Lumped Parameter Thermal Network Modeling for Online Temperature Prediction of Permanent Magnet Synchronous Motor for Different Drive Cycles in Electric Vehicle Applications 2020-01-0455
Electric vehicle is increasingly becoming popular and an alternative choice for the consumers because of its environment-friendly operation. Permanent magnet synchronous machines are widely and commonly used as traction motors since they provide higher torque and power density. High torque and power density mean higher current which eventually causes higher temperature rise in the motor. Higher temperature rise directly affects the motor output. Standard tests for UDDS (Urban Dynamometer Driving Schedule) and HWFET (Highway Fuel Economy Driving Schedule) drive cycles are used to determine performance of traction motors in terms of torque, power, efficiency and thermal health. Traction motors require high torque at low speed for starting and climbing; high power at high speed for cruising; wide speed range; a fast torque response; high efficiency over wide torque and speed ranges and high reliability. For both UDDS and HWFET driving conditions, it is essential to monitor the performance of the motor and predict the temperature of stator winding and magnet in order to maintain required torque and power generation. This paper proposes a simplified Lumped Parameter Thermal Network (LPTN) model which is an efficient, fast and reliable tool to determine thermal characterization of the motor. The integrated model takes input of motor operating parameters dynamically and regulate the required cooling to keep the operating temperature within the safe limit. An interior permanent magnet synchronous motor prototype has been used for the experimental validation of the proposed thermal model.
Citation: Towhidi, M., Ahmed, F., Mukundan, S., Li, Z. et al., "Lumped Parameter Thermal Network Modeling for Online Temperature Prediction of Permanent Magnet Synchronous Motor for Different Drive Cycles in Electric Vehicle Applications," SAE Technical Paper 2020-01-0455, 2020, https://doi.org/10.4271/2020-01-0455. Download Citation
Muhammad Towhidi, Firoz Ahmed, Shruthi Mukundan, Ze Li, Narayan C. Kar