Real-Time Fault Diagnosis for EVs With Multilabel Feature Selection and Sliding Window Control

Real-time fault diagnosis on vehicles can effectively avoid potential accidents, which, however, is difficult and challenging to be widely deployed due to the low computational capability and limited data storage of electric vehicles (EVs). To address this issue, we propose a vehicle-mounted fault diagnosis system with low computational complexity and small data storage, for achieving real-time monitoring of vehicle status. To facilitate the accurate and optimized feature selection, we had been collecting 6.52-GB real data from three EVs in 12 months. Motivated by those data, we first propose a multilabel feature selection algorithm to obtain the feature weights, based on which the optimal number of features is then calculated through the backpropagation neural network (BPNN), thus minimizing the computational cost of real-time fault diagnosis regarding sample dimensions. To further simplify the fault diagnosis system, i.e., reducing the minimum required capacity of data storage, we design a real-time diagnosis sliding window (RDSW) where the window moves forward as new samples arrive and the stale data outside the window are discarded. In particular, we calculate the optimal size of RDSW, which controls the minimum required number of samples to guarantee the accuracy of real-time fault diagnosis. Owing to the mechanism of RDSW, vehicles no longer need to store massive data to guarantee the accuracy of real-time fault diagnosis. In addition, the results of real-time fault diagnosis at each vehicle can be shared with other vehicles in cooperative intelligent transportation systems (C-ITS). Finally, comprehensive simulation is conducted to validate the effectiveness of the proposed diagnosis system in terms of accuracy, complexity and storage capacity.