基于增量能量法和BiGRU-Dropout的锂电池健康状态估计

State of health estimation of Lithium-ion batteries based on incremental energy analysis and BiGRU-Dropout

锂离子电池健康状态(SOH)的精确估计是电池管理系统面临的核心问题之一。针对实际的电池容量很难直接测量和容量再生导致的SOH估计误差问题,提出了一种基于增量能量法和双向门控循环网络(BiGRU)-Dropout的锂离子电池健康状态估计方法。首先分析增量能量曲线随电池老化的衰退规律,提取出最大峰值高度作为电池SOH的新健康因子。通过翻转层和门控循环网络层所搭建的BiGRU网络得出健康因子与SOH的映射关系,同时添加Dropout机制网络层防止出现过拟合现象,建立SOH估计模型用于电池SOH精确估计。实验结果表明,在不同充电倍率条件下,该方法均可快速、准确地估计电池SOH。

The accurate state of health(SOH)estimation of Lithium-ion battery is one of the core issues faced by battery management systems.Considering that it is difficult to directly measure the battery capacity in practice,and the capacity regeneration problem always cause SOH estimation errors,a SOH estimation method of Lithium-ion battery is proposed based on incremental energy analysis and bidirectional gate recurrent unit(BiGRU)-Dropout.The incremental energy curve is used to analyze the battery’s degeneration characteristic,and the maximum peak height is extracted as a new health factor of battery SOH.Through the BiGRU network built by flip layer and gate recurrent unit layer,the mapping relationship between health factor and SOH is obtained.At the same time,Dropout mechanism network layer is added to prevent overfitting,and a SOH estimation model is established to accurate estimate the battery SOH.The results indicate that the proposed method can estimate battery SOH quickly and accurately under different charging rates.