Influence of Auxiliary Loads on the Energy Consumption of Electric Vehicle – A Case Study

Abstract

Range anxiety and higher cost are the two important hurdles for electric vehicles (EV) in the automobile industry. Range anxiety depends on topological factors, driving styles, climatic conditions, traffic conditions and auxiliary power requirements. Though many researchers have extensively worked on various factors of Range Anxiety, an accurate residual range estimation algorithm has not yet been figured out. Machine Learning and Deep Learning based models have also come up to evaluate the residual range of EVs. Auxiliary loads are one of the prime factors for offering driving comfort to passengers. It is found that the lights, horns, power steering and media components contribute significantly to the energy storage loss. This paper presents a simulation-based analysis on the influence of auxiliary loads on the energy consumption and hence the range of an EV. The simulations are done on Advanced Vehicle Simulator (ADVISOR) software and the results are interpreted. The vehicle specifications correspond to a commercially available EV and two different drive cycles, namely the Urban Dynamometer Driving Schedule (UDDS) drive cycle and the NREL to VAIL drive cycle. Acceleration and gradeability tests were performed to understand the vehicle performance and the observations are tabulated.