A High-Fidelity and Computationally Efficient Electrothermally Coupled Model for Interior Permanent-Magnet Machines in Electric Vehicle Traction Applications

Accurate temperature predictions for permanentmagnet machines are essential to prevent irreversible demagnetization and undesirable reduction in lifetime. This paper proposes a high-fidelity and computationally efficient electrothermally coupled model for interior permanent-magnet machines (IPMs) in electric vehicle (EV) traction applications. First, a high-fidelity IPM model accounting magnetic saturation, spatial harmonics, iron loss, and temperature effects is presented. The temperature effects on both the dand q-axis flux-linkages and the torque in the proposed model are quantified. The IPM model with due account of temperature effect is integrated with a state-space lumped parameter thermal model to establish a high-fidelity and computationally efficient electrothermally coupled model for IPMs. Both the steady-state and driving cycle operations are simulated with the proposed model and the results are compared to those predicted by the machine model without considering temperature effect as well as by the machine model which only accounts for the temperature effect on the winding resistance. Considerable temperature differences between those predicted by the proposed model and those predicted by the latter two models are observed. Experimental validation of the proposed model is performed with a 10-kW IPM prototype machine operating in generating mode.