EV Charging Infrastructure: Predict, Plan, Deploy

Abstract

The rapid proliferation of electric vehicles (EVs) worldwide has created an unprecedented challenge in charging infrastructure deployment. While EV adoption continues to accelerate exponentially, the strategic placement and capacity planning of charging stations remain largely based on traditional approaches that fail to capture dynamic demand patterns and user behavior. This creates significant inefficiencies including underutilized stations in some areas while others experience persistent overcrowding, ultimately hindering widespread EV adoption.

This project presents a comprehensive predictive analytics framework that leverages advanced machine learning algorithms to forecast EV charging demand patterns and optimize charging infrastructure placement. The system integrates multiple data sources including historical EV sales data, charging station usage logs, traffic patterns, demographic information, and real-time grid conditions to provide accurate demand predictions and intelligent location recommendations. The framework employs Long Short-Term Memory (LSTM) networks for temporal forecasting, Random Forest algorithms for feature importance analysis, and clustering techniques for optimal station placement.

Experimental results demonstrate that the proposed system achieves 91.3% accuracy in predicting charging demand patterns with a Mean Absolute Error (MAE) of 2.1 sessions per hour. The location optimization algorithm successfully identifies high-demand zones with 89.7% precision, leading to a 34% improvement in charging station utilization rates. The solution addresses critical challenges in EV infrastructure planning while supporting sustainable transportation adoption and grid stability.