Physics-informed learning-based fault-tolerant control for robust wireless power transfer in electric vehicles

Wireless Power Transfer-based electric vehicles (EVs) emerge as a promising technology that can drastically reduce carbon emissions worldwide. Conventional ICE-based transportation has led to a significant increase in greenhouse gas emissions and exacerbated climate change. On the contrary, WPT-based EVs can offer extended range, contactless charging, enhanced safety, and reduced vehicle costs. However, the complex design of WPT is vulnerable to power converter failure and coil misalignment. These faults can significantly deteriorate the performance of the WPT-based EVs. A power converter failure can degrade WPT performance, and this paper introduces redundant switches to ensure smooth vehicle operation. This paper presents a Physics-informed Neural Network (PINN) that incorporates physical laws to observe signals from power converters and control the Active Fault-Tolerant Controller (AFTC). The AFTCS consists of the FDI block, which is responsible for detecting and isolating faults in real time. The PINN-based observer system monitors the signal in real time. The results clearly illustrate that the proposed framework provides accurate current tracking and ensures the residual signal remains in the threshold even in parameter variations and achieves a reduced settling time of 0.22s. Furthermore, the steady-state error is reduced to 0.3 A, outperforming the other control methods and ensuring the stability of the system in the event of faults. Thus, a PINN-based FTC is introduced to ensure the resilience and reliable operation of WPT-based EVs in case of power converter failure and coil misalignment.