Simulation and Analysis of Factors Influencing the Residual Range of Electric Vehicle

Electric Vehicles (EVs) have emerged as a green alternative in the automobile industry. In spite of having many advantages, their limited cruising range, long recharging times and the inadequate recharging infrastructure have caused lesser popularity in the automotive sector. Range anxiety indicates the concern of the vehicle driver that the EV battery is getting exhausted. Due to range anxiety problem existing with EVs, there is a need for precise route planning so that the recharging, if needed, is ensured and a possible battery drain out in between the recharging is avoided. The range of an EV depends on numerous factors including vehicle parameters, climatic conditions and terrain related factors. Range estimation techniques through machine learning algorithms and neural networks are currently being researched. This paper presents a study of the various factors that contribute to the range estimation of a commercially available EV. Simulations are done in Advisor (Advanced Vehicle Simulator) software, analyzed and interpreted.