基于ACO-BP神经网络的锂离子电池容量衰退预测

Lithium-ion battery capacity decline prediction based on ant colony optimization BP neural network algorithm

准确预测电池的容量衰退趋势对加强电池系统的管理和维护具有重要意义。本工作选择以锂离子电池为研究对象,根据NASA实验室公开的源数据集分析、预测锂离子电池的容量衰退趋势。在室温、恒流工况下对锂离子电池进行满充满放的循环充放电试验,得到各循环周期下电池的实际额定容量值,采用紧支集正交小波分析对获得的电池监测数据进行去噪优化处理,得到更加平稳规律的电池容量衰退过程,然后利用蚁群算法(Ant colony optimization,ACO)优化BP神经网络的初始权值和阈值,基于ACO-BP神经网络模型完成对锂离子电池容量衰退的预测,并与单独使用BP神经网络进行对比。结果表明,采用ACO-BP神经网络比单独使用BP神经网络具有更好的预测效果,且随着训练样本的增加,包含更多的电池容量退化信息,预测精度明显提高,当以前80个循环充放电周期作为训练样本时,预测的平均误差为1.46%,若继续扩大训练样本,预测效果将会更好。本研究有助于加强电池系统的健康管理,为高效预测锂离子电池的劣化轨迹提供技术参考。

Accurately predicting the declining trend with respect to the capacity of a battery is important for strengthening the management and maintenance of the battery system.Lithium-ion batteries are the research object;the battery capacity decline trend is predicted based on a source data set analysis published by NASA Laboratories.The data for a full-cycle charge-discharge test of a battery,obtained at room temperature and constant current,are denoised and optimized by a compact set orthogonal wavelet analysis to obtain a more stable and regular battery capacity decay process.The ant colony optimization(ACO)algorithm is subsequently used to optimize the initial weight of the BP neural network.And threshold,based on the ACO-BP neural network model to predict the capacity decline of lithium-ion batteries,and compared with BP neural network alone.The results denote that the ACO-BP neural network generates better prediction results when compared with that generated by the BP neural network alone;with more training samples,it contains more information on battery capacity degradation,and the prediction accuracy is significantly improved.The predicted average error is 1.46%when 80 charge and discharge cycles are used as training samples.If the training samples are further expanded,the prediction effect will improve.This study helps to strengthen the management of the battery systems and provides a technical reference for efficiently predicting the degradation trajectory of the lithium-ion batteries.