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A Multi-Fault Diagnosis Method for Battery Packs Based on Low-Redundancy Representation Learning

The fault diagnosis process of battery pack is restricted to its complex internal structure, chemical characteristics and nonlinearity. Internal short circuit (ISC) fault and virtual connection (VC) fault are two imperceptible fault types that can cause severe consequence, such as thermal runaway, which may lead to fire accident. The existing methods detect aforementioned faults by examining every cell in the battery pack. However, the inspection of normal cells is actually unnecessary. In real application occasion, the battery pack is composed of thousands of cells. If the algorithm is used to check each battery cell is faulty, the computing burden is significantly increased. Therefore, a method that can reduce the number of detected cells is urgently needed. A low-redundancy battery pack diagnosis method is proposed to address the data redundancy issue in electric vehicle battery pack fault detection of ISC and VC. The fault diagnosis efficiency can be improved dramatically if the fault diagnosis process is executed only in abnormal cells. Via extracting a novel extreme voltage sequences, a low-redundancy representation method is performed. Hence, the feature of battery pack cells is represented with less data. Then the sample entropy algorithm is employed to measure the extreme voltage sequences, of which only the abnormal cell voltage is detected. The method gained superior performance in reducing the data redundancy of the battery pack. The feasibility of proposed method is validated by experiment result.