关键词: 新能源汽车；动力电池；低荷电状态；逻辑回归算法；风险预测

Abstract: The traditional state of charge (SOC) prediction of power batteries is carried out under ideal experimental conditions, which may not be able to adapt to the real complex and changeable driving conditions. This work relies on the national big data alliance for new energy vehicles, using a data-driven approach, using real driving data to predict the current SOC of power batteries. The power battery type used in this paper is lithium iron phosphate battery, the alarm group and the prediction group are designed, and the logistic regression algorithm is used to extract the battery characteristic information of the low residual power SOC, and the model is trained, and the trained model is used to predict. The results show that the proposed model has high accuracy for low SOC diagnosis.

Key words: New energy vehicles; Power batteries; Low state of charge; Logistic regression algorithm; Risk prediction