Beyond diagnosis: Why current fault diagnosis methods for power batteries fall short

TY - JOUR

T1 - Beyond diagnosis

T2 - Why current fault diagnosis methods for power batteries fall short

AU - Lin, Ni

AU - Chen, Kang

AU - Zhang, Zhaosheng

AU - Chen, Shuaiheng

AU - Wang, Zhenpo

N1 - Publisher Copyright: © 2025

PY - 2025/9/15

Y1 - 2025/9/15

N2 - The proliferation of electric vehicles has been catalyzed by advancements in battery technology, heightened environmental awareness, and supportive governmental policies. However, thermal runaway, which is a catastrophic failure mode characterized by an uncontrollable temperature increase leading to fires or explosions, remains a paramount safety issue. This work first elucidates the multifaceted causes of thermal runaway and evaluates state-of-the-art detection methodologies, including temperature and voltage monitoring, and highlights the integration of machine learning and electrochemical parameters in fault diagnosis algorithms, followed by challenges in enhancing the reliability and accuracy of early warning systems and the practical implementation of real-time diagnostic tools. To deepen our comprehension of the underlying issues, a case analysis is presented to elucidates critical considerations for fault diagnosis. While our interpretation remains incomplete, hopefully the resultant findings provide significant insights that may inform and guide future research endeavors. In the end, we propose avenues for future research focused on robust modeling, innovative sensor technology, as well as a deeper understanding of electrochemical processes to enhance battery safety.

AB - The proliferation of electric vehicles has been catalyzed by advancements in battery technology, heightened environmental awareness, and supportive governmental policies. However, thermal runaway, which is a catastrophic failure mode characterized by an uncontrollable temperature increase leading to fires or explosions, remains a paramount safety issue. This work first elucidates the multifaceted causes of thermal runaway and evaluates state-of-the-art detection methodologies, including temperature and voltage monitoring, and highlights the integration of machine learning and electrochemical parameters in fault diagnosis algorithms, followed by challenges in enhancing the reliability and accuracy of early warning systems and the practical implementation of real-time diagnostic tools. To deepen our comprehension of the underlying issues, a case analysis is presented to elucidates critical considerations for fault diagnosis. While our interpretation remains incomplete, hopefully the resultant findings provide significant insights that may inform and guide future research endeavors. In the end, we propose avenues for future research focused on robust modeling, innovative sensor technology, as well as a deeper understanding of electrochemical processes to enhance battery safety.

KW - Electric vehicles

KW - Fault diagnosis

KW - Lithium-ion batteries

KW - Thermal runaway

UR - https://www.scopus.com/pages/publications/105008007677

U2 - 10.1016/j.est.2025.117225

DO - 10.1016/j.est.2025.117225

M3 - Review article

AN - SCOPUS:105008007677

SN - 2352-152X

VL - 130

JO - Journal of Energy Storage

JF - Journal of Energy Storage

M1 - 117225

ER -