Efficient Estimator of Rotor Temperature Designing for Electric and Hybrid Powertrain Platform

Abstract

<jats:p>This paper presents an efficient method of estimation of rotor cage temperature for induction machine design, applied for electric and hybrid vehicles. This factor influences the torque produced by the induction machine with a field-oriented control algorithm. Equipping sensors to measure the temperature of a rotation component is expensive and is not representative of mass production. The approach of estimation of rotor cage temperature is based on the good knowledge of motor parameters and the estimation of the flux of the machine. For an accuracy inductance taking account of the saturation, the no-load test can be performed. The machine flux will be estimated taking account of the voltage drop of the system on the test-bench. The rapid prototyping in a real-time motor control platform will be presented that integrates this estimator of rotor temperature. We finally show the experimental testing results compared to the measurement of the rotor cage on a prototype asynchronous low-cost motor designing for battery electric city cars.</jats:p>

Induction Machine On-Line Parameter Identification for Resource-Constrained Microcontrollers Based on Steady-State Voltage Model

“…This set of parameters can vary based on the complexity of the mathematical model used, the purpose of the model, or the phenomena of the machine that are being studied. One of the most common sets of parameters representing an induction machine model consists of the following five parameters: stator resistance R s , rotor resistance R r , stator leakage inductance L σs , rotor leakage inductance L σr , and magnetizing inductance L m [1,5,[10][11][12].…”

“…Different control algorithms stress the importance of different parameters [5,15,18]. For the rotor-flux-oriented indirect field-oriented control (IFOC), which is among the most widespread control strategies, rotor resistance and magnetizing inductance can be considered the most crucial ones [5,12,21,22]. The main idea behind IFOC is to control the induction machine similarly to a DC machine with a separate control of the so-called torque and flux component of the induction machine stator current.…”

“…The controllers in the control structure try to correct these errors, but if, for example, the controller expects a lower flux component than the real one, it can lead to an unwanted oversaturation of the magnetic circuit. With an error in the torque component, it can take longer for the controller to adjust the torque to the desired value, thus reducing the acceleration of the drive [5,12]. Tracking the machine parameter changes is thus beneficial even in the case of drives with control systems with limited computational resources.…”

“…The combination of a steady-state voltage model and information from a mechanical speed sensor was used in [12]. Stator voltages and currents, stator frequency, and mechanical frequency measured with a speed sensor were used to calculate rotor resistance to obtain the temperature change of the rotor of the induction machine in a battery electric vehicle drive.…”

A Combined Model and Data-Driven Approach for the Determination of Rotor Temperature in an Induction Machine

“…Contrary to invasive methods, optimal or sub-optimal filters can be used to estimate the temperature of electrical machines considering the dependence between the magnetic flux and rotor temperature. These approaches require prior knowledge of the electrical subsystem and often assume Gaussian uncertainties [ 13 , 14 ]. A method is described for estimating the temperature of the magnet of a PMSM using the frame model within a Kalman filter.…”