Review in Fire Safety Strategies in Electric Vehicle

Abstract

Electric vehicles, or EVs, are becoming a more cost-effective, emission-free mode of transportation. The primary source of power for these electric vehicles is the battery. Faulty battery management systems, cell thermal management, and thermal runaway can cause batteries to catch fire. Safety is a crucial factor that prevents catastrophic fire hazards and the loss of human life and precious property. Many fire detection and handling systems have been created that include a variety of sensors that measure different parameters, research at the battery pack level, and external cooling methods to control cell temperature and minimize thermal runaway. This study employs a unique method for detecting and analysing fire behaviour by combining Internet of Things (IoT) sensors with artificial intelligence (AI) and machine learning (ML) frameworks. Fire has a nonlinear character. In a conventional fire system, a single passive sensor system can falsely indicate a fire. The primary goal is to outline a neural network-based intelligent fire detection system. Based on trained data, the system gathers real-time data from several sensors and tracks the impact of these parameters' values. Achieving efficient battery management allows the system to notify the user in the event of danger. Energy storage systems and electric vehicles are better designed and function effectively using artificial neural networks. Performance metrics like State of Charge (SOC), State of Health (SOH), and State of Power are tracked to ensure the battery system operates safely. For early fire detection, various algorithms are discussed for their efficacy, future selection, and enhanced accuracy and efficiency.