Comparison of Simulation- and Regression-Based Approaches to Estimating Electric Car Power Consumption - Emil Nagy; Árpád Török (Applied Sciences)
The main objective of this paper is to present a methodology for the reliable estimation of the energy consumption of electric vehicles, focusing on the main electrical subsystems of passenger cars. This paper presents a comparative analysis of the available regression models and the results of our simulation experiments. While numerous regression models have been documented in the literature, their accuracy is not always satisfactory. Consequently, there is a need to develop a sufficiently accurate and comprehensive generalized simulation framework, which is presented in the paper. Currently, most of the major vehicle manufacturers have developed pure electric vehicle platforms and are using them in the production of many models available on the market. The estimation of consumption data for these vehicles is still based on traditional techniques, namely, prediction from historical operation data. To overcome this problem, in this article, we have constructed a multi-element, model-based simulation for the purpose of implementing an energy consumption monitoring system. In order to create a simulation that reflects real-life vehicle behavior, the input data are based on empirical measurements, while the simulation model is based on actual electric vehicle parameters. In the main simulation model, it is possible to simulate the energy consumption of the vehicle’s drive system and to extract the requisite input data for the simulation of the other vehicle subsystems. In regard to the simulation, the subsystems that have been incorporated are the electric vehicle steering system, the vehicle lighting system and the HVAC system. After running the simulation, the total system consumption for a given trip segment is obtained by running each vehicle subsystem simulation. The findings were validated with real data and compared with two relevant regression models. Our preliminary expectation is that, given the level of detail of our simulation, the developed model can be considered validated if the error of the estimate remains below 4% and if the simulation model in question yields superior results in comparison to other regression models.