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<p>Energy efficiency is crucial in modern high-speed rail, particularly with the emergence of hybrid energy storage for sustainable transportation. Traditional energy management methods, such as rule-based control and heuristics, lack scalability and adaptability for real-time, multi-unit operations. Although model predictive control (MPC) effectively manages energy flow within constraints, most implementations rely on centralised, single-layer architectures that struggle with flexibility and practical uncertainties. This paper presents a hierarchical energy management framework featuring a two-layer MPC structure and an adaptive state observer. The upper layer optimises long-term energy distribution, while the lower layer manages real-time torque tracking and power control. The adaptive observer enhances model accuracy and control reliability by estimating unmeasurable internal states, such as battery resistance and thermal degradation. Simulations using a high-fidelity train model demonstrate that the proposed framework enhances energy efficiency, mitigates battery stress, and maintains operational stability under varying loads. Its computational efficiency supports real-time onboard use, providing a scalable and robust solution for next-generation intelligent rail systems.</p>

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Dual-Layer Predictive Energy Control in High-Speed Trains Using Adaptive Observers

  • 期刊论文

  • 发表日期：

    2025年9月30日

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  • Vo Thanh Ha,
  • Bao Dan

摘要

Energy efficiency is crucial in modern high-speed rail, particularly with the emergence of hybrid energy storage for sustainable transportation. Traditional energy management methods, such as rule-based control and heuristics, lack scalability and adaptability for real-time, multi-unit operations. Although model predictive control (MPC) effectively manages energy flow within constraints, most implementations rely on centralised, single-layer architectures that struggle with flexibility and practical uncertainties. This paper presents a hierarchical energy management framework featuring a two-layer MPC structure and an adaptive state observer. The upper layer optimises long-term energy distribution, while the lower layer manages real-time torque tracking and power control. The adaptive observer enhances model accuracy and control reliability by estimating unmeasurable internal states, such as battery resistance and thermal degradation. Simulations using a high-fidelity train model demonstrate that the proposed framework enhances energy efficiency, mitigates battery stress, and maintains operational stability under varying loads. Its computational efficiency supports real-time onboard use, providing a scalable and robust solution for next-generation intelligent rail systems.