Evaluating Reliability and Economics of EV Charging Configurations and Deep Reinforcement Learning in Robotics and Autonomy

Abstract

Growing EV popularity drives companies to focus on reliable charging station designs despite challenges in maintaining reliability. A proposed 36-ported design combines uniform and non-uniform port arrangements, tested with 50-350 kW systems. Failure rates are estimated using MILHDBK217F and MILHBK-338B standards, assessing port reliability and station success rates through binomial distribution and cost analysis. This design improves voltage stability and reduces maintenance costs through enhanced port reliability. In robotics and autonomous systems, Deep Reinforcement Learning (DRL) excels but faces challenges from unsafe policies leading to hazardous decisions. This study introduces a reliability assessment framework for DRL-controlled systems, using formal neural network analysis. A two-level verification approach evaluates safety locally using reachability tools and globally by aggregating local safety metrics across tasks. Experimental validation confirms the framework's effectiveness in enhancing RAS safety.