基于物理的混合增强学习用于估计离子电池健康状况
Nermin M Salem1, Ahmed Mohamed2
1Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt (FUE), Cairo, 11835, Egypt. nfawzy@fue.edu.eg.
Scientific reports
|December 14, 2025
概括
这项研究引入了一种混合长短期记忆 (LSTM) 和强化学习 (RL) 模型,用于准确预测离子电池健康状况 (SOH). 这种新的方法提高了SOH估计的解释性和稳定性.
科学领域:
- 电池技术 电池技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 电池健康状况 (SOH) 估计的数据驱动方法缺乏解释性和概括性.
- 现有的模型很难有效地捕捉复杂的降解模式.
研究的目的:
- 开发一种混合方法,将长短期记忆 (LSTM) 网络和强化学习 (RL) 结合起来,以提高SOH预测的准确性和可解释性.
- 解决电池健康监测中纯数据驱动方法的局限性.
主要方法:
- 一个混合模型,集成LSTM以捕获时间依赖和近接政策优化 (PPO) 以基于物理约束的自适应性SOH校正.
- 在NASA和CALCE CS2电池数据集上进行培训和验证.
- 与独立的LSTM和RL模型进行比较.
主要成果:
- 与独立模型相比,混合LSTM-RL模型显著提高了SOH预测的准确性和稳定性.
- 降低了平均绝对误差 (MAE) 和根平均平方误差 (RMSE),[公式:参见文本]得分显著增加.
- 通过基于物理降解行为的RL反增强模型解释性.
结论:
- 拟议的混合LSTM-RL方法为准确和可解释的电池SOH估计提供了一种卓越的方法.
- 这种方法为电池管理系统和预测性维护提供了更可靠的工具.
关键词:
电池的退化降低了电池的性能.电池健康估计 电池健康估计电池管理系统 (BMS) 是一种电池管理系统.数据驱动的预测数据驱动的预测.深度强化学习的学习.混合建模混合建模这是LSTM的LSTM.离子电池 (LIB) 是一种离子电池.这就是PPO PPO PPO.基于物理的奖励函数.RL RL RL 的意思是这就是SOH.时间序列预测时间序列预测更多相关视频
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