Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks

Yongjian Zhou, Rong Yang, Song Zhang, Kejun Lan and Wei Huang ()
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Yongjian Zhou: School of Mechanical Engineering, Guangxi University, Nanning 530004, China
Rong Yang: School of Mechanical Engineering, Guangxi University, Nanning 530004, China
Song Zhang: Guangxi Yuchai Machinery Company Limited, Yulin 537000, China
Kejun Lan: School of Mechanical Engineering, Guangxi University, Nanning 530004, China
Wei Huang: School of Mechanical Engineering, Guangxi University, Nanning 530004, China

Energies, 2023, vol. 16, issue 17, 1-21

Abstract: Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.

Keywords: power-split hybrid heavy-duty trucks; energy management; powertrain parameters; dynamic programming; improved particle swarm optimization; optimal DP-RCS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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