Abstract
The need for protection of electrical machines comes as a demand of safety regulations in the automotive industry as well as a result of the general desire to obtain a robust and reliable electric powertrain. This paper introduces a hybrid method for estimating the temperature of the rotor of an Induction Machine (IM) based on a Nonlinear Autoregressive Network with Exogenous inputs (NARX) used as a prediction function within a particle filter. The temperature of the stator case is measured, and the information is used as an input to a NARX network and as a variable to a thermal process with first-order dynamics which serves as an observation function. Uncertainties of the NARX and thermal model are determined and used to correct the posterior estimate. Experimental data are used from a real IM test-bench and the results prove the applicability and good performance.
Highlights
Determination of Rotor TemperatureThe topic discussed in this paper is contextualized in the field of electric machine protection
The posterior estimate is obtained after merging with the particle filter the two information channels: the NARX and the thermal model
The Belt-Driven Booster (BDB) consists of a three-phase Induction Machine (IM) with an integrated inverter and control unit which is capable of offering a continuous power of 6KW and a torque of 60NM on a 48 V
Summary
The topic discussed in this paper is contextualized in the field of electric machine protection. 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. The method proposed in this paper has the advantage of being simple despite the complicated-look of the particle filter, it is straightforward and provides good results It is decoupled and can be modularized, allowing an update or change to the prediction and observation model in a ‘plug and play’ manner. The posterior estimate is obtained after merging with the particle filter the two information channels: the NARX and the thermal model This corrects the prediction of the neural network in the prediction stage.
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