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离子电池状态估计基于使用光学频域反射计的阳极应变场重构

Kaijun Liu¹, Zhijuan Zou², Guolu Yin^1,3

¹The Key Laboratory of Optoelectronic Technology and Systems (Ministry of Education), Chongqing University, Chongqing 400044, China.

|April 15, 2025

概括

这项研究使用光学反射计来监测电池应变,使用神经网络准确预测充电状态 (SOC) 和健康状态 (SOH). 仅靠应变数据就能提供高精度的电池性能洞察力,而无需进行电气测量.

科学领域:

材料科学材料科学材料科学
电气工程电气工程
数据科学数据科学数据科学

背景情况:

精确的充电状态 (SOC) 和健康状态 (SOH) 对电池系统的性能和安全至关重要.
传统的监控依赖于电气参数,在某些应用中可能受到限制.

研究的目的:

调查分布式应变数据的使用,以实时预测电池SOC和SOH.
评估光学频域反射计和用于电池诊断的神经网络的有效性.

主要方法:

采用相位敏感的光学频域反射计,用于实时监测电池阳极中的应变场.
使用feedforward和长期短期记忆循环神经网络,用于SOC和SOH预测的应变和应变率数据.
提取了诸如最大应变和累积残余应变等特征,以提高预测.

主要成果:

分布应变数据在SOC预测中达到98.3%的准确性,超过单点测量 (88.8%) 和匹配电参数预测 (98.5%).
长期短期记忆网络使用菌株数据预测SOH的准确率为96.3%.
基于应变的预测实现了高精度,而不需要电池数据.

结论:

纯粹基于应变的数据可以高精度地预测电池SOC和SOH.
分布式光学测量为同时监控多个电池组提供了强大的非电气方法.
这种方法支持先进的电池管理系统,以提高安全性和性能.

关键词:

离子电池是一种离子电池.神经网络的神经网络的神经网络光学频率域反射计反射计国家的预测预测预测.应变测量测量应变测量

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