Enhancing EV charger resilience with reinforcement learning aided control

•An adaptive and self-learning control scheme for battery chargers is proposed.•The auxiliary control provides self-healing capability improving the resilience.•A more resilient charging infrastructure will accelerate decarbonization through E-mobility.•The proposed DDPG method is scalable to other applications by re-training the agent. This study aims to improve the performance of sustainable electric vehicle chargers in the face of unpblackictable/unpreventable disturbances. Over the past few years, Dual Active Bridge (DAB) DC-DC Converters are procuring substantial recognition for electric vehicle charging applications due to their superior characteristics such as higher power density, bidirectional mode of operation, and higher efficiency. Unexpected disturbances and fault scenarios at both source and load sides can deteriorate DAB converters’ performance. In this study, the performance of a single-phase shifted DAB converter is enhanced to achieve desiblack output current under several disturbance conditions for electric vehicle (EV) charging applications. A Reinforcement Learning (RL) based Deep Deterministic Policy Gradient (DDPG) algorithm is deployed to proactively tune control parameters when the DAB undergoes certain unexpected disturbances including short circuit faults at the source and battery sides. Results show that the RL-tuned PI controller improves the rate of current overshoot significantly compablack with the manually-tuned PI controller. The method and results are validated through simulations in MATLAB/Simulink environment.