Robust lithium-ion battery state of health estimation based on recursive feature elimination-deep Bidirectional long short-term memory model using partial charging data

IF 1.3 4区 化学 Q4 ELECTROCHEMISTRY
Fengxun Tian , Shuwen Chen , Xiaofan Ji , Jiongyuan Xu , Mingkun Yang , Ran Xiong
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引用次数: 0

Abstract

Accurate perception of the state of health (SOH) of lithium-ion batteries is crucial for their safety and reliable operation. To meet this demand, a recursive feature elimination-deep bidirectional long short-term memory (RFE-DBiLSTM) model suitable for partial charging data is proposed to effectively estimate the SOH of lithium-ion batteries. In this study, the recursive feature elimination (RFE) method is used to screen multiple charging features for obtaining the key features that best represent the SOH under two scenarios with different charging segment lengths. Due to the robust noise-filtering capability and strong ability to capture complex and multi-level temporal dependencies, the deep bidirectional long short-term memory (DBiLSTM) model is used for time series data training, verification, and testing during aging. Experimental results show that compared with benchmark time series models such as long short-term memory (LSTM) and gated recurrent unit (GRU), the proposed method significantly reduces the estimated mean absolute error (MAE) and root mean square error (RMSE) on diverse batteries in the above scenarios. In the scenario for missing partial constant current (CC) charging data, the MAE and RMSE of B0005 cell are 0.0062 and 0.0094, the MAE and RMSE of B0006 cell are 0.0294 and 0.0314, the MAE and RMSE of CS2_36 cell are 0.0510 and 0.0601, the MAE and RMSE of B0029 cell are 0.0057 and 0.0072, and the MAE and RMSE of B0030 cell are 0.0088 and 0.0102. This research innovatively combines the RFE method with the DBiLSTM model to improve the accuracy and robustness of SOH estimation.
基于递归特征消除的锂离子电池健康状态鲁棒估计——基于局部充电数据的深度双向长短期记忆模型
准确感知锂离子电池的健康状态(SOH)对锂离子电池的安全可靠运行至关重要。针对这一需求,提出了一种适用于局部充电数据的递归特征消除-深度双向长短期记忆(RFE-DBiLSTM)模型,以有效估计锂离子电池的SOH。本研究采用递归特征消去(RFE)方法对多个充电特征进行筛选,获得两种不同充电段长度场景下最能代表SOH的关键特征。由于深度双向长短期记忆(deep bidirectional long - short- memory, DBiLSTM)模型具有鲁棒的噪声滤波能力和较强的捕获复杂和多层次时间依赖性的能力,该模型被用于时间序列数据在老化过程中的训练、验证和测试。实验结果表明,与长短期记忆(LSTM)和门控循环单元(GRU)等基准时间序列模型相比,该方法在上述场景下显著降低了不同电池的估计平均绝对误差(MAE)和均方根误差(RMSE)。在缺少部分恒流充电数据的情况下,B0005电池的MAE和RMSE分别为0.0062和0.0094,B0006电池的MAE和RMSE分别为0.0294和0.0314,CS2_36电池的MAE和RMSE分别为0.0510和0.0601,B0029电池的MAE和RMSE分别为0.0057和0.0072,B0030电池的MAE和RMSE分别为0.0088和0.0102。本研究创新性地将RFE方法与DBiLSTM模型相结合,提高了SOH估计的精度和鲁棒性。
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来源期刊
CiteScore
3.00
自引率
20.00%
发文量
714
审稿时长
2.6 months
期刊介绍: International Journal of Electrochemical Science is a peer-reviewed, open access journal that publishes original research articles, short communications as well as review articles in all areas of electrochemistry: Scope - Theoretical and Computational Electrochemistry - Processes on Electrodes - Electroanalytical Chemistry and Sensor Science - Corrosion - Electrochemical Energy Conversion and Storage - Electrochemical Engineering - Coatings - Electrochemical Synthesis - Bioelectrochemistry - Molecular Electrochemistry
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