Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm

Jingxian Hao, Zhuoping Yu, Zhiguo Zhao, Peihong Shen and Xiaowen Zhan
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Jingxian Hao: School of Automotive Studies, Tongji University, Shanghai 201804, China
Zhuoping Yu: School of Automotive Studies, Tongji University, Shanghai 201804, China
Zhiguo Zhao: School of Automotive Studies, Tongji University, Shanghai 201804, China
Peihong Shen: School of Automotive Studies, Tongji University, Shanghai 201804, China
Xiaowen Zhan: School of Automotive Studies, Tongji University, Shanghai 201804, China

Energies, 2016, vol. 9, issue 12, 1-24

Abstract: The rule-based logic threshold control strategy has been frequently used in energy management strategies for hybrid electric vehicles (HEVs) owing to its convenience in adjusting parameters, real-time performance, stability, and robustness. However, the logic threshold control parameters cannot usually ensure the best vehicle performance at different driving cycles and conditions. For this reason, the optimization of key parameters is important to improve the fuel economy, dynamic performance, and drivability. In principle, this is a multiparameter nonlinear optimization problem. The logic threshold energy management strategy for an all-wheel-drive HEV is comprehensively analyzed and developed in this study. Seven key parameters to be optimized are extracted. The optimization model of key parameters is proposed from the perspective of fuel economy. The global optimization method, DIRECT algorithm, which has good real-time performance, low computational burden, rapid convergence, is selected to optimize the extracted key parameters globally. The results show that with the optimized parameters, the engine operates more at the high efficiency range resulting into a fuel savings of 7% compared with non-optimized parameters. The proposed method can provide guidance for calibrating the parameters of the vehicle energy management strategy from the perspective of fuel economy.

Keywords: fuel economy; hybrid electric vehicle; energy management strategy; logic threshold value; DIRECT; parameters optimization (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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