A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application

The impact of SOC and temperature on external short circuit (ESC) faults characteristics of lithium-ion batteries, including the current and voltage variation and temperature increase, are analyzed. A fractional-order model (FOM) and a first-order RC model are both employed to describe the electrical behavior of the battery cells with the ESC fault. While the model parameters are identified by the genetic algorithm (GA). A comparison study is made on the prediction accuracy for the two models. An effective classification method based on a random forests (RF) model is proposed to recognize the electrolyte leakage behavior that occurs during the ESC fault experiments. Based on the above efforts, the three steps model-based diagnosis algorithm for identifying the ESC fault and even electrolyte leakage of the battery in real-time is proposed. Two indicators of the root mean square error (RMSE) of battery predicting voltage are applied to diagnose for the ESC fault only and ESC-leakage merged fault. The result of the leakage condition is obtained by a pre-trained RF classifier to confirm the leakage detection result based on the RMSE indicator. Several cases are verified that all the ESC cells can be diagnosed efficiently. (C) 2018 Elsevier Ltd. All rights reserved.