Model identification of battery dynamics is a central problem in energy research; many energy management systems and design processes rely on accurate battery models for efficiency optimization. The standard methodology for battery modelling is traditional design of experiments (DoE), where the battery dynamics are excited with many different current profiles and the measured outputs are used to estimate the system dynamics. However, although it is possible to obtain useful models with the traditional approach, the process is time consuming and expensive because of the need to sweep many different current-profile configurations. In the present work, a novel DoE approach is developed based on deep reinforcement learning, which alters the configuration of the experiments on the fly based on the statistics of past experiments. Instead of sticking to a library of predefined current profiles, the proposed approach modifies the current profiles dynamically by updating the output space covered by past measurements, hence only the current profiles that are informative for future experiments are applied. Simulations and real experiments are used to show that the proposed approach gives models that are as accurate as those obtained with traditional DoE but by using 85\% less resources.

翻译：电池动力学模型辨识是能源研究中的核心问题；许多能源管理系统和设计过程依赖精确的电池模型以实现效率优化。电池建模的标准方法是传统实验设计（DoE），该方法通过多种不同电流分布来激励电池动态特性，并利用测量输出估计系统动态特性。然而，尽管传统方法能够获得有用的模型，但由于需要扫描大量不同的电流分布配置，该过程耗时且成本高昂。本研究提出了一种基于深度强化学习的新型实验设计方法，该方法根据以往实验的统计特性动态调整实验配置。与依赖预设电流分布库不同，所提方法通过更新已有测量覆盖的输出空间来动态修改电流分布，因此仅施加对后续实验具有信息价值的电流分布。仿真与真实实验结果表明，所提方法能够获得与传统DoE精度相当的模型，但资源消耗降低85%。