An improved route planner-simulator with battery performance considerations for Electric Vehicles

Abstract

This paper shows the results of a development project that aims to provide Electric Vehicle (EV) drivers the opportunity to know, before making a trip, how the battery will progress during the route. This means drivers will know if they have to stop to recharge the battery and where they should do it, ruling out the idea that makes people reject buying an EV because they can´t make long trips for fear of running out of power supply halfway. This Planner, besides showing the technical data of the route, will display on a road map the selected route from "A to B" with the possibility of one or more intermediate stops to recharge, simulating all the route with a vehicle icon moving along it and a battery image that discharge following the real progress of that vehicle with the conditions of each trip. The data obtained from the Internet, in this case from Google Maps, will be sent to several mathematical algorithms already developed in Matlab. With these data and those obtained from sensors of the car the progress of the State of Charge (SOC) of the battery will be calculated, getting the planner-simulator these data back to display it during the simulation.

A Model for Range Estimation and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

“…A number of solutions for energy-efficient routing of electric vehicles are proposed in literature. In [27], the platform for a route planner is presented which implements a standard routeplanner in combination with an EV vehicle model, which was presented in [28]. However, this platform assumes the speed profile is known and inserted into the vehicle model.…”

“…However, this platform assumes the speed profile is known and inserted into the vehicle model. No evaluation of that proposed model was presented in [27] or found in other publications of the same authors. In [29], an energy consumption estimation model was constructed using multiple linear regression based on historical data of average speed, accelerations, road gradient and future data of the driving speed assumed to be known.…”