Abstract:

In order to adapt to the high power density of automotive high-speed motors and the high thermal load under extreme working conditions， the current motor cooling mostly adopts the direct contact oil cooling heat dissipation method， and it is necessary to establish a motor oil temperature model suitable for the study of thermal control methods. Existing methods are mainly based on finite element simulation calibration， which cannot meet the real-time application requirements， while the multi-physical field coupling of the complex oil-water heat transfer circuit makes it difficult for the online reconstruction of oil temperature. In this paper， a second-order lumped-parameter oil temperature model is proposed to strengthen the time-sequence cyclic process and consider the strong autocorrelation. The oil circuit unit is modeled according to the calibration， and the motor loss response is determined based on bench-top measurements. The time-sequence convolution method is adopted to describe the heat transfer process， and a cyclic dynamic recursive model with high and low oil temperature coupling is established. Oil temperature-sensitive parameters are introduced to improve the adaptability of the working conditions to solve the difficult problem of describing the oil temperature distribution in the flow path. Finally， the model accuracy is verified online by road spectrum working conditions， with the average absolute estimation error of the oil coolant temperature within 1°C， which can support the refined thermal management of the motor.

Key words: oil-cooled motor, temperature estimation, electric vehicles, parameter identification, automotive high-speed motor