基于电压频域特征和异常系数的动力电池故障诊断方法

Power battery systems are the key component and the main source of faults in electric vehicles. Therefore, it is of great importance to improve the efficiency and accuracy of battery fault diagnosis. Accordingly, a fault diagnosis method was proposed based on the fast Fourier transform (FFT) and abnormal coefficient evaluation for voltage inconsistency faults of a battery system. Six accident vehicles and one normal vehicle were selected from the National Monitoring and Management Center, and big-data preprocessing techniques, such as data cleaning and data transformation, were adopted for the full life-cycle operating voltage data. Then, the data were transformed in the frequency domain by using F F T, and the amplitude in the frequency domain was proposed as the characteristic indicator of fault diagnosis. Furthermore, the abnormal coefficient based on the Z-score was introduced to quantitatively evaluate the fault degree so that faulty cells may be detected and located. In addition, in the case of multiple faulty cells, the fault degree was determined and sorted by calculating the abnormal cell rate. Thereby, the influence of the voltage data length, date sampling time, and number of FFT sampling points on the model was analyzed in detail. Finally, a comparison with the voltage fault diagnosis method based on entropy and Z-score indicates that the proposed diagnosis method do not produce false alarms for normal vehicles and can effectively detect severe voltage inconsistency faults in accident vehicles under the above research conditions. Specifically, the accuracy of the model increases by 3. 25%, whereas its time consumption is only 0. 55% of the entropy model, verifying the advantages of the proposed method, namely, more accurate fault location, better applicability, and faster calculation. The proposed method can effectively diagnose voltage inconsistency faults, and thus it has high engineering application value.