B ACKGROUND
Simulation Modeling of Fuel Consumption
GT-Power provides high fidelity engine simulation models that characterize performance at crank-angle resolution, effectively capturing transient phenomena like turbo lag and precise injection timing The "Fast Running Model" offered by GT-Power simplifies flow paths while maintaining crank-angle resolution, allowing for near real-time performance analysis, although it requires tuning for accurate results Additionally, performance maps can describe engine characteristics across various speed-load ranges, enabling simulations in vehicle software like Autonomie These map-based models are computationally efficient, making them ideal for drive cycle analysis and fuel efficiency calculations, significantly reducing computational time while still capturing essential vehicle-level phenomena.
Engine Transient Phenomena
The transient performance of an engine is influenced by several design features implemented by the original equipment manufacturer (OEM) A key factor is the design and calibration of the Variable Geometry Turbocharger (VGT), which significantly affects the engine's throttle response Engine manufacturers prioritize performance, emissions, and aftertreatment processes as essential criteria for transient operations, leading to notable variations in engine performance.
OEMs aim to optimize fuel economy while adhering to regulatory standards, leading to nuanced variations in their technology and control strategies observed between extended steady-state operation and transient activity.
Monitoring energy loss distribution during transient engine operation is challenging due to continuous changes influenced by factors such as DPF soot loading and desired EGR mass flow rates, which affect pumping losses Additionally, auxiliary devices like the water, oil, and fuel pumps introduce variable parasitic losses along the engine lug curve The thermodynamic state of the engine and oil also impacts lubrication and heat transfer from engine surfaces Consequently, there is a need for data-driven energy distribution models that characterize energy flow as a function of speed and torque across different regions of the lug curve.
Engine Efficiency and Loss Mechanisms
A hypothetical energy audit for an engine reveals the fuel conversion efficiency, which is determined by the proportion of fuel energy converted into indicated work on the piston Factors such as fluid friction, mixing, and rapid expansion during combustion contribute to irreversibility, impacting work extraction and resulting in energy losses as heat and in exhaust Not all energy converted into work reaches the engine shaft output; some is consumed overcoming friction, pumping air and exhaust gases, and powering accessories like water and oil pumps, cooling fans, and alternators Brake thermal efficiency, expressed as a percentage, represents the ratio of useful work at the engine shaft output to the fuel energy input, with the exclusion of certain accessories during testing depending on laboratory standards.
Figure 3 Energy loss mechanisms within an engine energy audit
1 Fuel energy = fuel mass (kg) x fuel lower heating value (MJ/kg)
Historical changes in heavy-duty engine brake thermal efficiency (BTE) are primarily driven by emissions regulations and advancements in engine technology, as illustrated in Figure 4, which utilizes data from WVU’s extensive engine and chassis dynamometer tests The figure also highlights peak BTE values presented by Volvo at the 2011 DEER conference, indicating variations in efficiency due to different cycle characteristics and operational loads Despite significant reductions in NOx and PM standards by over 95% from 1992 to 2010, heavy-duty engines experienced a decline in fuel efficiency post-consent decree (model year 2004), showing an approximate 6% decrease in BTE This drop is likely linked to the US EPA's stringent NOx standard of 2.4 g/bhp-hr, down from 4.0 g/bhp-hr in 1998, achieved without defeat devices or NOx aftertreatment systems However, advancements in both engine and aftertreatment technologies have since allowed manufacturers to produce cleaner and more efficient diesel engines while simultaneously reducing emissions.
Figure 4 Historical changes in heavy-duty engine efficiency from WVU FTP and Volvo peak BTE
Engine Model
Numerical models that simulate engine performance are vital to understand and predict the potential of
Future engine technologies aim to enhance efficiency, yet they necessitate a deep understanding of engine behavior and precise design inputs for accurate energy flow predictions Modern heavy-duty diesel engines utilize various control algorithms that original equipment manufacturers (OEMs) can adjust to meet emissions standards and fuel economy goals Consequently, data-driven engine models are crucial for effectively predicting energy flows and fuel consumption in both current and future heavy-duty diesel engines.
Simulation tools like Autonomie can effectively predict fuel consumption without needing intricate engine models, utilizing a fuel look-up table to interpolate fuel consumption rates based on speed and torque To enhance the accuracy of these predictions, a data-driven approach is essential for developing the fuel map inputs This project primarily aimed to create fuel map inputs for US EPA 2010 compliant heavy and medium-duty diesel engines By integrating these engine fuel maps with appropriate chassis parameters in the Autonomie tool, the project seeks to provide reliable fuel economy predictions across various real-world driving cycles.
Developing fuel lookup tables for Autonomie presents challenges due to the influence of engine transients on fuel consumption patterns Factors such as EGR rate, water pump load, oil pump load, DPF soot loading, and VGT actuation can lead to discrepancies between actual fuel consumption and steady-state measurements This study aims to create fuel map surfaces that accurately reflect fuel consumption rates during both steady-state conditions and transient acceleration and deceleration However, caution is necessary, as over-fitting data from transient events may increase prediction errors compared to using steady-state data To mitigate this risk, a systematic data collection and post-processing routine was implemented.
In a recent study, two US EPA 2010 emissions compliant engines, the 12.8L Mack MP8 and the 6.7L Cummins ISB, along with a legacy US EPA 2004 compliant engine, the 12.8L Mercedes Benz OMB 460, were evaluated on an engine dynamometer test bench This testing aimed to characterize their fueling maps and quantify various energy loss mechanisms through an energy audit The 2010 compliant engines served as a baseline for predicting future fueling maps and energy audits for 2017 and beyond Additionally, the 12.8L Mercedes Benz engine, representing pre-aftertreatment technology, provided valuable insights into technological advancements over the past decade and the impact of aftertreatment systems on fuel consumption.
US EPA 2010-C OMPLIANT H EAVY - DUTY D IESEL E NGINE
WVU conducted a fuel map characterization and energy audit for a US EPA 2010 compliant heavy-duty diesel engine by testing the Mack MP8 505C, as detailed in Table 1 To measure the indicated power, a similar Volvo D13 engine was utilized due to its in-cylinder pressure measurement capabilities The Volvo D13 testing aimed to correlate frictional power from in-cylinder pressure with friction power derived from motoring tests Energy audit and fuel consumption assessments were specifically carried out on the Mack MP8 engine, as illustrated in Figure 5, which depicts the baseline 2010 Mack engine set up in the test cell.
Table 1 Engine specifications for USEPA 2010 heavy-duty diesel engine
Displacement (L) 12.8 Rated Horsepower (hp) 505 Rated Speed (rpm) 1800 Peak Torque @Speed 1810 ft-lb@1100rpm Aftertreatment system DPF-SCR
EGR High pressure cooled EGR
Fuel Injection Electronic unit injectors (2400 Bar) Compression Ratio 16:1
Bore and Stroke 131mm and 158 mm
Figure 5 Mack MP8 505 C equipped with DPF and SCR
L EGACY US EPA 2004 C OMPLIANT H EAVY - DUTY D IESEL E NGINE
In order to understand the technology and fuel consumption progression from pre-2010 engines to current
The 2010 US EPA compliant Mercedes MBE 4000 engine, Model Year 2005, was evaluated for its fuel map and energy efficiency without the use of a DPF or SCR system Certified to emit 2.4 g/bhp-hr NOx and 0.1 g/bhp-hr PM, the engine specifications are detailed in Table 2, while Figure 6 illustrates the engine's installation in the test cell.
Table 2 Engine specification of US EPA 2004 engine
Displacement (L) 12.8 Rated Horsepower (hp) 386 Rated Speed (rpm) 1986 Peak Torque @Speed 1381 ft-lb @1080rpm Aftertreatment system None
EGR High pressure cooled EGR
Fuel Injection Electronic unit injectors (2000 Bar) Compression Ratio 17.75:1
Bore and Stroke 128mm and 166mm
US EPA 2010 C OMPLIANT M EDIUM -D UTY D IESEL E NGINE
The 2013 Cummins ISB 6.7L engine was evaluated to analyze its fueling map and energy efficiency, adhering to US EPA 2010 standards for medium-duty diesel engines Detailed engine specifications can be found in Table 3, while Figure 7 illustrates the testing setup used for the Cummins engine.
Table 3 Engine Specification of USEPA 2010 Medium-duty Engine
Peak Torque (ft-lbs) @ Speed 750@1600 rpm
High pressure cooled EGR with intake throttle valve for pumping loss reduction Fuel Injection
High Pressure (1800 Bar) Bosch CP3 Common rail fuel injection
Bore and Stroke 107 and 124 mm
Figure 7 MY 2013 Cummins ISB 6.7 equipped with DPF and SCR
Engine dynamometer experiments were performed at WVU CAFEE’s §40 CFR 1065 compliant Engine Research Center (ERC) in Morgantown, WV WVU CAFEE is equipped with two DC dynamometers
The heavy-duty engine testing utilized an 800 HP DC dynamometer, supporting speeds up to 2500 RPM, while medium-duty testing was conducted on a 400 HP DC dynamometer with a maximum speed of 2900 RPM Engine installation required adapting the engine flywheel to the dynamometer shaft, linking the engine to the DC dynamometer Throttle input and speed control were managed using WVU CAFEE’s proprietary engine dynamometer test cell software.
The heavy-duty engine was installed in the test cell along with its aftertreatment system, while auxiliary components like the cooling fan and alternator were excluded from power consumption measurements during engine dynamometer testing Although these auxiliaries typically consume power based on the vehicle's duty cycle, they do not impact the engine fueling map or energy audit In contrast, essential engine-related auxiliary components, including water, oil, and fuel pumps, were measured during testing and are already accounted for in the fueling maps.
Table 4 Engine Auxiliaries Included and Excluded During Engine Fueling Map Testing
Included (accounted for in the map) Excluded (not accounted for in the map)
Fuel pump Air conditioning compressor
Air compressor Power-steering pump Power take-off
T EST C ELL I NTEGRATION
The Mack MP8 505C engine, a heavy-duty powerhouse, was taken from a Class 8 tractor and placed in a test cell for evaluation To ensure proper functionality, a wiring harness was acquired to connect the engine control unit (ECU), aftertreatment ECU, and the test cell control system, facilitating seamless integration with various vehicle components.
The engine and aftertreatment systems utilize a dedicated controller area network (CAN) bus for communication with the vehicle interface, necessitating specific vehicle parameters such as ambient temperature, vehicle speed, and ECU clock from the test cell computer for optimal performance Volvo North America facilitated this study by supplying essential CAN messages and procedures for the engine's integration within the test cell Given the complexity of aftertreatment integration, ensuring effective communication between the engine and all aftertreatment components was crucial to avoid engine de-rate and inaccurate open loop fuel control The diesel particulate filter (DPF) and selective catalytic reduction (SCR) systems were tested in their original condition without prior regeneration Research indicates that a typical DPF exhibits a differential pressure of about 5 kPa when clean and 10 kPa under loaded conditions at rated exhaust flow rates (Tan et al., 2011).
The Cummins ISB6.7 was provided by Cummins Inc as test a cell ready engine with all necessary components, wiring harness and detailed test cell integration procedures
This section outlines the test procedure designed to evaluate fuel consumption and energy flows in heavy and medium-duty diesel engines, detailing the engine instrumentation, testing methodology, and modeling techniques employed.
E NGINE I NSTRUMENTATION
The instrumentation setup on the test engines, as depicted in Figure 8, included thermocouples installed throughout all fluid flow pathways to estimate energy flows in air, exhaust, coolant, and oil streams Measurements were taken for the intake air mass flow rate, coolant flow rate, and exhaust flow rate Additionally, the EGR circuit was equipped with thermocouples positioned before and after the EGR cooler to evaluate the change in enthalpy of EGR gases All data channels were recorded at a frequency of 10 Hz, with the engine instrumentation also encompassing the aftertreatment system; however, the energy audit focused solely on the outlet of the turbocharger.
The study utilized an AVL fuel flow meter to measure fuel flow, employing the Coriolis principle for instantaneous readings This advanced meter provides accurate fuel flow and density measurements with a precision of 0.12% Additionally, ECU-reported fueling data was also recorded for comprehensive analysis.
Figure 8 Schematic of engine instrumentation
To determine the EGR fraction at the intake manifold, West Virginia University utilized an enthalpy balance approach, presuming adiabatic fluid mixing This method necessitated temperature measurements from the intake manifold, post-CAC air, and post-EGR cooler gas.
Cp(T): Specific heat of gas at respective temperature T [kJ/Kg.K]
C ha rg e A ir C oo le r (C A C )
C oo la nt H ea t- E xc ha ng er
C oo lin g W at er In C oo lin g W at er O u t
T1 Engine Inlet Air (Pre Compressor) T2 Pre Intercooler (Post Compressor) T3 Post Intercooler
T4 Engine Intake Manifold T5 Test Cell
The T6 engine exhaust flows post-turbine, while the T7 intercooler receives water input and the T8 intercooler releases water output EGR gas enters the EGR cooler at T9 and exits at T10, with coolant flowing into the EGR cooler at T11 and out at T12 Additionally, coolant is directed into the heat exchanger at T13 and exits at T14, while water enters the heat exchanger at T15 and exits at T16 Finally, the intake for cylinder 6 is designated as T17.
T18 Exhaust Cylinder 6T19 Fuel InletT20 Fuel ReturnT21 Coolant after ThermostatT22 Engine Oil
Tintakemanifold: Temperature of charge air post CAC and EGR mixer [K]
Tintakeair: Temperature of intake air to compressor side of turbocharger [K]
In this study, the heat capacity (Cp) values for EGR gases were assumed to match those of air under specific temperature and pressure conditions These heat capacity values were sourced from the NIST Reference Fluid Thermodynamic and Transport Properties Database (REFPROP) Version 9.1.
The adiabatic mixing assumption is a commonly utilized method in the industry for determining EGR fractions, which helps validate EGR flow measurement devices in engines Alternative EGR calculation methods, such as oxygen sensor and CO2 measurements, were not accessible for this study.
E NGINE L UG C URVE
Engine mapping procedures are essential for measuring the peak torque and power curves of an engine across different speeds This process is crucial for ensuring that all engine components operate optimally, allowing the engine to achieve the peak torque and power levels indicated on its specification tag.
Before initiating the engine mapping procedure, the engine is warmed to stabilize the coolant and oil temperatures, monitored by WVU test cell software Once stability is achieved, the control software conducts a wide-open-throttle (WOT) sweep across the engine's speed range, increasing speed at a rate of 4 rpm/s To ensure accuracy, three consecutive tests are performed, resulting in final torque and power curves These curves serve as upper boundaries for the fueling mapping process and are essential inputs for the engine dynamometer test bench during test cycles.
M OTORING P ROCEDURE
The engine underwent a motoring procedure without fuel to assess its frictional torque, resulting in a motoring map that highlights frictional and pumping losses at various engine speeds It's important to note that this map does not account for the additional friction from piston rings during actual combustion, where cylinder pressures are significantly higher To accurately characterize in-cylinder friction during combustion, pressure measurements are essential The difference between indicated power from these measurements and brake power reveals the true frictional and pumping losses during combustion events West Virginia University (WVU) utilized in-cylinder pressure measurement for the 2005 Mercedes engine and the Volvo D13, which had its exhaust backpressure set to mimic the Mack MP8's rated power This setting effectively simulates the pumping loss from the aftertreatment system However, the Cummins ISB 6.7 and Mack MP8 505C engines lacked in-cylinder pressure measurement capabilities, prompting the use of the sum of brake power and frictional power to determine the indicated power for these platforms.
F UEL M AP D EVELOPMENT
To effectively explore the operational range of the engine, two distinct design of experiments (DOE) methods—Gaussian and Latin Hypercube—were employed to map the engine's fuel consumption Detailed descriptions of these methods can be found in the Appendix Each approach generated 25 data points along the lug curve, resulting in a combined total of 50 points A randomized testing procedure was implemented to navigate these points, allowing for the measurement of fuel consumption and energy audit parameters in a shorter timeframe compared to traditional Cartesian grid methods.
The study examines the steady-state points on the lug curve of a 2010 heavy-duty engine, capturing both steady-state and transient fueling data at 50 points, as well as three additional points at 100% load to assess peak fueling rates Data was collected for two minutes at each steady-state point The transient data obtained may be used to create correction factors for the steady-state fueling maps, addressing potential discrepancies due to engine transients This report later compares fuel maps generated from steady-state data alone with those developed from a combination of steady-state and transient data.
The Gaussian fitting process is more complex and computationally intensive than a simple second-order fit, which led to the decision to utilize the latter for generating fuel maps This study aimed to compare fuel maps derived from both steady-state and transient test points, resulting in the creation of two distinct fuel maps: one based solely on steady-state points and the other incorporating a mix of steady-state and transient points These second-order surface fuel maps were then employed to predict fuel consumption during a transient FTP cycle, allowing for an evaluation of the differences between the two approaches.
Figure 9 DOE test matrix with Gaussian and Latin Hypercube test points
The fuel consumption characteristics analyzed in this study accurately reflect engine performance on the test bench, as dictated by the engine dynamometer conditions This includes various engine management strategies, such as aftertreatment thermal management and extreme operational adjustments.
Manufacturers utilize 17 calibration adjustments for ambient conditions, DPF regeneration events, and fuel-saving strategies during highway cruising, which rely on various parameters and sensor feedback not accounted for on engine test benches Consequently, on-road fuel economy and emissions may differ significantly from results obtained in engine dynamometer tests.
To create comprehensive energy distribution maps across the entire operating range of an engine, a robust fitting method, such as the Gaussian fit procedure, is essential This approach enhances the development of energy audit maps for the entire region of the lug curve, addressing the non-linear nature of engine losses, which can compromise accuracy in certain areas Optimizing combustion characteristics in heavy-duty engines is crucial for achieving peak performance, and failure to capture the various optimized regions can lead to inaccurate loss predictions Therefore, extensive engine dynamometer testing is necessary to accurately characterize losses and efficiency throughout the lug curve, ensuring precise predictions and improved engine performance.
* http://cta.ornl.gov/bedb/appendix_a/Lower_and_Higher_Heating_Values_of_Gas_Liquid_and_Solid_Fuels.pdf
E NERGY A UDIT M ETHODOLOGY
Test engines were equipped with instruments to monitor temperature, pressure, and flow rates of fluid streams that transfer energy to and from the engine To accurately assess energy losses and efficiencies, an appropriate control volume was established This control volume, depicted in Figure 10, is defined by the turbine's exhaust outlet and the compressor's air intake inlet.
Figure 10 Schematic showing the engine control volume
Certain key assumptions are required to characterize the energy flows in an internal combustion engine:
1) The LHV of diesel fuel is assumed to be 42.8 MJ/Kg *
To assess the exhaust conditions and energy, air is used as the assumed fluid While utilizing the actual molar flow rate of emission constituents would enhance the accuracy of the energy audit, measuring emissions was not included in the scope of this study.
Indicated power for the Mercedes and Volvo D13 engines was measured using in-cylinder pressure data, while for Cummins and Mack MP8 engines, it was calculated by summing brake power and frictional power obtained from motoring tests This method of calculating indicated power showed a strong correlation with the measurements taken from in-cylinder pressure data The relationship between these two methods is illustrated in Figure 11 Additionally, the frictional power of the engine will be assessed through motoring tests at various engine speeds, which were conducted immediately after a hot engine mapping process to ensure accurate representation of lubrication oil viscosity and in-cylinder temperature conditions.
C ha rg e A ir C oo le r (C A C )
C oo la nt H ea t- E xc ha ng er
Coolant to EGR Coolant from EGR
Figure 11 Scatter plot of in-cylinder measured indicated power vs calculated indicated power for the Volvo D13
The frictional power obtained experimentally through engine dynamometer procedure will include the combined losses of many factors, mainly
6 EGR related pumping losses as a result of VGT actuation
7 Frictional losses due to piston seal, bearings and lubrication oil viscosity
In heavy-duty diesel engines, piston rings and bearings are major contributors to friction and pumping losses, particularly under high load conditions A study analyzed manufacturer data to evaluate the energy consumption of components such as the oil pump, water pump, and air compressor, aiming to isolate their energy use from the overall friction and pumping work It's important to note that motoring tests may show lower frictional torque at high loads, as the friction experienced during actual combustion is considerably higher than that observed in dynamometer tests.
An experiment was conducted to decouple the backpressure component by varying it across five different settings, measuring the corresponding changes in fuel consumption Five hot engine tests were performed on the Volvo D13, with backpressure settings ranging from 0.5 psig (3.4 kPa) to 4.5 psig (31 kPa), to evaluate fuel consumption changes per psig variation The tests were carried out without the aftertreatment system to isolate the effects of soot loads in the DPF Subsequently, the changes in fuel consumption were normalized as a percentage of total fuel energy to assess the energy loss associated with engine backpressure.
W ASTE H EAT R ECOVERY S IMULATION
WVU has created a MATLAB simulation tool to model a waste heat recovery system utilizing an Organic Rankine Cycle (ORC), focusing on the recovery of waste heat from EGR cooling and the coolant circuit This simulation employs a suitable working fluid identified from existing literature and incorporates key assumptions based on both literature findings and sound engineering judgment Fluid properties are sourced from the REFPROP manager developed by NIST, with the working fluid selected as R245fa, in line with the Cummins WHR system, rather than the ethanol system proposed by Park et al Key assumptions for the simulation are detailed in Table 5.
Table 5 List of Key Assumption in WVU WHR ORC Model (Park et al., 2011, Teng et al., 2011,
EGR gas properties Air properties assumed Engine Coolant Heat
Mass flow rate of Working Fluid @ each mode
Based on Cummins ORC specifications (between 0.3 and 0.8 kg/sec)
Expander out Pressure 101.3kPa Condenser out State 25°C Saturated Liquid State
As part of the SuperTruck DOE project, OEMs have developed various Waste Heat Recovery (WHR) systems, with Cummins Inc.'s design being extensively documented Consequently, West Virginia University (WVU) adopted a similar simulation approach, utilizing design parameters such as turbine expansion ratio and generator efficiencies based on sound engineering judgment This simulation leverages measured temperature and flow data from different streams to assess the energy recovery potential of the cycle, offering a realistic estimate of WHR capabilities across the engine's operating range The schematic of the simulated WHR Organic Rankine Cycle (ORC) system illustrates the heat transfer regions between the working fluid and engine fluids.
Figure 12 Schematic of ORC with energy recovery from EGR and Engine Coolant
The model of the ORC system consists of five major components -pump, pre-heater, evaporator, expander and condenser Each sub-system and the processes are described below:
The feed pump is designed to enhance the working fluid's pressure and achieve the desired flow rate, with specifications based on Cummins (Nelson, 2008b) It delivers a pressure head of 2068 kPa and a mass flow rate between 0.3 and 0.8 kg/sec The fluid enters the pump at state 3, maintaining a saturated temperature of 25°C, as illustrated in the T-s and P-v diagrams The pump effectively pressurizes the fluid to 2216.7 kPa at state 4, with minimal temperature increase, adhering to the ideal Rankine cycle conditions that assume saturated fluid states at both the condenser and boiler inlets.
The preheater in this model leverages the engine's coolant circuit to preheat the working fluid entering at state 4, utilizing the energy recovered from the coolant to elevate the fluid's temperature at a specific pressure to state 5 The efficiency of heat transfer is significantly influenced by the mass flow rate of the working fluid in the heat exchanger The simulation considers various flow rates, ensuring that the working fluid reaches a vapor state at the evaporator's outlet For the preheater analysis, a counter-flow shell and tube heat exchanger is employed, assumed to have an effectiveness of 90% with minimal pressure drop across the system.
The evaporator, specifically the EGR cooler, utilizes preheated working fluid from state 5, leveraging exhaust gas recirculated heat from the engine for effective vaporization Achieving vaporization requires the use of a preheater and lower mass flow rates of the working fluid The system is designed to maintain the evaporator's outlet at state 1 in the single-phase vapor region, preventing the working fluid from entering the expander as a two-phase mixture For our evaporator analysis, we selected a counter-flow shell and tube heat exchanger, with exchanger effectiveness determined using the NTU method, while assuming the pressure drop across the evaporator heat exchanger to be negligible.
The vaporized working fluid, R245fa, is expanded to a condenser pressure of 101.3 kPa, with an isentropic efficiency of 85% R245fa is favored for its ability to undergo phase change at lower temperatures and remain in the gas phase post-expansion, making it an ideal working fluid for waste heat recovery (WHR) in internal combustion engines It is recognized as a safe and environmentally friendly fluid that does not contribute to ozone depletion Pressure-specific volume and temperature-entropy diagrams for R245fa demonstrate that it remains in a superheated state during isentropic expansion, effectively preventing turbine cavitation Additionally, R245fa exhibits optimal isentropic efficiency at temperatures between 380 and 400 K, with a maximum temperature limit of 500 K, which is crucial for determining the mass flow rate to avoid fluid decomposition.
The condenser plays a crucial role in the thermodynamic cycle, where the working fluid from the expander at state 2 enters at constant pressure During this process, heat is rejected to the environment via the condenser heat exchanger According to the model's standard assumptions, the fluid exits the condenser at state 3, reaching saturation at a selected temperature of 25°C.
Figure 13 T-S & P-v diagram of the simple R245fa Rankine cycle WHR thermodynamic steady-state model
F UEL M AP C HARACTERIZATION
MY 2005 Mercedes OM460
Figure 14 illustrates the fuel consumption contours along the operating curve of the Mercedes OM460 engine, with the dots beneath the lug curve representing the test points derived from the DOE test matrix used in the engine mapping process Meanwhile, Figure 15 compares the predicted fuel consumption rate from the Gaussian model with the actual measured fuel rate during the FTP cycle.
Figure 14 Contour plot prediction for Mercedes OM460 fuel flow rates [g/s]
Figure 15 Predicted vs Measured Fuel Flow Rate for Mercedes OM460 on FTP cycle
Figure 16 illustrates the linear correlation between the predicted and measured fuel rates of the Mercedes OM460 during the FTP cycle, with the total cycle fuel measured in grams Detailed statistics of this fit are presented in Table 6.
Figure 16 Scatter Plot of Fuel Flow Rate for Mercedes OM460 using the Gaussian Process Fit over
Table 6 Summary of Linear Fit for Mercedes OM460 over FTP for the Gaussian Process Fit
Percentage error of integrated fuel 1.1%
Total FTP Work (kW-hr) 21.1
Table 6 presents the statistical data from the Gaussian fit applied to estimate the fuel consumption rate of the 2005 Mercedes during the FTP cycle The analysis revealed an integrated fuel consumption error of 1.1%, indicating a slight bias in favor of the predictions.
MY 2011 Mack MP8 505C
Figure 17 illustrates the fuel consumption contours for the US EPA 2010 compliant Mack MP8 engine, with test points indicated by dots beneath the lug curve, derived from the DOE test matrix Figure 18 presents the predicted fuel consumption rate using a Gaussian model, compared to the measured fuel rate during the FTP cycle Additionally, Figure 19 demonstrates the correlation between the measured and predicted fuel rates for the Mack MP8 engine throughout the FTP cycle.
Table 7 lists the statistics of the fit between total measured and predicted fuel consumption in grams over the FTP cycle for the Mack MP8 engine
Figure 17 Contour plot prediction for MY2011 Mack MP8 fuel flow rates [g/s]
Figure 18 Predicted vs Measured Fuel Flow Rate for MY2011 Mack MP8 on FTP cycle
Figure 19 Scatter Plot of Gaussian prediction vs measured fuel flow for Mack MP8 over FTP
Table 7 Summary of Linear Fit for Mack MP8 over FTP
Actual Total Fuel [g] 6228 Predicted Total Fuel [g] 6230 Percentage error of integrated fuel 0.01%
Total FTP Work (kW-hr) 25.9
Difference between steady-state and transient maps
Engine fuel maps can be created through two main methods: collecting extensive real-world data to fit fuel consumption under the engine's lug curve or conducting controlled steady-state tests at selected points along the curve However, both methods have limitations that can lead to discrepancies in estimating fuel consumption for untested speed or torque combinations Highly transient engine operations may not yield stabilized fuel consumption readings, resulting in deviations in absolute fuel consumption values Additionally, simple steady-state tests often overlook the transient behaviors of engine components like the water pump, oil pump, fuel pump, and turbocharger, which can affect the accuracy of fuel consumption rates during transitions between different speed and torque settings.
This study addresses existing limitations by implementing a combined testing approach that features a cycle of steady-state points interconnected by transient movements Fuel consumption is recorded in real-time at a frequency of 10 Hz throughout the cycle Consequently, this testing procedure generates a comprehensive fuel consumption profile across 50 steady-state points, along with multiple transient data points captured during the transitions.
The study aimed to utilize Gaussian process fitting for training fuel consumption data to create a 25 x 25 fuel consumption matrix However, due to limitations in the JMP statistical software with large datasets, this methodology was limited to predicting energy audit maps, while a simpler second-order surface fit was employed for generating fuel maps for heavy-duty and medium-duty engines Figure 19 illustrates the prediction of a transient FTP cycle using the Gaussian process fit, which was trained solely on steady-state fuel consumption points from the test cycles The findings indicate that, despite using only steady-state data for training, the predictions of transient FTP fuel consumption align closely with the measured fuel consumption rates throughout the FTP cycle.
To assess the differences in fuel consumption profiles between steady-state testing and a comprehensive transient test cycle, a 2nd order surface fit was employed in JMP, offering reduced computational demands compared to the Gaussian fit method This analysis utilized two sets of training data, with the initial dataset focusing solely on
The study utilized 50 steady-state test points to establish a second-order fit, while the secondary dataset incorporated the complete dataset, which included instantaneous fuel consumption data collected during transitions between the steady-state points.
Two 2 nd order fit equations were derived from the datasets Table 8 shows the summary of fit for the 2 nd order equation developed using the transient and steady-state data points A R 2 of 0.99 and root mean square error (RMSE) of 0.30 suggests a good surface fit of the data Table 8 shows the summary of fit for the 2 nd order equation developed using the transient and steady-state data points A R 2 of 0.99 and root mean square error (RMSE) of 0.25 suggests a good surface fit of the data
Table 8 Summary of fit for equation 1
Table 9 summarizes the fit results for the second-order equation derived from 50 steady-state data points, revealing a mean absolute percent error of 19% In contrast, the fit equation based on Equation 1 achieved a lower error of 12.1%.
Table 9 Summary of Fit for Equation 2
The two equations were utilized to forecast instantaneous fuel consumption during the FTP, highlighting the variations in surface fit resulting from steady-state and transient datasets Figure 20 illustrates the scatter.
The scatter plot comparing predicted and measured fuel consumption over the FTP cycle demonstrates the differences in fuel maps derived from steady-state testing versus a combination of steady-state and transient testing Both Equation 1 and Equation 2 achieved a high R² value of 0.99, with RMSE values of 0.65 and 0.64, indicating negligible differences in the fuel maps generated from the two datasets This suggests that both datasets can accurately predict transient FTP fuel consumption, leading to the conclusion that there are no significant differences in using either dataset for training fuel consumption models Additionally, Equation 1 was utilized to populate the 25 x 25 Autonomie fuel matrix.
Figure 20 Actual Vs Predicted Fuel consumption over FTP using 2 nd order fits of Equation 1 and
Figure 21 Percent difference in fuel map between steady-state and transient
Figure 21 illustrates the contour plots depicting the differences in fuel maps generated from a second-order fit of steady-state and transient engine operation data The analysis reveals that the discrepancy in fuel consumption between steady-state and transient data is generally less than 2% across most areas of the lug curve However, significant differences in fuel consumption are noted at lower loads, which can be attributed to lower signal-to-noise ratios and instability in engine loads at these low torque conditions, resulting in considerable variations in the measured fuel flow.
Differences in fuel maps can occur depending on whether steady-state or transient engine data is utilized, primarily due to variations in losses associated with stable engine operation versus fluctuating speed and torque characteristics This study aimed to assess these differences by creating two fuel maps: one based solely on steady-state data and another combining both steady-state and transient fueling rates The fuel consumption predictions for the 2010 heavy-duty engine were calculated using fit equations derived from these datasets, revealing that the transient data predicted FTP fuel consumption to be 0.48% lower than actual consumption, while the steady-state map showed a 0.9% higher bias and the transient map a 1% higher bias for the SET cycle Notably, the lower fueling rates during idle mode led to larger percent errors, prompting the exclusion of idle points from the SET error estimation Overall, the differences in the fuel maps were found to be minimal, likely influenced by the testing methodology and the number of transient data points employed.
30 maps Fuel maps developed from real-world data collection with a larger dataset could show larger differences between steady-state and transient fuel-maps
Table 10 Correction factor between steady-state and transient fuel maps
Transient + Steady- state Prediction Steady-state
MY2011 Cummins ISB6.7
Figure 22 illustrates the fuel consumption contour for the Cummins ISB6.7 engine, which complies with US EPA 2010 emissions standards, highlighting steady-state points derived from the DOE methodology Meanwhile, Figure 23 compares the measured and predicted instantaneous fuel consumption during the FTP cycle, providing insights into performance accuracy.
Figure 22 Contour plot prediction for Cummins ISB6.7 fuel flow rates [g/s]
Figure 23 Comparison of predicted vs actual instantaneous fuel consumption over FTP
Figure 24 Scatter Plot predicted vs measured fuel flow for Cummins ISB over FTP
Table 11 Fit statistics for the predicted vs measured fuel consumption for Cummins ISB
Actual Total Fuel [g] 4120.9 Predicted Total Fuel [g] 3980.5 Percentage error of integrated fuel 3.41%
Total FTP Work (kW-hr) 14.6
The scatter plot in Figure 24 illustrates the relationship between predicted and measured instantaneous fuel consumption for the Cummins ISB 6.7 during the transient FTP cycle The predicted fuel consumption was derived from a second-order fit applied to steady-state testing data According to the statistics presented in Table 11, there is an observed under-prediction of 3.41% in fuel consumption when comparing the steady-state fuel map to the measured values over the FTP cycle To align the predicted fuel consumption with the measured FTP results, a correction factor of 1.034 must be applied.
A UTONOMIE F UEL M AP
The second-order fit equation, derived from the combined steady-state and transient dataset, was utilized to create the 25 x 25 Autonomie fuel consumption matrix A significant technological advancement between the 2005 and 2010 model years lies in the aftertreatment system The 2005 Mercedes featured a different configuration compared to its 2010 counterpart.
The 2010 Mack engine is equipped with advanced aftertreatment systems, including a Diesel Particulate Filter (DPF) and Selective Catalytic Reduction (SCR), which enable it to meet the stringent US EPA NOx emissions limit of 0.20 g/bhp-hr In contrast, the 2005 model lacks such systems and is certified at a higher NOx standard of 2.4 g/bhp-hr The increased Exhaust Gas Recirculation (EGR) rate in the 2010 model may lead to higher pumping losses due to the Variable Geometry Turbocharger (VGT) restricting exhaust flow Additionally, the DPF contributes to increased exhaust backpressure, resulting in a fuel efficiency penalty However, advancements in engine technologies and the implementation of SCR have mitigated some of these fuel penalties The brake-specific fuel consumption for the Mercedes was measured at 17.4 g/bhp-hr, while the 2010 Mack demonstrated improved efficiency at 16.6 g/bhp-hr.
E NERGY A UDIT
USEPA 2010 Heavy-Duty Engine Energy Audit
The energy distribution results for the USEPA 2010 compliant heavy-duty diesel engine indicate that, on average, 39% of the total input fuel energy is converted into useful work, while approximately 34% is lost as exhaust gas at the turbocharger exit Additionally, frictional and pumping losses account for around 6% of the total input energy The coolant circuit is responsible for nearly 10% of the input fuel energy rejected from the combustion chamber, engine oil, and EGR circuit Notably, the energy from the EGR cooler is transferred to the coolant and contributes to the overall coolant heat rejection; however, it is not represented as a separate energy flow in the results Assuming complete heat transfer between the EGR and coolant circuit, it is estimated that about 46% of the coolant energy originates from the EGR circuit.
Operational curves for auxiliary devices and experimental calculations of engine backpressure losses were utilized to separate total frictional and pumping losses from motoring tests Frictional losses, primarily from bearings, piston and cylinder contact, and lubrication oil viscosity, represented approximately 2% of the total fuel energy and accounted for an average of 47% of the overall frictional and pumping losses in the engine Additionally, pumping losses contributed roughly 2% of the input fuel energy and made up about 29% of the total frictional and pumping losses.
The energy distribution and loss magnitude in an engine fluctuate across different regions of the lug curve, with variations in brake thermal efficiency (BTE) closely linked to changes in frictional power influenced by engine speed, in-cylinder pressure, and lubrication Additionally, pumping losses are affected by factors such as EGR flow, soot buildup in the DPF, boost pressure, and turbocharger efficiency.
The peak Brake Thermal Efficiency (BTE) for the heavy-duty platform was found to be around 40%, while EPA certification data indicates a thermal efficiency of 42.8% at peak torque It is important to consider that variations in BTE between original equipment manufacturer (OEM) literature and this study may arise from factors such as engine age, the state of Diesel Particulate Filter (DPF) loading, and fuel properties The engine tested in this study had been removed from a truck that had accumulated over 250,000 miles, highlighting the potential differences in BTE between new OEM products and engines that are actively in use.
Figure 27 USEPA 2010 heavy-duty energy audit for select operating points
USEPA 2010 Medium-Duty Engine Energy Audit
The energy distribution results for the USEPA 2010 compliant medium-duty diesel engine indicate that approximately 40% of fuel energy is transformed into useful work, while nearly 36% is lost as exhaust heat at the turbocharger exit Additionally, engine friction accounts for an average energy loss of 4.4%, with pumping and engine accessories contributing losses of 1.7% and 0.8%, respectively About 9% of fuel energy is also rejected through the coolant circuit, with the Exhaust Gas Recirculation (EGR) circuit contributing an average of 49% of the coolant energy, assuming complete heat transfer The peak Brake Thermal Efficiency (BTE) for this engine was found to be 41%.
Figure 28 USEPA 2010 medium-duty engine energy audit for select operating points
F UTURE E NGINE P REDICTION
Oil Pump
The oil pump work was based on research from Lasecki and Cousineau (2003), which focused on a 15-liter heavy-duty diesel engine The linear curve of the oil pump, as depicted in Figure 29, was utilized to calculate the speed-based oil pump work for this engine platform.
Water Pump
The power requirements for heavy-duty engine water pumps are determined by OEM specifications, which include idle and rated speed power requirements A two-point linear relationship is utilized to calculate the speed-dependent power needs of the baseline 2010 heavy-duty diesel engine This calculation is visually represented in Figure 30, which illustrates the linear water pump curve based on discussions with the OEM.
Air Compressor
During testing, the air compressor connected to the engine was disconnected, leading to the calculation of only the unloaded air compressor friction component Specifications from a Cummins report on an 18.7 CFM WABCO air compressor were utilized to establish a linear relationship between engine speed and the power requirements of the air compressor Figure 31 demonstrates the power consumption of an air compressor based on engine speed in heavy-duty diesel engines.
Exhaust Backpressure
To analyze the impact of exhaust backpressure on pumping loss, the Volvo D13 engine was tested at wide-open throttle across various exhaust backpressure levels The study established a correlation between total fuel consumption and exhaust backpressure, highlighting how these changes affect fuel mass.
To overcome a unit of 1 psig of backpressure, 0.075 kg of fuel is consumed, equating to an energy requirement of 3209.3 kJ This relationship allows for the scaling of fuel energy based on the percentage reduction in exhaust backpressure.
Engine Friction
The measurable components of friction and pumping were identified, while the residual friction and pumping power were linked to factors such as engine piston seals, connecting rod and crankshaft bearings, the viscosity of lubrication oil, and fuel pumps.
The summation of the afforementioned frictional and pumping components will equal the frictional fraction of input energy shown in the 2010 reference engine energy audit results.
Prediction Methodology
An extensive literature survey was conducted to identify suggested improvements by OEMs for the 2017 and 2020 fuel maps, leading to a fractional enhancement in fuel economy These improvements were incorporated as reduced energy consumption in various components, resulting in an updated energy audit fraction that totals less than 100% Consequently, the updated fuel energy is derived from the newly reduced energy distribution across different fractions For the 2020+ maps, the waste heat recovery (WHR) potential was considered as an equivalent reduction in fuel energy for specific speed and torque conditions, without altering the loss category distribution Since WHR is anticipated as a 2020+ technology, it was not included in the 2017 engine maps.
In the development of the 2017 and 2020+ maps, various loss categories such as friction, pumping, and exhaust were adjusted by a consistent scaling factor to reflect advancements in technology The specific technology improvements considered for calculating percentage reductions in each category are detailed in the following discussion.
The EGR circuit and exhaust aftertreatment backpressure significantly contribute to baseline engine pumping losses Enhancements in low-temperature operation of SCR catalysts and partial hybridization of thermal management strategies can lower EGR mass fractions This reduction decreases the need for higher exhaust manifold pressure to facilitate EGR flow into the intake manifold, ultimately minimizing the engine's parasitic pumping work.
Daimler's innovative use of a patented asymmetric turbocharger, which features no moving vane actuation, allows for the creation of smaller and lighter components compared to traditional variable geometry turbochargers (VGT) This advanced design enhances air delivery performance and achieves EGR rates of up to 35% at full load on Detroit Diesel's heavy-duty platform By utilizing a dedicated group of cylinders as an EGR pump while another set focuses on turbocharging, significant efficiency gains are realized Improvements in turbomachinery technology can lead to over a 5% reduction in fuel consumption, and strategically grouping exhaust manifolds based on firing order minimizes pumping losses, facilitating efficient turbocharging and supporting elevated EGR fractions.
Another contributor to engine backpressure is the DPF Research from Corning Inc has shown the possibility of thin walled DPF substrates to lower exhaust backpressure by approximately 35% (Corning)
Overall, this study assumes that changes in exhaust and air handling components of the engine can lower pumping losses by close to 30%
Variable flow oil and coolant pumps can reduce fuel consumption by up to 2% in heavy-duty applications The 2013 Detroit Diesel DD15 engine utilizes a viscous clutch design, enhancing water pump efficiency over traditional gear-driven models By decoupling the oil and water pumps at lower engine loads, pump friction is minimized during closed thermostat operation This innovative design could be a key feature for future engines post-2017, allowing for oil and water pump decoupling when cooling is not necessary.
Electric oil pumps, adaptable to engine load and oil temperature, effectively minimize parasitic and frictional losses during cold starts and low-load scenarios However, it is crucial to manage the interactions with alternator loading to optimize overall performance This study anticipates that future engines could achieve a reduction in power consumption for engine accessories by approximately 10%.
Research indicates that integrating advanced bearing and piston coating materials, along with low-friction boundary lubrication technologies, can lead to a 5% reduction in fuel energy consumption due to friction (Fenske et al., 2014) Additionally, synthetic lubrication oils and innovative oil formulations have been shown to significantly decrease engine friction For instance, Cummins achieved a 30% reduction in friction in their 2009 ISX model by implementing reduced-friction shaft seals, variable flow oil pumps, and optimizing components such as water and fuel pumps and the gear train (Delgado and Lutsey, 2014) Furthermore, Grant's study highlights that a low-friction piston ring design can result in a 30-35% reduction in engine friction, translating to a 0.5% to 1% improvement in brake thermal efficiency (BTE) (Grant, 2004) This study posits that future engines could see a 25% reduction in friction.
Improvements in turbomachinery and combustion parameters, such as higher compression ratios and increased in-cylinder pressures, are key to achieving significant fuel savings Advanced turbocharger designs, like Daimler's asymmetric turbocharger, enhance power density by optimizing exhaust energy use and improving air and EGR delivery to intake manifolds Enhancing turbine efficiency in turbochargers leads to reduced exhaust energy consumption by efficiently converting exhaust energy to achieve high-pressure ratios in the compressor Cummins' innovative two-stage turbocharging concept, featuring patented exhaust flow control, aims to provide ultra-high boost and a 1 to 2 percent fuel consumption benefit According to NRC, turbocharging technologies can yield a 2-5 percent decrease in fuel consumption while maintaining necessary EGR flow, with projections indicating a 10.5% reduction in fuel consumption through exhaust energy reduction by 2017 This includes an anticipated 15% reduction in exhaust energy due to turbocharging technology, translating to a 5% decrease in fuel consumption Additionally, mechanical or electrical turbo-compounding can harness exhaust energy downstream to generate mechanical work for flywheels or electrical energy for battery charging and auxiliary systems.
The thermodynamic assessment of engine loss reduction presents challenges; however, this study highlights turbo-compounding as an effective mechanism for reducing exhaust energy loss and enhancing fuel efficiency Research by Callahan et al indicates that the mechanical turbo-compounding system in the Detroit Diesel DD15 can achieve up to a 2.5% reduction in fuel consumption under full load conditions, even accounting for pumping losses from the additional turbine in the exhaust system Anticipated advancements in turbo-compounding technology in the 2020+ timeframe could yield further improvements, with potential fuel savings reaching up to 3.5% (Cooper et al., 2009) Thus, this study forecasts a 3.5% reduction in fuel consumption for the 2020+ platform.
Improving the conversion of combustion energy to work is crucial for reducing fuel consumption Enhancements in engine thermodynamics, such as optimized combustion chamber design and improved in-cylinder charge motion, lead to more efficient energy distribution, resulting in lower exhaust energy Techniques like increased fuel injection pressures enhance fuel atomization, achieving a uniform fuel/air ratio and maximizing energy conversion A study by Southwest Research Institute indicates that a two-step piston design combined with an 8-nozzle fuel injector can reduce brake-specific fuel consumption (BSFC) by nearly 5% compared to traditional designs However, these benefits are primarily observed under medium speeds and high load conditions Assuming a 3% reduction in fuel consumption due to improved designs, an additional 8.5% reduction in exhaust energy is projected compared to a baseline engine Navistar has demonstrated a 3% absolute improvement in brake thermal efficiency (BTE) through higher compression ratios and advanced fuel systems, correlating to an 8% decrease in fuel consumption Similarly, Cummins achieved a 2% absolute improvement in BTE through design optimizations It is important to note that these improvements in BTE may reflect benefits in specific operating conditions rather than across the entire performance spectrum.
Advancing injection timing enhances engine efficiency, reduces brake-specific fuel consumption (BSFC), and lowers exhaust temperatures, but it may lead to increased NOx emissions To fully harness the advantages of advanced injection timing, improvements in NOx aftertreatment technology are essential Original Equipment Manufacturers (OEMs) can effectively implement this engine calibration only when paired with aftertreatment systems that achieve significant NOx reductions at lower exhaust temperatures Research by Ardanese et al introduced a low fuel consumption engine calibration map that employed an optimized urea dosing strategy to comply with US EPA 2010 emissions standards while minimizing fuel consumption, resulting in a 4% reduction in exhaust temperatures.
Stanton (2009) found that an advanced SCR aftertreatment system can reduce fuel consumption by 10% while increasing engine-out NOx emissions eightfold The study suggests that combining this strategy with an advanced NOx reduction aftertreatment can enhance the fuel savings achieved through advanced injection timing Implementing closed-loop controls, such as in-cylinder combustion feedback and optimized fueling strategies using neural networks, can further lower fuel consumption and exhaust energy profiles in future engine technologies (NRC, 2010) Additionally, real-time combustion control, innovative fuel injection methods, and alternative combustion strategies may yield an extra 1 to 4% reduction in fuel consumption (NRC, 2014) This reduction positively affects the engine's closed-cycle efficiency, leading to decreased heat rejection through exhaust gases Consequently, the study indicates that a maximum 4% reduction in fuel consumption corresponds to a 10% decrease in exhaust energy based on the baseline engine energy audit.
This study anticipates a 15% decrease in exhaust energy for the 2017 engine platform, with an additional 25% reduction expected from the 2020 engine technology Ongoing research indicates that viable technological pathways have been previously identified, supporting the reduction targets established in this analysis.
Reducing engine friction and EGR fractions leads to decreased heat transfer to the coolant circuit Future engines are expected to feature lower EGR rates due to advancements in NOx aftertreatment technologies The integration of DPF and SCR systems enhances catalyst performance at lower temperatures, allowing for a reduction in EGR fractions necessary for in-cylinder NOx control This decrease in EGR not only minimizes pumping losses but also lessens the heat expelled into the coolant circuit from the EGR cooler.
Improving the utilization of combustion energy for expansion work can significantly reduce heat rejection to the coolant from the combustion chamber, thereby increasing the available energy for expansion According to Stanton (2013), advancements in materials technology, enhanced insulation of the combustion chamber, lower friction components, and reduced EGR fractions can decrease heat loss through the coolant circuit, leading to nearly a 3% improvement in fuel consumption A reduction in heat transfer from the combustion chamber to the coolant circuit markedly enhances thermal efficiency This study indicates that a 3% decrease in fuel consumption due to reduced coolant energy corresponds to a 30% reduction in coolant energy losses Specifically, a projected 1.6% reduction in fuel consumption is expected with a 15% decrease in coolant energy for the 2017 platform, while a 2.2% reduction is anticipated with a 25% reduction in coolant heat loss for 2020+ engine technology.
Table 12 shows the contribution of the different technologies to the various energy audit categories as well as forecasting availability of the various improvements
Table 12 Efficiency technologies, energy loss mechanism they influence, and approximate timeframe of technology availability
Engine technology that offers potential efficiency improvement over representative model year 2010 technology
Engine loss areas where the technology reduces the energy loss a
Heat loss engine coolant system
Parasitic load reduction (fuel pump) X X X
Parasitic load reduction (oil pump) X X X
Turbocharging and other air handling system improvements X X X X
Fuel injection improvements (e.g., increased fuel rail pressure) X X X
Advanced waste heat recovery systems X X
Turbocompounding X X X a Technologies (left) that impact each energy loss mechanism (top) marked with “X”
R EGULATORY C YCLE P REDICTIONS
Heavy-duty SET Prediction
The energy distribution for heavy-duty engine technology is illustrated in Figure 38, comparing the reference 2010 model with the predicted advancements for 2017 and beyond The 2017 engine technology is forecasted to achieve a 13.3% reduction in fuel consumption relative to the 2010 reference model Meanwhile, the 2020+ engine technology is expected to deliver the maximum feasible reduction, with a projected 17.8% decrease in fuel consumption over the SET cycle when compared to the 2010 heavy-duty engine.
Figure 38 Heavy-duty energy loss distribution for 2010, 2017 and 2020+ engine technologies over
Medium-duty FTP Prediction
The energy distribution for medium-duty engines from 2013, 2017, and 2020+ over the regulatory FTP cycle is illustrated in Figure 39 The 2017 engine technology is projected to achieve a fuel consumption reduction of 10.6%, while the 2020+ engine technology has a maximum feasible reduction of 19.5% It is important to note that the reductions in ambient heat transfer depicted in the figure are due to errors in predicting energy distribution during the transient FTP cycle, rather than an actual decrease in ambient heat transfer.
Figure 39 Medium-duty energy loss distribution for 2010, 2017 and 2020+ engine technologies over
W ASTE H EAT R ECOVERY
USEPA 2010 Heavy-Duty Diesel Engine WHR Model
Figures 40 and 41 illustrate the total available waste heat from the engine coolant and EGR circuits under the engine lug curve At high load operating points, the WHR ORC simulation recovers approximately 15% of the total available waste heat The WHR output power from WVU simulations closely aligns with the outputs observed in Cummins WHR design, as reported by Nelson (2008a), who noted a 19.4 hp power output at 1600 rpm with a 450 bhp engine.
Figure 40 Recoverable energy from engine coolant circuit for USEPA 2010 heavy-duty engine
Figure 41 Recoverable energy from EGR cooler for USEPA 2010 heavy-duty engine
The contour of Waste Heat Recovery (WHR) output work potential from an Organic Rankine Cycle (ORC) simulated on the Mack MP8 heavy-duty diesel engine indicates that the majority of WHR potential is accessible primarily during high-load engine operations, which are associated with elevated Exhaust Gas Recirculation (EGR) energy and coolant energy Near the rated engine speed, the WHR potential reaches approximately 25 kW, while medium load regions of the lug curve exhibit a WHR potential of around 16 kW.
Figure 40 Result for total recoverable turbine output work from WHR model for USEPA 2010
USEPA 2010 Medium Duty Diesel Engine WHR Model
The Cummins ISB6.7 engine's coolant and EGR circuit demonstrate significant waste heat recovery potential, with a maximum of 54 kW rejected by the coolant under the medium-duty engine's lug curve and 42 kW available from the EGR circuit Notably, the EGR heat energy in medium-duty engines is considerably lower than that in heavy-duty platforms, primarily due to reduced EGR fractions resulting from enhanced NOx control achieved through SCR activity and close-coupled aftertreatment systems.
As a result the available WHR work output is lower than results achieved in the heavy-duty platform
Figure 43 Recoverable energy from engine coolant circuit of medium-duty USEPA 2010 engine
Figure 44 Recoverable energy from EGR circuit for medium-duty USEPA 2010 engine
The WHR output work potential for the medium-duty Cummins ISB6.7 engine platform, as illustrated in Figure 45, peaks at 11 KW during high engine speed and full load conditions However, since medium-duty diesel engines are primarily utilized in vocational vehicles that operate under transient and lower average speed duty cycles, they often function outside these optimal regions Consequently, the potential for waste heat recovery (WHR) in medium-duty platforms is not a significant contributor to enhancing overall engine efficiency.
Figure 45 Result for total recoverable turbine output work from WHR model for medium duty
This study aimed to enhance the understanding of efficiency, energy losses, and potential improvements in diesel engines used in heavy- and medium-duty vehicles The research methodology included laboratory testing of two baseline diesel engines that meet the compliance standards for the specified model year.
The 2010 U.S Environmental Protection Agency emissions standards focused on a 12.8-liter heavy-duty diesel engine, typical for Class 8 tractor-trailers, and a 6.7-liter medium-duty engine, representative of Class 4-6 trucks such as urban delivery and vocational vehicles To enhance and validate efficiency data, additional engine information was incorporated, alongside insights from industry peers and research literature, to analyze the shifts in energy flows and losses associated with various efficiency technologies.
The project primarily focused on characterizing engine maps for modern heavy-duty and medium-duty engines, specifically those from 2010 This involved conducting detailed energy audits under various engine load conditions, analyzing torque and speed variations The engine fuel maps were created through rigorous testing across a series of steady-state points, interspersed with transient conditions, resulting in a comprehensive 25 x 25 fuel matrix derived from a second-order surface fit of the fuel consumption data Additionally, energy audits were performed at 50 steady-state points to evaluate the distribution of input energy, distinguishing between engine losses and useful work produced.
The heavy-duty diesel engine achieved a brake power conversion of 39.1% from fuel energy during the SET engine cycle, with significant energy losses: 35.5% through exhaust heat, 10.6% via coolant heat transfer, and 6% from the charge air cooler (CAC) Additional losses included 3.4% to ambient air, 2.3% to friction, 1.7% to pumping, and 1.3% to parasitic losses from accessories Notably, EGR cooling accounted for approximately 46% of the total heat in the coolant circuit In contrast, the medium-duty diesel engine converted 29.2% of its fuel energy to brake power over the FTP cycle, with 31.4% lost through exhaust gases and 18.4% attributed to friction and pumping losses The coolant circuit rejected 10% of fuel energy, while the CAC accounted for 5%, with additional losses of 3.6% to ambient air and 2.4% consumed by engine accessories.
The study employed energy audits and technology forecasting to evaluate how emerging technologies can minimize fuel consumption by addressing specific loss mechanisms It analyzed two future engine models: a "2017 engine" meeting heavy-duty vehicle standards and a "2020+ diesel engine" incorporating advanced technologies for enhanced fuel efficiency Key technologies investigated for improving efficiency included higher compression ratios, optimized control systems, advancements in exhaust gas recirculation, low-friction lubricants, reductions in engine friction and parasitic loads (such as those from the piston, water pump, oil pump, and fuel pump), turbocharging enhancements, improved fuel injection systems, optimized cylinder head designs, and waste heat recovery systems.
Emerging engine efficiency technologies have demonstrated a significant potential to lower fuel consumption, with the 2017 engine cycle achieving an average reduction of 7.9% compared to the SET engine cycle Furthermore, the advanced 2020+ engine cycle showed an impressive average reduction of 18.3% over the SET cycle when compared to the baseline 2010 heavy-duty engine The 2017 engine's performance improvements are attributed to factors such as reduced engine friction, enhanced turbomachinery, increased efficiency of engine accessories, and decreased pumping losses.
The study focused on aftertreatment and EGR loop backpressure technologies, alongside a waste heat recovery (WHR) simulation aimed at enhancing engine efficiency and predicting fuel consumption beyond 2020 It projected a peak brake thermal efficiency (BTE) of 49% for the 2020+ engine, which improved to 52% with WHR integration Promising advancements, including turbo compounding, engine down speeding, and the integration of engines and transmissions, are geared towards achieving a BTE target of 55% The analysis was limited to thermodynamic loss mechanisms within the engine and their effects on fuel consumption.
The study conducted a simple Waste Heat Recovery (WHR) analysis using the Organic Rankine Cycle (ORC) design with R245fa as the working fluid The turbine's work output was integrated into the 2020+ engine prediction to optimize fuel energy consumption The WHR simulation revealed a peak power output of 25 kW for heavy-duty platforms, while medium-duty platforms achieved a maximum output of 12 kW However, the medium-duty platform demonstrated limited WHR potential across its primary operational range due to lower Exhaust Gas Recirculation (EGR) fractions and reduced coolant heat rejection This combination of effective Selective Catalytic Reduction (SCR) operation and diminished coolant heat rejection results in less available energy for the WHR system.
This research methodology did not account for potential cross effects between various factors, such as how higher injection pressures impact fuel pump performance or how efficient engines reduce exhaust heat and, consequently, waste heat recovery (WHR) potential By focusing solely on positive impacts and neglecting possible negative cross-effects of engine technologies or efficiency improvements, the study's findings may not accurately reflect real-world engine efficiencies achievable with future technologies Additionally, the assumption of fuel consumption reduction across the entire lug curve overlooks the practical expectation that engines would be calibrated for optimal efficiency within a narrow speed range While this study presents a holistic thermodynamic analysis targeting major energy loss categories in engines, it does not delve into integrated engine and transmission efficiency gains related to specific speed and load conditions Furthermore, the efficiency of diesel engines is often limited by the necessity to comply with strict emissions and durability standards, making the relationship between engine efficiency improvements and exhaust aftertreatment a significant area for future research The implementation of WHR technology in thermal management strategies may help mitigate challenges associated with aftertreatment activities due to reduced available exhaust energy.
This research provides a comprehensive analysis of energy loads and losses in modern heavy- and medium-duty diesel engines, particularly within a broad range of the lug curve It also evaluates potential fuel consumption improvements that could arise from advancements in heavy-duty diesel technology The findings highlight several avenues for future research aimed at enhancing diesel efficiency Notably, integrating this work with full-vehicle simulations will be essential for understanding the interactions between engine, transmission, and powertrain-vehicle loads, especially in the context of emerging efficiency technologies for the 2020s.
By 2030, energy savings in engines will vary significantly across different load points, engine sizes, and vehicle duty cycles, leading to diverse real-world outcomes.
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8 APPENDIX a) Latin Hypercube design: Latin hypercube sampling is a form of stratified sampling described by McKay (Mckay et al., 1979) and elaborated upon by Iman et al., (Iman et al.,