Conceptual Framework and Performance Forecasting of a Terrain-Adaptive Plug-and-Play Range Optimization System for Electric Four-Wheelers

This research presents a conceptual framework and predictive simulation for a terrain-adaptive, plug-and-playenergy optimization module designed for electric four-wheelers. Building upon proven success in two-wheeler EVs, this paperextends the architecture to heavier, multi-axle platforms. By integrating AI-based terrain analysis, driving pattern recognition,and intelligent power routing, the proposed system aims to deliver up to 22–28% improvement in driving range withoutmodifying core drivetrain components. Simulations based on real urban and semi-urban Indian driving data validateperformance forecasts. The system is particularly relevant to passenger EVs, fleet operators, and last-mile delivery vehicles ininfrastructure-limited markets.