Application of physics-informed neural networks in fault diagnosis and fault-tolerant control design for electric vehicles: A review

The application of physics-informed neural networks (PINNs) in fault-tolerant control (FTC) systems of electric vehicles has gathered considerable interest in using underlying physics to improve the fault diagnosis and mitigation process. PINNs, which include the governing physical equations in the neural network training process, allow for accurate modeling of the EV components, such as motors and inverters. This review aims to evaluate neural networks, especially PINNs, for fault diagnosis and FTC development in the context of EVs. It includes neural network structures, algorithms for training, methods based on physical analogies, and the application of physical principles to enhance the algorithms. The comparative analysis presents the merits of PINNs against conventional techniques, including PID, LQR, and Kalman Filters, regarding model fitness, data utilization, adaptability, computational footprint, resilience, and extensibility. Future research directions include extension works of PINNs integrating them into conventional approaches, dynamic adaptation, multidisciplinary, and EV self-powered systems.