Charging Pile Control Management System using Adaptive Heuristic Guidance based Firefly Approach

A charging pile control management system is considered to effectively handle and manage Electric Vehicle (EV) charging stations. It determines charging schedules, ensures billing, and user authentication, optimizes energy distribution which increase total charging experience while supportive grid stability. However, the control management system involves potential interoperability problem across various EV approach and charging standards which impacts accessibility of user. This research proposes Adaptive Heuristic Guidance based Firefly Approach (AHG-FA) for charging pile control management system in EV. In traditional FA, the AHG model is incorporated which enhances convergence speed rapidly. It increases decision-making and ensures optimal performance and allocation. The objective function is controlled to EV reachability with obtainable State of Charge (SoC) to the selected charging station by without irrelevant maximum allowable depth of discharge limit which enable for charging rate. The optimization approach allows the users for prioritizing the variable function depending on battery specifications in various analysis. The proposed AHG-FA achieves a minimum total time of 55.21 m compared to existing techniques such as AHG-Grey Wolf Optimization (GWO), AHG-Pelican Optimization Algorithm (POA), AHG-Bald Eagle Optimization (BEO) and AHG-Butterfly Optimization Algorithm (BOA).