The rapid development of electric vehicles (EVs) creates a need for charging systems that provide convenience and energy efficiency while supporting the electric grid. One possible solution is Bidirectional Inductive Power Transfer (BIPT), which enables contact-less energy transfer between the EV battery and the electric grid. However, variations in magnetic coupling, such as coil misalignments, changes in battery load, and disturbances on the electric grid, can significantly degrade power-transfer efficiency, voltage regulation, and system stability. To address these issues, this research proposes a Multi-Objective Particle Swarm Optimisation (MOPSO) tuned Robust Model Predictive Control (RMPC) system for grid-connected BIPT systems. This system will combine uncertainty-aware power regulation with coordinated voltage and frequency support. A MATLAB/Simulink model includes bidirectional power converters, series-series resonant compensation, magnetic coupling variations, battery characteristics, and grid-side active and reactive power control. The proposed RMPC strategy accounts for bounded uncertainty in the coupling and load. It optimises voltage tracking, frequency regulation, power-transfer accuracy, and control effort subject to converter and battery constraints. MOPSO will determine Paretooptimal controller parameters, eliminating the traditional trialand-error tuning process. The proposed system will have a nominal power rating of 3.3 kW, an operating frequency of 85 kHz, and a magnetic coupling coefficient ranging from 0.15 to 0.35. Simulation scenarios will include battery resistance variations of ±20%, grid voltage disturbances of ±10%, frequency deviations of ±0.5 Hz, and switching between grid-to-vehicle and vehicle-to-grid modes of operation. Performance evaluation will compare the proposed system with conventional proportionalintegral control and nominal MPC using voltage tracking error, transient overshoot, settling time, frequency recovery, power transfer efficiency, and compliance with operational constraints. This investigation aims to determine whether the optimised RMPC framework improves disturbance rejection, maintains stable bidirectional energy transfer, and coordinates active and reactive power responses under simultaneous uncertainties. This research’s primary contribution is integrating robust predictive regulation, Multi-objective controller optimisation and coordinated gridsupport capabilities into a single wireless charging architecture. This proposed approach may increase charging reliability, reduce sensitivity to coil misalignment, and enable EV batteries to play a greater role in enhancing grid flexibility and integrating renewable energy sources. However, this approach needs experimental verification and an assessment of real-time computational requirements, battery degradation, communication reliability, and hardware limitations. This study provides a systematic approach for evaluating intelligent, grid-responsive wireless EV charging, which could lead to more robust electric grids, more sustainable transportation infrastructure, and the transition toward low-carbon mobility.
