Modified Relative Entropy-Based Lithium-Ion Battery Pack Online Short-Circuit Detection for Electric Vehicle
Zhenyu Sun, Zhenpo Wang, Yong Chen, Peng Liu, Shuo Wang, Zhaosheng Zhang, David G. Dorrell
Abstract
Thermal runaway of an electric vehicle (EV) battery can cause severe loss of property and human life. With the increasing market share of EVs, this issue becomes more critical since one single cell short circuit could easily cause thermal runaway in a few minutes. Therefore, battery short-circuit detection systems are important for the prevention and limitation of EV fire incidents. This article proposes a short-circuit detection and isolation method for lithium-ion battery packs based on relative entropy and the Z-score method, which identifies the cell voltage dropping behaviors caused by a short circuit with the sliding window processing method. Taking the optimal sliding window width, the proposed detection algorithm can be performed in real time without any significant delay. The effectiveness and efficiency of the proposed method are verified using real-world data measured from EVs that experienced fire incidents caused by thermal runaways. Results indicate the method in this article is capable to recognize the data pattern of the potential threat in real time and send an early alarm to the driver.