Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm

• Published in: Journal of energy storage 2018-08, Vol.18, p.26-39
• Main Authors: Feng, Xuning, Pan, Yue, He, Xiangming, Wang, Li, Ouyang, Minggao
• Format: Article
• Language: English
• Subjects: Battery safety; Fault diagnosis; Internal short circuit; Lithium-ion battery; State estimation
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2018.04.020

•Model-based fault diagnosis algorithm detects internal short circuit of battery.•The algorithm only relies on commonly used signal of voltage and temperature.•The internal short circuit is identified before it develops into thermal runaway.•The algorithm can estimate the resistance of short circuit with high accuracy. The spontaneous internal short circuit that sporadically occurs during operation is an unsolved safety problem that hinders the widespread application of lithium ion batteries. An online fault-diagnosis algorithm is an urgent requirement for early detection of the spontaneous internal short circuit of lithium-ion batteries to guarantee safe operation. This paper presents a model-based fault-diagnosis algorithm for online internal-short-circuit detection. Relying on the theory of model-based control, the algorithm transforms the measured voltage and temperature to the intrinsic electrochemical status that can reflect typical internal-short-circuit features, i.e. the excessive depletion of capacity and abnormal heat generation. The estimated status of the suspicious cell deviates from the average value of the battery pack, therefore the algorithm can capture the internal-short-circuit fault by evaluating the levels of deviation. Simultaneously considering the diagnosis result calculated from both the voltage and temperature signal helps enhance the robustness of the algorithm with few false alarms. Substitute internal-short-circuit tests confirm that the algorithm is capable of identifying the internal-short-circuit fault before it develops into a severe hazard, e.g., thermal runaway. The equivalent short resistance, which can reflect the level of the internal short circuit, can be estimated with small error by the online fault-diagnosis algorithm.