A Method for Abnormal Battery Charging Capacity Diagnosis Based on Electric Vehicles Operation Data

Abstract

Overcharging due to an abnormal charging capacity is one of the most common causes of thermal runaway (TR). This study proposes a method for diagnosing abnormal battery charging capacity based on electric vehicle (EV) data. The proposed method can obtain the fault frequency and output the corresponding state of charge (SOC) when a fault occurs. First, a machine-learning-based data cleaning framework is developed to overcome the limitations of the interpolation method. Then, offline training is implemented, based on big vehicle operation data and an improved Gaussian process regression (GPR). Thereafter, online monitoring of the discrete capacity increment (DCI) is used to identify the abnormal charging capacity. The abnormal charging capacity fault is identified by the absolute error between the GPR outputs and the true DCI, and the thresholds are determined using a Box–Cox transformation with a value of 3σ. The diagnostic results indicate that the abnormal charging capacity of the TR vehicle is identified two months in advance, and the fault frequency of the abnormal and normal vehicles is 0.5221 and 0.0311, respectively. EV operation data and various methods are used to validate the robustness and applicability of the proposed method.