Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques

Electric mobility is getting prominence in modern transportation as government policies aim to reduce greenhouse gas (GHG) emissions. In the context of real-time testing, numerical modelling and simulation of electric vehicle (EV) powertrains play a vital role in developing an efficient electric powertrain and charging infrastructure as it consumes less time and cost. Also, it enhances the overall performance by optimizing the size and configuration of the EV powertrain under different driving conditions. Thus, the review paper explores the different modelling approaches used for estimating the energy consumption (EC) and driving range (DR) initially. Further, the vehicle analytical model is discussed in detail with sub-models of powertrain components and vehicle dynamics, which have the mathematical correlation related to power losses and energy flow. Additionally, the necessity, development process, characterization and accuracy of localized driving cycles (DCs) and commonly used driver controller models for EVs are critically elaborated. Further, the impact of various influential input parameters such as vehicle parameters and driving conditions on EV performance characteristics is analyzed along with different improvisation methods utilized in the existing literature. From this extensive review, it can be concluded that simulation results by using an analytical vehicle model have good accuracy with chassis dynamometer-based testing and it can be used for optimizing the size and configuration of EV powertrain components under different scenarios. Finally, the present status and future research required in the field of EV powertrain development through modelling and simulation are summarized to extend the application of EVs in transportation sectors.