On-Board Implementation of Thermal Runaway Detection in Lithium-Ion Battery Packs: Methods, Metrics, and Challenges

Effective thermal runaway (TR) detection is critical for the safety of lithium-ion battery packs, particularly in electric vehicles. However, deploying laboratory-validated methods into resource-constrained battery management systems (BMS) presents significant engineering challenges. This review surveys the state of the art in on-board TR monitoring, with an emphasis on the practical constraints of automotive applications. We first examine available precursor signals, including thermal, electrical, gas, and acoustic emissions, and evaluate their trade-offs regarding response speed and integration complexity. Second, diagnostic algorithms, from threshold-based logic to deep learning, are assessed against key performance metrics such as computational latency, false alarm rates, and lead time. Furthermore, the review discusses essential deployment considerations, including model compression techniques, inference hardware architectures, and compliance with functional safety standards. Specifically, the review discusses the implementation challenges of multi-modal data fusion, with a particular focus on the constraints imposed by limited hardware resources and long-term sensor reliability. Future directions regarding data standardization and cloud-edge collaboration are also discussed.