Homogeneous charge compression ignition technology ...

Special Issue Article

Homogeneous charge compression ignition technology implemented in a hybrid electric vehicle: System optimal design and benefit analysis for a powersplit architecture

Proc IMechE Part D: J Automobile Engineering 227(1) 87–98 Ó IMechE 2012 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0954407012453237 pid.sagepub.com

Kukhyun Ahn, John Whitefoot, Aris Babajimopoulos, Elliott Ortiz-Soto and Panos Y Papalambros

Abstract Homogeneous charge compression ignition technology can improve fuel economy by providing increased efficiency at low-load operation. This article examines the implementation of this technology in hybrid propulsion systems. To assess the benefits, a physics-based model for a spark ignition–homogeneous charge compression ignition dual-operation engine is developed, together with system and component models, and is used to optimize a crossover sport utility van with a power-split hybrid powertrain. Comparison of optimal designs for the pure spark ignition and dual homogeneous charge compression ignition cases indicates the reduction in the fuel consumption based on our modeling assumptions to be in the range 2.5–5%, depending on the test cycle. These benefits increase substantially when the acceleration performance requirements increase. An analysis method is presented to show how such engine-level changes affect the entire powertrain characteristics, and mode maps are developed to indicate when the benefits are expected.

Keywords Hybrid electric vehicle, homogeneous charge compression ignition, power split, optimal design, energy management

Date received: 21 November 2011; accepted: 6 June 2012

Introduction Homogeneous charge compression ignition (HCCI) engines have shown much promise in reducing the fuel consumption of light-duty vehicles because of their high efficiency for part-load operation, an area where spark ignition (SI) engines have a low efficiency. Similarly to compression ignition (CI) engines, HCCI offers increased efficiency owing to very-lean-burn combustion, but without nitrogen oxide (NOx) and soot emissions that can result from heterogeneous fuel– air mixtures. The shortcomings of HCCI combustion are that its operating area is limited in both the load range and the speed range, and controlling the combustion process (mixture composition, temperature, etc.) is very challenging. Much research has been dedicated to developing HCCI engine technology and control strategies for conventional powertrains in order to stabilize combustion and to extend this range;1–7 multipleinjection multiple-ignition strategy, spark-assisted HCCI combustion strategy, exhaust gas recirculation and air–fuel regulation have been proposed to achieve

a reduction in the excessive noise for high-load operation, HCCI idle combustion, and quick convergence of the combustion. However, little research has addressed how HCCI technology could benefit a hybrid electric vehicle (HEV) and improve the fuel economy. The US Environmental Protection Agency (EPA)8 studied the use of an HCCI engine in a series hydraulic hybrid where the engine speed and load can be independent of the vehicle speed and thus transient operation can be reduced. The study focused on developing and testing an HCCI engine optimized for smooth transitions along an optimal efficiency power curve, which is the expected operation of an internal-combustion engine for a series hybrid. The study used engine testing on a dynamometer and did not include in-vehicle University of Michigan, Ann Arbor, MI, USA Corresponding author: Kukhyun Ahn, University of Michigan, 3200 EECS, 2350 Hayward Street, Ann Arbor, MI 48104, USA. Email: [email protected]

88 results. Musardo et al.9 studied the control strategies for NOx reduction in a parallel hybrid electric truck with a combined-mode CI–HCCI engine to trade off reduction in the NOx emissions and the fuel consumption. Their results used the estimated HCCI operation based on test data and future projections of HCCI operation, with some assumptions such as HCCI operation for zero load. Recent research at Argonne National Laboratory and the University of Michigan studied the potential improvements of using an HCCI engine in vehicles with various levels of powertrain hybridization, from a conventional vehicle to a plug-in HEV with an all-electric-vehicle (allEV) range of 40 miles. Heuristic powertrain sizing and control strategies were used to predict improvements of 16% and 12% in a conventional vehicle and a power-split HEV respectively for the combined EPA Urban Dynamometer Driving Schedule (UDDS)–Highway Fuel Economy Test (HWFET) driving cycles.10 Other research at the University of Michigan studied the fuel economy improvement of an HCCI engine in a conventional hybrid, a mild- and a medium-level parallel hybrid, and a power-split hybrid. These studies used fixed powertrain component sizes with customized rule-based controllers to shift more operation of the engine into the HCCI region. The results showed significant fuel economy improvement for the combined UDDS–HWFET driving cycles for the conventional and parallel hybrids (17–35%), but limited improvement for the power-split hybrid, owing to its inherent flexibility to shift its engine operating points away from low-efficiency part-load conditions.11 The present study uses a high-fidelity simulation of an HCCI engine to represent the operation and limitations of the HCCI combustion and to generate engine maps. The HCCI engine is implemented in a conventional vehicle and a power-split hybrid vehicle to analyze the fuel consumption benefits on multiple driving cycles. These benefits are examined by looking at the operating points and fueling rates, as well as by examining the energy management strategy of the hybrid vehicle. A metamodel-based design framework is used to find the optimal HCCI and non-HCCI HEV designs and to quantify the trade-off between the fuel economy and the performance. In the remainder of the paper, the second section presents the models used in the study. The third section and the fourth section present the HCCI benefits and optimization results respectively for the conventional crossover vehicle. The fifth section presents the HCCI benefits and optimization results for the HEV, along with Pareto-optimal operating point (POP) curves and operation mode maps. The sixth section offers conclusions.

Model description Vehicle A crossover sport utility van of mass 1700 kg was used for simulations and analyses in this study. Its hybrid

Proc IMechE Part D: J Automobile Engineering 227(1) configuration is the (input) power-split type and will be explained in a following section together with its components. The basic specifications are as follows: the frontal area, the drag coefficient, the tyre radius, and the rolling coefficient are 3.5 m2, 0.3, 0.35 m, and 0.01 respectively. The curb mass is 1340 kg plus the combined weight of an engine, two motors, and a battery pack, which will vary according to their sizes.

Engine The steady-state engine load and fuel consumption maps for both SI and HCCI engines were obtained using the engine to driving-cycle modeling framework developed by Ortiz-Soto et al.12 A brief summary of the map generation approach is provided in the following section. The engine system model was created using GTPower13 for a single-cylinder configuration with a displaced volume of 500 cm3 and square geometry. The SI engine had fixed valve lifts and a standard compression ratio of 10:1. The load was controlled by intake air throttling and the air-to-fuel ratio of the mixture was kept stoichiometric. The HCCI engine had a higher compression ratio of 12.4:1. Load control was achieved by varying the fueling rate and combustion phasing through the use of a variable-lift recompression strategy, where the full SI lifts were symmetrically scaled to control negative valve overlap, while maintaining the exhaust valve opening and the intake valve closing fixed. The SI engine was constrained by the occurrence of knock, which was enforced by a spark-timing optimizer within the simulation. Constraints for the HCCI engine were enforced during post-processing, ensuring that the mapped results were below the imposed limits for a ringing intensity of 5 MW/m2, NOx emissions of 1 g/(kg fuel), and an equivalence ratio of 1.0. The results for cases displaying instability (associated typically with late combustion phasing) or misfiring cases were also discarded. The final engine maps were interpolated along the speed and load (brake mean effective pressure) ranges at 250 r/min and 0.25 bar increments respectively. The dual-mode SI–HCCI engine map was obtained by combining the two maps and replacing part of the SI brake specific fuel consumption (BSFC) map with the BSFC values of the HCCI map. Although these two engines had different geometric compression ratios, it is in practice possible to achieve a variable effective compression ratio within the same engine using variable-valve-phasing techniques, such as early or late intake valve closing. In practice, transition between the two combustion modes involves many issues, such as controllability, stability, vibration, and noise. Therefore, making the transition smoother is a major challenge for this technology. In the simulation and quasi-stationary analysis here, we assume that there is no cost in making this

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Figure 1. Input-split hybrid powertrain. M–G 1: motor–generator 1; M–G 2: motor–generator 2.

transition. Accounting for transition costs is of future research interest.

Hybrid powertrain Configuration. We chose an input power-split configuration for the vehicle’s hybrid propulsion, which is the type most adopted for production and has been well studied and reported in numerous publications.14–19 A brief description is provided here. Figure 1 shows the schematic diagram of the propulsion system, which consists of an engine, two motor– generators (M–Gs), and a planetary gear set. The input torque from the engine (the carrier) is split into two paths: series (the sun gear) and parallel (the ring gear). The series torque is transmitted to M–G 1, and the parallel torque is combined with the M–G 2 torque to meet the output torque demand. In normal driving situations, M–G 1 absorbs a certain fraction of the engine torque and converts it to electric power, which either drives M–G 2 or is stored in the battery. The system achieves continuously electrically variable transmission, and engine operation can be decoupled from the road load to a large extent.17 This characteristic, together with power leveling of the electric propulsion, provides high flexibility in determining the partload condition at which the engine runs. This flexibility, however, is constrained when it accompanies a significant amount of power conversion loss at the M–Gs.18

Figure 2. Battery and motor operation simulated for the US06 driving cycle: (a) state of charge; (b) M–G 1 operation; (c) M–G 2 operation. M–G 1: motor–generator 1; M–G 2: motor–generator 2.

Hybrid components. The baseline design for M–G 1 is an interior permanent magnet (IPM) a.c. synchronous motor, whose rated power is 45 kW. This high-speed motor has a maximum speed of 10,000 r/min. The M– G 2 baseline design is also an IPM machine. Its maximum speed and rated power are 6000 r/min and 50 kW respectively. The full-load torque and efficiency plots for the motors are presented later in Figure 2. The battery pack is not scaled and is held fixed with power and voltage values at 45 kW and 375 V respectively. M–G and inverter power conversion efficiencies are referenced from analysis data in the form of look-up tables. The battery charging–discharging efficiency calculation is based on the internal resistance model with empirical internal resistance and voltage data over the battery charge state range.

Performance modeling. Performance modeling focuses on the fuel economy because the HCCI technology aims to improve the brake thermal efficiency in part-load operation conditions. The acceleration performance, however, is also considered to ensure that the vehicle has the realistic powertrain specifications which lead to a commercially viable design. The following sections describe the simulation models developed for predicting the two attributes. Fuel economy. The fuel economy of a vehicle can be predicted by simulating a scheduled travel on a test cycle, such as the US EPA US06 and FTP-75 driving cycles.

90 The fuel economy model developed here is based on backward-looking calculation and assumes a quasistationary scenario. The absence of controls and neglecting the transient responses tend to result in over-prediction; however, the use of this simulation type is considered adequate when relative assessments are made for improvement prediction or design optimization. The equivalent fuel consumption (EFC) minimization strategy, also known as the equivalent consumption minimization strategy, was employed for energy management.20,21 The strategy is shown to lead to a robust fuel efficiency that is close to the theoretical optimum obtained by optimal control.19,22 It makes a selection at each instant by finding the optimal operation that minimizes the EFC, which is a linearly weighted sum of the fuel consumption and the electric energy consumption.19 The weight works as a conversion factor from one energy form to another and is held constant throughout a simulation run. Charge-sustaining logic is developed by finding a conversion factor that leads to no change or tolerably small changes between the initial and the final state-of-charge (SOC) values. This also ensures charge-sustaining (SOC-corrected) fuel consumption for fair comparison of different powertrain designs. We shall revisit this energy management logic in the fifth section (see also the paper by Ahn et al.19). The graphs in Figure 2 show the operation information on the battery, M–G 1, and M–G 2 during a fuel economy simulation for the US06 test cycle. Figure 2(a) shows that the SOC returned to its original value at the beginning of the trip, leading to charge-sustaining operation of the powertrain. Each operation point in Figure 2(b) or (c) represents quasi-stationary motion of M–G 1 or M–G 2 during one time step, which was 1 s in the simulation.

Acceleration performance The 0–60 mile/h acceleration time is chosen here as the performance metric. The time can be estimated using the full-load characteristic information on the propulsion system. The simulation finds the maximum torque for a given output speed by solving an output torque maximization problem.23 Repeating this procedure for a pre-defined output speed array, the fullload torque curve can be obtained. See Figure 15 later for an example. Then, assuming that the powertrain exerts the maximum torque along its output speed, information on the drag, the road friction, and the vehicle inertia is taken into account to compute a profile of the velocity versus the time, which gives the 0– 60 mile/h acceleration time. As noted, this neglects transient motion of the powertrain components, and the performance is over-predicted. This overprediction is moderated in the model by setting the gear efficiencies to lower values.

Proc IMechE Part D: J Automobile Engineering 227(1)

Figure 3. Change in the optimal engine operation line for the conventional vehicle: (a) pure SI operation; (b) HCCI dual operation. BSFC: brake specific fuel consumption; M–G 1: motor–generator 1; M– G 2: motor–generator 2.

Conventional vehicle benefits The HCCI engine improves the fuel consumption at part-load, where the SI operation typically results in poor brake thermal efficiency. In the case of the engine models that we used here, the HCCI technology can reduce the fuel consumption by 16–32%. These pointto-point comparisons, however, will not be directly reflected in the overall fuel consumption reduction with the existence of a transmission because the shift logic tries to avoid running in that low-efficiency area. Thus, it is more useful to compare the two cases using the engine optimal operation line, which is a trace of the best BSFC operation for a given power output. We assumed an ideal transmission device with no loss and continuous gear ratios between zero and infinity. As can be seen in Figure 3, the HCCI operation with improved efficiency competes with the SI operation, not at the same load point, but at a point that would have been achieved with the optimal shifting. The figure shows that some HCCI operation points were sufficiently competitive to change the optimal operation line of the pure SI case. Now it is of our interest to estimate the difference that this change makes.

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Figure 4. The engine fuel consumption rate as a function of the output power for the conventional vehicle. SI: spark ignition; HCCI: homogeneous charge compression ignition.

Using the optimal operation line, the fueling rate of the engine can be calculated for any power output required. Overlaying the SI and HCCI fueling rates demonstrates the difference in fuel use for any power output, as shown in Figure 4. As seen in the figure, the HCCI operation results in a reduction in the fuel consumption of up to 20% in the output power range 10– 30 kW. As predicted, this is substantially lower than the point-to-point case. In addition to these two comparisons, a more practical approach is taken assuming a conventional vehicle model (the vehicle described above with a five-speed conventional transmission) and two test cycles (US06 and FTP-75) using quasi-stationary backward mapping. Although it does not capture engine and transmission transient behavior under shift and clutch control constraints, it will give a general sense of how different engine operations occur in the presence of the HCCI region. The engine operation from vehicle simulation is presented for the pure SI and HCCI dual cases in Figure 5, showing the shift in engine operation. For the FTP-75 cycle, most of the operating points lie along the optimal operation line, and for the HCCI engine this trace shifts from low-speed high-torque operation to similar power points within the HCCI operation region (medium-speed medium-torque operation). However, the US06 operation is at a higher vehicle speed, and thus a higher engine speed, and so fewer of the operation points shift to the HCCI region. The benefits seem to arise more from the improved efficiency for the operation that already had to be in the region owing to the transmission ratio limits. The simulated fuel consumption reductions are 6.1% and 7.7% for the US06 driving cycle and the FTP-75 driving cycle respectively. Aside from the potential fuel economy benefits of a dual-mode SI–HCCI engine, the results presented in

Figure 5. Engine operation points for the conventional vehicle: (a) pure SI operation; (b) HCCI dual operation. BSFC: brake specific fuel consumption.

Figures 3 and 5 also indicate some difficulties that might be encountered in the practical implementation of the concept. In order to obtain any benefits from HCCI in a power-split system, the engine would have to operate mostly near the high-load limit of the HCCI region. High-load HCCI operation is associated with high pressure rise rates, which may necessitate the use of advanced valve and/or spark-timing strategies.3 Another practical issue to consider is HCCI control and mode transitions. As indicated previously, a steady-state engine model was used for the generation of the engine maps. Therefore, issues associated with stability, cycle-to-cycle variability, controllability, and mode transitions pose real challenges that will have to be addressed in order for the technology to be applied in practice.

Design optimization Before we discuss the benefits of implementing the HCCI technology in a power-split hybrid vehicle, a baseline vehicle design needs to be found. The design optimization method proposed here also finds designs that meet a series of performance requirements, showing how the benefits vary with the performance.

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Proc IMechE Part D: J Automobile Engineering 227(1) components, and so the design variables were simply component scaling variables and gear ratios. In order to reduce the search design space the size of M–G 1 was assumed to vary with the other component designs,24 and the battery size was fixed at 45 kW, as noted previously. The design variables and their simple bounds are summarized in Table 1.

Metamodel-based optimization

Figure 6. Comparison of metamodel performance with three candidate types. ANN: artificial neural network; RBF: radial basis function.

Metamodel-based design optimization is attractive when function evaluations are computationally expensive simulations or have significant numerical noise.25– 28 In the present model, numerical noise in the simulation makes the use of metamodels desirable. As mentioned in the above section on the fuel economy, fuel economy estimation using SOC correction causes irregular responses that do not necessarily reflect the physics of a design. This will be discussed in more detail in the following section.

Formulation The optimization objective is to minimize the fuel consumption during a cycle test subject to an upper bound on the 0–60 mile/h acceleration time. If this upper bound is active, then solving for different bounds will generate the Pareto trade-off set between the fuel economy and the performance according to min FCi ðxÞ with respect to x = Peng , PM=G2 , rpg , rfd

subject to tðxÞ4t j and upper and lower bounds on Peng , PM=G2 , rpg , and rfd

where FC, P, r, t, and t are the fuel consumption, the peak power of a component, the gear ratio, the 0–60 mile/h acceleration time, and the target 0–60 mile/h acceleration time respectively and the subscripts j, eng, pg, and fd denote the performance requirement level, the engine, the planetary gear, and the final drive respectively. Two test cycles, namely US06 and FTP75, and five levels of performance requirements were used. For each cycle–performance combination, the SIonly and the HCCI dual-operation cases were studied. Thus, 20 optimization problems were solved in total. The purpose of this optimization is to find the baseline for analysis and to show the trend of the HCCI benefits rather than to design the powertrain

Building metamodels. To select a proper metamodel, we considered an artificial neural network (ANN), polynomial regression, and a radial basis function (RBF) network. A good metamodel should ignore any local optima resulting from numerical noise. A onedimensional slice of the four-dimensional design space, presented in Figure 6, was used to compare metamodeling options. The figure shows a partial variation in the fuel consumption (pure SI engine and US06 cycle) with respect to the normalized engine size when the other variables are held constant. Note that the metamodels were built not on the basis of the simulation results in the graph but on the basis of a separate sample set, as discussed later in this section. The solid curve from the simulation results, which is presented as a reference line, fluctuates owing to numerical noise. The noise comes mainly from interpolation errors involved in SOC correction and can be as high as 0.3%. All three candidates performed well in terms of smoothing out the noise, but the feedforward ANN and the RBF network tended to create local optima that did not necessarily reflect the physics. The quadratic regression sometimes lacked flexibility and failed to capture some local variations found in the other cases investigated. However, it showed adequate accuracy over the entire design space and captured the overall physics, and hence it was the metamodel chosen for the study.

Table 1. Design variables and their simple bounds. Bounds for the following

Upper Lower

Peak engine power (kW)

Peak M–G1 power (kW)


Planetary gear

Final drive

110 80

Varies with the rest

100 70

2/3 1/3

4.5 3

M–G 1: motor–generator 1; M–G 2: motor–generator 2.

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93 Results tables for the other cases are not presented here, but the fuel economy and acceleration results are shown in Figure 8. The improvements from using the HCCI technology were calculated and are presented in Figure 9. Two predictions can be made from the optimization results. The benefits from using the HCCI technology are expected to be greater for the FTP-75 cycle than for the US06 cycle, and the benefits will increase with more stringent performance requirements. The next section discusses how the HCCI benefits vary with the load characteristics, along with an analysis of the obtained predictions.

HCCI benefits in hybrid applications Figure 7. Pareto frontier in the attribute (fuel consumption– acceleration) space.

In this section, we predict how application of the HCCI technology will affect the fuel economy of hybrid propulsion systems based on simulated engine operation on test cycles and instantaneous optimal operation analyses.

To calculate the 15 quadratic coefficients, including cross-terms, 3000 optimal Latin hypercube samples were used, and all four design variables were normalized between zero and one.

Engine operation simulation on test cycles

Metamodel-based solution. Following the procedure described, response surface models were built for the four fuel economy test cases and acceleration performance. The scatter plot in Figure 7 shows part of the attribute space for the fuel economy for the FTP-75 cycle with the HCCI dual engine and 0–60 mile/h acceleration performance. Although the dots are from a limited number of sample designs, the cloud reveals the trade-off between the two attributes. Solving this optimization problem for a sufficient number of acceleration bounds creates the Pareto frontier for these two attributes. In solving each problem, Pareto-optimal designs among the 3000 samples were used as initial points for the sequential quadratic programming algorithm employed in the MATLAB optimization toolbox. The solid curve with open circles shows the solutions obtained using optimization described in more detail below.

Assuming that a vehicle travels along a pre-defined test cycle, its engine operation can be analyzed using the hybrid vehicle simulation model described in a previous section. We compare two cases where one power-split HEV uses a purely SI engine and the other adopts the HCCI dual-engine-operation technology. Both vehicles use the optimal design variables found for the case of pure SI engine operation, the US06 cycle, and 0 mile/h to 60 mile/h in 8.5 s. Figure 10(a) and (b) give the results for the US06 cycle, and Figure 10(c) and (d) those for the FTP-75 cycle. Figure 10(b) and (d) show how the engine operation will change when the technology is applied for respective cycles. The HCCI region attracts some operation points from the pure SI region, and the amount and location of the attraction will decide the improvement in the fuel economy. The operation points throughout the simulations were analyzed and summarized in Table 3. The time step used in the simulations was 1 s, and the times when the vehicles were at standstill are not included in the analysis.

Optimization results The optimized design variables in the case of 8.5 s for 0–60 mile/h acceleration are shown in Table 2.

Table 2. Optimization results for 0–60 mile/h acceleration in 8.5 s. Cycle

Operation mode

Peak engine power (kW)



Planetary gear

Final drive

Fuel consumption (l/100 km)

US06

SI HCCI SI HCCI

95.2 95.9 90.9 91.3

66.1 53.7 50.9 51.1

93.7 92.7 90.7 89.4

0.449 0.333 0.333 0.333

4.18 4.13 4.5 4.5

9.68 9.44 5.80 5.51

FTP-75

M–G 1: motor–generator 1; M–G 2: motor–generator 2; SI: spark ignition; HCCI: homogeneous charge compression ignition.

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Figure 8. Minimized fuel consumptions at target acceleration levels.

Figure 9. Fuel consumption reductions at target acceleration levels.

From the graphs and the table, it can be inferred that the attractions were mainly from either the engine-off region or the lower-efficiency regions. The most noticeable difference between the two cases is that twice as many HCCI hybrid operations were generated in the case of the FTP-75 cycle. Also, there is a difference as to where the operation points came from. For the US06 cycle, similar amounts of points moved from the all-EV and SI hybrid modes whereas,

for the FTP-75 cycle, almost three times as many points moved from the all-EV mode than from the other modes. This is largely because of the original composition of the operation and implies that the HCCI benefits might greatly depend on the load characteristics of a cycle. In the following sections, we discuss how the HCCI operation can contribute to improving the fuel economy for specific load conditions.

Figure 10. Engine operation points with and without HCCI operation for the US06 and FTP-75 cycles: (a) US06, pure SI; (b) US06, HCCI dual operation; (c) FTP-75, pure SI; (d) FTP-75, HCCI dual operation. BSFC: brake specific fuel consumption.

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Figure 11. Electric power and fuel consumption trade-offs for four output speed–torque combinations.

Figure 12. Minimum-EFC operation: 100 rad/s, 350 N m, and 6.353 1025 g/J. SI: spark ignition; HCCI: homogeneous charge compression ignition; EFC: equivalent fuel consumption.

Table 3. Operation breakdown in the SI-only and HCCI dualoperation cases for the US06 and FTP-75 cycles. Cycle

US06 FTP-75

Operation mode

SI only HCCI dual SI only HCCI dual

Operation breakdown All-EV

SI hybrid

HCCI hybrid

219 (39%) 154 (28%) 1205 (79%) 734 (48%)

337 (61%) 281 (50%) 313 (21%) 147 (10%)

— 121 (22%) — 637 (42%)

All-EV: all-electric-vehicle; SI: spark ignition; HCCI: homogeneous charge compression ignition.

Pareto-optimal operation point curve analysis Analyzing the HCCI benefits for HEVs is more complicated than for conventional vehicles because of the multiple power sources and options available with the power-split transmission. Instead of simply comparing the fueling rates for required power outputs, we need to determine the optimal operating mode for individual torque–speed combinations of the propulsion system. The POP has been previously defined in order to discuss the optimal operation of hybrid propulsion systems when there are multiple energy sources.19 A complete set of POPs, i.e. a POP curve, reflects the energy losses along the power transmission paths and presents the eventual trade-off between the fuel and the battery energy. Thus, the curve can serve as an effective tool for analyzing a propulsion system’s energy-efficient operation for specific output loads of interest. Some examples of POP curves are presented in Figure 11. The POP curves show how the gasoline mass flow rate can be traded with the battery power, considering the component power conversion losses and the mechanical power transmission losses. Each POP curve is defined for a different output speed–torque

combination, and there can be different curves even for the same output power, depending on the combination. See the case of 200 rad/s and 200 N m and the case of 100 rad/s and 400 N m. Since a POP curve represents the collective characteristics of the propulsion system, its shape changes when some of the components are changed. The dotted curves show the changes made from applying the HCCI technology in the engine. Recalling that the lower left side is towards the utopia point, the technology is apparently leading to energy savings. The change, however, is not the same for every case, as can be seen for the lower three curves. No change is observed for the case of 300 rad/s and 300 N m, where the HCCI region cannot provide power for the output demand. This implies that the HCCI benefits can be sensitive to the output load. Another important factor is the energy management strategy used to determine the optimal operation among POPs. As discussed by Ahn et al.,19 one way to choose a POP from the curve is to use a weighted sum or EFC. This method can provide a general sense of how the energy management affects the HCCI benefits and how the benefits depend on the output load demand. Figure 12 shows two POP curves for the pure SI and HCCI dual-operation cases when the output speed and torque are 100 rad/s and 350 N m respectively. Two isoEFC lines are presented, assuming that the EFC is in the form of the linear weighted sum given by EFC = FCR + w EPC

where FCR is the fuel consumption rate in grams per second, w is a constant power conversion factor equal to 6.35 3 1025 g/J, and EPC is the electric power consumption in watts.

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Figure 13. Minimum-EFC operation: 100 rad/s, 350 N m, and 6.65 3 1025 g/J. SI: spark ignition; HCCI: homogeneous charge compression ignition; AllEV: all-electric-vehicle; EFC: equivalent fuel consumption.

Proc IMechE Part D: J Automobile Engineering 227(1) affects the choice of the optimal operation, leading to more fuel use and less battery charge use. Another difference to notice is that applying the HCCI technology does not improve the energy consumption of the pure SI operation, unlike in the previous case. In Figure 14, the energy management remains the same as the first analysis case, but the output speed and torque are 100 rad/s and 100 N m respectively. With the different POP curves, the iso-EFC line with the same slope as that of Figure 12 makes the last contact at the all-EV point as it moves in the direction of minimization, which is towards the bottom left corner. Again, there are no HCCI benefits with this energy management for the load demand. In the previous three cases, the HCCI benefits were found to depend on both the energy management and the load demand. With this analysis method, it is possible to predict whether there will be HCCI benefits at a certain load demand for an assumed energy management strategy. The following section discusses this HCCI benefit prediction.

5.3. Operation mode maps on the output load plane

Figure 14. Minimum-EFC operation: 100 rad/s, 100 N m, and 6.35 3 1025 g/J. SI: spark ignition; HCCI: homogeneous charge compression ignition; AllEV: all-electric-vehicle; EFC: equivalent fuel consumption.

The solid line with multiplication signs passes through the triangle point on the pure SI POP curve, where the EFC is minimized for the case. The HCCI dual-operation POP curve has its minimum EFC at the rectangle point, and the solid line with plus signs is the iso-EFC line that passes through the point. Comparing the two iso-EFC lines shows that the dual operation can improve the power consumption of the pure SI operation, although not by much (1% in EFC). Two more cases are studied to investigate how the HCCI benefits depend on the load demand and the energy management. In Figure 13, the load demand is the same as in the previous figure, but the energy management is slightly modified; the conversion factor is now increased to 6.65 3 1025 g/J. This change directly

As shown in Figures 12 to 14, we can predict whether or not running the engine in the HCCI region will lead to an energy saving given a certain load combination. When this analysis is repeated for a set of load combinations that cover the entire operation region of the propulsion system, a map can be created to indicate the regions where there can be HCCI benefits. The map can also show where the all-EV or SI hybrid mode is expected to be more efficient, as in some other hybrid mode maps that have been presented previously.19,29,30 As discussed earlier, there is no unique way of creating such maps. However, we again assume EFC energy management and a conversion factor of 6.6 3 1025 g/J. Sweeping meshed load points results in the optimal operation maps for the US06 cycle, the FTP-75 cycle, and the US EPA New York City Cycle (NYCC) in the output load plane shown in Figure 15(a), (b), and (c) respectively. The map shows three regions: the all-EV region for low-power operation, the SI engine region for high-power operation, and the HCCI engine operation islands in between the two modes. Note that the boundaries between these regions are somewhat fuzzy because multiple modes have very similar EFC values at the boundary. Thus the choice of one particular mode is somewhat arbitrary. The graph also gives operation frequency information overlapped on the mode map. The larger the circle, the more frequently the vehicle meets the corresponding load demand. Thus, this overlapped map gives a sense of the portion of a cycle that falls on to the HCCI mode regions, which enables us to make a prediction of how great the HCCI benefits will be. The operation frequency information depends on the cycle, and Figure 15(a), (b), and (c) show the information for the US06 cycle, the FTP-75 cycle, and the NYCC respectively.

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Figure 15. Cycle load density on the operation mode map: (a) US06 cycle; (b) FTP-75 cycle; (c) NYCC. SI: spark ignition; HCCI: homogeneous charge compression ignition; All-EV: all-electric-vehicle.

The maps now provide further insight into the HCCI benefits that were analyzed with the operation breakdown in Table 3. Figure 15(a) and (b), for example, visually explain the reason why the HCCI benefits can be greater for the FTP-75 cycle than for the US06 cycle. It is also shown that more operation clusters of the FTP-75 cycle are placed near the border between the all-EV and HCCI regions than between the HCCI and SI regions, which again is in agreement with the analysis in Table 3. In the last map, most of the operation clusters of the NYCC fall on to the all-EV region. The mode map will appear different and the all-EV region will lose some of the clusters shown if we use a chargesustaining conversion factor. However, with the map we can still make a prediction that the benefits will be relatively limited. When verified by simulation, the fuel consumption reduction was 2.55%, which is as low as that for the US06 cycle, while it is 5.03% for the FTP75 cycle.

Conclusions The benefits from employing HCCI technology in hybrid propulsion systems were examined. We began with directly comparing the engine fueling of the pure SI and SI–HCCI dual-operation cases. Then, benefits were assessed taking into account the use of a transmission device for a conventional powertrain. In a hybrid powertrain, we studied the change in the engine use due to the availability of HCCI operation and predicted the resulting fuel consumption reduction. We then analyzed how the dual operation affects the system operation decisions from an energy management viewpoint. A metamodel-based optimization framework was developed for finding the optimal HCCI and non-HCCI HEV designs efficiently. Optimization showed the Pareto trade-off between the fuel economy and the performance for the two powertrain cases. Mode map analysis enabled quick estimates of the magnitude of expected HCCI benefits for various driving cycles that can inform early powertrain design directions. The key findings and future research suggestions are summarized as follows.

As flexibility in powertrain operation increases, the effects of an improved HCCI operation efficiency in a local area diminish. In the case of our engine model, the average efficiency improvement of 25% in the area led to a reduction of 6–8% in the fuel consumption for the US06 and FTP-75 cycles with conventional propulsion (albeit not an optimized transmission), and the values further decreased to 2.5–5% with power-split hybrid propulsion. It should be noted that the predicted benefits could be higher with other types of hybrid propulsion architecture with lower flexibility in the decoupling engine operation and the road load. For example, a parallel type with a conventional transmission would benefit more because engine operation is forced into the low-speed low-torque region more frequently owing to transmission behavior, as discussed by Lawler et al.11 The fuel economy benefits of the technology increase with more aggressive acceleration performance requirements. The reductions almost doubled as we lower the 0–60 mile/h performance requirement from 9.3 s to 7.7 s. When engine downsizing effects are limited because of an engine peak power demand for high acceleration, HCCI technology can be an option for improving the fuel economy of an HEV. Some good examplesof this are the sport sedan and luxury sport utility vehicle classes. The development of a metamodel-based design optimization framework led to fair comparison between the pure SI and HCCI dual-operation cases. POP curves can be used to analyze the changes that a particular engine technology will make on the overall powertrain operation and efficiency. Inversely, the desired directions of powertrain improvement might be translated into guidance for new engine technology development. Funding This work was supported by the General Motors Collaborative Research Laboratory at the University of Michigan. The opinions presented here are solely those of the authors.

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