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Optimized Charging Management for Electric Vehicles Using Neural Networks Considering Fast Charging and Vehicle-To-Grid Operations
Conference proceeding

Optimized Charging Management for Electric Vehicles Using Neural Networks Considering Fast Charging and Vehicle-To-Grid Operations

A. V. V. Sudhakar, Rajesh G, Sandeep. C S, Aswani K, Sivaganesan Sivanantham and Anvesh Perada
2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT), pp 1-6
05 Sep 2025
DOI: https://doi.org/10.1109/ICICNCT66124.2025.11233030

Abstract

Accuracy Computational modeling convolutional neural network (CNN) Convolutional neural networks Costs Data models Electric vehicle charging electric vehicles (EV) hybrid energy storage system (HESS) internal combustion (IC) Load modeling Numerical models sparse principal component analysis (SPCA) Strain Batteries
The emergence of electric cars as a cleaner and more efficient substitute for ICE vehicles has raised hopes for a lessening of pollution and strain on the power system. There are a number of obstacles that need to be overcome before electric vehicles can be widely adopted. These include higher initial costs, longer charging times, shorter driving ranges caused by restricted battery capacity, and the added expense of increasing battery capacity to alleviate range anxiety. Market penetration is slowed down by these problems. A data-driven solution was created utilising powerful machine learning techniques to address these limitations and improve Electric Vehicles Charging Management. Prior to feature selection using SPCA, the dataset was subjected to preparation procedures like data cleaning and numerical feature normalisation. The training model used real-world charging data from Temixco and is a two-layer CNN-LSTM. By surpassing previous models, the suggested model achieved an outstanding accuracy rate of 98.56 % when tested on two popular benchmarks. These findings demonstrate the promise of the CNN-LSTM architecture for improved management of electric vehicle charging. Scalable electric vehicle integration is supported by this model, which optimizes charging procedures, allowing for smarter and more sustainable energy management and assisting in the overcoming of important adoption hurdles.

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