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Multiobjective Optimization for a Li-Ion Battery and Supercapacitor Hybrid Energy Storage Electric Vehicle

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  • Gang Xiao

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China
    Wuhan Digital Engineering Institute, Wuhan 430074, China)

  • Qihong Chen

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Peng Xiao

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Liyan Zhang

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Quansen Rong

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

Abstract

The acceptance of hybrid energy storage system (HESS) Electric vehicles (EVs) is increasing rapidly because they produce zero emissions and have a higher energy efficiency. Due to the nonlinear and strong coupling relationships between the sizing parameters of the HESS components and the control strategy parameters and EV’s performances, energy consumption rate, running range and HESS cost, how to design the HESS EVs for different preferences is a key problem. How to get the real time performances from the HESS EV is a difficulty. The multiobjective optimization for the HESS EV considering the real time performances and the HESS cost is a solution. A Li-ion battery ( BT ) semi-active HESS and optimal energy control strategy were proposed for an EV. The multiobjectives include energy consumption over 100 km, acceleration time from 0–100 km per hour, maximum speed, running range and HESS cost of the EV. According to the degrees of impact on the multiobjectives, the scaled factors of BT capacity, the series number of Li-ion BTs, the series number of super-capacitors (SCs), the parallel number of SCs, and charge power of the SCs were chosen as the optimization variables. Two sets of different weights were used to simulate the multiobjective optimization problem in the ADVISOR software linked with MATLAB software. The simulation results show that some of the multiobjectives are sensitive to their weights. HESS EVs meeting different preferences can be designed through the weights of different objectives. Compared with the direct optimization algorithm, the genetic algorithm (GA) has a stronger optimization ability, and the single objective is more sensitive to its corresponding weight. The proposed optimization method is practical for a Li-ion BT and SC HESS EV design.

Suggested Citation

  • Gang Xiao & Qihong Chen & Peng Xiao & Liyan Zhang & Quansen Rong, 2022. "Multiobjective Optimization for a Li-Ion Battery and Supercapacitor Hybrid Energy Storage Electric Vehicle," Energies, MDPI, vol. 15(8), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2821-:d:792526
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    References listed on IDEAS

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    1. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.

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