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This paper presents a comprehensive bi-level convex optimization framework for eco-driving of fuel cell hybrid electric vehicles (FCHVs) at signalized intersections. The proposed approach analyses the complex co-optimization problem into upper-level speed planning using quadratic programming (QP) and lower-level energy management using Model Predictive Control (MPC) being adapted as QP. The upper level ameliorates vehicle’s path taking into consideration traffic light constraints, while the lower level works on fuel cell and battery power distribution through a predictive control strategy. Complete state-space matrices are derived from first principles, and the MPC problem is systematically reformulated into standard QP form for efficient real-time implementation.

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Bi-level Energy Management Based on Convex Optimization for Hybrid Electric Vehicle

  • 会议论文

  • 发表日期：

    2026年

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  • Akram Nedjaoui,
  • Sofiane Bououden,
  • Mohammed Chadli,
  • Ilyes Boulkaibet,
  • Bilel Neji

摘要

This paper presents a comprehensive bi-level convex optimization framework for eco-driving of fuel cell hybrid electric vehicles (FCHVs) at signalized intersections. The proposed approach analyses the complex co-optimization problem into upper-level speed planning using quadratic programming (QP) and lower-level energy management using Model Predictive Control (MPC) being adapted as QP. The upper level ameliorates vehicle’s path taking into consideration traffic light constraints, while the lower level works on fuel cell and battery power distribution through a predictive control strategy. Complete state-space matrices are derived from first principles, and the MPC problem is systematically reformulated into standard QP form for efficient real-time implementation.