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A Real-Time Adaptive Model Predictive Control for Improving Energy Economy in Vehicle Systems

Author

Listed:
  • Wei Pan

    (College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China)

  • Zhouyuan Qian

    (Shanghai Aerospace Control Technology Institute, Shanghai 201100, China)

  • Lanqi Zhou

    (College of Automotive and Energy, Tongji University, Shanghai 201804, China)

  • Yiding Hua

    (Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China)

  • Jiaxing Lu

    (Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, China)

  • Tao Cao

    (Shanghai Aerospace Control Technology Institute, Shanghai 201100, China)

  • Hongqing Chu

    (College of Automotive and Energy, Tongji University, Shanghai 201804, China)

  • Lin Zhang

    (College of Automotive and Energy, Tongji University, Shanghai 201804, China)

Abstract

The real-time performance bottleneck of energy management strategies (EMS) based on model predictive control (MPC) severely restricts their vehicle-grade deployment in series-parallel plug-in hybrid electric vehicles (SPPHEVs). This research develops a real-time adaptive-mode MPC (RTAM-MPC) designed to jointly minimize fuel consumption, electricity usage, and battery aging under strict vehicle-grade execution constraints. An adaptive framework is established by integrating driving pattern recognition (DPR) with MPC, which dynamically adjusts the prediction time grid, solver initialization, and speed prediction configurations. To ensure computational efficiency suitable for embedded systems, a fast numerical optimization method is proposed, alongside a DPR-guided speed prediction model based on a coyote optimization algorithm-optimized kernel extreme learning machine. The results show that RTAM-MPC achieved 98.54% dynamic programming (DP) performance. Compared to the equivalent consumption minimization strategy (ECMS), it demonstrated a 5.37% improvement in economic efficiency and a 24.67% reduction in battery aging. Compared to standard MPC, the average computation time is 11.07 ms, a decrease of 94.48%.

Suggested Citation

  • Wei Pan & Zhouyuan Qian & Lanqi Zhou & Yiding Hua & Jiaxing Lu & Tao Cao & Hongqing Chu & Lin Zhang, 2026. "A Real-Time Adaptive Model Predictive Control for Improving Energy Economy in Vehicle Systems," Sustainability, MDPI, vol. 18(10), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:18:y:2026:i:10:p:4739-:d:1939050
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