The growing demand for Electric Vehicles (EVs) has highlighted the importance of efficient and accurate simulation tools for design and performance optimization. The architecture of electric vehicles is distinct from that of internal combustion engine vehicles. It consists of on-board charger, DC-DC converter, Lithium ion battery pack, Inverter, electric motor, controllers and transmission. The battery pack supplies electric current to the traction motor, which then converts this electrical energy into mechanical energy, resulting in the rotational motion needed to drive the vehicle. Wide range of Multiphysics is involved in the simulation which involves Power electronics, Electromagnetics, Fluid Mechanics, Thermal engineering.
This paper presents an integrated simulation and range prediction methodology for Electric Vehicles (EVs) using the Reduced Order Model (ROM) approach. The methodology includes simulation in both 3D and 1D domain. Computational fluid dynamics (CFD) simulation is performed to understand the thermal behavior of battery pack. The temperature distribution results of battery pack module is utilized to create a reduced order model (ROM) for all the similar modules thereby eliminating the repetition of CFD simulation. Thereafter, FEA based 2D electromagnetic simulation of a permanent magnet synchronous motor is performed in order to generate the performance map of traction motor over various range of speeds and State of charge (SOC). The consolidated results data is used to generate another Reduced Order Model (ROM) for traction motor which can be imported in the 1D model environment as a FMU model.
This ROM approach proposes a simplistic way of simulation based on data driven approach with better computational efficiency, accuracy, and real-time applicability. Both Reduced order models are imported in the 1D electric vehicle plant model. The 1D EV model is then simulated by using Modified Indian drive cycle (P1P2 Cycle) and the range predicted this model is validated with the ARAI test data.
Keywords: Li-ion battery, PMS motor, Computational fluid dynamics (CFD), Electromagnetics, temperature distribution, SOC, Range prediction, MIDC cycle