基于短时弛豫电压与IBAS-BP网络的SOH估算

SOH estimation based on short-time relaxation voltages and IBAS-BP networks

为提高锂电池健康状态(SOH)的在线估算精度,提出一种基于短时弛豫电压与IBAS-BP网络的SOH估算方法.首先,通过分析不同静置时间的弛豫电压与SOH的关系,选取10秒时的弛豫电压曲线进行特征提取,采用主成分分析法对所提取特征融合与降维,降低模型复杂度和特征间的冗余.其次,通过动态步长和S型权重改进BAS算法并对BP网络的初始权阈值寻优,建立IBAS-BP网络.再次,利用MD-MTD增强的数据训练IBAS-BP网络实现SOH估算.结果表明,由10秒内的弛豫电压中所提取的特征能有效反应电池的老化,可用于SOH的估算;与其它模型相比,所建IBAS-BP模型的估算精度更高,误差均保持在0.5%以内.最后,基于所提方法利用LabVIEW搭建了一种模拟电池在环管理系统,为电池安全管理提供了参考依据.

To improve the accuracy of online estimation of lithium battery state of health(SOH),a SOH estimation method based on short-time relaxation voltage and IBAS-BP network is proposed.Firstly,by analyzing the relationship between relaxation voltage and SOH at different resting times,the relaxation voltage curve at 10 seconds is selected for feature extraction,and principal component analysis is used to fuse and reduce the dimensionality of the extracted features to reduce the model complexity and the redundancy among features.Secondly,the IBAS-BP network is established by improving the BAS algorithm with dynamic step size and S-type weights and seeking the initial weight threshold of the BP network.Again,the IBAS-BP network is trained to implement SOH estimation using MD-MTD enhanced data.The results show that the features extracted from the relaxation voltage within 10 seconds can effectively reflect the aging of the battery and can be used for SOH estimation;compared with other models,the estimation accuracy of the proposed IBAS-BP model is higher and the errors are kept within 0.5%.Finally,a simulated battery in-loop management system is built based on the proposed method using LabVIEW,which provides a reference basis for battery safety management.