Intelligent Trip Planning for Battery Electric Vehicles Using Real-Time Map and Elevation Data in MATLAB-Simulink

Abstract

The increasing tendency of electric mobility needs proper modelling and prediction of the performance of a vehicle in real-world situations. The traditional drive cycles, NEDC and WLTP, are not likely to capture the local driving conditions, road slopes and weather conditions. This work overcomes this shortcoming by creating an intelligent drive cycle generation and simulation system in MATLAB and Simulink, with Google Maps, Elevation, and Weather APIs. The system automatically determines a route-based cycle in a drive between any two points by deriving geographic coordinates, elevation profiles, and weather conditions like wind speed. The computation of these data is done using the Haversine formula and interpolation to create a uniform data cycle of one second as a time-based drive. It is then connected to a MATLAB-Simulink model of the Tata Nexon Electric Vehicle (EV) that has subsystems of vehicle dynamics, battery modelling, and motor drive control. The results of the simulation contain vehicle speed, slope change, power demand, and battery state of charge (SOC) under the various modes of driving, which are Eco, Normal, and Sport. The findings indicate that the suggested system is able to measure the effects of elevation and weather on vehicle performance and energy consumption. The combination of these allows the realistic and location-sensitive EV simulations, which can be used in energy prediction, route planning, and sustainable transportation design. The model developed may be expanded with reinforcement learning to select optimal routes and with real-time IoT data to control adjustments.