Long-Term Estimation of SoH Using Cascaded LSTM-RNN for Lithium Batteries Subjected to Aging and Accelerated Degradation

Energy Storage Pub Date : 2024-11-05 DOI:10.1002/est2.70066
Y. K. Bharath, V. P. Anandu, U. Vinatha, Shetty Sudeep
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Abstract

Accurate estimation of state of health (SoH) of the battery over long-term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short-term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet-based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM-RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM-RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery.

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使用级联 LSTM-RNN 对老化和加速退化锂电池的 SoH 进行长期估算
准确估算电池的长期健康状况(SoH)是电动汽车电池管理系统面临的一项重大挑战。这是因为对电池在不同工作条件下造成的加速老化和退化现象进行精确建模是一项挑战。本文提出了一种具有长短期记忆(LSTM)的级联递归神经网络(RNN),用于估计电池的内阻和 SoH,同时考虑到电池的各种异常工作条件。使用衰减方程开发了基于数据表的电池衰减模型。LSTM-RNN 的训练和验证数据集是在电池模型受到快速充电、工作温度变化、过度使用和电池失衡等各种加速退化因素影响的情况下生成的。级联 LSTM-RNN 只在每个充放电周期结束后进行一次估计 SoH 的训练。所提出的 SoH 估计器的训练误差指数参数完全在 1% 以内,证明了该估计器在电池的不同工作条件下的可靠性和鲁棒性。
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