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An Incipient Multifault Diagnosis Method for Lithium-Ion Battery Pack Based on Data-Driven With Incremental-Scale
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DOI：10.1109/TTE.2024.3363238.png)
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Abstract
En 中文
The effective fault diagnosis method is a key measure to enhance the safety of lithium-ion batteries (LIBs). Nevertheless, it is challenging for conventional threshold diagnosis methods to detect minor faults in the early stages. Herein, an incipient multifault diagnosis method based on data-driven with incremental-scale is proposed. First, a lightweight long short-term memory (LSTM) is used to obtain the healthy mapping relationship between the voltage-current interleaved sequence (VCIS) and cell voltage increment (CVI). Second, the faults are detected by determining the residual between the predicted CVI and the actual CVI. Eventually, the incremental diagnosis model (IDM) is constructed to distinguish the fault type and quantify the fault severity by evaluating fault resistance. The experimental results demonstrate that the proposed method performs excellent generalization to different battery types, fault degrees, and combinations of noise. Moreover, the minimum detectable fault CVI is as low as 15 mV, and the mean error of fault resistance evaluation is 4.0%. Compared with conventional approaches, the proposed method has a higher fault detection accuracy and provides richer fault information. More importantly, the method is proven to work efficiently on battery management system (BMS).
Keywords:
Circuit faults
Integrated circuit modeling
Fault diagnosis
Voltage
State of charge
Logic gates
Long short term memory
incremental-scale
lithium-ion battery (LIB)
long short-term memory (LSTM)
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