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<p>The performance of Fuel Cell Hybrid Electric Vehicles (FCHEVs) is critically dependent on the optimization of energy management strategies (EMS), powertrain component sizing, and associated cost factors. Achieving the full potential of FCHEVs necessitates sophisticated optimization techniques that balance competing objectives of fuel efficiency, durability, and performance. This paper introduces a novel AI-driven multi-objective optimization framework that simultaneously optimizes both the EMS and powertrain component sizing, incorporating real-world driving conditions, degradation models, and vehicle dynamic constraints such as acceleration, top speed, and gradeability. The methodology begins by employing a machine learning approach using a Random Forest classifier to construct a representative driving cycle, categorizing traffic conditions into four distinct operational scenarios: congested, urban, extra-urban, and highway. An advanced hybrid optimization approach is then developed by combining Deep Q-Networks (DQN) with the NSGA-II evolutionary algorithm. This framework dynamically selects genetic operators (crossover and mutation) based on population performance, enhancing convergence and Pareto front quality. Both Type-2 Fuzzy Logic Controller parameters for EMS and powertrain component sizes are co-optimized to improve efficiency and durability. The proposed co-optimization framework improves both efficiency and durability, reducing fuel consumption by 21% compared to sizing-only optimization, while increasing battery durability by 7% and fuel cell durability by 30% compared to EMS-only approaches. Finally, the practical feasibility of the approach is demonstrated through hardware-in-the-loop (HIL) testing, where the optimized Type-2 fuzzy logic controller is executed in real-time by an Electronic Control Unit (ECU) via a data acquisition interface, confirming the system’s applicability.</p>

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AI-driven multi-objective optimization of FCHEV sizing and energy management considering degradation and vehicle dynamics under realistic machine learning-based traffic conditions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月13日

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  • Morteza Montazeri-Gh,
  • Afshin Mostashiri

摘要

The performance of Fuel Cell Hybrid Electric Vehicles (FCHEVs) is critically dependent on the optimization of energy management strategies (EMS), powertrain component sizing, and associated cost factors. Achieving the full potential of FCHEVs necessitates sophisticated optimization techniques that balance competing objectives of fuel efficiency, durability, and performance. This paper introduces a novel AI-driven multi-objective optimization framework that simultaneously optimizes both the EMS and powertrain component sizing, incorporating real-world driving conditions, degradation models, and vehicle dynamic constraints such as acceleration, top speed, and gradeability. The methodology begins by employing a machine learning approach using a Random Forest classifier to construct a representative driving cycle, categorizing traffic conditions into four distinct operational scenarios: congested, urban, extra-urban, and highway. An advanced hybrid optimization approach is then developed by combining Deep Q-Networks (DQN) with the NSGA-II evolutionary algorithm. This framework dynamically selects genetic operators (crossover and mutation) based on population performance, enhancing convergence and Pareto front quality. Both Type-2 Fuzzy Logic Controller parameters for EMS and powertrain component sizes are co-optimized to improve efficiency and durability. The proposed co-optimization framework improves both efficiency and durability, reducing fuel consumption by 21% compared to sizing-only optimization, while increasing battery durability by 7% and fuel cell durability by 30% compared to EMS-only approaches. Finally, the practical feasibility of the approach is demonstrated through hardware-in-the-loop (HIL) testing, where the optimized Type-2 fuzzy logic controller is executed in real-time by an Electronic Control Unit (ECU) via a data acquisition interface, confirming the system’s applicability.