Xu Peng, Huang Yuan, Ran Wenwen, Wan Shibin, Guo Cheng, Su Xin, Yuan Libing, Dan Yuanhong
School of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, China.
School of Computer Science and Technology, Chongqing University of Technology, Banan, Chongqing, 400054, China.
Heliyon. 2024 Feb 9;10(4):e25808. doi: 10.1016/j.heliyon.2024.e25808. eCollection 2024 Feb 29.
Accurate estimation of a battery's state of health (SOH) is essential in battery management systems (BMS). This study considers a complete analysis of combining incremental capacity (IC), differential thermal voltammetry (DTV), and differential temperature (DT) for SOH prediction in cases of discharge. Initially, the IC, DTV, and DT curves were derived from the current, voltage, and temperature datasets, and these curves underwent smoothing through the application of Lowess and Gaussian techniques. Subsequently, discerning healthy features were identified within the domains where the curve exhibited substantial phase transitions. Utilizing Pearson correlation analysis, features exhibiting the utmost correlation with battery capacity degradation were singled out. Finally, the state-of-health (SOH) prediction model was constructed using a bidirectional long short-term memory (BILSTM) neural network. Two datasets were used to validate the model, and the experimental results demonstrated that the SOH prediction had a root mean square error (RMSE) below 1.2% and mean absolute error (MAE) below 1%, which verified the feasibility and accuracy. This approach quantifies the internal electrochemical reactions of a battery using externally measured data, further enabling early SOH predictions.
在电池管理系统（BMS）中，准确估计电池的健康状态（SOH）至关重要。本研究考虑对增量容量（IC）、差分热伏安法（DTV）和差分温度（DT）进行全面分析，以预测放电情况下的SOH。首先，从电流、电压和温度数据集中导出IC、DTV和DT曲线，并通过应用局部加权散点平滑法（Lowess）和高斯技术对这些曲线进行平滑处理。随后，在曲线呈现明显相变的区域内识别出有区别的健康特征。利用皮尔逊相关分析，挑选出与电池容量退化相关性最大的特征。最后，使用双向长短期记忆（BILSTM）神经网络构建健康状态（SOH）预测模型。使用两个数据集对模型进行验证，实验结果表明，SOH预测的均方根误差（RMSE）低于1.2%，平均绝对误差（MAE）低于1%，这验证了该方法的可行性和准确性。该方法利用外部测量数据对电池内部的电化学反应进行量化，进一步实现了对SOH的早期预测。
