Electric vehicle model parameter estimation with combined least squares and gradient descent method

Abstract

Energy management algorithms have a crucial role in electric vehicles due to their limited driving range. For an energy management algorithm to be effective, we should model the vehicle as accurately as possible. That is, not only the structure of the model should be accurate, but also the parameters of the model should be accurate. In this work, we take the model of an electric vehicle and tune three parameters in it based on trip data, namely, vehicle mass, air drag coefficient, and rolling resistance coefficient. We do this by using Least Squares method to set the initial guess and then by optimizing the parameters using Gradient Descent. To the best of our knowledge, this is the first work that simultaneously estimates these three parameters. Our work is also unique in the sense that it combines Least Squares and Gradient Descent.