Realistic Electric Motor Modelling for Electric Vehicle Performance Prediction 2021-26-0152
Costlier engine exhaust gas treatment systems as a result of stringent emission norms and increasing awareness about industrial effects on climate have pushed the automotive industry around the globe to shift its focus from fossil fuel driven vehicles to electrically powered ones. While Battery Electric Vehicles (BEVs) have some problematic issues such as lower range, lesser energy density and higher cost owing to not fully mature battery technology, they do provide some benefits such as lower carbon footprint and simpler transmission systems. The torque and power characteristics vary greatly between IC engines and electric motors. The longitudinal dynamics of a vehicle depends greatly on the nature of its powertrain. As a result, new challenges have emerged for simulation engineers who were until very recently accustomed only to IC engine driven vehicles. Now the demand within the industry is to come up with accurate models of the electric powertrain to replicate the behavior of real-world electric vehicles. Currently, very few Multi-Body Dynamics (MBD) software provide standard methods and templates for modelling of electric powertrain. This work aims to define a process for modelling an accurate electric powertrain and integrating it with a vehicle model in an MBD software, thereby studying the effects of electric powertrain on vehicle dynamics. Here, the key area of interest is the study of acceleration performance of the BEV. Two separate models were built in two different software tools. Modelling of the electric powertrain system consisting of a motor and single gear transmission was performed using MSC ADAMS and an electric vehicle was modelled using CarSim. Extensive electrical architecture was used to create the models. Acceleration tests were carried out and the result obtained from both approaches was then correlated with data obtained from test data of actual vehicle and the accuracy of the results obtained was found to be good.