Thermal runaway (TR) of lithium-ion batteries (LIBs) represents a critical safety challenge in EV applications. This study explores the potential of data-driven safety management strategies for mitigating TR risks in EVs. To minimize the impact of external environmental factors on the degradation of LIBs, experiments were conducted using an accelerating rate calorimeter (ARC). The intrinsic thermal behavior of six nickel–cobalt–manganese (NCM) cells at different states of health (SOH) and operating temperatures has been captured in created adiabatic conditions. Multiple sensors were deployed to monitor the temperature and electrochemical and environmental parameters throughout the degradation process until TR occurred. The results show that both the thermal and electrochemical stability of LIBs have been affected, exhibiting consistent thermal patterns and early electrochemical instability. Furthermore, even under adiabatic conditions, the degradation of LIBs show synergistic effects with environmental parameters such as chamber temperature and pressure. Correlation analysis further revealed the coupling relationships between the monitored parameters. Through calculating their correlation coefficients, the results indicate advantages of combining thermal, electrochemical, and environmental parameters as being to characterize the degradation of LIBs and enhance the identification of TR precursors. These findings stress the importance of considering the battery-environment system as a whole in safety management of EVs. They also provide insights into the development of data-driven safety management strategies, highlighting the potential for achievement and integration of anomaly detection, diagnosis, and prognostics functions in current EV management frameworks.

With the rapid development of recycling and secondary utilization of end-of-life battery materials, it is crucial to clarify the impact of full-lifecycle degradation on the thermal safety limits of lithium-ion batteries. This study focuses on a 16 Ah NCM613|graphite pouch battery. First, it analyzes the evolution of capacity decay, thickness expansion, and internal resistance during cycling at room temperature (25 °C) and high temperature (45 °C). Furthermore, an adiabatic accelerated calorimeter (ARC) is employed to investigate the influence of different states of health (SOH) levels (95% and 85%) on the battery’s thermal runaway characteristics. The findings indicate that, macroscopically, batteries in all states follow similar voltage–temperature failure pathways, with mass loss rates confined to a narrow range of approximately 16%, emphasizing the low catastrophic potential of mid-nickel chemistry. However, the microscopic kinetic mechanisms exhibit significant anisotropy: although thickness and internal resistance display no apparent abrupt increase during the late stage of room temperature aging, the capacity exhibits a highly nonlinear plunge behavior. The severe internal lithium plating side reaction triggered by this phenomenon causes the self-heating onset temperature to drop rapidly from 130.0 °C in the fresh state to 79.7 °C. Concurrently, the activation energy of the exothermic side reaction, fitted using a simplified Arrhenius equation, exhibits a non-monotonic variation with aging progress. In the early stages of aging at 95% SOH, due to high temperatures promoting more significant growth of the interfacial film or moderate film formation at room temperature enhancing interfacial thermal stability, the activation energies for both aged batteries increase, and the energy barrier at high temperatures is slightly higher than at room temperature; however, during the deep aging stage at 85% SOH, due to the degradation of active material components and the emergence of lithium plating characteristics, the energy barrier significantly decreases, with high-temperature-aged batteries exhibiting a greater reduction, highlighting the cumulative negative impact of prolonged high-temperature exposure on thermal safety. The research provides a core scientific basis for establishing a battery safety early warning and dynamic health management system covering the entire lifecycle.

This study focuses on the thermal runaway (TR) risk of lithium-ion batteries (LIBs) and explores data-driven multi-sensor early warning and risk-oriented prognostics based on the intrinsic thermal behavior of batteries. A framework is proposed to support health information extraction using multi-sensor monitoring data, where data-driven approaches are adopted to address multicollinearity among parameters and explore their synergistic effects. The identified health-related features help characterize abnormal behavior and model health index propagation under TR evolution, which support the design of warning points and estimation of remaining time before TR (time-to-TR). The framework is validated through a designed experiment, where the Accelerating Rate Calorimeter (ARC) is employed to create adiabatic conditions for LIBs. The experiment helps minimize external impacts during TR evolution and validate the framework according to the intrinsic characteristics of batteries related to TR risks. The results from Leave-One-Sample-Out cross-validation preliminarily demonstrate the application potential of identified feature behavior patterns in early warning and risk-oriented prognostics. The integration of these functions can enhance the current TR risk management solutions, transforming monitoring data into decision support information for proactive risk mitigation.