AI – Driven Reinforcement Learning System for High Traffic Electric Vehicle Charging Station with Linear Optimization Algorithm
TL;DR
A hybrid AI-based architecture that integrates real-time Traffic pattern, distance of EV, arrival and departure time of EV state of charge as input, and two proposed optimization algorithm improves the operational effectiveness of EVCS.
Abstract
Electric Vehicles are thought to be among the best options for lowering gas emissions and oil consumption. EV users can from a charging station with a well-thought-out schedule and price plan. Advanced energy management techniques are required to guarantee the sustainable, dependable, and effective operation of charging infrastructure due to the quick rise in the usage regarding electric vehicle (EV). In light of recent research, current design restrictions as well as the erratic conduct of EV consumers make classic scheduling techniques, such as set costs for Time-of-Use (ToU), insufficient. Uncoordinated charging results Voltage instability caused by transformer overloading, also wasteful utilization of electricity from renewable as EV use rises. AI is becoming more widely acknowledged in a crucial facilitator of scalable, durable, effective EV charging facilities with intelligence. This paper proposes a hybrid AI-based architecture that integrates real-time Traffic pattern, distance of EV, arrival and departure time of EV state of charge as input. Through real-time monitoring and charge optimization, the EVCS enable intelligent EV charging. The AI framework employs a non-uniform Poisson process in order to dynamically assess user demand also enhances schedule of charging. While the charging demand of electric vehicles (EVs) is intrinsically heterogeneous, decentralized, and stochastic, the intermittent and weather-dependent nature of PV power results in considerable output uncertainty. The purpose of the EV Charging Grid Optimization is to facilitate research on AI-driven energy management for EV charging infrastructure. The two proposed optimization algorithm improves the operational effectiveness of EVCS. Using threshold value Reinforcement Learning make the real-time decision that dynamically schedule the EV. Threshold value is determined from customer preferences. The proposed technology demonstrates scalability, durability, and cost-effectiveness and provides a feasible substitute for upcoming metropolitan EV charging system.