Optimization of Energy Management Strategy for the EPS with Hybrid Power Supply Based on PSO Algorithm

Bin Tang, Di Zhang, Haobin Jiang and Yinqiu Huang
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Bin Tang: Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Di Zhang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Haobin Jiang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Yinqiu Huang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2020, vol. 13, issue 2, 1-13

Abstract: The traditional vehicle power supply is unable to meet the power requirement of electric power steering system (EPS) in heavy-duty vehicles at low speeds. A novel EPS with hybrid power supply (HP-EPS) is constructed in this paper, and a new optimized rule-based energy management strategy of hybrid power supply system is designed. The strategy determines the power distribution of the vehicle power supply (VPS) and super capacitor (SC), as well as the charging or discharging of SC. Furthermore, to minimize the output current fluctuation of the VPS, the optimization model of parameters in the strategy is established and the particle swarm optimization algorithm (PSO) algorithm is applied to optimize the rules in the energy management strategy. The verification for the designed energy management strategy is carried out in MATLAB/Simulink and results show that the output current peak of VPS decreases by 33% and its fluctuation depresses significantly. In addition, the SC is charged timely and fast, which is beneficial to guarantee enough state of charge (SOC) of SC. In conclusion, the optimized rule-based energy management strategy used for the HP-EPS system can meet the current requirement of EPS and effectively reduce the peak and fluctuation of the VPS output current.

Keywords: heavy-duty vehicle; EPS; hybrid power supply; energy management strategy; PSO (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: 2020
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