A Hyper-Adaptive Chaos-Aware Asynchronous Twin SAC² Controller for 100 kW Dynamic Wireless Power Transfer in Electric Vehicles

This research introduces HAC-SAC², a novel hyper-adaptive chaos-aware controller based on twin reinforcement learning, for 100 kW dynamic wireless power transfer (DWPT) in electric vehicles. Fully implemented on the industrial Siemens S7-1516F-3 PN/DP PLC, the controller features three coordinated layers: (1) a dynamic Hyper Fuzzy-PID with 49 adaptive rules and real-time coefficient tuning, (2) a Twin SAC² reinforcement learning engine with parallel 5-layer neural networks and dual-entropy regularization for robust optimization in uncertain environments, and (3) a chaos-aware switcher driven by a novel Instantaneous Power Chaos Index for sub-2ms decision-making.An asymmetric compensation topology is proposed: LCC-S on the transmitter (roadside) side and LCL-P on the receiver (vehicle) side, operating at 87 kHz in alignment with emerging SAE J2954 guidelines for high-power dynamic charging. Initial simulations were performed in integrated MATLAB/Simulink 2025b and PLECS 4.9 using the virtual Iran-1408 highway model, followed by C++ implementation via ODK 1500S on the PLC and rigorous Hardware-in-the-Loop (HiL) validation.Key results demonstrate power transfer efficiency of 93.8–96.7% across all misalignment conditions (±35 cm), secondary current ripple below 3.9%, control settling time under 14ms, PLC cycle time up to 5.8ms, program memory usage at 68%, data at 42%, and full system stability at 140 km/h. Global asymptotic stability of the closed-loop system is formally proven using the Lyapunov–LaSalle theorem for the first time in this context.This work represents a pioneering deployment of advanced reinforcement learning-based control on an S7-1500 series industrial PLC for EV dynamic charging, offering a scalable pathway for national highway infrastructure integration by 2029 (1408 SH). Due to severe financial constraints as an independent researcher without institutional or grant funding, full-scale physical prototyping was not feasible; results are based on high-fidelity simulation and preliminary Hardware-in-the-Loop validation.