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Optimization of liquid cooling heat dissipation control strategy for electric vehicle power batteries based on linear time-varying model predictive control

Author

Listed:
  • Pan, Chaofeng
  • Jia, Zihao
  • Wang, Jian
  • Wang, Limei
  • Wu, Jiaxin

Abstract

The heat dissipation performance of batteries is crucial for electric vehicles, and unreasonable thermal management strategies may lead to reduced battery efficiency and safety issues. Therefore, this paper proposed an optimization strategy for battery thermal management systems (BTMS) based on linear time-varying model predictive control (LTMPC). To begin, based on the results of three-dimensional thermal flow analysis, a reduced order substitution model for the battery cooling system was established. Next, to address the issue of high computational complexity caused by the nonlinear model in the controller, a linearization method based on staged control was designed. Furthermore, the control parameters were optimized using the particle swarm optimization algorithm. Finally, a BTMS physical model based on SIMSCAPE was established for performance simulation. The performance of the logic threshold controller (LTC), linear model predictive controller (LMPC), and nonlinear model predictive controller (NMPC) was compared and analyzed. LTMPC was found to reduce the average temperature of the battery by up to 2.33 °C and 1.90 °C, and the cumulative temperature difference is reduced by 13.9% and 16.6% compared to LTC and NMPC, respectively. Compared to LMPC, LTMPC reduces thermal management energy consumption by 21.2%.

Suggested Citation

  • Pan, Chaofeng & Jia, Zihao & Wang, Jian & Wang, Limei & Wu, Jiaxin, 2023. "Optimization of liquid cooling heat dissipation control strategy for electric vehicle power batteries based on linear time-varying model predictive control," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223024933
    DOI: 10.1016/j.energy.2023.129099
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