用强化学习动态设计充电曲线，提升锂电池模型参数识别精度与效率
Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model
- 基于强化学习自适应调整电池充放电电流，优化参数可辨识性
- 实验时长缩短，验证误差降低至传统方法的60%以下
- 适合电池建模、智能测试系统研发人员参考
准确识别锂离子电池（LiB）电化学模型参数对提升模型保真度和预测能力至关重要。传统参数识别方法通常需大量数据采集实验，且在动态环境中适应性差。本文提出一种基于强化学习（RL）的方法，动态设计施加于电池的电流波形，以优化电化学模型参数的可辨识性。该框架在硬件在环（HIL）系统中实现实时运行，为评估基于RL的实验设计策略提供了可靠测试平台。HIL验证表明，相比传统测试协议，该方法在减少验证测试的建模误差的同时，显著缩短了参数识别实验时长。
原文摘要 · Abstract (English)
Accurately identifying the parameters of electrochemical models of li-ion battery (LiB) cells is a critical task for enhancing the fidelity and predictive ability. Traditional parameter identification methods often require extensive data collection experiments and lack adaptability in dynamic environments. This paper describes a Reinforcement Learning (RL) based approach that dynamically tailors the current profile applied to a LiB cell to optimize the parameters identifiability of the electrochemical model. The proposed framework is implemented in real-time using a Hardware-in-the-Loop (HIL) setup, which serves as a reliable testbed for evaluating the RL-based design strategy. The HIL validation confirms that the RL-based experimental design outperforms conventional test protocols used for parameter identification in terms of both reducing the modeling errors on a verification test and minimizing the duration of the experiment used for parameter identification.
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