Integration of a Dual-Mode SI-HCCI Engine Into Various Vehicle Architectures

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Benjamin J. Lawler ◽  
Zoran S. Filipi
Keyword(s):  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Si Engine ◽  
Dual Mode ◽  
Engine Operation ◽  
Conventional Vehicle ◽  
Hcci Engine ◽  
Parallel Hybrid ◽  
Mode Transitions

A simulation study was performed to evaluate the potential fuel economy benefits of integrating a dual-mode SI-HCCI engine into various vehicle architectures. The vehicle configurations that were considered include a conventional vehicle and a mild parallel hybrid electric vehicle. The two configurations were modeled and compared in detail for a given engine size (2.0 L) over the EPA UDDS (city) and highway cycles. The results show that the dual-mode engine in the conventional vehicle offers a modest gain in vehicle fuel economy of approximately 5–7%. The gains were modest because the baseline (the SI engine in the conventional vehicle) is relatively advanced with a six-speed automated manual transmission. The mild parallel hybrid with the SI engine achieved 32% better fuel economy than the conventional vehicle in the city, but only 6% on the highway. For the dual-mode engine in the mild parallel hybrid, a specific control strategy was used to manipulate engine operation in an attempt to minimize the number of engine mode transitions and maximize the time spent in HCCI. The parallel hybrid with the dual-mode engine and modified control strategy provides dramatic improvements of up to 48% for city driving, demonstrating that the addition of HCCI has a more significant impact with mild parallel hybrids than with conventional vehicles. Finally, a systematic study of engine sizing provides guidelines for selecting the best option for a given vehicle application.

Integration of a Dual-Mode SI-HCCI Engine Into Various Vehicle Architectures

2011 ◽  
Author(s):  
Benjamin J. Lawler ◽  
Zoran S. Filipi
Keyword(s):  
Fuel Economy ◽  
Control Strategy ◽  
Planetary Gear ◽  
Dual Mode ◽  
Engine Operation ◽  
Conventional Vehicle ◽  
Hcci Engine ◽  
Parallel Hybrid ◽  
Power Split ◽  
Mode Transitions

A simulation study was performed to evaluate the potential fuel economy benefits of integrating a dual-mode SI-HCCI engine into various vehicle architectures. The vehicle configurations that were considered include a conventional vehicle, a mild parallel hybrid, and a power-split hybrid. The three configurations were modeled and compared in detail for a given engine size (2.0 L for the conventional vehicle, 2.0 L for the mild parallel, and 1.5 L for the power-split) over the EPA UDDS (city) and Highway cycles. The results show that the dual-mode engine in the conventional vehicle offers a modest gain in vehicle fuel economy of approximately 5–7%. The gains were modest due to an advanced 6-speed transmission and a practically-based shift schedule, with which only 30% of the operating points were in the HCCI range for the city cycle and 56% for the highway cycle. The mild parallel hybrid achieved 32% better fuel economy than the conventional vehicle, both with SI engines. For the dual-mode engine in the mild parallel hybrid, a specific control strategy was used to manipulate engine operation in an attempt to minimize the number of engine mode transitions and maximize the time spent in HCCI. The parallel hybrid with the dual-mode engine and modified control strategy provides dramatic improvements of up to 48% in city driving, demonstrating that the addition of HCCI has a more significant effect with parallel hybrids than conventional vehicles. The power-split hybrid simulation showed that adding a dual-mode engine had an insignificant effect on vehicle fuel economy, mostly due to the ability of the planetary gear set to act as an e-CVT and keep the engine at relatively high loads. Finally, a systematic study of engine sizing provides guidelines for selecting the best option for a given vehicle application by characterizing the vehicle level interactions, and their effect on fuel economy, over an engine displacement sweep.

Energy Management and Control of a Hybrid Electric Vehicle With an Integrated Low Temperature Combustion (LTC) Engine

2014 ◽  
Author(s):  
Ali Solouk ◽  
Mahdi Shahbakhti ◽  
Mohammad J. Mahjoob
Keyword(s):  
Low Temperature ◽  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Hybrid Electric Vehicle ◽  
Low Temperature Combustion ◽  
Si Engine ◽  
Hcci Engine ◽  
Temperature Combustion ◽  
Hybrid Electric

Low Temperature Combustion (LTC) provides a promising solution for clean energy-efficient engine technology which has not yet been utilized in Hybrid Electric Vehicle (HEV) engines. In this study, a variant of LTC engines, known as Homogeneous Charge Compression Ignition (HCCI), is utilized for operation in a series HEV configuration. An experimentally validated dynamic HCCI model is used to develop required engine torque-fuel consumption data. Given the importance of Energy Management Control (EMC) on HEV fuel economy, three different types of EMCs are designed and implemented. The EMC strategies incorporate three different control schemes including thermostatic Rule-Based Control (RBC), Dynamic Programming (DP), and Model Predictive Control (MPC). The simulation results are used to examine the fuel economy advantage of a series HEV with an integrated HCCI engine, compared to a conventional HEV with a modern Spark Ignition (SI) engine. The results show 12.6% improvement in fuel economy by using a HCCI engine in a HEV compared to a conventional HEV using a SI engine. In addition, the selection of EMC strategy is found to have a strong impact on vehicle fuel economy. EMC based on DP controller provides 15.3% fuel economy advantage over the RBC in a HEV with a HCCI engine.

scholarly journals Intelligent Control of Parallel Hybrid Electric Vehicles

1999 ◽  
Author(s):  
Bradley Glenn ◽  
Gregory Washington ◽  
Giorgio Rizzoni
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Logic Controller ◽  
Hybrid Electric Vehicles ◽  
Conventional Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Reduce Emissions

Abstract Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. To demonstrate the potential of an advanced control strategy for HEV’s, a fuzzy logic control strategy has been developed and implemented in simulation in the National Renewable Energy Laboratory’s simulator Advisor (version 2.0.2). The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy. Results with advisor show that the vehicle with the Fuzzy Logic Controller can achieve (56) mpg in the city, while maintaining a state of charge of .68 for the battery pack, compared to (43) mpg for a conventional vehicle. This scheme has also brought to light various rules of thumb for the design and operation of HEV’s.

Hierarchical energy management strategy considering switching schedule for a dual-mode hybrid electric vehicle

2021 ◽  
pp. 095440702110297
Author(s):  
Hui Liu ◽  
Rui Liu ◽  
Riming Xu ◽  
Lijin Han ◽  
Shumin Ruan
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Control Strategy ◽  
Power Distribution ◽  
Hybrid Electric Vehicle ◽  
Hierarchical Control ◽  
Management Problem ◽  
Energy Management Strategy ◽  
Dual Mode ◽  
Hybrid Electric

Energy management strategies are critical for hybrid electric vehicles (HEVs) to improve fuel economy. To solve the dual-mode HEV energy management problem combined with switching schedule and power distribution, a hierarchical control strategy is proposed in this paper. The mode planning controller is twofold. First, the mode schedule is obtained according to the mode switch map and driving condition, then a switch hunting suppression algorithm is proposed to flatten the mode schedule through eliminating unnecessary switch. The proposed algorithm can reduce switch frequency while fuel consumption remains nearly unchanged. The power distribution controller receives the mode schedule and optimizes power distribution between the engine and battery based on the Radau pseudospectral knotting method (RPKM). Simulations are implemented to verify the effectiveness of the proposed hierarchical control strategy. For the mode planning controller, as the flattening threshold value increases, the fuel consumption remains nearly unchanged, however, the switch frequency decreases significantly. For the power distribution controller, the fuel consumption obtained by RPKM is 4.29% higher than that of DP, while the elapsed time is reduced by 92.53%.

Optimal adaptation equivalent factor of energy management strategy for plug-in CVT HEV

2018 ◽  
Vol 233 (4) ◽  
pp. 877-889 ◽  
Author(s):  
Xinyou Lin ◽  
Qigao Feng ◽  
Liping Mo ◽  
Hailin Li
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Adaptation Strategy ◽  
Management Control ◽  
Energy Management Strategy ◽  
Driving Mode ◽  
Driving Cycles ◽  
Equivalent Factor

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

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