Abstract
Since it can be difficult to directly estimate the temperature of permanent magnets (PM) in high‐density permanent magnet motor rotors, this paper proposes a method based on lumped parameter thermal networks (LPTN) for online estimation of PM temperature. Firstly, a parameter model is established that contains a four‐node thermal network model and a loss model. For the online estimation, an extended Kalman filter algorithm with fading factor is used to dynamically update the thermal network parameter model in order to estimate the PM temperature. The simulation and experimental results show that the parameter model can quickly converge to a value that is close to the true value. Therefore, this online estimation method for PM temperature can accurately monitor the PM temperature online without increasing the required motor hardware; thus not affecting the motor operation. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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