PREDICTIVE MODELING FRAMEWORK FOR ENERGY ECONOMY OF ELECTRIC TRANSIT BUSES

Abstract

This research develops a predictive modeling system for assessing and improving the energy efficiency of electric cars under a variety of operating and environmental conditions. This research develops a data-driven model for energy consumption and efficiency forecasts by utilizing real-time operational data such as route parameters, passenger load, traffic patterns, driving behavior, battery performance, and weather conditions. In order to determine the main factors impacting battery discharge rates and variations in range, we use sophisticated statistical approaches and machine learning algorithms to examine energy consumption trends. Public transportation that is good for the environment and the bottom line may be more easily developed with the help of this platform, which allows scenario-based simulations to look at charging schemes, route design, and fleet deployment methods. By improving the accuracy of energy consumption projections, the proposed methodology enhances operating efficiency, decreases lifecycle costs, and facilitates strategic decision-making for urban electric transportation systems.