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

• Published in: Energies (Basel) Vol. 9; no. 12; p. 997
• Main Authors: Hao, Jingxian, Yu, Zhuoping, Zhao, Zhiguo, Shen, Peihong, Zhan, Xiaowen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2016
• Subjects: DIRECT; Electric vehicles; Energy efficiency; Energy management; energy management strategy; fuel economy; hybrid electric vehicle; logic threshold value; Optimization; parameters optimization
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en9120997

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.