Comparative study of hybrid architectures integrated with dual-fuel intelligent charge compression ignition engine: A commercial powertrain solution towards carbon neutrality

[Display omitted] •Hybrids with low-temperature combustion engines contribute to carbon neutrality.•Power-split structure performs better than parallel or series structures.•Dynamic programming provides optimal and robust solutions in the design phase.•Using a non-dominated sorting genetic algorithm...

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Veröffentlicht in:Energy conversion and management Jg. 292; S. 117423
Hauptverfasser: Zhang, Yaoyuan, Wu, Haoqing, Mi, Shijie, Zhao, Wenbin, He, Zhuoyao, Qian, Yong, Lu, Xingcai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 15.09.2023
Schlagworte:
administrative management
algorithms
batteries
carbon
comparative study
Dynamic programming
energy conversion
energy use and consumption
fuels
Hybrid electric vehicle
Low-temperature combustion engine
Multiple-objective optimization
Dynamic programming
Hybrid electric vehicle
Multiple-objective optimization
Low-temperature combustion engine
ISSN:0196-8904
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Zusammenfassung:[Display omitted] •Hybrids with low-temperature combustion engines contribute to carbon neutrality.•Power-split structure performs better than parallel or series structures.•Dynamic programming provides optimal and robust solutions in the design phase.•Using a non-dominated sorting genetic algorithm optimizes multiple objectives. The dual-fuel intelligent charge compression ignition (ICCI) engine, equipped with double direct injection systems, can simultaneously improve fuel consumption and gaseous emissions by substituting low-carbon fuels for conventional fuels. This work investigates the optimal application of the ICCI engine on hybrid powertrains under various standard driving conditions. The purpose is to evaluate the profit of combining the advantages of hybrid powertrains with the advanced engine. Dynamic programming (DP) is applied to the energy management control, realizing the global optimization in the design phase. Three typical hybrid configurations are compared, including series (SHEV), parallel (PHEV), and power-split (PSHEV) hybrid electric vehicles. Except for the charging sustaining stage, the optimal state of charge trajectories are also researched to verify the reasonable parameters in the charging stage. Considering multiple objectives, a non-dominated sorting genetic algorithm (NSGA-II) is coupled to optimize fuel consumption, gaseous emissions, and battery cost. The ICCI engine is mapped by the test bench results, while the involved hybrid configurations are modeled in Matlab/Simulink. The results illustrate that PSHEV achieves the best fuel economy and emission purification among the involved structures. With the ideal solution provided by DP, fuel consumption and gaseous emissions for PSHEV are reduced by 10% and 50%, and the low-carbon fuel utilization ratio has tripled, contributing to carbon neutrality. The engine was operated in several working conditions for SHEV, achieving the cleanest gaseous emissions. For PHEV, the engine performance was rarely improved compared with the engine without the assistance of hybrid systems. The optimization provided by NSGA-II indicates that the minimum values of multiple objectives cannot be reached simultaneously. A further 50% reduction in emissions would cause fuel consumption to double the original.
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ISSN:0196-8904
DOI:10.1016/j.enconman.2023.117423