International journal of automotive technology, tập 12, số 1, 2011

International Journal of Automotive Technology, Vol 12, No 1, pp 1−9 (2011) DOI 10.1007/s12239−011−0001−4 Copyright © 2011 KSAE 1229−9138/2011/056−01 IMPROVING THE PREDICTIVENESS OF THE QUASI-D COMBUSTION MODEL FOR SPARK IGNITION ENGINES WITH FLEXIBLE INTAKE SYSTEMS T.-K LEE and Z S FILIPI* Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48105, USA (Received 24 March 2009; Revised 10 September 2009) ABSTRACT−Fast and predictive simulation tools are prerequisites for pursuing simulation based engine control development A particularly attractive tradeoff between speed and fidelity is achieved with a co-simulation approach that marries a commercial gas dynamic code WAVETM with an in-house quasi-dimensional combustion model Gas dynamics are critical for predicting the effect of wave action in intake and exhaust systems, while the quasi-D turbulent flame entrainment model provides sensitivity to variations of composition and turbulence in the cylinder This paper proposes a calibration procedure for such a tool that maximizes its range of validity and therefore achieves a fully predictive combustion model for the analysis of a high degree of freedom (HDOF) engines Inclusion of a charge motion control device in the intake runner presented a particular challenge, since anything altering the flow upstream of the intake valve remains “invisible” to the zeroD turbulence model applied to the cylinder control volume The solution is based on the use of turbulence multiplier and scheduling of its value Consequently, proposed calibration procedure considers two scalar variables (dissipation constant Cβ and turbulence multiplier CM ), and the refinements of flame front area maps to capture details of the spark-plug design, i.e the actual distance between the spark and the surface of the cylinder head The procedure is demonstrated using an SI engine system with dual-independent cam phasing and charge motion control valves (CMCV) in the intake runner A limited number of iterations led to convergence, thanks to a small number of adjustable constants After calibrating constants at the reference operating point, the predictions are validated for a range of engine speeds, loads and residual fractions KEY WORDS : Spark ignition (SI) engine, Quasi-dimensional combustion model, Variable intake system, Intake charge motion control, Calibration NOMENCLATURE Af B Cβ CM K k P mb me u' ε inherent high power density, low cost, effective exhaust aftertreatment, and smooth operation The continued success hinges upon continuous improvements over time As shown by Heywood (2009), the main performance attributes have been improved at the rate of 2% per year To compete with turbocharged common rail direct injection diesel engines, gasoline engine developers have adopted a slew of new technologies, many of which pertain to the flexible devices for improving engine breathing This expands the operating range and allows unprecedented opportunities for optimizing the engine system, but also increases the complexity as well On the business side, cost-reduction requires further shortening of the development cycle Achieving design objectives within severe cost constraints critically depends on effective use of predictive simulation tools Simulations allow early explorations, optimization of design, and full characterization of the engine system for controls work In order to accomplish these goals, the simulation must be sensitive to the variations of process variables resulting from the action of devices under consideration This provides the impetus for the work presented here As an alternative to costly engine experiments, computer : flame front area : cylinder bore diameter : adjustable constant of the quasi-D combustion model : adjustable constant of the quasi-D combustion model : mean flow kinetic energy : turbulent kinetic energy : production rate of turbulent kinetic energy : mass of burned products : mass entrained : turbulent intensity : dissipation rate of turbulent kinetic energy per unit mass INTRODUCTION The gasoline spark ignition (SI) engine dominates the light vehicle markets in US and many other regions due to its *Corresponding author e-mail: filipi@umich.edu T.-K LEE and Z S FILIPI simulations have been widely used to predict engine performance characteristics Simulations range in complexity from highly detailed three dimensional computational fluid dynamics (CFD) models (Choi et al., 2005; Haworth, 2005; Gosman, 1994) to simplified mean value engine models (Cook and Powell, 1998; Hendricks and Sorenson, 1990) Every class of models has its place, and the selection depends on the simulation goals Development of engine control strategy requires a large number of simulations that cover all possible engine operating conditions Therefore, fast computations are highly desired, but with sufficient accuracy of predictions Clearly, a need for high computational speed eliminates CFD codes, while simplifications in the mean value models limit their predictiveness An optimum compromise can be found with a one-dimensional (one-D) gas dynamic simulation coupled to a thermodynamic cycle simulation, as long as the latter includes models capable of capturing the physics of all relevant phenomena As an example, application of a flexible valve actuation system will lead to variations of the flow parameters, including turbulence, and the residual fraction in a very wide range Hence, the use of a semi-empirical model such as the Wiebe function (Wiebe, 1956; Katsumata, 2007) will not suffice as the combustion model needs to be able to predict the effect of turbulence and residual fraction on burn rates A promising approach is the application of a two-zone quasi-dimensional (quasi-D) combustion model It includes the effect of turbulence on the rate of flame entrainment, and the effect of laminar flame speed on the burn-up of entrained mixture The concept was first pro-posed in the previously published literature (Tabaczynski et al., 1977, 1980), but until recently the quasi-D simulations were viewed as relatively computationally intensive tools intended primarily for engine development work Advances of the computer technology and refinements of the models open the doors to a wider use of quasi-D tools for simulation-based engine control development A much more predictive tool can replace the mean-value model and allow explorations in a much wider range of operating conditions The objectives of our work are to maximize the fidelity of such tools through a co-simulation approach marrying a commercial gas dynamic code and an in-house combustion model, and to subsequently propose a systematic methodology for calibrating model constants based on the limited set of experimental data In doing so, we address a particular challenge and capture the effect of a charge motion control device mounted in the intake runner, upstream of the cylinder The challenge stems from the fact that a zero-dimensional turbulence model can normally be applied only to the cylinder control volume, so anything altering the flow upstream of the intake valve remains “invisible” After characterizing the sensitivity of the flow and combustion predictions to model constants, we propose a solution based on the use of a turbulence multiplier and scheduling of its value Figure Illustration of the procedure for building a fast and predictive simulation tool using a co-simulation approach The co-simulation approach combines strengths of the commercial code Ricardo WAVETM in gas dynamics modeling and strengths of the in-house quasi-dimensional Spark Ignition Simulation (SIS) in combustion modeling WAVETM has been widely used for engine performance predictions (e.g Kim et al., 2005) We use it to model gas dynamics in the intake and exhaust systems from the air filter to the tailpipe SIS is a research code written in FORTRAN language that has been refined over time and used routinely at the University of Michigan for a variety of simulation studies (Filipi and Assanis, 2000; Wu et al., 2005, 2006) The combustion sub-model in the code is based on the turbulent flame entrainment model proposed by Tabaczynski et al (1977, 1980) and further refined by Poulos and Heywood (1983) Figure illustrates our vision for a co-simulation approach The experiments are required only in the development stage for the calibration of model constants and the validation of predictions However, it is important to note that the real engine does not necessarily have to comprise all technologies under consideration Once the code has been validated within a given range of operating variables, it will be suitable for studies of many configurations producing similar changes of in-cylinder conditions The development of the calibration methodology and the predictiveness of the co-simulation tool are demonstrated using an engine with a dual-independent variable valve timing (di-VVT) system and charge motion control valves (CMCVs) The CMCV is an air flow restriction device located upstream of the intake valves It generates turbulence in the flow entering the combustion chamber in order to produce faster burning rates – see Figure The CMCV is simple and inexpensive to use, but developing control strategies requires full characterization of its impact on combustion Therefore, creating a fast and accurate “virtual engine” is critical for efficient engine control design This paper is organized as follows First, the predictive physics-based simulation is created based on the co-simulation approach Then, we propose a calibration procedure to improve the prediction accuracy of the quasi-D simulation The calibration procedure considers the dissipation constant Cβ , and the multiplier CM in the turbulence model IMPROVING THE PREDICTIVENESS OF THE QUASI-D COMBUSTION MODEL FOR SPARK IGNITION Figure Integration of a one-D gas dynamics simulation and quasi-D combustion model Figure Schematic of the engine configuration with dualindependent cam phasing (di-VVT) and the charge motion control valve (CMCV) In addition, it discusses the need for an in-depth look at the early flame growth in the combustion chamber, and introduces adjustments of the flame maps based on the actual distance from the spark to the combustion chamber wall Finally, the accuracy of predictions is demonstrated through the comparison with experimental data obtained with the engine equipped with the CMCV ENGINE CONFIGURATION The engine used as the platform for simulation development, calibration and validation is a Chrysler dualoverhead camshaft 2.4 liter inline four (I4) cylinder spark ignition (SI) engine with the di-VVT device and the CMCV Two intake valves and two exhaust valves are used per cylinder and are actuated by the dual overhead camshaft The CMCV is introduced upstream of the combustion chamber in the intake runner to generate high turbulence for fast combustion and reduced combustion variability at low loads The relevant engine parameters are summarized in Table Table Engine specifications Displacement Bore/Stroke Compression ratio Max intake valve lift Max exhaust valve lift Default intake valve timing Closes/Opens/Centerline Default exhaust valve timing Closes/Opens/Centerline Default valve overlap Intake cam-phasing range Exhaust cam-phasing range 2.4 liters 87.5/101.0 mm 9.4:1 8.25 mm 6.52 mm 51o ABDC/1o BTDC/ 115o ATDC 9o ATDC/51o BBDC /111o BTDC 9o@0.5 mm lift 15o Crank angle 15o Crank angle SIMULATION TOOL The high-fidelity simulation consists of a one-D gas dynamics simulation model, a quasi-D combustion model, and an integration module To achieve the combustion predictiveness over all possible engine operating conditions, the quasi-D combustion model is integrated into the one-D gas dynamics simulation Engine states related to gas exchange process, such as mass flow rate, gas velocity, temperature and composition through intake and exhaust valves, are predicted by the one-D simulation Engine responses related to the combustion process are predicted by the quasi-D combustion simulation 3.1 Integration of One-D and Quasi-D Models to Create a Versatile Engine System Simulation Tool A top-level program written in the C++ language is introduced to realize the co-simulation approach by integrating the WAVETM–based gas dynamic model with the quasi-D combustion model The program was originally developed by Wu et al (2005) and refined for this study The integration procedure is illustrated in Figure The integration program calls the one-D simulation with an initial guess of the burning rate profile to calculate the gas flows through the intake and exhaust valves Next, the program transfers gas flow predictions to the in-cylinder quasi-D simulation, which calculates the burning rate profile, in-cylinder pressure, engine output and emissions Then, the predicted burning rate profile is passed back to the one-D simulation for the next iteration The convergence is established based on the error tolerances for indicated mean effective pressure (IMEP), residual fraction, and volumetric efficiency 3.2 One-D Gas Dynamics Simulation Model The one-D gas dynamics model is created using the commercial software Ricardo WAVETM It includes all air flow paths from the air box to the intake valve as well as from the exhaust valve to the tail pipe Figure shows the gas dynamics simulation model of the entire engine system First, the cylinder block is modeled Each cylinder has two intake and exhaust valves and ports Gas flow paths are connected to the cylinder head via the intake and exhaust runners Air flow coefficients through the valves are found by using experimental data provided by Chrysler LLC, and T.-K LEE and Z S FILIPI Figure One-dimensional gas dynamics simulation model built with the Ricardo WAVETM™ software these values are critical for correct estimation of air mass flow rate into the cylinders Then, the piping and manifolds are modeled by using duct and junction components Using exact three-dimensional CAD data and two-dimensional drawings provided by Chrysler LLC guarantees the accuracy of gas exchange predictions The throttle valve is emulated by an orifice The maximum orifice diameter at the wide open throttle (WOT) is restricted to the maximum intake air path diameter at the throttle body For part load conditions, an equivalent orifice diameter is determined to achieve the air mass flow rate corresponding to a given throttle position Finally, the integration program establishes an interface between the WAVETM and the external combustion model at the valve seat 3.3 Quasi-D Spark-ignition Combustion Model The quasi-D model is based on mass and energy conservation and phenomenological models for mean flow, turbulence, combustion, and heat transfer in the cylinder (Tabaczynski et al., 1977, 1980; Poulos and Heywood, 1983) The quasi-D model includes detailed physics and has previously been validated for a range of applications (Poulos et al., 1983; Filipi, 1994), hence it has the capability to extrapolate once calibrated at several important engine operating conditions Flame is assumed to propagate spherically from an ignition point The main governing equations are given here to facilitate further discussions, while the details of the model can be found in the already referenced papers The rate of mass entrainment is dm-e -= ρu Af ( u′ + SL ) , dt (1) Figure Turbulent energy cascade model for estimating mean and turbulent flow parameters where mb is the mass of burned products, λ is the Taylor microscale, and τ = λ/SL Clearly, everything affecting the laminar flame speed will have significant influence on the rate of burn-up The combustion model is complemented by a zerodimensional turbulence model, since turbulence intensity plays a major role in the prediction of the flame entrainment, and Taylor microscale is essential for determining the rate of burn-up in the reaction zone The model calculates crank-angle resolved global turbulence throughout the whole cycle based on the energy cascade concept shown in Figure The equations for the zero-dimensional energy cascade are as follows m· e dK - = - m· iv2i − P − , (3) dt m m· e dk - = P – m ε – k , (4) dt m where m· i and m· e are mass flow rates into and out of the cylinder respectively vi is the gas flow velocity into the cylinder, and ε is the dissipation rate of turbulent kinetic energy per unit mass by assuming turbulence is isotropic P is the production rate of turbulent kinetic energy and it is calculated assuming analogy to the turbulence production over flat plates K is the mean kinetic energy and k is the turbulent kinetic energy defined as: where me is the mass entrained, t is time, ρu is density of unburned charge, Af is the flame front area, u' is turbulent intensity, and SL is laminar flame speed The flame area term takes into account the effect of combustion chamber geometry, and turbulence intensity captures the effect of charge motion, while the laminar flame speed ensures sensitivity to residual fraction and air-to-fuel ratio The rate of burning is K = - mU , (5) k = - mu′2 , (6) dm -b = ( me – mb )/τ , dt L is the integral length scale assumed to be determined by (2) u′- ( 2k/3m ) ε = = , (7) P = 0.3307Cβ ( K/L )( k/m )1/2 , (8) L 3/2 L IMPROVING THE PREDICTIVENESS OF THE QUASI-D COMBUSTION MODEL FOR SPARK IGNITION the minimum vessel dimension: (9) L = V/( π B2 /4 ) ≤ B/2 , where V is the instantaneous volume of the combustion chamber, and B is the cylinder bore diameter Cβ is an adjustable constant that tunes the production and dissipation rate of turbulent kinetic energy during the compression and expansion processes After ignition, the conservation of mass and angular momentum of individual eddies leads to the following expressions, L/L0 = ( ρu0/ ρu )1/3 , (10) u′/u0′ = CM ( ρu0/ρu )1/3 , (11) where subscript “0” denotes conditions at the time of ignition Multiplier CM is a tunable parameter useful for any situation involving additional devices for generating turbulence 3.4 Implementation of the CMCV in the High-Fidelity Simulation Implementing the CMCV into the high-fidelity simulation requires the prediction of its impact on air mass flow rate into the cylinder and turbulence intensity The air mass flow rate can be easily predicted by adding an orifice at the CMCV position in the one-D gas dynamics model in order to emulate the pressure drop across the CMCV Since the one-D code provides only the mean flow parameters and the calculation of the energy cascade begins with the flow velocity through the intake valve, there is no sensitivity of the in-cylinder calculations to the turbulence-enhancing devices mounted upstream Another way must be found to simulate the effect of turbulence generation in the intake runner A promising solution is to use the multiplier CM in equation (11) of the quasi-D combustion model and tune it until burn rates with the CMCV blocked match the measured burn rates Meanwhile, the overall behavior of the incylinder turbulence model depends on the values chosen for the dissipation constant Cβ CALIBRATION PROCEDURE OF A QUASI-D COMBUSTION MODEL The ultimate goal of model calibration is to select the smallest number of constants that will be evaluated over a relevant range of operation This is achieved by investigating governing equations of the quasi-D model Flame front area maps in equation (1) have a very direct impact on predictions of flame entrainment The dissipation constant Cβ in equation (8) influences predictions of turbulence intensity used in (1) throughout compression, while the multiplier CM in equation (11) allows adjusting the turbulence intensity level after ignition to simulate the impact of the CMCV The mass fraction burned profile is highly influenced by the flame front area maps The maps need to be prepared in Figure Schematic diagram of the quasi-D combustion model calibration procedure advance using a dedicated code for calculating the interaction between the spherical front and the combustion chamber walls While this is a purely geometric calculation and there is no possibility for adjustments, one aspect of the map generation process deserves special attention The proximity of combustion chamber walls to the spark, i.e gap between electrodes, determines the flame kernel growth In many cases the predictions of the flame development stage are crucial for the overall accuracy of the calculated mass fraction burned, and yet this detail can easily be overlooked Thus, we include assessments of the ignition delay predictions based on the spark location into the overall calibration procedure 4.1 Overall Calibration Procedure The overall calibration procedure is illustrated in Figure The flame front area maps are generated from 3-D CAD data of the combustion chamber geometry by considering the interaction between a spherical front growing outwards from the spark and the combustion chamber walls A separate map is generated for every piston position The first iteration is carried out using the best available information about the spark electrode length, but small adjustments are made in case there are obvious deficiencies in predictions of the early part of the mass fraction burned profile Details of the flame frontal area calculation are presented in the next sub-section Next, the multiplier CM is calibrated to account for a device such as the CMCV that manipulates the turbulent intensity upstream of the combustion chamber Then, the parameter Cβ is calibrated to emulate the realistic energy cascade process It is worth noting that adjustments of Cβ allow capturing the global effect of 3-D flow patterns on turbulence in the context of the zero-dimensional model T.-K LEE and Z S FILIPI In general, a single value for CM and Cβ , respectively, may not be sufficient to cover the whole operating range, but a small set of values will still provide a robust simulation tool If the engine includes a device that significantly alters the flow in the intake system, as is the case with the CMCV, the scheduling of the constant accounts for the state of the device The iterations continue until the satisfactory match between the predicted and experimental mass fraction burned is achieved, and the overall procedure is then repeated for several selected operating conditions 4.2 Flame Front Area Maps and Their Effect on Combustion The flame front area is critical for the accuracy of combustion predictions, such as the mass fraction burned profiles The mass fraction burned profile is a function of crank angle, and has a typical S-shaped curve It consists of the flame-development angle (∆θd) and the rapid-burning angle (∆θb) The flame-development angle (the 0~10% mass burned) is the crank angle interval between the spark discharge and the time when a small but significant fraction of the cylinder mass has burned The flame-development stage is influenced by mixture composition and charge motion in the vicinity of the spark plug Initially, the flame develops freely around the point of ignition, as shown in Figure 7(a) When the flame touches the surface of cylinder head, the interaction between the flame front area and the combustion chamber walls becomes a factor as well – see Figure 7(b) Hence, the exact location of the spark can be very influential for the growth of the flame kernel, e.g longer electrodes will allow more space for the spherical flame kernel and lead to a shorter ∆θd The rapid-burning angle (the 10~90% burn duration) characterizes the main stage of combustion During this stage, shown in Figures 7(c) and 7(d), the details of combustion chamber geometry, Figure Illustration of flame front area propagation with respect to the crank angle Figure Pre-processed and simplified combustion chamber 3-D geometry using finite element pre-processor tools including the shape of the piston top, become dominant The complexity of combustion chamber geometry poses a special challenge In our case, the combustion chamber is a pent-roof shape and the piston top is raised up to maintain compression ratio The 3-D CAD geometry is converted to adequate 3-D mesh data for calculating the flame front area maps using a finite element pre-processing tool Re-meshing procedure generates coarse mesh shown in Figure and enables fast calculations of geometric interactions The Figure Comparison of flame front area maps: (a) with an inaccurate spark plug position; (b) with the accurate spark plug position IMPROVING THE PREDICTIVENESS OF THE QUASI-D COMBUSTION MODEL FOR SPARK IGNITION accuracy is confirmed by verifying the clearance volume and compression ratio Next, we assess the sensitivity of the flame area calculations to the location of the spark Figures 9(a) and 9(b) illustrate the flame front area development for two spark locations Each plot contains a set of lines, each calculated for a different piston position The first line from the bottom corresponds to the piston located at the top dead center (TDC), and the top line corresponds to 120 degCA position The slope of the flame front area line at the very beginning largely influences the flame-development angle The peak and the slope observed for larger flame radius influence the rapid burning stage When the spark is located near the wall, only mm from the back surface of the head, the flame touches the wall early and a significant portion of the front is cut out This leads to a mild slope of the flame area line with respect to flame radius, and a relatively flat appearance of the profiles shown in Figure 9(a) When the distance between the spark and the wall is increased to mm, the flame area profiles become much sharper thus leading to larger flame front size for a given radius − see Figure 9(b) The flame front area maps are obviously highly sensitive to the spark plug position Different flame front area maps are expected to produce significant variations of mass fraction burned profiles Figure 10 compares burning rate and mass fraction burned profiles predicted using flame area maps shown in Figures 9(a) and 9(b) Indeed, burn rates predicted for case (i.e flame area maps shown in Figure 9(a)), are very different from those obtained for case (i.e flame area maps shown in Figure 9(a)) Case produces an asymmetric burn rate profile with a retarded peak, as shown in Figure 10(a) This leads to a reduced slope of the mass fraction burned during the main stage of combustion – see Figure 10(b) In addition, Case demonstrates slower burning during the flame development stage Experiments confirm that Case captures the flame front evolution during the cycle much more accurately In summary, flame area calculations deserve special attention, and in case there is any uncertainty about the details of the geometric interaction close to the sparkplug electrodes, the experimentally measured burn rates can indirectly verify the accuracy of flame area maps that are subsequently being used as input the quasi-D simulation 4.3 Sensitivity to CM The parameter CM in equation (11) is introduced as a multiplier for adjusting the turbulent intensity when additional devices are mounted upstream of the combustion chamber to increase the turbulent intensity Figure 11 shows the influence of the CM on the mass fraction burned profiles Multiplier values larger than unity imply enhanced turbulent intensity due to a device such as the CMCV This significantly increases the slope of the mass fraction burned curves In other words, combustion predictions are very sensitive to the multiplier CM and its value will indicate the success in enhancing turbulence with the CMCV 4.4 Sensitivity to Cβ The dissipation constant Cβ in equation (9) influences the zero dimensional energy cascade by varying the rate of mean kinetic energy dissipation and turbulence production Larger Cβ implies faster conversion of the mean kinetic energy into turbulent kinetic energy The effect on turbu- Figure 10 Influence of different flame front area maps: (a) normalized burning rate profiles; (b) mass fraction burned profiles Figure 11 Influence of the CM on the mass fraction burned profiles T.-K LEE and Z S FILIPI Figure 12 Influence of the Cβ on the mass fraction burned profiles lence during combustion is somewhat non-intuitive Greater turbulence production leads to high values of u' at the beginning of intake process, but this is accompanied by relatively faster dissipation of mean kinetic energy Consequently, the mean kinetic energy drops to lower levels by the end of intake and beginning of compression, and higher turbulence intensity values cannot be sustained Close to the TDC, when it matters for combustion, the turbulence intensity is lower for higher values of Cβ , and combustion speed is reduced as well (see Figure 12) Calibrating Cβ based on burn-rates enhances the versatility of the quasi-D combustion model by indirect compensation of in-cylinder flow patterns CALIBRATION RESULTS The proposed calibration procedure is validated for the diVVT engine with the CMCV using experimental results obtained in the University of Michigan Automotive Laboratory The proposed procedure completes calibration with a small number of iterations due to only three calibration parameters and the sequential approach First, the flame front area maps are generated from the 3D CAD geometry using the methodology introduced in the section 4.2 Then, the adjustable constants CM and Cβ are separately calibrated for the CMCV blocked and unblocked cases When the CMCV is blocked, CM value is swept from a unit value to larger value to account for the increased turbulent intensity upstream of the combustion chamber Then, the Cβ value is fine tuned in order to reproduce an experimentally measured combustion profile When the CMCV is unblocked, CM value is set to a unit value because there is no increase in turbulence upstream of the valve, and Cβ is adjusted in the range to until the experimental combustion profile is reproduced Validation of the predictions at the engine speed of 2000 rpm and the break mean effective pressure (BMEP) of bar is shown in Figure 13 Mass fraction burned profiles change significantly between the two CMCV positions, but in both cases the agreement between predicted and experi- Figure 13 Comparison of predictions and experimental results for the mass fraction burned at the CMCV unblocked and blocked cases; engine speed of 2000 rpm and BMEP of bar mental curves is excellent Similar agreement is observed for all low to medium engine speeds, for load ranging from idle to WOT, and residual fractions ranging from 0% to 34% Therefore, after calibrating the constants for the CMCV blocked and unblocked cases at a reference point, the quasi-D combustion model can be used over the entire range of engine operating points relevant for fuel economy studies Prediction errors may be larger under some extreme conditions, but the combustion sensitivity related to main control variables, such as throttle input, EGR, engine speed, and variable valve actuation, is preserved in the whole range Thus, the co-simulation approach coupled to a systematic calibration procedure yields a truly predictive tool for HDOF engine optimization and control development CONCLUSION This work proposes a systematic calibration procedure for the predictive SI engine simulation tool that maximizes its range of validity The simulation is based on the co-simulation approach marrying a commercial gas dynamic code WAVETM and an in-house quasi-dimensional combustion model The latter is based on the turbulent flame entrainment concept and it is chosen because of its ability to capture the effects of key process variables on combustion In particular, the model is sensitive to the changes of combustion chamber shape, engine speed, manifold absolute pressure, air-to-fuel ratio, residual fraction, and turbulence level in the cylinder A particular challenge arises with the introduction of a charge motion control device in the intake runner, upstream of the cylinder The zero-dimensional turbulence model follows the energy cascade that starts with mean kinetic energy generation in the intake gas jet, and it is insensitive to the phenomena occurring upstream of the valve In order to mitigate this problem, a multiplier CM is introduced in the equation that tracks the turbulence intensity evolution IMPROVING THE PREDICTIVENESS OF THE QUASI-D COMBUSTION MODEL FOR SPARK IGNITION during combustion The dissipation constant Cβ , which affects the rate of mean kinetic energy dissipation and turbulence production, is considered next Sensitivity analysis emphasizes a need for in-depth look at the flame area maps and their impact on early flame growth Flame area maps depend on the combustion chamber shape and the spark location A detail that plays a big role is the distance of the spark from the cylinder head surface dictated by the electrode length Greater distance leads to delayed contact of a spherical flame front and the wall, increased flame areas, and faster burn rates In case there is any uncertainty, the experimentally measured burn rates should be used to indirectly verify the accuracy of flame area maps In summary, calibration of only two constants pertaining to the in-cylinder model and possible adjustments of the flame area maps are sufficient to provide a predictive SI engine simulation based on a gas dynamics model and a quasi-D combustion model A sequence of steps begins with the assessment of flame area maps before moving on the adjustments of the turbulence multiplier and the dissipation constant In case the engine is equipped with a device for altering charge motion in the intake runner, calibration needs to be repeated for different settings of the device The procedure is demonstrated using an SI engine system with dual-independent cam phasing and charge motion control valves in the intake runner A limited number of iterations led to convergence, thanks to a small number of adjustable constants After calibrating constants at the refer-ence operating point, the predictions were validated for a range of engine speeds, loads and residual fractions The results indicate that the co-simulation approach combined with a systematic calibration procedure yields a predictive and robust tool for HDOF engine optimization and control development ACKNOWLEDGEMENT−The authors wish to acknowledge Robert Prucka for providing the experimental results, Chrysler LLC for financial support, and Roger Vick, Denise Kramer and Greg Ohl for providing engine geometry data REFERENCES Choi, H., Lim, J., Min, K and Lee, D (2005) Simulation of knock with different piston shapes in a heavy-duty LPG Engine Int J Automotive Technology 6, 2, 133− 139 Cook, J A and Powell, B K (1988) Modeling of an internal combustion engine for control analysis IEEE Control Systems Magazine 8, 4, 20−26 Filipi, Z S and Assanis, D N (1991) Quasi-dimensional computer simulation of the turbocharged spark-ignition engine and its use for 2- and 4-valve engine matching studies SAE Paper No 910075 Filipi, Z S (1994) Investigation of variable valve area strategies for a turbocharged SI-engine Proc IMechE 1994-6, 5th Int Conf Turbocharging and Turbochargers, 93–102 Filipi, Z S and Assanis, D N (2000) The effect of stroketo-bore ratio on combustion, heat transfer and performance of a homogeneous-charge spark-ignited engine of given displacement Int J Engine Research 1, 2, 191−208 Gosman, A D (1994) CFD modeling of flow and combustion for IC engines Proc 2nd European Computational Fluid Dynamics Conf 5, 1, 133−143 Haworth, D C (2005) A review of turbulent combustion modeling for multidimensional in-cylinder CFD SAE Paper No 2005-01-0993 Heisler, H (1995) Advanced Engine Technology Society of Automotive Engineers, Inc Hendricks, E and Sorenson, S C (1990) Mean value modeling of spark ignition engines SAE Paper No 900616 Heywood, J B (1998) Internal Combustion Engine Fundamentals McGrawHill Heywood, J B (2009) Trends in performance characteristics of modern automobile SI and diesel engines SAE Paper No 2009-01-1892 Katsumata, M., Kuroda, Y and Ohata, A (2007) Development of an engine torque estimation model: Integration of physical and statistical combustion model SAE Paper No 2007-01-1302 Kim, C.-S and Oh, B (2005) Performance simulation of an in-line engine considering VVT strategy using wave SAE Paper No 2005-01-0036 Poulos, S G and Heywood, J B (1983) The effect of chamber geometry on spark-ignition engine combustion SAE Paper No 830334 Tabaczynski, R J., Ferguson, C R and Radhakrishnan, K (1977) A turbulent entrainment model for spark-ignition engine combustion SAE Paper No 770647 Tabaczynski, R J., Trinker, F H and Shannon, B A (1980) Further refinement and validation of a turbulent flame propagation model for spark-ignition engines Combustion and Flame 39, 2, 111−121 Taylor, C F (1985) The Internal-combustion Engine in Theory and Practice Volume I: Thermodynamics, Fluid Flow, Performance The M.I.T Press Wiebe, I I (1956) The combustion speed in internal combustion piston engines; Collected works of piston engine research, Laboratory of Engines, Academy of Sciences, USSR, Moscow 1956 (Translated to English by M Kiisa, KTH 1993) Wu, B., Prucka, R G., Filipi, Z S., Kramer, D M and Ohl, G L (2005) Cam-phasing optimization using artificial neural networks as surrogate models – maximizing torque output SAE Trans J Engines, SAE Paper No 2005-013757 Wu, B., Prucka, R G., Filipi, Z S., Kramer, D M and Ohl, G L (2006) Cam-phasing optimization using artificial neural networks as surrogate models – Fuel consumption and NOx emissions SAE Trans J Engines, SAE Paper No 2006-01-1512 132 B SUH et al braking can recover energy that is wasted as heat during mechanical braking, and the energy recovered to electricity can be stored in the battery (4) On-off engine operation can eliminate idle speed operation of the engine and turn off the ICE when the electric motor works alone to propel the HEV Therefore, this operating control strategy can save energy and reduce emissions by minimizing the unnecessary use of the ICE (Burke, 1993; Zeng et al., 2005) The Advanced Vehicle Simulator (ADVISOR) created by the National Renewable Energy Laboratory (NREL) has been used as an analytical tool to simulate the fuel economy, emissions and performance of a variety of vehicle types (Markel and Wipke, 2001; Wipke et al., 1999) In 2003, ADVISOR was commercialized by AVL (Anstalt für Verbrennungskraftmaschinen; Institute for Internal Combustion Engines) In Korea, Hyundai Heavy Industries (HHI) has been developing a series hybrid electric bus (HEB) in cooperation with Enova systems, Daewoo Bus, and the HEV Center of UC Davis Many engineers in China have vigorously conducted research and development into hybrid-powered buses, rather than small and medium-size hybrid vehicles In addition, many U.S companies have been developing medium and heavy-duty hybrid electric drive systems for buses and trucks (Wang et al., 2003) Currently, the majority of hybrid electric systems for trucks and buses implement series hybrid electric drive system technology However, development of the parallel hybrid electric drive system is undergoing remarkable growth for these types of heavy-duty vehicles In particular, plug-in hybrid electric systems for transit buses are expected to save more hydrocarbon fuel and reduce emissions more dramatically The objective of this study was to define the component selection of a hybrid powertrain system to achieve the hybridization and specification of components for the HEB using the ADVISOR software package BACKGROUND 2.1 HEV Powertrain Configuration HEV powertrain configurations can be classified into parallel hybrid, series hybrid, and power split (series-parallel) hybrid categories These divisions are based on the roles and mechanical connections of the ICE, electric motor, generator, transmission, and clutches A series HEV uses the engine as an auxiliary power unit (APU) to drive an electric generator This electric generator provides electric power to a battery pack, ultracapacitor, or the propulsion motor, while an electric motor directly propels the vehicle The APU must be large enough to provide the average power for the worst conditions that the vehicle is expected to operate in The series HEV is configured with only the electric motor providing the required power to drive the vehicle, while the ICE is not mechanically connected to the wheels The electric motor can extract energy from the battery pack or the ICE, or from both The electric motor must be big enough to provide the torque needed at low speeds and the power required at high speeds The system can be implemented with or without a transmission (He et al., 2006) A parallel HEV uses the ICE and electric motor together to propel the vehicle, typically through a transmission The electric motor and ICE are both coupled to a transmission through a separate clutch The electric motor plays the role of assisting the ICE in supplying the required power The ICE can spin the electric motor as a generator to produce electric energy that is stored in the battery, as in the series HEV The single electric motor/generator in this system is much smaller than the motor of the series HEV This is because the electric motor for the parallel configuration can work with the ICE to supply the total power The parallel HEV can be operated in several operational modes, such as the electric motor-only mode, electric regeneration mode, and hybrid mode of the ICE and motor, according to the driving and battery conditions Recently, most hybrid vehicles have adopted the parallel hybrid configuration The UC Davis HEV Center has constructed and developed many parallel hybrid vehicles There is some disadvantage in using this parallel system, as the control strategy or software becomes more complex because the controller must optimize the power that is regulated and combined from two different power sources Despite this, the highly developed hardware and software of this technology can offset these drawbacks In this study, the parallel hybrid system was used for the powertrain configuration of the diesel HEB A power split hybrid electric powertrain contains characteristics of both the parallel and series hybrid electric configurations The Toyota Hybrid System (THS) is a representative case of the power split hybrid configuration The power split configuration of this THS is based on the parallel hybrid configuration However, it consists of an electric motor and a generator, as in the series hybrid configuration so that the engine operates at optimal points to minimize fuel consumption The hybrid Ford Escape, which is currently in production, also uses the power split hybrid powertrain system (Wang et al., 2007) 2.2 Pre-transmission Configuration Parallel hybrid configurations can be classified into pretransmission and post-transmission parallel configurations according to the location of the electric motor Pre-transmission parallel configurations have a transmission that is inserted between the electric motor and the final drive Post-transmission configurations have an electric motor between the transmission and the final drive on the drive shaft Most HEVs employ the pre-transmission configuration However, post-transmission powertrain systems are also used in some systems For example, International Inc has been developing a post-transmission parallel hybrid electric school bus The reason for using the post-transmission configuration is to reduce the manufacturing POWERTRAIN SYSTEM OPTIMIZATION FOR A HEAVY-DUTY HYBRID ELECTRIC BUS cost of the hybrid electric drive system by minor modification of existing conventional powertrain systems However, the post-transmission configuration cannot receive the benefits of gear ratio shifting because the electric motor is located beyond the transmission Consequently, a high torque electric motor should be utilized and the volume and weight of the electric motor should be increased, as more rotor surface area is required to produce higher torque The in-wheel motor HEV is another example of the post-transmission hybrid system In the present work, the plug-in parallel HEB was utilized in a pre-transmission parallel configuration 2.3 Plug-in Hybrid Electric Vehicle The plug-in HEV combines the features of the electric vehicle and common HEV The plug-in HEVs implement a relatively bigger battery pack so that more electrical energy can be effectively used In the electric motor-only mode, the plug-in HEV can operate on electric power alone, with a significant driving range that is similar to an EV However, the capacity of the battery remains within the predefined state of charge (SOC) of the battery In the charge-depleting mode, the ICE and electric motor work together However in the charge-sustaining mode, the electric motor is used to a greater extent than the electric motor The charge-depleting mode is a control strategy that is unique to the plug-in HEV The charge-depleting mode and the electric-only mode are in a processing state until the battery reaches an assigned low SOC Once the battery is depleted down to a low SOC of the battery, the operation mode of the plug-in HEV switches to the charge-sustaining hybrid mode In this state, the ICE acts to sustain the SOC of the battery as a main power source Overall, the implication of this process is for a potential improvement in vehicle efficiency during both the charge-depleting and charge-sustaining hybrid operations Consequently, several benefits are expected from the plug-in HEVs, as follows (1) Reducing the consumption of hydrocarbon fuels (2) Lowering air pollutant emissions from exhaust gases (3) Augmenting the use of electricity generated by diversifying renewable and non-renewable energy sources and avoiding petroleum sources Renewable energy includes solar energy, wind power energy, and geothermal heat energy, among others Petroleum, natural gas, and coal are considered nonrenewable energy sources that cannot naturally be regenerated or reformed An Electric Power Research Institute (EPRI) report by M Duvall has compared the fuel economy and emissions (NOx, HC) of conventional vehicles, non plug-in HEVs, and plug-in HEVs versus types of compact cars, mid-size SUVs, and full-size SUVs The plug-in HEVs achieved the best results in terms of fuel economy and emissions Unnasch and Pont at Bluewater Network have reported greenhouse gas (GHG) emission results for the Gasoline Internal Combustion Engine Vehicle (ICEV), Ethanol 133 (85%) ICEV, LPG ICEV, LPG Bi-Fuel, CNG ICEV, and plug-in HEV (HEV 20) The plug-in HEV exhausted the least amount of carbon dioxide (CO2), including CO2 from electric generation The HEV Center at UC Davis has successfully developed plug-in HEVs Plug-in HEV prototypes at UC Davis have a reasonable promise of being successful In the present study, the main focus is on energy management and powertrain control for the plug-in diesel HEB COMPONENT SELECTION FOR THE HYBRID POWERTRAIN SYSTEM This section describes different component selection philosophies for achieving the hybridization and specification of components for the plug-in parallel HEB 3.1 Engine The engine selection process in conventional vehicles is generally focused on having the power of the ICE meet the peak acceleration requirement of the vehicle Driving conditions that require peak acceleration power include climbing hills, passing vehicles on the highway, and stopand-go traveling However, these driving situations occur over a relatively short time period, when compared with cruising at a steady speed The ICE of conventional vehicles is scaled up for the momentary power requirements associated with obtaining peak acceleration The cruising condition demands relatively low power, compared to peak acceleration situations When a conventional vehicle is driven at a steady (cruise) speed, its ICE uses only a fraction of its ability and runs in an inefficient region On the other hand, HEVs consider the power requirement of high speed cruising to select the ICE size The engine is downsized to deliver only the power Table Power required by a heavy-duty bus on the highway (1COMMUTE 60 is a driving cycle including constant speed cruising at 60 mph based on the COMMUTE driving cycle in ADVISOR, which is a simulation tool developed by the U.S Department of Energy's National Renewable Energy Laboratory's (NREL) 2COMMUTE 50 is a driving cycle including constant speed cruising at 50 mph based on the COMMUTE driving cycle.) Average power required for highway driving (kW) Power required for high-speed cruise driving (kW) COMMUTE 601 111.51 108.63 COMMUTE 100.53 95.87 COMMUTE 50 90.12 83.71 HWFET 96.20 - WVUINTER 96.22 - Driving cycles 134 B SUH et al necessary to drive the vehicle at a steady cruising speed Additional power required for acceleration to pass on the highway or to climb a hill is supplied by the electric motor of HEVs Therefore, the HEV engine can be operated in the most efficient zone To determine the engine size for simulation of the plugin parallel HEB, it was necessary to know the power requirement of a heavy-duty bus cruising at high speed ADVISOR was used to simulate a conventional bus with a 350 kW engine, based on a Daewoo bus (model BS211M) The simulated mass of the bus was 13,000 kg Table shows the simulation results, the average power demanded by the bus when driving on the highway and the power requirement of the bus when cruising at high speed The simulations determined that an engine power of 130 kW is feasible for high-speed cruising with the engine operating within a more fuel-efficient range This would be capable of maintaining the SOC of the battery in the chargesustaining hybrid mode The diesel engine is a vehicle propulsion source that most conventional heavy-duty vehicles (e.g., buses and trucks) use widely This type of engine was selected as the ICE of the plug-in parallel HEB Diesel engines have a self-ignition property, which is the main distinctive characteristic of com-pression ignition (CI) engines That is, diesel engines intake and compress only air, and diesel fuel is injected without an ignition spark and thus operating Diesel engines have better energy efficiency compared to the equivalent gasoline engine, known as the spark ignition (SI) engine The better fuel efficiency of the diesel engine results from its fuel-lean combustion, high compression ratio, and use of high energy density diesel fuel The compression ratios of diesel engines are typically from 15 : to 23 : 1, which is higher than the compression ratios of gasoline engines (8 : to 12 : 1) A sufficiently high temperature in the combustion chamber of the diesel engine is also required for auto ignition In general, the Figure Diesel engine efficiency map of 130 kW high power diesel engines of heavy-duty trucks and buses feature high torque and a low engine speed range of less than 2500 rpm Conversely, passenger cars and SUVs tend to make use of diesel engines that have low torque and a high-speed range However, a diesel engine with low torque and high speed capabilities was utilized for the heavy-duty hybrid bus This is because the diesel engine was downsized, making it similar to an SUV engine, and the electric motor worked together with the ICE Figure shows the thermal efficiency map and maximum torque line of the diesel engine that was selected for simulation of the plug-in parallel HEB The thermal efficiency map comes from the ADVISOR data The map shows the energy conversion of the engine at all possible combinations of output torque and rotational speed The drawback of diesel engines is that they discharge a greater amount of exhaust emissions, compared to gasoline engines, because diesel fuel contains more carbon than gasoline fuels Typically, exhaust emissions from ICEs contain nitrogen oxides (NOx), particulate matter (PM), carbon monoxide (CO), and unburned hydrocarbons (HC) However, in the case of the parallel hybrid electric powertrain, in which an ICE and electric motor are directly coupled to a transmission, the ICE can be downsized compared to the engine of a conventional powertrain This is thanks to the supplemental power of the electric motor A hybrid electric powertrain controller can maintain the high thermal efficiency of the downsized ICE and immediately make the ICE operate in an engine operation zone to reduce exhaust emissions Furthermore, in diesel engines, diesel aftertreatment devices, such as a Diesel Particulate Filter (DPF) and Lean NOx Trap (LNT), can contribute to reducing the tailpipe emissions of diesel engines 3.2 Electric Motor The electric motor of a hybrid electric powertrain can be determined using several factors, including the battery capacity, engine size, and type of hybrid powertrain configuration The electric motor of an HEV should have the ability to act as a generator so that it can recharge the battery and carry out the function of braking energy regeneration An alternating current (AC) induction motor is suitable for use as the electric motor in the powertrain of the HEB AC induction motors not require a brush because the current is supplied directly to the stator Therefore, maintenance costs can potentially be lower than for direct current (DC) brushed machines An inverter would be necessary in this case to convert DC to AC Recently, AC induction motors have been widely integrated into the powertrain of heavy-duty HEVs because of their lower maintenance cost, adequate controlling ability and ease of regenerative braking The electric motor in plug-in HEVs can be sized to operate independently for stop-and-go POWERTRAIN SYSTEM OPTIMIZATION FOR A HEAVY-DUTY HYBRID ELECTRIC BUS 135 Figure Power distribution required for a bus in diverse driving cycles driving in the city, as well as to produce power for acceleration to supplement power in the case of an ICE power shortage Stop-and-go operation demands high torque and low speed Thus, high power electric motors are required to achieve this performance The high power electric motor enables the HEV to operate in a high efficiency region with a wide speed range where the ICE has low thermal efficiency Figure shows the power distributions required when a standard diesel bus of 12,000 kg drives in city and highway cycles based on a preliminary simulation for a conventional diesel bus As shown in Figure 2, when a 12,000 kg heavy-duty bus is driving in the city zone, the average power requirement is approximately 80 kW to 120 kW, corresponding to the continuous power of the electric motor For acceleration, the peak power of the electric motor requires nearly 180 kW to supplement the maximum engine power of 130 kW Thus, the electric motor for the plug-in HEB was sized for a peak power of 180 kW with a continuous power of 100 kW Moreover, the 180 kW electric motor enables more effective regenerative braking to absorb the relatively large negative power of mechanical braking In Figure 3, the maximum torque of the electric motor is shown to be about 760 Nm and its maximum rotational speed 136 B SUH et al Figure Electric motor/generator efficiency map is about 7,660 rpm The efficiency map and specifications shown here were based on data from the AC induction motor in ADVISOR 3.3 Transmission The powertrain system of vehicles requires a transmission system to increase the engine efficiency The transmission system is also a crucial component in HEVs, increasing the efficiency of the powertrain Automotive transmissions can be classified into three types, including automatic transmission, manual transmission, and continuously variable transmission (CVT) Manual transmissions (MTs) have many advantages, such as lower fuel consumption, higher performance, lighter mass, and smaller volume, compared to automatic transmissions (AT) Despite this, MTs have yielded their premier position to the AT The reason for this is that the shifting operation required for a MT is inconvenient for the operator, and it is difficult to select an efficient gear ratio to meet the required power However, AT systems have also been confronted with high technical desires, including a need to lower the gearshift shock, optimize the gear ratio change pattern, increase the performance and improve the efficiency To solve these technical desires, ATs with multi-gear ratios have been developed Ultimately, the concept of multi-gear ratio led to the continuously variable transmission The CVT in HEVs plays the significant role of automatic gear shifting and improved mechanical efficiency Currently in the automotive industry, the belt drive type of CVT is widely popularized among various CVT types, including the traction drive, fluid drive, and belt drive types The fundamental structure of a belt type CVT is simple, as shown in Figure In general, it consists of a few components, including a high-density belt or chain, primary and secondary pulleys actuated by hydraulic cylinders, hydraulic pumps supplying hydraulic pressure, and several sensors measuring the hydraulic pressure, pulley positions, and shaft speed According to the gear ratio and driving conditions, the Figure Fundamental structure of a belt-type CVT Figure CVTs of the HEV cylinders actuated by pumps can work to make space between the two sides of the pulley, causing it to be wide or narrow The mechanism of the CVT has become as simple as for the MT, and the mechanical efficiency of the CVT is nearing that of the MT The most recent CVT has even better mechanical efficiency than the MT The CVT has many advantages, such as better drivability by elimination of shift shock, improved fuel efficiency, better acceleration, and better control of engine speed In parallel HEVs, which include two or more propulsion driving systems, the continuously shifting ability of CVT can enable steady operation of the HEV engine within the peak efficiency region, called the “ideal operation line” (IOL) In the parallel hybrid configuration the engine speed tends to be coupled with the vehicle speed The engine can be driven by the CVT independently of the vehicle speed Moreover, in the electric use-only mode of the HEV, the CVT is able to stretch its ability to control the operation of the electric motor within the most efficient operation line The engine shaft is disconnected from the driving shaft by POWERTRAIN SYSTEM OPTIMIZATION FOR A HEAVY-DUTY HYBRID ELECTRIC BUS the clutch in the electric use-only mode The electric motor in the parallel configuration can make up for the poor transient behavior of the CVT Compared to the AT and MT, the CVT enables more energy to be efficiently recovered via regenerative braking The ability to regeneratively brake is one of the energy saving advantages of the HEV and electric vehicle The HEV Center at UC Davis used a Van Doorne belt CVT in the Coulomb, the UCD hybrid electric passenger vehicle built in 1999 The Van Doorne belt is a kind of metal v-belt A CVT was used to mechanically and electrically rebuild the Nissan 2.0 L CVT Since then, high torque CVTs have been developed at the HEV center at UC Davis for SUVs, trucks, and buses As shown in Figure 5, the HEV center replaced the Van Doorne belt of the CVT with a high torque Gear Chain Industries (GCI) chain This type of chain can transfer high input torque from the ICE and electric motor to the final drive The high torque GCI chain was specifically designed by Gear Chain Industrial B.V to reduce noise and losses The GCI chain CVT achieved a mechanical efficiency of 94.97% From a mechanical efficiency point of view, the CVT is now a viable transmission choice for hybrid vehicles The CVT was selected as the transmission out of the powertrain in the plug-in parallel HEB 3.4 Battery The battery, as an electric energy storage unit, is a significant component that can influence the performance and efficiency of HEVs All batteries used for HEVs are rechargeable batteries, also known as secondary cells Recently, certain HEVs have made use of ultracapacitors for electric energy storage, instead of batteries Compared to the battery, the benefits of the ultracapacitor are its higher power density, quick charging and discharging capacity, long life, and high efficiency However, the ultracapacitor has a fatal weakness in its much lower energy density, compared to the battery Because of this, the ICE must turn on at all times during vehicle travelling to recharge the ultracapacitor Thus, ultracapacitors are often used as an assistant to batteries, taking advantage of their high specific power and rapid charge/discharge characteristics The battery type should be selected by first considering its specific energy, specific power, internal resistance, voltage, and capacity The performance, capability and weight/volume of the battery pack depend on the kind of battery Therefore, choosing the battery type is a significant factor in designing the powertrain components of an HEV Battery selection criteria include its energy density, power density, cost, life cycle, and efficiency Rechargeable batteries can be classified into lead-acid (Pbacid), nickel-metal hydride (NiMH), and lithium-ion (Li-ion) batteries, which are used for most HEVs Pb-acid batteries have been used as rechargeable batteries for the longest time The Pb-acid battery is composed of lead plates in grids that are suspended in an electrolyte solution of sulfuric acid and water 137 Even though this type of rechargeable battery has the advantages of low cost and reliable operation, its drawbacks include a low energy density, environmental problems and short cycle life This has caused them to be withdrawn from the EV and HEV markets NiMH batteries are composed of a hydrogen storage metal alloy, nickel oxide cathode, and potassium hydroxide electrolyte These batteries have been successfully used in HEVs Compared to Pb-acid batteries, NiMH batteries are advantageous because they have a better energy density and are recyclable, quickly rechargeable, and reliable On the other hand, some disadvantages of NiMH batteries are their high cost and low cell efficiency The Toyota HEV Prius, Honda Civic Hybrid, Saturn Vue Hybrid, and many HEVs use NiMH batteries The UC Davis HEV Center has utilized NiMH batteries for UCD HEVs, including the Sequoia, Yosemite, and Coulomb, for over ten years This team has achieved good marks in future truck competitions Li-ion batteries can be more expensive than NiMH batteries However, their higher specific energy and low self-discharge make them widely used in most small electronic products, such as cellular telephones, laptop computers, and portable A/V players Furthermore, recently the technological development of Li-ion batteries has increased their safety and durability, as well as reduced their cost Drop-in prices of Li-ion batteries with higher energy densities has caught the attention of HEV researchers and manufacturers The Trinity, a new hybrid electric SUV from the HEV Center at UC Davis, uses Li-ion batteries as the preferred electric energy storage system over NiMH batteries Table 2, which was provided by Compact Power Inc (CPI), compares the differences between NiMH and Li-ion batteries The Li-ion battery CPI is the North American subsidiary of LG Chemical in Korea The specific energy and specific power of Li-ion batteries are more than twice as high as the NiMH battery If Li-ion batteries replace NiMH batteries, the weight and size that batteries occupy in hybrid vehicles will shrink significantly This fact is a critical point for the design of practical HEVs In particular, the Li-ion battery is ideal for plug-in HEVs, which we would like to build Unlike other parallel HEVs, plug-in HEVs can be classified by an all-electric range of up to 40, 60, or 100 miles Because of this requirement, the battery pack must be considerably larger than for other HEVs Thus, the weight of the battery pack is increased The high Table Li-ion and NiMH battery NiMH Li-ion 80 200 1600 >3000 Self discharge (% per month) 15 Efficiency (%) 90 >95 300 K 300 K Specific energy (Wh/kg) Specific power (W/kg) Cycle life for HEV 138 B SUH et al specific energy, high specific power, and low internal resistance of the Li-ion battery makes it possible to use a larger battery pack with a higher power, compared to a Pbacid or NiMH battery, while still achieving a reasonable weight This large battery pack has a lower internal resistance than a smaller pack of the same battery Therefore, the efficiency of the battery is increased The total mass of the vehicle, which is increased by this large battery pack, can be compensated for by using more electrical energy and downsizing the engine via the high power electric motor Li-ion batteries are nearly ready for HEVs and were selected for the plug-in HEB in the present research Furthermore, the battery life cycle in HEVs should be more than 100,000 miles The data for Li-ion batteries used in this simulation were from ESS_LI7_temp.m in ADVISOR and the data sheet of the 60 Ah HE 602040 Li-ion battery of GAIA Currently, the Li-ion battery cells of GAIA are equipped on a plug-in HEV at UC Davis, named Trinity The battery pack selected for the present simulation has the capability to supply a voltage of 355 V and a capacity of 240 Ah; that is, the battery energy becomes 85.4 kWh The plug-in HEB equipped with this Li-ion battery pack has an all-electric range of 40 miles Table shows the relationship between the open circuit voltages and internal resistances of the selected battery pack for each of the SOCs of the battery at the ambient temperature of 25oC The internal resistance decreases with increasing SOC until the middle SOC Then, it increases slightly until the SOC approaches its full state, at which point it increases For the plug-in parallel HEB in the present research, the battery pack should supply a power of at least more than 200 kW to the 180 kW electric motor, when the discharging efficiency of the battery is considered According to the computation, the battery pack used for the plug-in HEB can produce an output power of about 2,400 kW at 100% SOC Figure Efficiency map of the battery and 25 oC Therefore, this battery pack has the capacity to supply enough power to the 180 kW electric motor A relatively large battery pack should be selected for the plug-in HEB to improve its all-electric functionality and regenerative braking capability, as well as to operate the engine efficiently by using the powerful electric motor Moreover, an extension of the battery lifecycle can be expected The efficiency map of the battery selected for the plug-in HEB is shown in Figure This map displays the efficiencies under the discharge and charge conditions of the battery SUMMARY Configurations for the powertrain and energy management battery for building a hybrid electric bus were determined Also, the components and component sizes were selected based on the demands for hybridization of a heavy-duty conventional bus As shown in Table 4, the HEB employed the parallel and pre-transmission powertrain configurations The plug-in hybrid Table Open circuit voltage and internal resistance of the battery pack SOC (%) 10 20 30 40 50 60 70 80 90 100 Voc (V) 309 332 340 344 348 354 358 365 371 379 386 0.0375 0.0208 0.0165 0.0122 0.0124 0.0125 0.0131 0.0137 0.0139 0.0149 Rint (Ω) 0.1782 Table Conceptual design and component specification of the HEB Design and components Selection Specifications Comparison with a standard bus Electric energy charging system Plug-in N/A Hybrid powertrain configuration Parallel N/A Pre-transmission N/A Electric motor location Internal combustion engine Diesel engine 130 kW 280 kW Electric motor AC induction 180 kW N/A Transmission CVT Mechanical pulley with GCI chain MT (Manual trans) Battery Lithium-ion 85.4 kWh N/A Electric energy storage POWERTRAIN SYSTEM OPTIMIZATION FOR A HEAVY-DUTY HYBRID ELECTRIC BUS Figure Conceptual design plan of the proposed hybrid electric bus system, which has the benefit of a charge-depleting mode, was adopted as the method for recharging the battery pack For hybridization of the powertrain in the plug-in parallel HEB, a diesel engine was used, as for the conventional bus However, its power capacity was downsized to 130 kW An AC induction electric motor at 180 kW was selected A Li-ion battery pack at 80 kWh with high-voltage was utilized as the electricity storage system A high-torque CVT was equipped in the HEB for the transmission The downsized engine is capable of propelling the vehicle at a steady high cruise speed of 50~60 mph and, if necessary, the electric motor can assist the engine to obtain supplemental power The electric motor drives the HEB by itself when the operation efficiency of the engine is very low and, if necessary, it can charge the battery through regenerative braking and engine power, which is accomplished using the generator Because the Li-ion battery has high power and high energy densities, a relatively large battery pack could be used without greatly increasing the weight, in comparison with other types of batteries The large battery pack enables the high power electric motor to be equipped in the hybrid powertrain Using a high power electric motor can improve the all-electrical driving range, regenerative braking capability, and stopand-go performance Figure shows the conceptual design plan of the plug-in parallel HEB, which consists of the components proposed above CONCLUSIONS To hybridize a heavy-duty diesel bus, diverse hybrid powertrain configrations have been proposed in this paper The plug-in parallel hybrid configuration was finally chosen for the hybrid electric bus (HEB) based on preliminary simulations This is the first time that an attempt has been made to design a plug-in parallel diesel HEB A new design paradigm is given here to build a plug-in HEB, including the development of proposed emission standards (HDVs) for heavy-duty hybrid vehicles The duty of the powertrain controller is to control and manage the complicated connected components of the 139 powertrain, such as the internal combustion engine, electric motor/generator, transmission, clutch, and battery To validate the optimization concepts of the powertrain, a simulation was performed The Simulink block diagrams of specific components in ADVISOR were modified and newly built for the new powertrain control strategies and components These included the CVT gear shifting, hybrid mode selection, emission optimization application, driving pattern classification approach, and CVT mechanical dynamics A variety of simulations were conducted to validate and test the design and hybridization, as well as the proposed concepts of the powertrain REFERENCES Anderson, C D and Anderson, J (2005) Electric and Hybrid Cars: A History Burke, A F (1993) On-Off Engine Operation for Hybrid/ Electric Vehicle: Electric and Hybrid Vehicle Advancements SAE Publication SP-969 Warrendale Chan, C C and Chau, K T (2001) Modern Electric Vehicle Technology Oxford University Press Oxford He, B., Ouyang, M and Lu, L (2006) Modeling and PI control of diesel APU for series hybrid electric vehicles Int J Automotive Technology 7, 1, 91−99 Markel, T and Wipke, K (2001) Modeling grid-connected hybrid electric vehicles using ADVISOR NREL/CP540−30601 Matheson, P (2003) Modeling and simulation of a fuzzy logic controller for a hydraulic-hybrid powertrain for use in heavy commercial vehicles SAE Paper No 2003-013275 Trigui, R., Badin, F., Jeannert, B., Harel, F., Coquery, R., Lallemand, R., Ousten, J P., Castagne, M., Debest, M., Gittard, E., Vangreefshepe, F., Morel, V., Baghli, L., Reaaoug, A., Labbe, A., Labbe, J and Biscaglia, S (2003) Hybrid light duty vehicles evaluation program Int J Automotive Technology 4, 2, 65−75 Wang, B H., Zhang, J W and Luo, Y G (2007) The rapid development of parallel hybrid propulsion control system by an online calibrating system J Automobile Engineering, 221, 1555−1565 Wang, W., Zeng, X and Wang, Q (2003) Develop hybrid transit buses for chinese cities SAE Paper No 2003-010087 Wipke, K B., Cuddy, M R and Burch, S D (1999) ADVISOR 2.1: A user-friendly advanced powertrain simulation using a combined backward/forward approach NREL/JA-54026839 Zeng, X., Zeng, X., Qingnian, W., Weihua, W and Liang, C (2005) Analysis and simulation of conventional transit bus energy loss and hybrid transit bus energy saving SAE Paper No 2005-01-1173 Zhou, R S and Hashimoto, F (2004) Highly compact electric drive for automotive applications SAE Paper No 200401-3037 International Journal of Automotive Technology, Vol 12, No 1, pp 141−147 (2011) DOI 10.1007/s12239−011−0018−8 Copyright © 2011 KSAE 1229−9138/2011/056−18 APPLICATION STUDY ON A CONTROL STRATEGY FOR A HYBRID ELECTRIC PUBLIC BUS B H WANG* and Y G LUO Department of Automobile Engineering, Hubei Automotive Industries Institute, Hubei 442002, China (Received 30 March 2009; Revised July 2010) ABSTRACT−This paper first describes the control strategy used in a hybrid electric public bus and then proposes a torquebalancing control strategy Simulations were performed using the designed control strategies, and the results were analyzed under different conditions The torque-balancing control strategy was improved on the basis of the efficiency-first ideas of the hybrid system Finally, experiments were performed to verify that the efficiency-first and torque-balancing control strategy (EFCS) is both feasible and reliable The simulation results showed that, compared with a conventional public bus, the hybrid electric bus could save approximately 27.3 percent on fuel consumption using the EFCS control strategy in a public bus in China, while under the Wuhan urban driving cycle KEY WORDS : Hybrid electric bus, Control strategy, Simulation INTRODUCTION 2.1 Optimal Operating Point Control Strategy The optimal working point control strategy is also known as thermostat control The hybrid control system switches the engine working modes in real time according to the state of charge (SOC) of the battery, and the engine working points are the pre-determined best fuel consumption working point Regardless of the traffic load or the battery SOC changes, as long as the engine starts, the engine will always work at the best fuel consumption point, namely, It is possible for the hybrid electric vehicle (HEV) to overcome the disadvantages inherent in both the conventional vehicle (CV) and the pure electric vehicle (EV), while inheriting their advantages The potential of HEVs to reduce both emissions and fuel consumption has been widely accepted by many researchers The primary differences among the HEV, the CV and the EV are their drivetrains and the key technologies including the control strategies and the control system development for the hybrid propulsion system This paper describes the application of control strategies in HEVs and proposes an efficiency-first and torque-balancing control strategy (EFCS) Finally, by comparing simulation results for different strategies and different conditions, the effectiveness of the EFCS control strategy was verified ⎧ Pe = Popt ⎨ ⎩ ne = ne = nopt Pe = when when SOC = ON SOC = OFF (1) In equation (1), Popt is the engine power at the best fuel consumption point (kW), and nopt is the engine speed at the best fuel consumption point (rpm) The battery SOC is used as a logic parameter for the engine optimal operating point control strategy, and the control logics are expressed as follows: (1) If the current battery is lower than the minimal setting threshold soclow, (soc < soclow) then the engine starts (2) The engine continues to work and charge the battery pack until the battery SOC reaches the pre-set maximum threshold sochi, or the bus enters a low-emission or zeroemission section; then the engine is shut off The engine working point control strategy is a singleparameter control strategy with a simple control Because most of the engine's output power is used to charge the battery pack for the control strategy, the energy-use efficiency of the hybrid system is low and is often used in PRINCIPLES OF HYBRID SYSTEM CONTROL STRATEGIES Based on the control methods of engine working points, the control strategies for a hybrid propulsion system can be divided into three types creating the foundation to control the working points of an engine and the motor: the optimal working point control strategy, the optimal working curve control strategy and the optimal working region control strategy *Corresponding author e-mail: patriot_wang@163.com 141 142 B H WANG and Y G LUO Figure Engine minimal fuel consumption curve the control system with the series hybrid propulsion system Furthermore, due to the constraints of rechargeable batteries, the engine’s output power cannot be set very high To increase the output power of the vehicular battery and the battery-balanced ability, a high-power energy storage battery component, such as a super capacitor or a flywheel, should be added 2.2 Minimum Fuel Consumption Curve Control Strategy The minimum fuel consumption curve as it varies with different speeds, which is part of an engine characteristic map, is shown in figure For the control strategy, the engine always works along the lowest fuel consumption rate curve in accordance with the power demand of the hybrid electric bus and maintains the battery SOC at the normal states To meet the requirements needed when accelerating and starting from a standstill, the battery will supply additional power When starting a bus from a standstill, only the motor drives the bus The control model is expressed as follows: (1) If the battery SOC is lower than the pre-set value soclow, or the output power of the battery pack is insufficient to meet the traffic requirements, then the engine starts, and the battery operates in the charging mode The engine output power of the engine must satisfy the following: ⎧ Pe = PL + Paux + Pch ⎪ n = f ( Pe ) ⎨ ⎪ ( ne, Pe ) ∈ f( ne, Pe) ⎩ Pe_min ≤ Pe ≤ Pe_max (2) In equation (2), PL is the external load power, Paux is the accessory consumption power, Pch is the generating (charging) power of the electric machine, and f(ne, Pe) is a function of the engine’s minimum fuel consumption curve (2) After the engine starts, it continues to charge the battery until the battery SOC reaches the pre-set upper limit SOChi or until the bus enters a low- or zero-emission driving cycle region, which would cause the control strategy to force the engine to shut down The actual working modes are determined by the PL+Paux sum and the battery SOC for the control strategy The engine output power is used to track the road load, which not only improves the energy efficiency of the hybrid system but also reduces the power-balancing requirements The battery pack reduces the battery participation proportion and extends the battery life cycle, thus, a battery with a smaller power can be selected To avoid increasing emissions and energy consumption caused by frequent switching engines, the engine’s switch frequency must be restricted, and the smallest turn-on time of the engine must be set in advance The real-time detection and prediction of the power demand for automobiles is a complex issue during a driving cycle, which leads to decision-making difficulties in the engine control strategy 2.3 Best System Efficiency Control Strategy The best system efficiency control strategy regards the entire power requirement of a hybrid system as the control target and considers the characteristics of the battery pack, the electric machine and the multi-energy control system Depending on the location of the acceleration pedal, the driving power of a bus is divided into three areas: the highload power demand area, the medium-load power demand area and the low-load power demand area In the low-load area, the engine is off or works at the operating points to meet the charging power demands of the battery In the medium- or high-load areas, the engine works at the pre-set operating points but has enough power to recharge the battery pack When a driver pushes the brake pedal down, the engine either idles or is off, and the electric machine uses the braking power to charge the battery pack The control models are expressed as follows: (1) When, SOC < SOClow the engine starts, and the engine operating points are determined by the acceleration pedal opening αpedal and the current battery SOC Input commands of the motor controller are described by a function that depends on the acceleration pedal opening αpedal Depending on the motor torque output conditions, the available power demand of the bus depends on the above-mentioned conditions and the actual working speed of the motor (2) After the engine starts, it continues to charge the battery until the upper limit threshold sochi is reached When the bus enters a low-emission region, the engine is shut off DESIGN OF AN EFFICIENCY-FIRST AND TORQUE-BALANCING DISTRIBUTION STRATEGY 3.1 Torque-balancing Distribution Control Strategy APPLICATION STUDY ON A CONTROL STRATEGY FOR A HYBRID ELECTRIC PUBLIC BUS The torque-balancing control strategy consists of two parts: first, the torque-balancing management strategy based on the engine’s low-fuel economy and low-emissions areas, taking into account the efficiency of the hybrid system and the emissions; and second, the torque-balancing control algorithms that ensure that the vehicle drives with a smooth power transfer The operating state judgment, the torquebalancing management strategy and the torque control algorithms are the three important issues that the torquebalancing control of a hybrid electric bus must achieve The torque-balancing management strategy belongs to the energy management strategy of a hybrid electric bus During the actual operation of a bus, when the transmission stalls and the clutch is engaged, the engine speed and the motor speed are partially related; thus, it is more difficult to control the engine speed and the motor speed but relatively simple to control the engine torque and the motor On the basis of the steady-state efficiency of the engine, it is possible for the torque management strategy to use the torques as the major control variables to achieve a reasonable torque distribution between the engine and the motor During normal traffic, the demand torque of the transmission input end consists of two parts: first, the driver’s demand torque, which can be acquired according to the acceleration pedal stroke, the velocity and the speed ratio of transmission, etc.; and second, the demand torque for recharging the battery, which is relative to the demand torque and the battery’s SOC After the demand torque of the transmission input end is calculated, the torque management strategy can determine the motor efficiency and the operating mode of the hybrid propulsion system Because an engine is less efficient at low speeds or low loads, the hybrid electric bus operates in the pure motordriving mode when at a small or low-load signal Figure illustrates the characteristic diagram of a common rail diesel engine with 150 hp, where it can be seen that the engine is not efficient and is not sensitive to the engine Figure Characteristic map of a common rail diesel engine with 150 hp 143 speed at a small load In figure 2, the curve Lopt represents the optimal engine fuel consumption curve The curves LR_hi and LR_lo divide the entire steady-state efficiency characteristics map of the engine into three regions: the pure motor-driving mode, the pure engine-driving mode and the hybrid driving mode The torque-balancing control strategy is a rule-based management strategy According to the demand torque of the transmission input end Tdrv and the battery SOC, the torque-balancing management strategy determines the appropriate operating mode and the running conditions and determines whether the state switch conditions are met If the conditions are met, then the state is switched to the expected-driving mode The torquebalancing distribution rules are expressed as follows: (1) If Tdrv ∈ [ Te_lo Te_hi] , then Te_req = Tdrv, Tm_req = (2) If Tdrv > Te_max + Tm_max, then Te_req = Te_max, Tm_req = Tdrv− Te_req (3) If Tdrv ∈[Thi+Tm_max Te_max+Tm-max] and soc > soclo, then Te_req = Te_hi, Tm_req = Tdrv-Te_req (4) If Tdrv ∈[Thi+Tm_max Te_max+Tm_max] and soc ≤ soclo, then Te_req = Tdrv, Tm_req = hi + soc lo , the Te_req = (5) If Tdrv < Te_lo and soclo ≤ soc ≤ soc Te_lo, Tm_req = Tdrv−Te_lo hi + soclo and < Tdrv < Tm_max(nm), (6) If Tdrv < Te_lo, soc > soc then Te_req = 0, Tm_req =Tdrv hi + soclo and Tdrv > Tm-max(nm), then (7) If Tdrv < Te_lo, soc > soc Te_req = Te_hi, Tm_req = Tdrv−Te_req (8) If Tdrv < (namely, braking or sliding condition, and soc < sochi, then Te_req = 0, Tm_req = -Tdrv (9) If Tdrv < (namely, braking or sliding condition), and soc < sochi, then Te_req = 0, Tm_req = Here, Te_hi is the upper limit threshold of the engine torque, which corresponds with the engine torque curve LR_hi Te_lo is the lower limit threshold of the engine torque, which corresponds with the engine torque curve LR_lo 3.2 Efficiency-first Control Strategy The control strategies are complex and are one of the key technologies in the hybrid propulsion control system When designing the control strategies, many factors must be considered First, the operating conditions should be tested and analyzed The operating conditions can be divided into the engine start, the engine idling, the parking charge, the starting and accelerating process, the cruising process, the coast process, the braking or deceleration for the hybrid electric bus, etc Based on the identified conditions, input variables such as the acceleration pedal, the brake pedal, the velocity, the engine speed, the motor speed, etc., are analyzed, as well as the electronic control unit (ECU) of the hybrid electric public bus, the battery’s ECU and the motor’s ECU in different working conditions The control strategies are analyzed and collated, which is the basis for the preparation of the software (Oh et al., 2005) In view of the above analysis, the hybrid system 144 B H WANG and Y G LUO efficiency must be accounted for when designing the hybrid control strategy Based on the combined results of the previous optimization, the efficiency-first and the torque-balancing control strategy to balance the torques will be proposed, and its design will subsequently be discussed The basic ideas are as follows (Wang et al., 2007): (1) All the initial energies for the hybrid propulsion system originate from the engine (including the bus taxiway energy, the regenerative braking energy and the recycling energy of downhill, etc.), because the potential energy and the kinetic energy of the bus are provided by the engine When the bus is operating in the hybrid-driving mode, the total working efficiencies of the hybrid system will equal the product of the efficiencies of every component (2) At any engine speed, the best power allocation is determined based on the balanced combination of the dynamic torque distribution strategy and the most efficient power distribution for the current engine speed; thus, this strategy is actually a local, optimally efficient strategy (3) When the engine torque of the current cycle is smaller than the torque of the previous cycle, the current engine order remains unchanged As a result, the rapid changes of the engine order are avoided, which will reduce dynamic emissions On the basis of the torque-balancing control rule, additional rules are added as follows: Rule 1: when Tdrv ∈ [0 Te_max(ne)], the best powerdistributing strategy is the largest of the synthesis efficiencies for the engine-driving strategy, the optimal efficiency curve strategy and the torque-balancing distribution strategy Namely, ηtc_opt = max{ηe ( ne, Tdrv ), ηe_opt( ne, Tef_opt ) ⋅ ηbcd, ηe ( ηe, Te_req ) ⋅ ηmc( nm, Tm ) ⋅ ηbcd } (3) As seen in equation (3), ηe(ne, Tdrv) represents the net engine-driving efficiency ηe_opt (ne, Tef_opt) is the efficiency of the working point of the optimal efficiency curve, ηe_opt (ne, Tef_opt)·ηmc (nm, Tm)·ηbcd is the system efficiency of the optimal efficiency curve, ηe (ne, Te_req)·ηmc(nm, Tm) is the hybrid system efficiency of the torque-balancing control strategy, Tm equals the difference between Tdrv and the engine torque Te, and ηbcd is the battery charging/ discharging efficiency The ideal output torque of the engine and motor is the torque item corresponding to the greatest efficiency Rule 2: when, Tdrv > Te_max(ne), the best optimal torquedistributing strategy is the maximum of the synthesis efficiency of the three allocation strategies from the optimal efficiency curve, the torque-balancing distribution strategy and the largest engine torque distribution strategy That is, ηtc _opt = max { ηe_opt( ne, Tef_opt ) ⋅ ηmic( nm, Tm ) ⋅ ηbcd ), ηe_max ( ne, Te _max ) ⋅ ηmc ( nm, Tm ), ηe( ne, Te_req ) ⋅ ηmc ( nm, Tm )} ⋅ ηbcd (4) where he_max(ne, Te_max)·ηmc(nm, Tm) is the working efficiency of the hybrid system on the maximal engine torque; also, the decision-making torque corresponding with the largest efficiency item is the ideal output torque of the engine and motor Rule 3: when Te( k + ) – Te( k) < Tthreshold, Te should remain unchanged Te(k+1) denotes the command torque of the next moment, Te(k) denotes the command torque of the current moment, and Tthreshold denotes the setting threshold of the engine torque Based on the optimized efficiency-first torque distribution control strategy, the optimal results can be reasonably applied, and the hybrid control system will Table Basic specifications of the prototype buses Parameters Prototype bus A Prototype bus B Prototype bus C 14,000 kg 14,000 kg 14,000 kg 1.5 1.55 - 6.166 5.571 6.166 Common rail diesel engine Common rail diesel engine Diesel engine 110 kW /2500 rpm 110 kW /2500 rpm 162 kW /2500 rpm Motor model SR motor SR motor - Rated/maximal power of motor (kW) 35/60 kW 35/60 kW - Ni-MH Ni-MH - 336 V /40Ah 336 V /40 Ah - 6-speed 5-speed 6-speed Test weights (kg) Torque-coupling ratio Final drive ratio Engine type Engine power (kW) Battery type Rated voltage (V)/Rated capacity (Ah) Transmission Ratios of the gears 6.983:3.996:2.511:1.757:1.306:1 6.9:3.83:2.32:1.49:1 6.983:3.996:2.511:1.757:1.306:1 APPLICATION STUDY ON A CONTROL STRATEGY FOR A HYBRID ELECTRIC PUBLIC BUS 145 distribute the torques between the engine and the motor according to the most efficient torque distribution Thus, the rules are clear, and the calculation is simple and can be easily achieved ANALYSIS OF THE SIMULATION RESULTS 4.1 Basic Description To verify the control effect and the adaptability of the EFCS control strategy, the following three simulation studies were performed: (1) the effect study, which tests different control strategies using the same system parameters and the same driving cycles; (2) the dynamic performance and fuel economy study, where the same system parameters are used but with different driving cycles; (3) the performance comparison study, where the same driving cycles are used for different buses with different system configurations The four types of driving cycles selected were the federal test cycle (FTP), the city four-step urban driving cycle (CITY_4UDC), the urban driving cycle of the typical cities in China (CTC_UDC) and the Wuhan typical urban driving cycle (WH_UDC) The CITY_4UDC represents heavy-duty commercial vehicles operating at city driving conditions The WH_UDC is an actual Wuhan city urban driving cycle established from the characteristics of actual running routes of a Wuhan city public bus Wuhan is one of the three running-model areas to test the hybrid electric vehicle in China, and the four types of driving cycles constitute the current cycles used to test the fuel economy of hybrid electric buses (Wang, 2008) Table lists the basic specifications of the three prototype buses used in the simulation analysis Prototype A and prototype B are the hybrid electric buses with different system configurations, and prototype C is a conventional bus with a pure diesel engine Figure Torque distribution of the engine and motor with the BCS control strategy Figure Torque distribution of the engine and motor with the FOCS control strategy 4.2 Comparisons of Different Control Strategies with the Same System Configuration and the Same Driving Cycles In this section, prototype bus model A was selected, and the CITY_4UDC was used In the aforementioned Table Simulation results of different control strategies under the CITY_4UDC driving cycle for prototype bus A Fuel consumpAccelerating tion improveFuel time from Control consumption ment compared to 50 km/h strategies with the BCS (l/100 km) (sec) (%) BCS 25.1 0.00 28.9 FOCS 27.1 -7.96 41.4* EFCS 24.2 +3.59 23 * The technical requirement: the accelerating time from to 50 km/h is less than or equal to 35 seconds Figure Torque distribution of the engine and motor with the EFCS control strategy conditions, the three control strategies, which are the baseline control strategy (BCS), the fuzzy optimum curve control strategy (FOCS) and the efficiency-first and torquebalancing control strategy (EFCS), were selected and studied Table lists the simulation results under the conditions 146 B H WANG and Y G LUO of the battery SOC balance Clearly, the fuel consumption with the FOCS showed a 7.96 percent increase compared with the BCS, but the dynamic performance did not meet the requirement set for a city public bus The fuel consumption simulation result of the EFCS was 24.2 l/ 100 km, which was a reduction of 3.59 percent, and the dynamic performance improved because the acceleration time from to 50 km/h was 23 seconds Thus, the EFCS control strategy significantly improved the fuel economy and the driving performance of the EQ6110HEV buses compared with the other control strategies The following simulation results of the three control strategies can be seen in figures 3-5: (1) the greater the participation proportion of the motor, the higher the fuel consumption per 100 km for buses at the acceleration stage or the constant-speed stage; (2) for the deceleration process, the larger the energy-regenerative braking ratio, the smaller the proportion of the braking friction power, and the fuel-saving effect can be seen more clearly; (3) there are two reasons why using the FOCS control strategy resulted in a higher fuel consumption per 100 km First, the load rate of the engine was slightly higher due to the battery being recharged; and second, there was a higher proportion of the motor generation power, which reduced the average efficiency of the hybrid propulsion system; (4) when the bus starts from a standstill in the pure motordriving mode, in order to reduce emissions, the engine did not begin working effectively until a velocity threshold was reached (5 km/h) According to the optimization curve of the control strategy in line with the aforementioned principles, the optimal engine and motor torque distribution ratio was determined in accordance with the driving cycle curve to attain a better fuel economy but also to ensure the driving performance of the hybrid buses 4.3 Comparisons of Different Driving Cycles with the Same System Configuration The simulation analysis was separately performed based on the EQ6110HEV prototype A under the FTP, the CTC_UDC and the WH_UDC driving cycles The analysis compared their fuel economies and evaluated the control effect and the adaptability of the EFCS control strategies under different driving cycles Also, the simulation results Table Comparisons of fuel economies under different driving cycles for the hybrid bus A Fuel consumption (l/100 km) Driving cycles Improvement of the relative fuel economy （%） Figure Improvement of the fuel economy under different urban driving cycles were compared with the results when the BCS control strategy was used at the same test conditions It can be seen from the simulation results in table that the EFCS control strategy was better able to adapt to the different driving cycles The EFCS fuzzy logic controller could reasonably determine the torque distribution ratio of the engine and the motor and achieved a desired control effect with actual driving cycles As shown in figure 6, because the operating characteristics of the public buses in China were considered and optimized in advance and then used in the design of the fuzzy logic controller, the fuel economy using the EFCS control strategy was improved approximately 20.2 percent relative to the BCS control strategy under the CTC_UDC driving cycle Under the WH_UDC driving cycle, the fuel economy of the EFCS control strategy improved approximately 20.3 percent relative to the BCS strategy, and the fuel consumption was 35 l/100 km Using the EFCS control strategy, the fuelsaving rate was maintained above 18 percent under the different control strategies The results show that the EFCS control strategy adapted well to different operating conditions and achieved a better fuel economy 4.4 Comparisons with different driving cycles and different system configurations To verify the adaptability to different buses for the EFCS control system, the control effect of the EFCS control Table Robustness of the EFCS control strategy for different parallel hybrid buses Fuel consumption (l/100 km) EFCS control strategy BCS control strategy Improvements of the fuel economy (%) +18.5 Prototype A 35.0 42.1 +20.3 54.5 +20.2 Prototype B 35.9 40.1 +11.7 42.1 +20.3 Error of A to B 2.6% 5% - EFCS control strategy BCS control strategy FTP 40.4 49.5 CTC_UDC 43.5 WH_UDC 35.0 APPLICATION STUDY ON A CONTROL STRATEGY FOR A HYBRID ELECTRIC PUBLIC BUS strategy was analyzed using the prototype buses A and B The two buses differed in their transmission ratios and in their final ratios As shown in table 4, prototype buses A and B have almost identical fuel consumptions using the EFCS control strategy under the Wuhan actual driving cycle; the error was only 2.6 percent Thus, the EFCS strategy is able to adapt well to buses with different transmission parameters In comparison with the BCS control strategy, bus A improved the fuel economy by approximately 20.3 percent, and bus B improved the fuel economy by approximately 11.7 percent, where the error of the fuel consumptions for the two buses was nearly 5% under the BCS control strategy In comparison with the EQ6110 conventional bus with a pure diesel engine (prototype bus C), the average fuel consumption was approximately 44.53 liters per 100 km under the WH_UDC urban driving cycle, and the fuel consumption of the hybrid bus A was 35 liters per 100 km based on the EFCS control strategy using the same driving cycle Therefore, the efficiency-first and torquebalancing control strategy reduced the fuel consumption by approximately 27.3 percent for the EQ6110HEV hybrid bus The results show that the efficiency-first fuzzy logic control strategy not only improved the fuel economy but was also able to adapt to different buses CONCLUSION To further improve the performance of a hybrid city bus, the efficiency-first control strategy was proposed, and the hybrid controller was designed Simulations were performed and analyzed under different configurations, different control strategies and different driving cycles The analysis showed that using the efficiency-first control strategy resulted in excellent performance and adapted well to different conditions The study showed that the 147 efficiency-first control strategy effectively improved the fuel economy and power performance of the hybrid city bus and exhibited strong adaptability and robustness behavior Under the same conditions, the efficiency-first control strategy improved the fuel economy by approximately 27.3% compared with the EQ6110 conventional city bus ACKNOWLEDGEMENT−We are grateful for the valuable revising advises by Prof J-W, Zhang for the paper, discussing its functionality with us, and analyzing the test results REFERENCES Luo, Y G., Wang, B H and She, J Q (2005) Application of HC9S12 micro processor in integrated starter/ generator of hybrid electric vehicle IEEE Proc ICVES2005, 305−310 Oh, K., Kim, D., Kim, T., Kim, C and Kim, H (2005) Efficiency measurement and energy analysis for a HEV bench test and development Int J Automotive Technology 6, 5, 537−544 Wang, B H (2008) Optimal and Experimental Investigation to Control Strategy of Hybrid Electric Bus [D] Ph D Dissertation Shanghai Jiaotong University China Wang, B H., Luo, Y G and Zhang, J W (2008) Simulation of city bus performance based on actual urban driving cycle in China Int J Automotive Technology 9, 4, 501−507 Wang, B.-H., Zhang, J.-W and Luo, Y.-G (2007) The rapid development of parallel hybrid propulsion control system by online calibrating system Proc Instn Mech Engrs., 221, Part D: J Automobile Engineering, 1555− 1567