Because of the limited range of electric vehicles, there is an increased importance of accurate range estimation. The range of an EV is determined by the state of charge (SoC), and the power consumption. Power consumption for a vehicle is a function of kinematic parameters, driving behavior, use of auxiliaries and many other parameters. This project will focus on developing innovative predictive power consumption algorithms for real-life range estimation of EVs. Using the large data pool of the Living Lab platforms, real-life data of 200 electric vehicles can be analyzed. From the data, driving cycles representative for real-life use of electric vehicles can be developed. Using the newly developed driving cycle and on-road tests, the influence of the driving behavior,auxiliaries and other external parameters on power consumption will be studied. From these correlations, an important innovation can be applied to the current vehicle simulation tools by the development of longitudinal power flow simulation tools with the influence of external parameters included. Integrating this algorithm with shortest-route algorithms, it will lead to an innovative predictive power consumption algorithm.Together with input from an (accurate) SoC measurement, this predictive power consumption algorithm will allow to estimate, if you will be able to reach a certain destination and what the alternatives are, energy-efficient wise. This real-life range estimator (RLRE) for electric vehicles addresses the range-anxiety problem and can introduce an intelligent system in the vehicle.
Effective start/end date1/01/1331/12/16

    Research areas

  • Electric Installations, Computational Electromagnetics, Numerical Electromagnetic Simulations, Lighting, Computational Electrochemistry, Electric Vehicles, Electrochemistry, Traction Batteries And Battery Chargers, Cathodic Protection

ID: 3511661