Method and device for on-board temperature estimation of an electrical machine rotor

A method using a reduced thermal model and Kalman filter for rotor temperature estimation in electrical machines addresses the complexity of existing methods, providing accurate and robust temperature monitoring in motor vehicles.

FR3118840B1Active Publication Date: 2026-05-22RENAULT SA +1
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Patent Information

Authority / Receiving Office
FR · FR
Patent Type
Patents
Current Assignee / Owner
RENAULT SA
Filing Date
2021-01-11
Publication Date
2026-05-22

AI Technical Summary

Technical Problem

Existing methods for estimating the temperature of rotating parts of an electrical machine, such as the rotor, are complex and unsuitable for use in motor vehicles, and accurate real-time monitoring is difficult due to the challenge of placing sensors in these parts.

Method used

A method involving a complete thermal model reduction and a state observer, utilizing a Kalman filter, to estimate rotor temperature in an electrical machine, which includes defining a thermal model, reducing it for embedded systems, and using data acquisition and correction modules for precise estimation.

Benefits of technology

Enables accurate rotor temperature estimation suitable for motor vehicles, with reduced computational resources, suitable for all types of electrical machines, and robust against parametric variations, ensuring reliable estimation in both steady and transient regimes.

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Abstract

A method (27) for estimating the temperature of a rotor of an electric machine, comprising a first set of steps, intended to be implemented on a first computing device, comprising reducing a complete thermal model (20) so as to simplify its solution (28), and a second set of steps, intended to be implemented on a second device, comprising a step of providing a state observer (29) defined as a function of said reduced thermal model, and calculating the temperature estimate of the rotor as a function of the state observer, the reduced thermal model, and the acquired data. Figure to be published with the abstract: Fig. 1
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