Early Detection of Fire in EV Battery Using Machine Learning Approach

In recent years, Electric Vehicles (EVs) have revolutionized the automobile industry. Nowadays, EVs are the preferred means of transportation for people because they are easy to drive, convenient, and make less noise. EVs use Lithium-Ion Batteries (LIB) to power the vehicle. However, in past years, there have been multiple instances of EVs catching fire and becoming one of the most urgent problems in EVs. The leading causes of such mishaps include faulty Battery Management System (BMS), no proper venting mechanism for overheated cells to release heat, poor quality cells, and short-circuiting. Many researchers proposed fire handling systems that consider sensor values, such as temperature, voltage, etc. while some focus on battery health management. However, there is a need for a detection mechanism to detect fire in advance to save people's life and property from damage. Some researchers have proposed fire detection strategies considering only a few sensor readings that cannot detect the extent of a fire early and inform users about the situation. Therefore, in the proposed work, an approach is designed considering both environmental and battery data using multi-sensors for developing an intelligent early fire detection system. The proposed approach first performs exploratory data analysis on real dataset to get an insight into data for fault diagnosis, leading to early thermal runaway. Further, Machine Learning (ML) and ensemble methods are used to check the potentiality of the fire or detect fire in advance. Finally, the system is evaluated concerning accuracy, recall, precision, and Fl_Score.