However, when some of the cells in the actual battery module are faulty, the voltage profiles of the faulty cells produce a different degree of outliers from the F-F cells. Based on the above description, in this section we introduce the hypothesis of the reference voltage.
Table 1. Faults performance of the battery system and interrelationships. Mechanical deformation, Over-charge/Over-discharge fault, induction of active materials, thermal fault. It is often accompanied by discharge and exothermic, and the main fault activates BTR. Connection fault, mechanical deformation, aging fault, water immersion.
In battery system fault diagnosis, finding a suitable extraction method of fault feature parameters is the basis for battery system fault diagnosis in real-vehicle operation conditions. At present, model-based fault diagnosis methods are still the hot spot of research.
Yet the faults of batteries are coupled with each other, and the actual faults usually are the simultaneous occurrence of multiple faults, so the combination of information fusion technology and battery system fault diagnosis is the future tendency. The advantages and disadvantages of data-driven fault diagnosis methods are compared in Table 7.
As electric vehicles advance in electrification and intelligence, the diagnostic approach for battery faults is transitioning from individual battery cell analysis to comprehensive assessment of the entire battery system. This shift involves integrating multidimensional data to effectively identify and predict faults.
In short, the research of lithium battery fault diagnosis technology will continue to make progress in data processing, model optimization, parameter selection and real-time monitoring, to provide more effective solutions for the reliability and safety of batteries. Yuzhao Shang: Writing – original draft. Shanshuai Wang: Writing – review & editing.
Research progress in fault detection of battery systems: A review
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