Artificial Neural Network-Driven Adaptive Control for Multi-Port EV Fast Charging Stations

Paper ID: EIJTEM_2025_12_4_174-184

Author's Name: Parlapally Chaithanya Raj, Dr. K. Srinivas

Volume: 12

Issue: 4

Year: 2025

Page No: 174-184

Abstract:

There is a critical need for intelligent, efficient, and quick charging solutions due to the rising popularity of electric vehicles (EVs). An Adaptive Energy Management Technique (AEMT) developed for Multi-Port Fast EV Charging systems using Artificial Neural Networks (ANN) is presented in this paper. Efficient power distribution, shorter wait times, and improved energy efficiency are the goals of this technology, which uses intelligence to spread electricity across several electric vehicles linked to a single charging station. Data such as past charging habits, grid constraints, electric vehicle battery profiles, and user habits are used to train the ANN model. It enables dynamic decision-making by responding in real-time to variations in load and energy availability. Results from simulations show that when compared to traditional rule-based or fixed scheduling approaches, the ANN-driven AEMT improves energy optimization, reduces peak demand stress, and gives a better user experience.

Keywords: Electric Vehicles (EVs), Multi-Port Fast Charger, Adaptive Energy Management Technique (AEMT), Artificial Neural Network (ANN), Dynamic Power Allocation, Energy Efficiency, Load Balancing, Peak Load Reduction, Real-Time Control, Smart Charging Infrastruct

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