A Review On Machine Learning Models For Estimating Ev Charging Demand

Abstract

Machine Learning And Data Analytics Are Extensively Used For Optimizing The Performance Of Electric Vehicles. As Electric Vehicles (EVs) Become More Prevalent Globally, The Need For Efficient Infrastructure Planning And Energy Management Grows. One Critical Aspect Of This Transition Is Accurately Estimating EV Charging Demand, Which Is Vital For Grid Stability, Charging Station Placement, And Energy Cost Optimization. Traditional Forecasting Methods Often Fall Short Due To The Complex And Dynamic Nature Of EV Usage Patterns. Machine Learning (ML) Offers A Promising Solution By Leveraging Historical And Real-time Data To Make Accurate And Adaptive Demand Predictions. This Paper Presents A Comprehensive Review On Multiple Machine Learning Models Which Are Extensively Used For Estimating EV Charging Demand. The Analysis Of The Benchmark Models Would Allow Researchers In Developing An Optimized Data Driven Model With The Objective Of Attaining High Accuracy.

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