Vehicle electric motor temperature estimation using neural network model

Patent metadata

Field	Value
Publication number	US-12091027-B2
Application number	US-202117376990-A
Country	US
Kind code	B2
Filing date	Jul 15, 2021
Priority date	Jul 15, 2021
Publication date	Sep 17, 2024
Grant date	Sep 17, 2024

Abstract

A temperature estimation system and method for an electric motor of a vehicle include a set of sensors configured to measure a set of operating parameters of the electric motor including at least (i) phase current, (ii) speed, and (iii) coolant temperature and a controller configured to access a trained artificial neural network (ANN) temperature estimation model, using the trained ANN temperature estimation model with the set of electric motor operating parameters as inputs, estimate temperatures of a stator and a rotor of the electric motor, and control operation of the electric motor based on the estimated stator and rotor temperatures.

First claim

What is claimed is:

1. A temperature estimation system for an electric motor of a vehicle, the system comprising:
a set of sensors configured to measure a set of operating parameters of the electric motor including at least (i) phase current, (ii) speed, and (iii) coolant temperature, wherein the vehicle does not include a temperature sensor permanently associated with a stator of the electric motor or a rotor of the electric motor, or that remains implemented on the vehicle after a temporary calibration period; and
a controller configured to:
access a trained artificial neural network (ANN) temperature estimation model, wherein the trained ANN temperature estimation model is trained across all operating regions of the electric motor and based on temperature measurements from a first temperature sensor temporarily mounted on the stator and a second temperature sensor temporarily directed at the rotor during the temporary calibration period,
using only the trained ANN temperature estimation model with the set of operating parameters of the electric motor as inputs, estimate temperatures of the stator and the rotor of the electric motor, and
control operation of the electric motor based on the estimated temperatures of the stator and the rotor,

wherein the controller does not utilize empirical look-up tables for resistance-based estimation of the temperatures of the stator and the rotor.

2. The system of claim 1 , wherein the trained ANN temperature estimation model is a recurrent-type ANN that also uses the estimated temperatures of the stator and the rotor as inputs.

3. The system of claim 2 , wherein two of the inputs provided to the trained ANN temperature estimation model include the estimated temperatures of the stator and the rotor delayed by first and second delays, respectively.

4. The system of claim 3 , wherein the first delay and the second delay are approximately 100 milliseconds and 200 milliseconds, respectively.

5. The system of claim 1 , wherein the first temperature sensor is a thermocouple and the second temperature sensor is an infrared temperature sensor.

6. The system of claim 1 , wherein the set of operating parameters of the electric motor consists of (i) the phase current of the electric motor, (ii) the speed of the electric motor, and (iii) the coolant temperature of the electric motor.

7. A temperature estimation method for an electric motor of a vehicle, the method comprising:
measuring, by a set of sensors, a set of operating parameters of the electric motor including at least (i) phase current, (ii) speed, and (iii) coolant temperature, wherein the vehicle does not include a temperature sensor permanently associated with a stator of the electric motor or a rotor of the electric motor, or that remains implemented on the vehicle after a temporary calibration period;
accessing, by a controller of the vehicle, a trained artificial neural network (ANN) temperature estimation model, wherein the trained ANN temperature estimation model is trained across all operating regions of the electric motor and based on temperature measurements from a first temperature sensor temporarily mounted on the stator and a second temperature sensor temporarily directed at the rotor during the temporary calibration period;
using only the trained ANN temperature estimation model with the set of operating parameters of the electric motor as inputs, estimating, by the controller, temperatures of the stator and the rotor of the electric motor; and
controlling, by the controller, operation of the electric motor based on the estimated temperatures of the stator and the rotor,
wherein the controller does not utilize empirical look-up tables for resistance-based estimating of the temperatures of the stator and the rotor.

8. The method of claim 7 , wherein the trained ANN temperature estimation model is a recurrent-type ANN that also uses the estimated temperatures of the stator and the rotor as inputs.

9. The method of claim 8 , wherein two of the inputs provided to the trained ANN temperature estimation model include the estimated temperatures of the stator and the rotor delayed by first and second delays, respectively.

10. The method of claim 9 , wherein the first delay and the second delay are approximately 100 milliseconds and 200 milliseconds, respectively.

11. The method of claim 7 , wherein the first temperature sensor is a thermocouple and the second temperature sensor is an infrared temperature sensor.

12. The method of claim 7 , wherein the set of operating parameters of the electric motor consists of (i) the phase current of the electric motor, (ii) the speed of the electric motor, and (iii) the coolant temperature of the electric motor.

Assignees

• Luedtke Daniel R
• Mohamadian Mustafa
• Fca Us Llc

Inventors

• Luedtke Daniel R
• Mohamadian Mustafa

Classifications

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• B60L58/16

  responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH] · CPC title

• G06N3/02

  Neural networks · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• B60L2260/46

  by self learning · CPC title