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This paper presents an innovative approach to optimize the parameters of model predictive control (MPC) for adaptive cruise control (ACC) systems in pure electric vehicles using a co-evolution immune particle swarm optimization (CEIPSO) algorithm. The CEIPSO leverages the adaptive and robust characteristics of the immune system to dynamically adjust MPC parameters, ensuring optimal control performance under varying traffic conditions. The proposed method automates the tuning process, reduces computational burden, and enhances the performance of ACC-based driving process. Simulation studies and comparative analysis demonstrate the effectiveness of the proposed controller, showcasing its superiority over traditional fuzzy-based tuning methods and other widely used optimization algorithms.

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Adaptive Cruise Control for Pure Electric Vehicles Using MPC Optimized by Co-evolution Immune Particle Swarm Optimization Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Xiao Huang,
  • Li Feng,
  • Kenan Du

摘要

This paper presents an innovative approach to optimize the parameters of model predictive control (MPC) for adaptive cruise control (ACC) systems in pure electric vehicles using a co-evolution immune particle swarm optimization (CEIPSO) algorithm. The CEIPSO leverages the adaptive and robust characteristics of the immune system to dynamically adjust MPC parameters, ensuring optimal control performance under varying traffic conditions. The proposed method automates the tuning process, reduces computational burden, and enhances the performance of ACC-based driving process. Simulation studies and comparative analysis demonstrate the effectiveness of the proposed controller, showcasing its superiority over traditional fuzzy-based tuning methods and other widely used optimization algorithms.