Model Prediction and Rule Based Energy Management Strategy for a Plug-in Hybrid Electric Vehicle With Hybrid Energy Storage System
发表时间:2021-02-22     阅读次数:     字体:【


This article presents an energy management strategy (EMS) design and optimization approach for a plug-in hybrid electric vehicle (PHEV) with a hybrid energy storage system (HESS) which contains a Li-Ti-O battery pack and a Ni-Co-Mn battery pack. The EMS shares power flows within the hybrid powertrain, and it employs a dual fuzzy logical controller whose inputs are predictions for PHEV powertrain states. An elitist nondominant genetic algorithm using a model in loop simulation approach as fitness functions is implemented to multiobjective optimization for the EMS under worldwide light-duty test cycles. The optimal objectives are improving PHEV mileage, minimizing battery packs capacity fades, reducing HESS degradation inconsistency, and minimizing driving cost unit distance. A hardware in loop test bench has been established to verify EMS performances in embedded systems. The test results under new European driving cycles demonstrate that optimized EMSs remain appropriate for different driving cycles and their performances are close to dynamic programming based offline optimal solutions. Due to the contributions of both the HESS and the optimized EMS, the PHEV energy efficiency has been improved by 1.6%-2.5% and the PHEV energy storage system cycle life can be improved by 159%-203%.


图1 Cells used in HESS. (a) NCM cells. (b) LTO cells.

图2 Hybrid power system schematic. (a) PHEV powertrain configuration. (b) High-voltage system schematic.


S. Zhou and Z. Chen, "Model Prediction and Rule Based Energy Management Strategy for Hybrid Energy Storage System," 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC), Beijing, China, 2019, pp. 427-432, doi: 10.1109/CIEEC47146.2019.CIEEC-2019186.(下载链接


1. Z. Y. Chen, R. Xiong, and J. Y. Cao, “Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions,” Energy, vol. 96, pp. 197–208, Feb. 2016.(下载链接

2. Zhang, R. Xiong, and F. C. Sun, “Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system,” Appl. Energy, vol. 185, pp. 1654–1662, Jan. 2017.(下载链接

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