Battery Safety Risk Assessment in Real-World Electric Vehicles Based on Abnormal Internal Resistance Using Proposed Robust Estimation Method and Hybrid Neural Networks

Abstract

Battery safety issue is developing as one of the main hinders restricting the further application of real-world electric vehicles (EVs). Internal resistance (IR) is one of the important parameters to reflect battery safety, because bigger abnormal IR will cause more heat generation and make the battery easier to cross the critical condition of thermal runway. Safety risk assessment based on abnormal IR can locate this kind unsafe batteries and ensure the safe operation of EVs. Existing studies mostly focus on IR estimation in laboratory, yet is insufficient in real-world abnormal IR detection, which is influenced by random conditions and coupled factors. To cope with this issue, a method is proposed to detect unsafe battery, thereby predicting the thermal runaway. The method can be divided into three parts, i.e., IR estimation, normal IR prediction, and IR evolution law construction and safety risk assessment. First, we analyze the problems of battery IR estimation in real-world scenarios, and propose a robust method to estimate the IR only based on sparse voltage and current. Second, a novel hybrid neural network model is designed and trained to predict normal IRs inputted by temperature, mileage, and state-of-charge. The model combines the advantages of different neural network structures to improve the performance. A double-layer grid searching method is proposed to optimize hyperparameters of the hybrid neural network, aiming to achieve the best accuracy under constructed neural network structure. Finally, the safety boundary indicated by IR is formed by statistic method and the IR evolution law is constructed, then a strategy is proposed to make residual evaluation for safety risk assessment. All the proposed methods are driven by EV historical data to reflect real-world conditions. The results showcase that the method is effective in the cloud for the small IR prediction error with the mean-square-error, mean-relative-error, and max error of 2.45*e-4, 1.78%, and 0.15. In addition, the method can periodically classify the unsafe and normal batteries with 98.6% accuracy for the EVs with the same specification.