Enhancing battery durable operation: Multi-fault diagnosis and safety evaluation in series-connected lithium-ion battery systems

Precise fault identification and evaluation of battery systems are indispensably required to facilitate safe and durable operation for electric vehicles. With the core objective of addressing the challenges of inaccurate evaluation and misdiagnoses of multi-fault in existing methods, this paper proposes a deep-learning-powered diagnosis and evaluation scheme for series-connected battery systems. First, we conduct series-connected cycling experiments to simulate the two most common faults including capacity anomaly fault and short circuit fault happening concurrently to observe the failure phenomena of different faulty batteries and fault-free batteries. Then, the evolutional processes of various faults are analyzed and compared for a deeper understanding of the battery fault mechanism. In addition, we establish an elaborate deep-learning-based model, achieving satisfactory realizations on predicting the reference voltage (with the mean square error of 7.84 × 10−5 V) while categorizing the current fault state (with an accuracy of 98.2 %). At last, a comprehensive fault identification and quantification strategy is constructed to minimize the misdiagnosis. All proposed methodologies demonstrate the advancement compared to other state-of-the-art algorithms. And the results are thoroughly validated with two different experimental datasets and real-world cloud vehicle datasets, affirming the efficiency and practical applicability, contributing to enhancing the active safety capabilities of battery systems.