Fault Diagnosis Method for Lithium-ion Battery Based on Multi-algorithm Fusion
The safety and thermal runaway problems of the lithium-ion battery limit its wide range of applications in various fields.In order to more accurately detect internal short-circuit and open-circuit faults in abnormal battery conditions,this paper proposes a multi-algorithm fusion-based fault diagnosis method for the lithium-ion battery.Firstly,the sequential variational mode decomposition(SVMD)method is used to calculate the maximum voltage fluctuation of each battery cell,and the anomalous cells are identified by box-and-line diagram and outlier detection.Secondly,the absolute value of the voltage mean difference obtained from SVMD decomposition is used to detect potential internal short or open circuit faults by combining slope calculation and statistical analysis.Finally,the paper verifies this method using thermal runaway data from actual lithium-ion batteries.The results show that the method can provide early warning 55 minutes before thermal runaway occurs and accurately identify internal short-circuit or open-circuit faulty monoliths,which is highly reliable and has high value for engineering applications.
lithium-ion batteryinternal short circuit faultsequential variational mode decomposition(SVMD)outlier detectionsafety warning
万方数据
维普
