Timely Thermal Runaway Prognosis for Battery Systems in Real-World Electric Vehicles Based on Temperature Abnormality

Abstract

Hundreds of thermal-runaway-induced battery fire accidents have been occurring to real-world electric vehicles (EVs) in recent years, exposing life to danger and causing property losses. Timely and fast battery thermal runaway prognosis is essential but restricted by limited parameters and complex influencing factors during real-world operation of EVs, i.e., environment, driving behavior, and weather. To cope with the issue, several data-driven methods are combined, and the thermal runaway prognosis is realized by two steps, i.e., temperature prediction by the modified extreme gradient boosting (XGBoost) and then abnormality detection by the principal component analysis (PCA) and density-based spatial clustering of applications with noise (DBSCAN). The XGBoost is modified and trained by data of real-world EVs to couple the influencing factors during the real-world operation of EVs. For parameter optimization, the “pretraining and adjacent grid optimizing method” (P-AGOM) and the “adjacent grid optimizing method” (AGOM) are proposed to achieve locally optimal hyperparameters for XGBoost and DBSCAN. Verified results showcase that the XGBoost-PCA-DBSCAN achieves accurate 5-min-forward temperature prediction, and the mean square errors (mses) of four seasons are only 0.0729, 0.0594, 0.0747, and 0.0523, respectively. By modification of XGBoost, the mse of temperature prediction is reduced by 31.2%. In addition, the 35-min-forward thermal runaway prognosis by the XGBoost-PCA-DBSCAN will provide the driver sufficient response time to minimize the loss of life and property.