针对车载环境下电池容量难以预测,电池退役时间难以确定的问题,提出基于车辆日常充电片段数据来估算电池当前最大可用电量,构建电池当前可用最大容量与行驶里程的序列关系以描述电池的退化特征,为剩余寿命预测提供可靠依据。首先在电池的充电工况下以安时积分法估算电池的当前可用最大容量,利用卡尔曼滤波对得到的容量值进行修正,然后建立长短期记忆（LSTM）神经网络模型来预测在车辆行驶里程下的电池容量衰退轨迹。结果表明：该方法实现了电池容量的准确预测,为车辆电池退役时间确定提供了可靠依据。不同训练集下均方根误差均低于多项式回归模型和高斯回归模型,预测精度至少提高了12.7%,具有较强的适用性和实际意义。
Abstract
Aiming at the problem that battery capacity is difficult to predict and battery retirement time is difficult to determine in the vehicle environment, using the daily charging segment data of the vehicle to estimate the current maximum available power of the battery. Constructing the sequence relationship between the current maximum available capacity of the battery and the driving mileage to describe the degradation characteristics of the battery, which provides a reliable basis for the prediction of remaining life. Firstly, under the charging condition of the battery, estimating the current available maximum capacity of the battery by ampere hour integral method, and correcting the obtained capacity value by Kalman filter. Then, predicting the decline trajectory of battery capacity under vehicle mileage by long short-term memory （LSTM） neural network model. The results show that this method can accurately predict the battery capacity and provide a reliable basis for the determination of vehicle retirement time, the root mean square error of different training sets is lower than that of polynomial regression model and Gaussian regression model, and the prediction accuracy is improved by at least 12.7%, which has strong applicability and practical significance.
关键词
电动汽车 /
寿命预测 /
动力电池 /
梯次利用 /
电池容量 /
卡尔曼滤波 /
LSTM网络模型
Key words
electric vehicles /
life prediction /
power battery /
echelon utilization /
battery capacity /
Kalman filtering /
LSTM network model
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