数据驱动的动力电池能量特性预测研究

Research on Power Battery Energy Characteristic Prediction Based on Data-Driven

为实现纯电动汽车电池能量信息的准确预测,提出了一种基于充电型纯电动汽车大数据的电池能量分析和预测方法。首先,通过大数据平台获取搭载相同型号电池车型的不区分地域大数据,然后使用区间平均法和支持向量回归(SVR)方法对总数据和典型地域数据进行里程-总能量关系的拟合,完成电池总能量衰减的预测,最后,将预测结果与长短时记忆(LSTM)神经网络的预测结果进行对比,并利用实车试验验证所提出方法的准确性。验证对比结果表明:基于SVR的模型能够对分散电池容量进行量化拟合,具有较高的预测精度。

To achieve accurate prediction of EV battery energy information,this paper proposed a method for battery energy analysis and prediction based on big data of chargeable pure electric vehicles.Firstly,the big data of vehicles with the same battery model from different regions were obtained through a big data platform,and then the interval average method and Support Vector Regression(SVR)were used to fit the relationship between mileage and total energy for both the total data and typical regional data,to predict degradation of the battery total energy.Finally,the predicted results were compared with that obtained from Long Short-Term Memory(LSTM)neural network,and the accuracy of the proposed method was verified by vehicle test.The results show that:the SVR-based model can quantitatively fit the degraded battery capacity,which has high prediction accuracy.