International journal of automotive technology, tập 10, số 4, 2009

Copyright © 2009 KSAE 1229−9138/2009/047−01 International Journal of Automotive Technology, Vol 10, No 4, pp 405−411 (2009) DOI 10.1007/s12239−009−0046−9 DEVELOPMENT OF QUANTITATIVE MEASURING TECHNIQUE TO FIND CRITICAL FLOW CONDITIONS FOR PREVENTING SOOT DEPOSIT ACCUMULATED IN THE DIESEL EXHAUST SYSTEM USING MAIN MUFFLER COMPOSED OF THREE CHAMBERS B.-H SONG and Y.-H CHOI 1) 2)* Graduate School, Ajou University, Gyeonggi 443-749, Korea Department of Mechanical Engineering, Ajou University, Gyeonggi 443-749, Korea 1) 2) (Received 23 July 2008; Revised 28 October 2008) ABSTRACT−If a vehicle that meets emission regulations operates sufficiently for a long time under low speed and low load conditions, soot contained in the exhaust gas is accumulated on the inner surface of the exhaust system This soot deposition problem occurs frequently in all diesel cars However, when a vehicle is placed under the conditions of sudden start and sudden acceleration after city mode driving for a long time, the deposited soot is abruptly blown up with the soot produced during fuel combustion In the present study, the main cause of the abrupt outburst of deposited soot is investigated to overcome this adverse phenomenon First, we developed a method to quantify the amount of the exhausted soot particles (or the accumulated soot particles) by measuring the opacity that represents the contamination level of the exhaust gas due to soot particles Using this measuring scheme for deposited soot, we found the critical conditions for engine speeds and load conditions at which soot particles are emitted into the air without accumulation in the exhaust system using main muffler composed of three chambers In order to meet these critical conditions and thus to drastically reduce soot accumulation, the exhaust system using the main muffler applied in this study must be designed to ensure that the flow velocity of the exhaust gas is higher than 62 m/s when the back pressure at the exit of the turbocharger is under 0.08 bars KEYWORDS : Deposited soot, Opacity, Particulate matter, NOx, Char, Coke, Soot, Spherule, PAH NOMENCLATURE FSN N PM SOF FTP PAH In the harmful exhaust gas from the diesel engine, the concentration of nitrogen oxides (NOx), whose main components are nitrogen monoxide (NO) and nitrogen dioxide (NO ), is similar to that of a spark-ignited engine, but particulate matter, in contrast, is considerably higher The worst issue is that 0.2% to 0.5% of fuel weight is exhausted into the form of small particles below 0.1 µm in diameter (Heywood, 1988) In particular, ultrafine particles below 0.1 µm in diameter are exhausted into the atmosphere, and they can penetrate deeply in the human body through the respiratory organs Most medical experts warn that these ultrafine soot particles can cause chronic pulmonary disease, lung cancer, cardiovascular disease, influenza, asthma, etc Also, ozone is a known toxic material causing respiratory problems by damaging the lungs, and ground-level ozone (O ) is formed by chemical reactions between NOx and volatile organic compounds (VOCs) in the presence of sunlight Here, VOCs are unburned hydrocarbons produced in the process of fuel combustion Moreover, a chain reaction of NOx that produces ozone photochemically in daylight threatens to destroy the ozone layer within the stratosphere at night This results in an increase of the quantity of ultraviolet emissions from the sun and has a harmful influence on the : filter soot number : opacity [%] : particulate matter : soluble organic fraction : federal test procedure : polycyclic aromatic hydrocarbons INTRODUCTION The diesel engine has the potential to reduce the environmental pollution problem The diesel engine is not only more economical due to higher thermal efficiency compared to other internal combustion engines, but also shows lower concentrations of carbon dioxide in the exhaust gas However, as the interest in both environmental pollution and health increases dramatically, the harmfulness of diesel exhaust emissions has been highlighted due to the bad influence of its toxic components on the environment and on the human body Therefore, the exhaust emission standards of each country are becoming stricter * Corresponding author e-mail: ychoi@ajou.ac.kr 405 406 B.-H SONG and Y.-H CHOI earth's atmosphere Therefore, it is important to reduce harmful emission components from diesel engines, such as NOx and PM, during the design and the development of diesel vehicles Many studies have been made attempting to decrease harmful exhaust gas dramatically in the fields of pretreatment, after-treatment, and new combustion system development, and now most automobile manufacturers are producing passenger cars that meet present emission standards However, when diesel vehicles that satisfy the legal criteria for exhaust gas emissions are placed under conditions of low speeds and low loads for a long period of time, such as in city driving, most of the vehicles have difficulty avoiding the soot deposit phenomenon wherein soot particles contained in exhaust gas collect on the inner surface of the exhaust system In particular, the deposited soot particles are abruptly emitted into the atmosphere when a driver makes a sudden start or sudden acceleration, and a big soot cloud is formed behind the car because the quantity of emitted soot particles is extraordinarily greater than that of soot particles produced automatically through the general combustion of a direct injection (DI) diesel engine Sometimes this fine black soot cloud can even hide the vision of a driver Therefore, it is necessary to study this problem In the present study, we analyzed the cause of the soot deposit phenomenon to find a solution to the abrupt emission of deposited soot particles under conditions of sudden start and sudden acceleration In addition, we have developed a method to measure the quantity of soot particles deposited in the exhaust system, and we present a design guide to remove the soot deposition problem FORMATION AND OXIDATION OF SOOT 2.1 Soot Characteristics Particulate matters consisting of carbon compounds are produced in the combustion process, and their color in general is black According to formation processes, particulate matter is classified mainly into char, coke, and soot Figure Photomicrograph of a soot particulate formed during diesel combustion Char is the particulate matter made of solid components of residue after the volatile components of fuel are evaporated Particulate matter formed by the pyrolysis of hydrocarbon in the liquid phase is called coke In particular, soot is formed in the combustion process of gas phase fuel in a high temperature condition, and it includes a soluble organic fraction (Lee , 2006) Figure shows the soot configuration observed by an electron microscope We can see that the soot spherules of 10~80 nm in diameter (most of them are 15~30 nm) or its collectives are connected in a chain shape (Haynes and Wagner, 1981) et al 2.2 Soot Formation When the chemical energy contained in hydrocarbon fuel is not completely released into available energy through combustion, soot is produced Therefore soot formation due to incomplete combustion results in a decrease in fuel efficiency The process of soot formation also includes various physical and chemical steps Many theories have been proposed to explain these steps, and the general theory is as follows (Bockhorn and Schäfer, 1994) 2.2.1 Formation step of macro-molecule precursor The formation of molecular precursor that represents the origin of soot formation is the most important step in the process of soot formation, and is achieved through the chemical condensation of gas phase material and the coagulation reaction Even though it has not yet been clarified how the molecular precursor is changed into a soot particle, two types of transition processes, such as physical condensation and chemical condensation, have been suggested The physical condensation process occurs when a high molecular precursor is physically condensed into liquid phase soot when a high molecular precursor produced by a reaction of the gas phase is supersaturated The chemical condensation process occurs when soot particles start to be produced by the consecutive chemical reaction of a high molecular precursor (Amann and Siegla, 1982) 2.2.2 Surface growth step via the coagulation of the growth agents of the gas state The surface growth of soot particles occurs by the following heterogeneous processes: (1) Through the deposit of hydrocarbon in the gas state on the hot surface of a particle; (2) Through the chemical reaction on the particle surface where carbon atoms are added; and (3) Through the desorption of products from the particles Also, the mass of soot particles increases significantly through the surface growth process compared to the mass at the initial stage of soot formation Even though the surface growth process of soot particles has not yet been clarified, we can assume that the surface of a soot particle is similar to the border of polycyclic DEVELOPMENT OF QUANTITATIVE MEASURING TECHNIQUE TO FIND CRITICAL FLOW CONDITIONS Table Test engine specifications Engine type Displacement Bore Stroke Number of valves Valve arrangement Compression ratio Number of injectors 407 4-stroke, 5-cyl 2700 cc 86.2 mm 92.4 mm 20 DOHC 18:1 Figure Microstructure of a diesel soot particle aromatic hydrocarbons (PAH) covered with the chemical bonds of carbon and hydrogen As a hydrogen atom from the particle’s surface is isolated, the reaction of the particle surface is activated, resulting in the production of radicals that are highly reactive unpaired electrons The general theory of the surface growth mechanism of soot particles is that the surface is grown by the reaction of the produced radicals and the gas state hydrocarbons (Karlsson , 1998) et al 2.2.3 Collective coagulation step to form a bigger particle Since coagulation is the process of becoming a bigger particle through the merger of colliding particles, this process takes place immediately after soot formation, or when the soot particle is relatively small or immature The number of total particles decreases in this process Figure shows the detailed structure of a soot particle We can see that there are several fine particles in a soot particle 2.2.4 Agglomeration step to form a chain type collective In the latter stage of soot formation, the particles stick together and begin to agglomerate to make a collective dump of soot particles Since the reaction region of particle surfaces is reduced in this step, the chain-type unresolved particle collective is built up due to the agglomeration of small spherical particles (about 30~1800 particles) instead of surface growth due to particle integration Figure Measuring principle of AVL opacimeter based on the Beer-Lambert law real time Figure shows the measuring principles of this equipment which measures the opacity of the air contaminated by diesel exhaust emissions When the measuring chamber consisting of a nonreflecting surface with a length of 0.43 m is homogeneously filled with exhaust gas, the intensity of the light source diminishes This loss of light intensity is due to light dispersion from collisions between the light and the particulate matter in the exhaust gas Therefore, the loss occurring from the light source to the light receiver is measured as opacity The opacity of the exhaust gas is calculated using Equation (1) which is based on the Beer-Lambert law Equation (2) defines the opacity N [%] that is used to quantify the amount of exhausted soot = I ⋅ e –kL (1) N -I =1 (2) I I0 − 100 2.3 Soot Oxidation Oxidation reaction on the surface of a particle is found to occur during the entire process of soot formation In contrast to the growth process where atoms are added to soot particles, the carbon deposited on soot particles is consumed by the oxidation reaction where TEST CONDITIONS AND MEASURING EQUIPMENT EXPERIMENTAL RESULTS Our test was performed on a 2700 cc, 5-cylinder, DI diesel engine The specifications of the engine are shown in Table An opacity measurement instrument made by AVL was used to measure the pollution level of the exhaust gas in = Intensity of the light source = Intensity of the light after traveling the measuring length [m−1] = Absorption coefficient [m] = Measuring length I0 I k L 4.1 Derivation of a Correlation Formula Between the Opacity and the Mass Flow Rate of Exhausted Soot Some soot particles contained in the exhaust gas are deposited on the inner surface of the exhaust system when a vehicle operates at low speed and low load conditions for 408 B.-H SONG and Y.-H CHOI Figure Variation of exhaust soot mass flow rate and opacity according to engine speed Figure Exhaust system used for Diesel Euro III engine a long period of time This is the main cause of soot deposits, and results in much more soot than that produced by normal combustion Figure depicts the exhaust system of a 2700 cc, 5-cylinder engine that meets the emission regulations of Euro III Total soot quantity (f) emitted from the exhaust can be described as a function of mass flow rate of the exhaust gas (g) and soot concentration of exhaust gas (h) Equation (3) represents this physical relationship f ⎛ Exh Gas Flowrate, ⎞ = g ( rpm ) × ⎝ Soot Concentration ⎠ h ( Opacity ) (3) The contamination level of exhaust gas (or the soot concentration) according to operating conditions is measured to formulate a mathematical model for the relationship between the opacity representing the soot concentration and the mass flow rate of exhaust gas Figures and show the variation of exhaust soot mass flow rate and opacity according to engine speed, and the correlation of opacity and soot concentration in terms of the filter soot number (FSN), respectively Using the experimental results shown in Figures and 6, the correlation formula between the operating speed of the engine and the soot mass flow rate at each operating speed (Equation (4)) is derived and is depicted in Figure Exhaust Soot Mass Flowrate [g/s]= Exp [0.8033+ln(X)]×Opacity (4) 4.2 Method to Quantify the Deposited Soot To quantitatively estimate the amount of soot deposited in the exhaust system according to the operating conditions, we perform experiments to evaluate soot accumulation Figure shows the operating mode to completely blow out the soot remaining in the exhaust system before starting the soot accumulation for this study After performing the Figure Correlation of opacity and soot concentration versus FSN Figure Correlation of exhaust soot mass flow rate and engine speed blow-out mode for enough time to completely remove the remaining soot, the engine is operated for a certain time DEVELOPMENT OF QUANTITATIVE MEASURING TECHNIQUE TO FIND CRITICAL FLOW CONDITIONS Figure Operating mode for completely blowing out residual deposited soot Table Selected operating conditions for investigating soot deposit phenomena Test No Speed [rpm] 969.4 1598.1 1923.7 2233.2 Torque [Nm] 80.1 107.8 150.8 213.3 period to preheat the total system and to stabilize the oil and the coolant temperature Table summarizes the operating conditions to simulate the soot deposit phenomena These are four types of operating conditions that appear often in the city drive mode of the federal test procedure (FTP) After the soot is accumulated during steady-state operation of two hours for each experimental condition summarized in Table 2, the opacity of the exhaust gas is measured during the blow-out mode depicted in Figure In Figure 9, the shaded part of the opacity curve measured during this experiment can be defined as the Figure Operating mode for quantitatively measuring the mass of deposited soot after accumulation 409 abrupt emission quantity of the deposited soot The opacity that converges to constant value in the latter half of the measurement can be defined as the quantity of soot normally produced in the combustion process at a corresponding operating condition Therefore, the deposited soot quantity can be calculated using the opacity of exhaust gas measured during the blow-out mode (Figure 9) and the correlation of the mass flow rate of exhaust soot with engine speed (Equation (4)) Equation (5) describes the calculation method of deposited soot The total soot mass accumulated in the exhaust system= the total soot mass (calculated using Equation (3) and the opacity curve shown in Figure 9) − the soot mass produced by engine combustion itself (calculated using Equation (3) and the dotted line in Figure 9) (5) 4.3 Calculation of Soot Quantities Deposited According to Operating Conditions The morphological structure of soot particles produced in the combustion process is very similar to that of graphite, but the chemical composition of a soot particle can not be defined clearly as can that of graphite In addition, there is a considerable difference in chemical composition between immature soot and mature soot In particular, since the internal fine structure of a soot particle is sensitive to temperature change, it is known that there is also a considerable difference in structure between a soot particle in the exhaust gas and a soot particle in the initial production stage (Senda , 2002) All soot particles produced at the operating conditions described in Table are not completely emitted into the air by passing through the exhaust system from the combustion chamber In the present study, the quantity of soot particles accumulated on the inner surface of the exhaust system is calculated using the method described in Section 4.2 et al Figure 10 Time history of opacity during blowout test mode after depositing soot for hours at each test condition 410 B.-H SONG and Y.-H CHOI Table Accumulated soot quantity at selected operating conditions to investigate the soot deposit phenomenon Test No Speed [rpm] 969.4 1598.1 1923.7 2233.2 Torque [Nm] 80.1 107.8 150.8 213.3 Deposited soot [g] 0.678 0.748 0.644 1.357 Total soot [g] 8.355 11.672 12.590 16.173 Figure 10 shows the real time variation of opacity measured at the exit of the muffler during the blow-out mode after the soot is deposited for two hours of operation under the conditions shown in Table With this, we can evaluate the soot quantity quantitatively Table shows the calculated results for each curve in Figure 10 using the method described in Section 4.2 We can see that the amount of accumulated soot corresponds to the operating speed and the smoke level that represents the combustion characteristics of the engine used in this study 4.4 Critical Condition for Soot Particle Accumulation The critical condition of soot deposit occurs when the soot particles are emitted into the air without accumulating in the exhaust system Experiments are performed to determine the critical operating condition In the soot deposit phenomena, the most influential parameters are the flow velocity of the exhaust gas and the back pressure condition in exhaust system; these parameters are dominated by change of engine speed Among the test conditions listed in Table 2, an engine speed of 1598 rpm and torque of 108 Nm are used to accumulate the soot particles After running for one hour at this deposit mode, the engine speed is raised to 2000 rpm and 2500 rpm We then tried to find the critical condition at which the deposited soot particles start to blow out by measuring the opacity variation at the exit of the muffler with a gradual change of load conditions Figure 11 shows the experimental results in our attempt to find the critical condition of soot deposit From the experimental results used for the derivation of the correlation formula (Figure 5), the maximum opacity values that appear naturally at the operating conditions of 2000 rpm and 2500 rpm are 15 and 12, respectively In Figure 11, we can define the critical condition as the point that exceeds these natural maximum opacities Therefore, we can see that the engine speed and load conditions of 2000 rpm, 58% and 2500 rpm, 50% are the critical conditions for soot accumulation Figure 12 shows the back pressure measured at the exit of the turbocharger when the engine operated at 2000 rpm and 2500 rpm It can be seen that the back pressure condition at which soot particles are no longer deposited is about 0.08 bars at the exit of the turbocharger Figure 13 shows the exhaust gas velocity measured just before the exit of the muffler according to variation of engine speed It can be seen that the exhaust gas velocity at the critical Figure 11 Time history of opacity during blowout test mode after depositing of soot to find the critical condition of soot accumulation Figure 12 Turbocharger outlet pressure versus variation of pedal position at engine speeds of 2000 rpm and 2500 rpm Figure 13 Expected exhaust gas velocity at the exit of the main muffler depending on engine speeds and loads DEVELOPMENT OF QUANTITATIVE MEASURING TECHNIQUE TO FIND CRITICAL FLOW CONDITIONS condition is approximately 62 m/s This means that some of the soot particles produced during the combustion process is accumulated in the exhaust system at operating conditions such that the flow velocity of exhaust gas is less than 62 m/s Therefore, the solution for removing or drastically reducing the soot accumulation phenomena in the exhaust system is to decrease the flow resistance of the exhaust system, and to increase the flow velocity of exhaust gas to be greater than 62 m/s when the back pressure at the exit of the turbocharger is under 0.08 bars CONCLUSIONS When a vehicle operates at low speeds and low load conditions for a long period of time, some of the soot particles produced in the combustion process is not emitted to the air, and they are deposited on the inner surface of exhaust system This causes much more soot than that produced by normal combustion and they are abruptly exhausted when the vehicle is suddenly started or suddenly accelerated In this study, the soot deposit phenomena were investigated using a 2700cc, 5-cylinder engine having the specifications in Table and the main muffler depicted in Figure The results can be summarized as follows: (1) We developed a method to quantify the amount of the exhausted soot particles (or the accumulated soot particles) by measuring the opacity that represents the contamination level of the exhaust gas due to soot particles Using this quantification method, the deposited soot quantities at the operating conditions shown in Table were calculated and the results were summarized in Table Since the accumulated soot quantity at each operating condition in Table corresponds to the smoke level measured at each operating condition, we succeeded in quantifying the exhaust smoke level measured in terms of opacity which has a characteristic that as the amount of particulate matter in the exhaust increases, the opacity of the exhaust also increases (2) Experiments were performed to find the critical condition at which soot particles are emitted into the air without accumulation in the exhaust system using the main muffler composed of three chambers as depicted in Figure From the experimental results, we found that the critical conditions are the engine speeds and 411 the load conditions of 2000 rpm, 58% and 2500 rpm, 50% At each critical condition, the back pressure at the exit of the turbocharger was approximately 0.08 bars, and the emission velocity of the exhaust gas at the exit of the muffler was about 62 m/s Therefore, in order to remove or drastically reduce soot accumulation, the exhaust system using main muffler applied in this study must be designed to decrease the flow resistance and to ensure that the flow velocity of the exhaust gas is higher than 62 m/s when the back pressure at the exit of the turbocharger is under 0.08 bars ACKNOWLEDGEMENT−This research was financially sup- ported in part by the Ministry of Knowledge Economy (MKE) and Korea Industrial Technology Foundation (KOTEF) through the Human Resource Training Project for Strategic Technology REFERENCES Amann, C A and Siegla, D C (1982) Diesel particulateswhat they are and why Aerosol Science and Technology, 1, 73−101 Bockhorn, H and Schäfer T (1994) Growth of Soot Particles in Premixed Flames by Surface Reactions Soot formation in Combustion Springer-Verlag Berlin Haynes, B S and Wagner, H G (1981) Soot formation Progress in Energy and Combustion Science, 7, 229− 273 Heywood, J B (1988) Internal Combustion Engine Fundamentals McGraw-Hill International Karlsson, A., Magnusson, I., Balthasar, M and Mauss, F (1998) Simulation of soot formation under diesel engine conditions using a detailed kinetic soot model SAE Paper No 981022 Lee, D G., Miller, A., Park, K H and Zachariah, M R (2006) Effects of trace metals on particulate matter formation in a diesel engine: metal contents from ferrocene and lube oil Int J Automotive Technology 7, 6, 667−673 Senda, J., Choi, D., Iwamuro, M., Fujimoto, H and Asai, G (2002) Experimental analysis on soot formation process in DI diesel combustion chamber by use of optical diagnostics SAE Paper No 2002-01-0893 International Journal of Automotive Technology, Vol 10, No 4, pp 413−420 (2009) DOI 10.1007/s12239−009−0047−8 Copyright © 2009 KSAE 1229−9138/2009/047−02 EFFECTS OF END COILS ON THE NATURAL FREQUENCY OF AUTOMOTIVE ENGINE VALVE SPRINGS H LIU and D KIM 1) 2)* Graduate School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 680-749, Korea Department of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 680-749, Korea 1) 2) (Received December 2008; Revised 10 April 2009) ABSTRACT−In this paper, we present a method for estimating the natural frequencies of various engine valve springs such as constant pitch, two-step variable pitch, three-step variable pitch, and progressive springs Since a valve spring’s surging amplitude is magnified when the spring’s natural frequency coincides with the frequency of the cam profile harmonic components, estimating the natural frequency of the spring is the first step in predicting valve spring surging phenomena A new method for calculating the valve spring’s natural frequency is proposed in this paper that considers the end coil effect This method predicts not only the natural frequency of a helical spring at a fixed number of active turns, but also the change in the natural frequency as the spring is compressed The experimental results demonstrate that nonlinear characteristics of engine valve springs can be predicted from the given initial pitch curves KEY WORDS : Valve spring, Variable pitch spring, Natural frequency, Spring surging, End coil effect NOMENCLATURE capacity for the spring, which can cause premature failure and malfunctions in valve motion The fundamental natural frequency of the longitudinal vibration mode is the first indicator in valve spring surging estimation (Kim and Nguyen, 2007) However, because the number of active coils changes according to spring height, the natural frequency of the valve spring cannot be assumed to be constant during operation; even the natural frequency estimation for a fixed spring height is not a straightforward task due to the end coil effect at the boundaries The natural frequencies of helical springs have been studied for many years Some early studies derived simple equations for estimating the fundamental natural frequency of a compressed helical spring (Wahl, 1949; SAE: Spring Design Manual, 1990) The most comprehensive equation for the dynamics of helical springs was derived by Wittrick (1966) A set of six ordinary differential equations and six partial differential equations were obtained based on the Timoshenko beam theory Following Wittrick’s work, some researchers introduced a transfer matrix method and a finite element method to predict the natural frequencies of helical springs (Pearson, 1982; Yildirim, 1996; Mottershead, 1980; Kim, 1999) A pseudospectral method was employed to investigate the free vibrations of cylindrical and noncylindrical helical springs with fixed-fixed, free-free, fixedfree, and hinged-hinged boundary conditions (Lee, 2007a, 2007b) Lin and Pisano (1987) also derived the general dynamic equations of helical compression springs with a variable pitch angle and a variable helical radius Almost all of the literature presented above assumed that : spring wire diameter [mm] : spring mean coil diameter [mm] : spring mean coil radius [mm] : spring stiffness [N/mm] : equivalent spring constant [N/mm] : fundamental natural frequency [Hz] : number of active coils : Young’s modulus [Pa] : spring material shear modulus [Pa] ρ : density of spring material [kg/m ] υ : Poisson ratio I : transverse second moment of wire cross section [m ] J : polar second moment of wire cross section [m ] xn : number of spring turns p(xn) : pitch, function of spring turns g(xn) : accumulated gap, function of spring turns γ : shear coefficient of wire cross section d D R k keq f Na E G 4 INTRODUCTION The valve spring is an important valve train component that provides restoring forces for the intake and exhaust valves (Liu et al., 2009) The internal vibration of a valve spring is a critical factor that determines the dynamic characteristics of valve trains Traveling waves in a spring may produce a high level of dynamic stress, resulting in a reduced loading *Corresponding author e-mail: djkim@ulsan.ac.kr 413 414 H LIU and D KIM the boundary conditions of helical springs are fixed-fixed, free-free or hinged-hinged However, for a practical valve spring, the end coils are fixed only along the spring axis direction, which is the main cause of the mismatch between calculated and experimental results This paper focuses on two topics: estimating the natural frequency of the valve spring at a fixed height, and estimating the change in the natural frequency due to spring compression A new method for calculating the natural frequency of cylindrical valve springs is proposed by considering the end coil effect NATURAL FREQUENCY ESTIMATION OF A COMPRESSED HELICAL SPRING Figure shows a typical engine valve spring that is comprised of active coils, end coils, and dead coils The connection points between the active and end coils are defined as boundary points A simple equation for estimating the fundamental natural frequency of a compressed helical spring is widely used in the initial stages of valve spring designs By calculating the stress wave propagation time between the fixed boundaries, the fundamental natural frequency is estimated as follows (SAE: Spring Design Manual, 1990): f 3.58 × 10 d - (Hz) = (1) NaD where is the diameter of the wire in mm, a is the number of active coils, and is the mean coil diameter in mm Since the spring material is assumed to be steel, the shear modulus and density are set to 7.93×1010 Pa and 7850 kg/ m3, respectively The SAE (SAE: Spring Design Manual, 1990) accepts equation (1) as a standard estimation method for determining the fundamental natural frequency of a helical spring However, the natural frequency of an engine valve spring tends to be somewhat lower than the estimation provided by equation (1) The error between the measured and calculated frequencies may be as much as 10%, which is due to the end coil effect Since the error in this type of estimation is unacceptable, valve spring manufacturing companies generally use their own empirical equations Usually, 3/4~1.0 turns of engine valve springs are ground d N at both sides in order to satisfy the system assembly condition Since the ground surfaces are in contact with the cylinder head and retainer, the torsional deflection is tightly constrained Therefore, the ground coil can be assumed to be fixed and is hence called a dead coil In this paper, we define the dead-end point as the connection point between the end coils and dead coils Even if the motion of the boundary point in the direction of the spring axis is restrained, the torsional deflection of the spring wire may penetrate the end coils This phenomenon seems to be a main source of the error between the measured and estimated natural frequencies An FEM model was used to simulate the effect of a very small compression; a 3-D beam element was adopted and coil-tocoil contact was also considered in the model Figure shows the results of the FEM simulation of the torsional strain along the wire The results indicate that the torsional strain penetrates to the dead-end point Therefore, the end coils can be modeled by torsional spring elements as shown in Figure Finally, the valve spring can be described by the active coils and two torsional springs at the two extremities The stiffness of the end coils can be determined by applying Castigliano’s theorem (Ugural , 2003) The bending and torsional moments for any cross section are shown in Figure (2) M θ = T sin θ et al D Figure Helical valve spring Figure FEM analysis results for a small compression International Journal of Automotive Technology, Vol 10, No 4, pp 513−521 (2009) DOI 10.1007/s12239−009−0059−4 Copyright © 2009 KSAE 1229−9138/2009/047−14 DESIGN OF AN ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES USING A LOGIC THRESHOLD AND INSTANTANEOUS OPTIMIZATION METHOD Y.-J HUANG , C.-L YIN and J.-W ZHANG * Institute of Automotive Engineering, Shanghai Jiao Tong University, ShangHai 200240, China (Received 10 April 2007; Revised 28 October 2008) ABSTRACT−A novel parallel hybrid electric vehicle (PHEV) configuration consisting of an extra one-way clutch and an automatic mechanical transmission (AMT) is taken as the study subject of this paper An energy management strategy (EMS) combining a logic threshold approach and an instantaneous optimization algorithm is developed for the investigated PHEV The objective of this EMS is to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing engine fuel economy and maintaining the battery state of charge (SOC) in its rational operation range at all times Under the MATLAB/Simulink environment, a computer simulation model of the studied PHEV is established using the bench test results Simulation results for the behavior of the engine, motor, and battery illustrate the potential of the proposed control strategy in terms of fuel economy and in keeping the deviations of SOC at a low level KEY WORDS : Parallel hybrid electric vehicle (PHEV), energy management strategy (EMS), logic threshold control strategy (LTCS), instantaneous optimization INTRODUCTION tion and emission at possible operating points (Johnson et al., 2000; Paganelli et al., 2000, 2001) The third type employs global optimization techniques such as dynamic programming (Ao et al., 2008), mathematic programming (Galdi et al., 2001), and optimization algorithms based on a classical variational approach (Delprat et al., 2002) Control strategies that are based on the minimization of fuel consumption seem to be one step ahead of heuristic control strategies that are based upon simple rules and maps The former, also known as optimal controllers, in fact provide more generality and reduce the need for heavy tuning of the control parameters Global optimization techniques are not implementable in real-time control because they require an a priori known driving cycle They are currently used as bases of comparison for evaluating the qualities of other control strategies Contrary to the global optimization method, not requiring a priori knowledge of future driving conditions makes instantaneous optimization applicable in real-time control In this paper, a novel EMS using a logic threshold approach incorporating a stand-by optimization algorithm is proposed The objectives of the EMS are to achieve acceptable vehicle performance and drivability requirements while simultaneously maximizing engine fuel consumption and maintaining the battery state of charge (SOC) within its operation range at all times The remainder of this paper is organized as follows: in section 2, the powertrain system configuration and parameters of the specific PHEV investigated are briefly de- The powertrain of a parallel hybrid electric vehicle (PHEV) is a hybrid system of an engine and an electric drive system Under the control of the advanced vehicle controller unit (VCU), the drive force requested by the driver is optimally distributed between the engine and the motor The optimal distribution of the drive force is supervised by the vehicle energy management strategy (EMS), which is the kernel part of the real-time control algorithm of the PHEV, and it is one of the key PHEV technologies in which many researchers are engaged The goal of the EMS is to achieve a high efficiency, energy saving, and low emissions vehicle by controlling the hybrid powertrain system coordinately This means that the performance of a PHEV is strongly dependent upon the control of the hybrid powertrain system, which includes the engine, electric motor, electrical energy system, automatic clutch and transmission Many research efforts into the EMS of PHEV have been conducted in recent years They can be roughly classified into three categories The first type employs heuristic control techniques such as logic threshold, fuzzy logic, and neural networks for estimation and control algorithm development (Lin et al., 2001; Pu et al., 2005; Ahn et al., 2007; Zhong et al., 2008) The second type is an instantaneous optimization control strategy based on real-time computation of the equivalent fuel consump*Corresponding author e-mail: victor.huangyj@gmail.com 513 514 Y.-J HUANG, C.-L YIN and J.-W ZHANG scribed Then, the modeling of the PHEV system is introduced in section Next, a detailed description of the proposed EMS is explained in section Subsequently, the simulation results for minimum fuel consumption are given in section Finally, conclusions are presented in section SYSTEM CONFIGURATION The specific PHEV powertrain system considered in this paper is designed with a diesel engine that functions as the main power source and an electric machine that serves as a secondary power source, which is positioned before the transmission A schematic diagram of the PHEV powertrain is shown in Figure Both the output torque of the engine and the electric machine are coupled by the torque coupler (TC), whose output torque is then transmitted into the automatic clutch and transmission, through which the vehicle is ultimately propelled Between the engine and the torque coupler is located a one-way clutch The main component specifications of the hybrid powertrain system are listed in Table The retention of the mechanical connection between engine and driving system not only easily guarantees the kinetic performance of the vehicle but also makes acceptable the optimization of the engine working points, which leads to the engine working stably in the speed range leading to low fuel consumption, high efficiency, and low emission When the rotation speed of the input shaft of the one-way clutch is lower than that of the output shaft, the state of the one-way clutch is disengaged, and engine torque cannot be delivered The one-way clutch can only be engaged, and engine torque can only be delivered, when the rotation speed of the input shaft equals that of the Figure Schematic diagram of the PHEV powertrain Table Characteristics of hybrid electric vehicle powertrain Engine 119 kW Electric motor 45 kW continuous, 120 kW peak Power battery 312 VDC, 55 Ah Transmission 5-speed AMT Vehicle curb weight 111 40 kg output shaft Adopting a one-way clutch eliminates vehicle inverse traction toward the engine, and conventional engine braking torque can be substituted by the generating torque of the motor SYSTEM MODELING Due to the complexity of the engine, electric motor, battery, and other subsystems, using only a theoretical model makes it difficult to accurately and thoroughly describe the operation process of each component stated above Therefore, a model building method combining theoretical analysis and experimental data was adopted for the current research The concept of this method is that the performances of the key powertrain components such as the engine, motor, battery, etc are mainly depicted by their static data maps, with the exception of their kinetic equations, for which the analytical method is still used Under the Matlab/Simulink environment, a feed-forward computer simulation model, whose diagram is shown in Figure 2, was developed based on the powertrain system shown in Figure and on the control strategy proposed in section The vehicle controller unit is comprised of the input module, the output module, and the control algorithm module, which is the kernel of the simulation model The source code of the control algorithm implemented by using the C programming language can be compiled into a dynamic link library (DLL) file and wrapped by the Simulink S-Function for simulation Moreover, the control code can be compiled into executable object code and burned into the ROM of the controller without any modification for real-time control The simulation process is described as follows: for a single simulation step, the driver model (in our study, it is actually a PID controller) compares the vehicle velocity requested by target drive cycle with the actual vehicle velocity fed back by the vehicle simulation model The difference between the two speeds is calculated and then converted to the acceleration pedal command Acc_pedal and the brake pedal command Brake_pedal These two commands, along with other vehicle information such as battery SOC, current gear location, status of clutch, etc are all sent to the VCU model for optimization calculation After calculation, the control algorithm sends out drive commands to the motor and engine, an engage command to the clutch, and a shift gear command to the AMT Based on the simulation model of the engine, motor, and transmission system, applying the vehicle dynamic equation, the actual vehicle speed and battery SOC are calculated The signals sent by the VCU to the engine are the engine start/ stop signal engine_switch and the engine fuel control command engine_command; signals sent to the motor are the motor torque command motor_comand, the motor operating mode command motor_model1, and motor_model2 (driving mode, charging mode, racing mode, and speed regulating mode) DESIGN OF AN ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES 515 Figure Diagram of the PHEV simulation model ENERGY MANAGEMENT STRATEGY Based on the powertrain system scheme of the PHEV shown in Figure 1, all possible operating modes are listed in Table During the actual operating process of the PHEV, the operating modes of the PHEV are determined by the control algorithm according to conditions such as the driving force requested by the driver, the current operating mode of the PHEV, battery SOC, etc Therefore, the operating mode of the PHEV would switch among those modes listed in Table Figure illustrates the state transition diagram of the PHEV control summarized from the operating modes listed in Table In this figure, the blocks represent the PHEV operating modes, and the arrows between the blocks illustrate the transfer direction of the operating modes It can be seen from Figure that state transfers not exist between all of the operation modes As with the determination of the PHEV operating mode, the state transfer condition is also decided by the EMS based on interpreting the torque request by the driver, the current operating mode of the PHEV, and battery SOC In this study, a logic threshold control strategy (LTCS) is developed on the basis of engineering empirical experience and simple analysis of component efficiency tables and charts, which is a popular and effective design approach The control algorithm of the discussed LTCS is basically a rule-based control algorithm that combining the logic threshold and the instantaneous optimization method The features that facilitate handling switch operating modes and implementation into C programming language codes make the proposed LTCS acceptable for real time control applications The control strategy interprets the driver pedal motions as a torque request Tr, itself a function of the maximum torque available at the current vehicle speed If Tr < 0, then the vehicle is braking The motor recovers the maximum possible regenerative braking energy within the constraints imposed by the motor, battery, brake system, and vehicle stability considerations The friction brake system only supplies the remainder T T T R(i) (1) where Tr is the torque requested by the driver, Tb is the torque that the friction brake system is required to deliver, Tm is the torque that the motor is required to supply (the motor torque is constrained by the battery security as well as the motor capability), R(i) is the product of the ith gear ratio and the reduction ratio of the final drive If T ≥ , then the vehicle is under driving conditions; the requested torque is distributed between the engine and the motor, as following: r = m+ r T r ρ m = = b/ T +ρ ⋅ T e m n m n e m (2) 516 Y.-J HUANG, C.-L YIN and J.-W ZHANG Table Operation mode classification of PHEV Status of the hybrid powertrain components No Acc Engine Motor Clutch Trans pedal A Off Race Dis-engage neutral B Off Drive Dis-engage Engage >0 C Active Race Dis-engage Engage >0 D Drive Driving Engage Engage >0 E Drive Race Engage Engage >0 F Drive Charge Engage Engage − G Idle/Off Charge Engage Engage H Idle Speed control Dis-engage Shift − I Shut down Race Dis-engage Engage − J Idle/Off Charge Dis-engage Engage − K Off Race Dis-engage Engage − L Off Race Dis-engage Shift >0 M Off Race Dis-engage Shift Status description Brake pedal 0 0 0 Vehicle stop Pure electric driving mode Start engine Hybrid driving with both engine and motor Vehicle driven by engine alone Driving while charging the battery Coasting with engine on − Shift gear with engine on >0 Shutdown engine >0 Regenerative braking >0 Friction braking Shift gear with engine off Coasting with engine off Figure Operating mode transition diagram of the PHEV where ρm is the speed ratio between the motor shaft and the engine shaft; m , and e are the shaft speeds of the motor and the engine, respectively Figure Operating mode transition diagram of the PHEV The motor torque m in Equation (2), positive (driving) or negative (charging), is used to regulate the load of the engine according to the efficiency map of the engine (Figure 4) Figure also presents the optimal efficiency curve e_opt , the maximum torque curve e_max , the minimum operating torque curve e_off, and the engine minimum speed working curve e_min The engine shuts down because of excessive inefficiency whenever the torque request falls below e_off or the engine speed falls below e_min Charging the battery during driving by the engine-driven n n T T T T n T n motor is defined as active charge Charging the battery by regenerative braking is defined as passive charge Engine operation is maintained in the high efficiency range by load regulation through active charge and motor assist In order to guarantee the efficiency and security of battery charging and discharging performance whenever the PHEV is operating, the EMS should maintain the balance of battery SOC There are two kinds of methods to handle SOC control of the PHEV battery (Fukuo , 2001; Sasaki , 1997): (1) set high SOC limits ( H) and low SOC limits ( L), which are defined by the EMS, and control battery SOC within this operational range Whenever the value of battery SOC is less than L, the battery is actively charged by the hybrid system, and when it is greater than H, the et al al et S S S S DESIGN OF AN ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES Figure Torque distribution strategy for PHEUB hybrid system stops active charging as well as passive charging, which is actually regeneration during braking and decelerating (2) Set a target SOC value ( T) Whenever the SOC deviates from T, a charge or discharge demand is generated by the EMS to move the SOC closer to its target value T The more the SOC strays from its target, the higher power the battery requires Regardless of which kind of method is used, in order to decrease energy losses during charge and discharge, the SOC is always controlled in the region where both internal charge and discharge resistance are relative small Even so, only a relatively high efficiency of the battery can be achieved In order to increase overall energy efficiency, the concept of a “penalty function” is proposed to correct battery usage (Paganelli , 2001) The penalty function is used as a means of regulating the price of the electrical energy At high SOCs, the penalty function is low and makes using the motor and battery less “expensive.” At low SOCs, the value of the penalty function increases, making it more “expensive” to use the battery Therefore, the aim of the penalty function is to increase the percentage of the equivalent fuel consumption by the motor whenever SOC is low and to reduce it whenever SOC is high Because prices based on the SOC alone cannot reflect the real electrical energy cost, it is difficult to directly use the penalty function to compute the electrical energy equivalent fuel consumption In the Real-Time Control Strategy (RTCS) proposed by V.H Johnson (2000), both the losses during charge/discharge and the free electrical energy from regenerative braking, as well as SOC and ∆ (the variation of SOC in a single sample step), are all taken into account in calculating the instantaneous equivalent fuel consumption However, this method requires not only heavy computation but also real-time calculation while the PHEV is running, which makes it difficult to apply in a real control system Following the investigation of battery control and engine usage, we provide a detailed description of the control S S S et al 517 Figure Fuel consumption characteristics of the engine algorithm combining both the instantaneous optimization and the logic threshold that not only satisfies the requirement of real-time control but also considers both the charge/discharge losses and the free regenerative energy Since the objective of optimization is the engine operating point, once the system enters into the operation modes where the engine is on, the optimization algorithm is activated to run, and the control command is executed to shift the engine operating point closer to the points that optimize the overall efficiency of the hybrid system The optimization algorithm of the engine operating points is described as follows: (1) Set up the lookup table of fuel consumption map engine_table indexed by the engine’s torque and speed based on the fuel consumption characteristics of the engine, which are shown in Figure (2) Set up the lookup table for the battery SOC variation soc_table indexed by the motor’s torque and speed on the basis of the motor efficiency characteristics (shown in Figure 6), as well as the battery pack characteristics (shown in Figure 7), where , , and are the voltages of open-circuit, charging resistance, and discharging resistance of the battery pack, respectively Q Q Uo Rc Rd et al SOC Figure Efficiency characteristics of the motor 518 Y.-J HUANG, C.-L YIN and J.-W ZHANG table engine_table For the point where m = 0, the fuel consumption is denoted as (5) Look up the ∆ for each candidate operating point from the map of SOC variation table soc_table It is noteworthy that positive motor torque generates negative ∆ , while negative one generates positive ∆ (6) Calculate the regenerative energy ∆ c,reg based on the statistic of typical driving cycles and past driving data ∆ c,reg is used to correct the ∆ of step (5): ∆ = ∆ ∆ c,reg (7) Use the results drawn from steps (4) and (6) to set up lookup table engine_∆soc (8) During a control step time for each candidate operating point, the actual SOC change is ∆ actual In order to sustain the battery charge, the amount of SOC consumed at the current time step must be compensated with the same quantity in the future, which should be: ∆ compensate = −∆ actual Its corresponding engine fuel consumption is engine_compensate , which is looked up in the lookup table engine_∆soc resulted from step (7) The fuel consumption used to compensate ∆ compensate is denoted as compensate, calculated through the equation compensate = engine_compensate − (9) The equivalent fuel consumption needed to compensate the equal ∆ at different SOC should be different In order to regulate the price of electricity with different SOC, a penalty function shown in Figure is introduced to correct the equivalent fuel consumption: ), where engine_correct is the engine_correct = compensate · ( corrected equivalent fuel consumption (10) Compute the total fuel consumption total = + engine_correct (11) Repeat steps (5)~(7) and compute total for each candidate operating point (12) The control strategy determines the optimization working points of PHEV and the corresponding working mode at current time step based on the optimization calculation results of each candidate operating points from step (8) and the working mode at the previous time step, following the mode transition principle of PHEV state transition diagram (shown in Figure 3) At the same time, if PHEV operating mode switching is required, the control strategy will transfer the Q T Q S Q S S S S S S S + S Q S S S Q Q S Q Q Q Q S Q Q f soc Q Q Q Q Figure , , and Uo Rd Rc characteristics of the battery pack (3) Determine the possible working area using the following equation: (3) m = m_neg_max : step : m_pos_max where m is the motor output torque for each candidate operating point, m_neg_max is the maximum negative torque of the motor available at the current station, m_pos_max is the maximum positive torque of the motor available at the current station, and step is the calculation step of motor torque (the smaller the selected step, the finer the calculation result) (4) Look up the fuel consumption e for each candidate operating point from the map of the fuel consumption T T T T T T T T T Q Figure Penalty function for SOC correction Q DESIGN OF AN ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES 519 Figure Flowchart of the optimization algorithm control to the desired working mode smoothly In order to make the optimization easy to understand, the flowchart of the optimization is shown in Figure VALIDATION OF THE EMS Much computer simulation effort has been performed to validate the simulation model developed in section 3, and it is used to assess performance via the proposed control strategy developed in section (OCS) with the conventional logic threshold control strategy (LTCS) Simulation results for the two strategies using the China Typical Bus Driving Schedule in an Urban District (CTBDS_UD) are presented below Figure 10 presents the simulation results using the OCS with an initial SOC value of 0.5 Curves of the vehicle velocity (ua), driver’s requested torque (Treq) by interpreting acceleration and brake pedal, the engine torque (Te), motor torque (Tm), and battery SOC are presented Figure 11 presents the simulation results using the LTCS It is observed from the comparison between Figure 10 and Figure 11 that the engine load is relatively higher over the cycle in Figure 10 than in Figure 11, which means that the Figure 10 Simulation results using OCS on CTBDS_UD engine probably frequently operates in the high-efficiency range It can also be observed that the battery SOC controlled by OCS is much better than by LTCS For the purpose of further investigating the behavior of the engine, Figure 12 presents the engine operating points 520 Y.-J HUANG, C.-L YIN and J.-W ZHANG Table Fuel economy simulation results comparison between LTCS and OCS LTCS OCS Initial SOC (%) 50 50 End SOC (%) 43 52.5 Fuel economy (L/100 km) 24.48 24.59 Fuel economy with SOC correction 26.53 23.91 Q /(L/100 km) s Figure 11 Simulation results using LTCS on CTBDS_UD (sample time is s) scattered over the torque-speed plane with the initial SOC value of 0.5 for these two different control strategies The figure background consists of fuel efficiency contours and the maximum torque curve of the engine For the OCS, the operating points of the engine are more concentrated in the high efficiency range, while for the LTCS, the operating points of the engine are much more spread out over the map, and many of them are in the low load and low efficiency range This indicates that the OCS is more effective in controlling the engine to operate in the high efficiency range In addition, it is necessary to explain that some of the points near the coordinate axis are not real engine working points; they are the transition points of the engine during the start or stop processes, which are sampled by the simulation model The comparisons of the fuel economy simulation results between OCS and LTCS over CTBDS_UD are summarized in Table The data in Table demonstrates again that the OCS is more effective than the LTCS in keeping the engine operating at high efficiencies Although the fuel economy for the LTCS (24.48 L/100 km) is less than that for the OCS (24.59 L/100 km), the SOC increases by 2.5% for the OCS, while it drops by 6.5% for the LTCS Nine sets of fuel economy and SOC change results are obtained by simulation over the same driving cycle night times with different initial SOC from 0.3 to 0.7 for each run A linear regression is used to calculate the corrected fuel economy corresponding to the zero SOC change over the cycle The corrected fuel economy for the OCS is 23.91 L/100 km and for the LTCS is 26.53 L/100 km It is thus obvious that the OCS is superior to the LTCS in terms of fuel economy CONCLUSION Figure 12 Operating points of the engine with the initial SOC of 0.5 using (a) LTCS and (b) OTCS on CTBDS_UD The kernel of the real time control algorithm of a hybrid electric vehicle (HEV) is the energy management strategy (EMS) In this paper, a novel EMS using a logic threshold approach with incorporation of a stand-by optimization algorithm is presented The objective of the proposed EMS is to minimize the engine fuel consumption and maintain the battery state of charge (SOC) within its operational range while satisfying vehicle performance and drivability requirements Achieving this goal largely depends upon synergetic operation between different components of the hybrid powertrain to attain an overall high efficiency Experimental data for engine, motor and, battery were used for the construction of a feed-forward system model of DESIGN OF AN ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES PHEV on the Matlab/Simulink modeling platform Consequently, computation simulation results demonstrated that not only is the fuel economy of PHEV significantly improved, but also the battery SOC is maintained within its operating range while satisfying the requirements of the driver ACKNOWLEDGEMENT−This work was supported by the Key Technology Research for Hybrid City Bus Project of Science and Technology Commission of Shanghai China under contract No 033012017 REFERENCES Ahn, H S., Lee, N S., Moon, C W and Jeong, G M (2007) Fuel economy improvement for fuel cell hybrid electric vehicles using fuzzy logic-based power distribution control Int J Automotive Technology , , 651− 658 Ao, G Q., Qiang, J X., Zhong, H., Yang, L and Zhuo, B (2008) Exploring the fuel economy potential of ISG Hybrid electric vehicles through dynamic programming Int J Automotive Technology , , 53−59 Baumman, B M., Washington, G., Glen, B C and Rizzoni, G (2000) Mechatronic design and control of hybrid electric vehicles 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, 79−92 48 22 48 222 International Journal of Automotive Technology, Vol 10, No 4, pp 523−528 (2009) DOI 10.1007/s12239−009−0060−y Copyright © 2009 KSAE 1229−9138/2009/047−15 DIESEL HYBRID ELECTRIC VEHICLE HARDWARE SYSTEM J H SONG , J X WANG, H B TANG, X J MAO and B ZHUO * School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China (Received 19 June 2007; Revised 10 November 2008) ABSTRACT−A controller for a diesel hybrid electric vehicle based on a V-cycle development approach is investigated in this paper The hardware and infra program of the Hybrid Control Unit (HCU) are discussed in detail The hardware system is designed based on circuit simulation; while the infra system is written with assemble language Time sharing mode, buffer sharing mode and multi-task schedule method are used to ensure real-time communication in the infra program design Based on multi-thread technology, hardware in loop test system is also designed The hardware in loop and bench tests show that the controller could meet the requirements of the hybrid electric vehicle (HEV) and communicate in real-time Circuit simulation, HCU, infra program and hardware in loop test form the effective V-cycle development platform to design a hardware system for a diesel HEV controller KEY WORDS : Diesel HEV, HCU, V-cycle, Hardware design, Circuit simulation, Infra driver, Hardware in loop INTRODUCTION CONFIGURATION AND DEVELOPMENT METHOD Automobile industries will face great evolution in the 21st century Developing energy saving and low emission products have become the two main directions of the automobile industry Electronic vehicles have limitations because the life and performance problems of fuel batteries have not been solved Therefore, people have focused on hybrid electric vehicle (HEV) concepts since the 1970s HEVs integrate power devices, such as an engine, motor, or battery, and combine strong points from the pure electric and traditional automobiles The most famous HEV products are the Toyota Prius and the Honda Insight Additionally, city buses have a greater contribution to energy savings, low noise emission and the green project because of their special working conditions Ten years ago, China started 863 Project, which focused on a new energy source vehicle and achieved some success (Pu et al., 2005; Zhao et al., 2003) In this paper, a new controller of a diesel hybrid electric vehicle was developed The design process of the Hybrid Control Unit (HCU) is discussed The process applies the V-cycle development platform Circuit design is introduced and includes modularization design, circuit simulation and reliability design Methods of infra program are presented Hardware in the loop(HIL) simulation is applied to reduce the cost of development This HCU has been applied in both a bench test and a road test successfully 2.1 Configuration In general, according to the powertrain configuration, HEVs can be classified into three types, namely serial hybrid, parallel hybrid and serial parallel hybrid electric vehicle In this study, a parallel hybrid electric vehicle configuration is adopted, as shown in Figure The motor here is an Integrated Starter Generator (ISG).This design has many advantages, such as it better utilizes the inherited structure of former buses, easy to use and has a wider applied range Thus, this design is used widely in China The hierarchical control method is used in the control system of a HEV, and the kernel controller of this powertrain is a HCU The controllers of the lower lever include an Engine Management System (EMS), which is a diesel *Corresponding author e-mail: songjunhua203@yahoo.com.cn Figure Structure of diesel hybrid electric vehicle 523 524 J H SONG et al Figure Hierarchical control diagram of the hybrid vehicle powertrain system (5) Calibrating and monitoring functions based on the CAN Calibration protocol; (6) Having a high reliability and anti-jamming capability; (7) Adapting to a wide power range; and (8) Having an adequate hardware resource, RAM and velocity of the microcontroller HARDWARE DESIGN Figure V-cycle platform of HCU development controller for this system, a Battery Packets Control Module (BPCM), an Automation Disconnect Module (ADM) and a Drive Motor Control Module (DMCM) The main communication between controllers is the Controller Area Network (CAN) communication (Pillips, 1991) Main communication variables of a HEV powertrain are shown in Figure 2.2 Development Method With the quick development of the electronic vehicle, the V-cycle development platform became increasingly famous in the modern Electronic Control Unit (ECU) design process This method can raise the execution efficiency of code and ease the labor of experiments It also can reduce the cost and cycle of development The V-cycle development process is also used in the development of HCU, which is shown in Figure Based on a new powerful microcontroller MC9S12XDP512 (Freescale INC., 2006; Pan , 2005), an electronic control unit for a diesel hybrid electric vehicle is designed A MCU, signal processing module, power wake-up module, communication module and controller failure processing module are included in the hardware system The hardware diagram is shown in Figure et al 4.1 Method of Circuit Simulation HCU hardware is designed by means of circuit simulation, which uses component modeling and a circuit diagram to analyze signals of knots, so that a high class analysis and optimum design can beconducted more efficiently (Tan , 2005) In this study, a power circuit, signal sampling and processing circuit and output drive circuit are modeled with the American National Standard Simulation Program with Integrated Circuit Emphasis (SPICE) In this research, take the pulse signal processing circuit simulation as an example et al CONTROLLER DESIGN The HCU is the kernel part of the HEV It transmits control messages to other controllers according to information given by the driver’s control intention and the vehicle working condition At the same time, the HCU receives feedback messages from its subsystems In this way, the HCU controls the whole vehicle’s operation The functions of the HCU are designed as follows: (1) Getting driver’s control intention, such as clutch signal, air-conditioning signal, and others; (2) Sampling vehicle working condition, for example vehicle speed; (3) Communicating between the HCU and other controllers; (4) Managing power, low voltage and high voltage; Figure Structure of HCU hardware system DIESEL HYBRID ELECTRIC VEHICLE HARDWARE SYSTEM 525 Figure General diagram of fail-safe circuit Figure Simulation results of pulse circuit This system has one main pulse signal, namely vehicle speed An electromagnetic sensor is applied in this system The amplitude of the pulse signal is a function of speed The peak voltage of the sensor will vary from 0.5 V to 150 V A converter circuit is designed to transform the sinusoidal signal, where the frequency varies with speed, to an acceptable pulse signal for the microprocessor Two converse clamping diodes are used to input a signal to limit the input voltage to a certain range Then, the Smith processing circuit is used to transform the sine wave to a square wave and to send the signal to ports of the Enhanced Capture Timer (ECT) to capture the pulse number One of the simulation results for the speed processing circuit is shown in Figure 4.2 Reliability Design Reliability is very important in hardware design Many measures, including overheat protection, overvoltage protection and overcurrent protection circuits, are incoporated (Tan , 2003) A special fail-safe circuit is discussed in detail The fail-safe circuit of the HCU is a converter circuit (shown in Figure 6) of the pedal signal In normal conditions, the pedal signal is transmitted to the EMS from the HCU, which distributes energy based on the state of the vehicle working condition If the HCU has some faults or the HCU is powered off, the pedal signal will be sent directly to the EMS This method ensures that the EMS is operating normally The confirmation of faults is judged by a hardware circuit and a software watch dog When the HCU is powered off, the electronic analog switch is gated to follow the path of the dotted line in Figure by the hardware circuit When the HCU is powered on, the gated path is controlled by the software watch dog If the HCU program operates normally, then the watch dog chip outputs a certain signal The HCU receives this signal and sends a control signal to gate et al the continuous line path If the program runs abnormally, then the HCU will not receive this signal, and the control signal changes to gate the dotted line path The electronic analog switch uses the same power as the pedal, so it is always powered on, whether or not the HCU is powered on So diesel EMS can always receive the pedal signal SOFTWARE DESIGN Software for the HCU includes two parts: upper program and infra program The upper program is in charge of applying control strategy and energy distribution The infra program is in charge of the interactions between the upper and infra programs, sampling and output of signals, output of hardware faults and Diagnostic Trouble Code (DTC) communications between controllers In this research, the Diesel HEV has seven running modes, namely motor drive mode, engine drive mode, hybrid drive mode, idle speed charging mode, energy regenerating braking mode, auto starting and auto stopping mode (Chan, 2007; Oh , 2005) The control algorithms of the upper program are modeled by Matlab/Simulink/Stateflow C codes are generated from Simulink/Stateflow models by Targetlink In this paper, infra program is discussed in detail Infra program was the foundation for the control software in the HEV There are close relationships between the hardware and infra program, so the infra program is classified as one part of the hardware system Infra program is written with assembly language It includes reset of MCU registers, battery voltage check, signal processing, transmission and receiving of CAN messages, and so on The flow chart of the infra program is shown in Figure The infra driver contains functional modules as follows (1) Subroutines of power-on and power-off The power-on subroutine contains register settings, self-check of the system and parameter initialization The power-off subroutine is in charge of saving the data (2) Interactive port subroutine between infra program and upper program The port should follow some interaction protocol, which defines certain data forms for data exchange (3) Subroutine for signal sampling and control message output, which are processed in a ms interrupt subroutine et al 526 J H SONG et al Figure Flow chart of time sharing RT Figure Flow chart of the infra program (4) Subroutine of pulse signal processing (5) CAN driver, including transmission and receiving subroutine and communication strategy (6) Interface routine between software calibration and user monitoring It is based on the CAN Calibration Protocol (CCP) (Wang , 2005) Table Response time of control parameters Parameter Response time Pedal signal 10 ms Brake signal 10 ms Speed of engine ms Speed of vehicle 1000 ms State of charge (SOC) 1s Torque of driver motor 20 ms Torque of engine 20 ms et al 5.1 Algorithm for the CAN Driver Each CAN node has its own communication period Based on requirements for sampling velocity of vehicle control, each CAN node is set for a different communication period (20 ms, 50 ms, 100 ms and 1000 ms) Time sharing transmission and receiving (TR) are used in the CAN driver subroutine, as shown in Figure This method can reduce the load rate of the CAN bus and improve the real-time CAN communication In order to improve the resource utilization efficiency, buffer-sharing is applied in the infra program design The idea of buffer-sharing is that different CAN frames occupy the same buffer at different times 5.2 Optimization Design of CAN Drivers The CAN communication module is one of the most important subroutines of the infra program As an interrupt subroutine, the CAN driver subroutine may lead to a longer time for the interrupt relay and affect the interrupt performance ability of the system A Real Time Operating System (RTOS) is needed to optimize the control system software Multi-tasking is applied in the CAN subroutine design Setting the response time parameter should consider these two conditions (Yu , 2006) (1) Interrupt trigger is used in the events which need to be handled immediately, for example the control instruction between the HCU and EMS or the calibration instructions between the HCU and calibration tools (2) Considering the response time of control parameters are different, as shown in Table 1, CAN messages are divided into four segments – 20 ms, 50 ms, 100 ms and 1000 ms At the same time, the priorities of these tasks raise and the communication period reduces The new structure of CAN communication is shown in Figure The multi-tasking method can optimize the resources of the HCU and supply an effective means to extend the CAN communication program et al Figure Structure of CAN communication in the RTOS DIESEL HYBRID ELECTRIC VEHICLE HARDWARE SYSTEM 527 munication test, simulation test of vehicle working conditions, and so on The results of HIL show that the system operates well, and communication between controllers is stable The HCU is also applied in the bench test The content of the bench test includes check and optimization of control strategy, the efficiency of motor work and heat, the efficiency of battery charge, and so on CONCLUSION Figure 10 Block diagram of hardware in the loop APPLICATION The certification tests of the HCU hardware include three parts: hardware in loop (HIL) simulation test, bench test and road test The structure of the HIL simulation is shown in Figure 10 In the HIL system, there are two important parts: simulation ECU and upper program of the personal computer (PC) The models for the HEV, including engine model, motor model, battery model, ADM model and vehicle model, and the monitoring interface are handled in the PC (Nuksit , 2003; Lee , 2007) The diesel engine model is shown in Figure 11 Communication between the HCU and simulation ECU is a CAN communication, which uses an USB/CAN converter card The monitoring interface was developed with Labview CAN data can display clearly in the monitoring interface This method of HIL has many advantages It avoids double RAM communication The PC ability could be achieved as vehicle models are computed Multi-thread technology is used in the program design to reduce the response time of communication The content of HIL includes input and output of the HCU test, test of HCU diagnosing module, CAN comet al et al Figure 11 Model of diesel engine The scope of this contribution is to introduce the design methods of hardware system for diesel hybrid electric vehicle V-cycle platform is an effective means for controller development of Diesel HEV It can accelerate the design process and shorten the cost of controller design And circuit simulation can greatly increase the reliability of hardware design, so that the hardware of the HCU satisfies the requirements and durability of a Diesel HEV system The methods of software design are presented In which, time sharing and buffer sharing are used in infra program design to improve the communication response ability With HCU software becoming more and more complex, multi-tasking will be the trend in the software The ideas of RTOS and multi-tasking schedule can optimize software of the HEV Hardware in the loop simulation is applied in this system HIL can examine input and output of HCU hardware and the results of the production codes running in HCU, and it can facilitate the precalibration process of the entire control system The tests of HIL and bench show that the controller could meet the requirements 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