基于多特征融合的动力电池RUL预测

RUL prediction of power battery based on multi-feature fusion

针对车载动力电池容量测试成本高及剩余使用寿命(remaining useful life,RUL)预测精度低的问题,提出一种基于多特征融合的动力电池RUL预测方法。首先对某款纯电动客车的动力电池历史运行数据分析,挖掘出能够代表动力电池性能衰减的特征参数;然后利用小波变换(wavelet transform,WT)去除采集过程中的干扰信号,并通过主成分分析法(principal component analysis,PCA)得到降维并去除冗余后的融合特征因子,利用融合特征因子构建基于遗传算法优化的支持向量回归(support vector regression,SVR)预测模型;最后通过在哈尔滨、合肥及郑州3个城市的历史运行数据进行验证。结果表明:该方法能够准确地预测出3个动力电池的容量,且去噪后的预测误差平均降低60%。

Aiming at the high cost of vehicle power battery capacity test and the low accuracy of remaining useful life prediction,a prediction method of RUL of power battery based on multi feature fusion is proposed.Firstly,the historical operation datas of the power batteries of a electric bus was analyzed,and the characteristic parameters which can represent the performance degradation of the power battery were mined.Then,the interference signal in the acquisition process was removed by wavelet transform,and the fusion feature factor after dimension reduction and redundancy removal was obtained by principal component analysis,and the support vector regression prediction model based on genetic algorithm optimization was constructed by using fusion feature factors.Finally,through the historical operation data of Harbin,Hefei and Zhengzhou,the results show that the proposed method can accurately predict the capacity of three power batteries,and the prediction accuracy after denoising is improved by more than 60%.