A fault diagnosis method for lithium batteries based on optimal variational modal decomposition and dimensionless feature parameters

Due to the frequent occurrence of electric vehicles safety accidents caused by battery system failures, in order to ensure the normal operation of the vehicle, it is crucial to do fault diagnosis of the electric vehicle lithium battery. This paper presents a fault diagnosis method for lithium batteries based on optimal variational modal decomposition and dimensionless feature parameters for identifying faulty batteries. The method firstly preprocesses the voltage signal of lithium battery by optimal variable mode decomposition to obtain the high and low frequency components of the signal, and reconstructs the high and low frequency components. Then the dimensionless feature parameters are extracted according to the reconstructed signal and feature reduction of the dimensionless feature parameters is carried out by a local linear embedding algorithm. Finally, a local outlier factor algorithm is used to detect faulty batteries. After verified by the operation data before the real electric vehicles thermal runaway failure, this method can detect the faulty battery timely and accurately.