Intelligent trip-planning system for electric vehicles

Abstract

Abstract This thesis describes the development of an intelligent trip planner for battery elec tric vehicles that account for battery temperature and charging station availability. The primary goal is to minimize cost, time, and energy consumption for drivers. The trip planner uses historical data to predict charging station availability and incorporates stochastic and mathematical models to optimize charging and battery temperature management. The project is a proof of concept for the construction of a route planner. Delimitations were applied to ensure project manageability, includ ing data collection over a limited period, a predetermined driver profile, and three predefined routes for simulation. The algorithm combines vehicle dynamics, availability distribution, and battery tem perature management to calculate driving time and find the most optimal charging stations along the route. The predictive models for time to charge, energy consump tion, and battery temperature estimation were verified through tests and compared with existing data. The optimization algorithm successfully found the best route from Gothenburg to Uppsala, and its results were verified by comparing them with existing route planners. The study provides insights into the challenges and limitations of predicting energy loss during the charging process, highlighting the need for considering additional sources of energy loss in the model. Overall, the results demonstrate the potential for sing optimization algorithms to enhance the efficiency and convenience of battery electric vehicles.