Vehicle-to-vehicle dynamic wireless charging (V2V-DWC) is a contemporary electrified transportation technology in which a dedicated charging vehicle supplies power to a user vehicle. This technology is gaining popularity due to the slow progress in energy storage technology and the limited availability of plug-in charging stations. The concept of V2V wireless power transfer offers a means for EVs to replenish their batteries during a journey. Literature shows that the concept has been empirically validated through analysis and experimentation, however, the issue of misalignment has not been addressed from this perspective yet. For optimal power transfer in V2V, the coils need to be in perfect alignment. Lateral Misalignment (LTM) arises when these coils are not properly aligned which results in undesirable energy loss. Furthermore, there is still a deficiency in the development of appropriate controllers for V2V misalignment problem solution. This paper aims to develop Neural Network based Adaptive Fuzzy Logic Controller (ANFIS), to mitigate the misalignment problem of V2V-DWC system. The paper delves into a comparative analysis of the proposed controller with the existing Fuzzy Logic Controller (FLC) to assess their effectiveness for different degrees of LTMs. The effectiveness of the proposed ANFIS controller has been assessed using simulations in MATLAB/Simulink alongside experimental tests. Results show that proposed ANFIS outperforms the FLC in both simulated and experimental scenarios in solving the V2V misalignment issue.

Dynamic wireless charging (DWC) of electric vehicles (EVs) can greatly alleviate "short driving range" concern, making it a highly feasible prospect for electrified transportation. To achieve maximum power transfer in DWC, it is essential that the primary coil (under the road) and secondary coil (below the EV) are fully aligned. Lateral misalignment (LTM) occurs when the coils fail to align properly. The energy losses caused by LTM can be compensated using conventional controllers, however, they cannot perform well in higher misalignment scenarios and exhibit overshooting, and steady-state error (SSE). In this paper, a model prediction-based approach is implemented to design a controller that nullifies the LTM effect and compensates for the reduced power transfer to the EV. This work presents a comparative study between the model predictive controller (MPC) and conventional Proportional-Integral (PI) controller in terms of performance and suitability. The MPC controller uses an optimization algorithm to select the optimal control action over the prediction horizon, minimizing SSE and reducing overshooting in the system, thereby performing exceptionally well across all degrees of LTMs. The effectiveness of the proposed MPC has been evaluated through simulations in MATLAB/Simulink and experimental tests. The proposed MPC demonstrates superior performance compared to the PI controller in both simulation and experimental evaluations.