A Systematic Literature Review of Risk Assessment Methodologies for Battery Electric Vehicles

This systematic literature review investigates risk assessment methodologies for Battery Electric Vehicles (BEVs), highlighting their diversity and effectiveness in addressing emerging safety challenges. With the rapid global adoption of BEVs, there is an increasing need for robust methodologies to assess risks such as thermal runaway (TR), degradation, and operational failures. This review highlights techniques such as fuzzy failure mode and effect analysis (FMEA), hybrid neural networks, bayesian networks (BN), and entropy weight methods. These tools effectively identify and mitigate risks; however, they face challenges in providing holistic, system-level safety assessments and adapting to long-term, real-world conditions. Unlike previous works, this study integrates interdependent BEV subsystems into unified risk models and examines underexplored areas such as maritime transport safety. The transport of BEVs by vessels presents unique risks, including high humidity and confined cargo spaces, which intensify the battery safety challenges. Tools like FMEA and real-time monitoring systems are critical to mitigate these risks. The findings highlight the growing reliance on real-time diagnostics and advanced algorithms for enhancing BEV safety and reliability. By identifying gaps and proposing recommendations, this review aims to support the development of standardized frameworks to ensure BEV safety across various environments and operational scenarios, contributing to their continued global adoption.