Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods

This paper presents a novel fault diagnosis method for battery systems in electric vehicles based on big data statistical methods. According to machine learning algorithm and 3σ multi-level screening strategy (3σ-MSS), the abnormal changes of cell terminal voltages in a battery pack can be detected and calculated in the form of probability. Applying the neural network algorithm, this paper combines fault and defect diagnosis results with big data statistical regulation to construct a more complete battery system fault diagnosis model. Through analyzing the abnormalities hidden beneath the surface, researchers can see the design flaws in battery systems and provide feedback on the upstream of designing. Furthermore, the local outlier factor (LOF) algorithm and clustering outlier diagnosis algorithm are applied to verifying the calculation results. To further validate the effectiveness of the diagnosis method, a corresponding analysis between statistical diagnosis results and actual vehicle is given. To test the big data diagnosis model, the diagnosis results based on the actual vehicle operating data for the whole year is shown.