Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines

Kirchgässner, Wilhelm; Wallscheid, Oliver; Böcker, Joachim

doi:10.1109/isie.2019.8781195

Cited by 30 publications

(19 citation statements)

References 15 publications

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“…Both models also provide better results than the signal injection method presented in [23], and additionally, it does not require interference with the motor power system. The proposed sensorless model (model 1) gives similar results to the linear models presented in [34], but it does not require any additional temperature sensors if the ambient temperature does not significantly affect the motor temperature. However, it is important to note that the comparison of the results with those available in the literature is indicative, because different engine types and models were tested among the researchers.…”

Section: Discussionmentioning

confidence: 79%

“…Many of the articles on PMSM temperature prediction using machine learning available in the literature use the motor coolant temperature as an input variable of the algorithm [ 9 , 29 , 30 , 31 ]. Moreover, the authors in [ 34 ] emphasize that the stator temperature is strongly correlated with the exponentially weighted moving average of the PMSM motor coolant temperature, and removing this variable from the feature vector results in a significant decrease in the effectiveness of the prediction algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Czerwiński

Gęca

Kolano

2021

Sensors

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In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Czerwiński

Gęca

Kolano

2021

Sensors

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“…Moreover, learning curves for varying training set sizes and input feature attribution by means of expected gradients to the eventual target temperature estimates are presented. Subject of all investigations is the data set 1 from [1], [10]. In the following, we will briefly report on the utilized machine learning pipeline including basics of the investigated neural network topologies.…”

Section: )mentioning

confidence: 99%

“…where w i = (1 − α) i with α = 2/(s + 1) and s being an arbitrary span. Multiple, differently spanned EWMAs and EWMS' lead to various smoothed versions of all time-series and their standard deviation, which denote strong linear regressors [10]. Only four possible values for the span in EWMA and EWMS calculation are simultaneously explored in order to limit memory demand.…”

Section: A Data Preprocessing and Feature Engineeringmentioning

confidence: 99%

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Kirchgässner

Wallscheid

Böcker

2021

IEEE Trans. Power Electron.

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Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this work, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors (PMSMs). Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multi-head architecture, lean models are presented that exhibit strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented.

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“…• Static ML Models: Those cannot map any system dynamics on their own and, therefore, require special feature engineering or post-processing to capture the heat transfer processes (cf. [116], [117]). The reported temperature estimation accuracies are promising and motivate further research in that field.…”

Section: Available Publications and Interim Conclusionmentioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

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Temperature sensing in electric motors is an important task to ensure component protection against excessive heat while maximizing the power and torque capabilities. In order to optimize the dynamic performance limits of a motor during online operation, important motor temperatures must be known in real time. Since temperature measurements are associated with costs and integration efforts, model-based estimation methods became highly relevant. In recent years, many promising contributions have been made to this field leading to a vast literature basis. This paper organizes the literary landscape and gives a bird's-eye overview of the three most important estimation classes: Indirect methods, which track temperature-sensitive electrical motor parameters, and direct methods, namely lumped-parameter thermal networks as well as supervised machine learning. Practical limitations as well as special challenges of the respective methods are emphasized throughout the text. The aim of the paper is therefore to facilitate the entry for interested researchers and engineers in this field as well as to stimulate new research impulses. Hence, open problems as well as future research questions are addressed at the end of this review.

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Empirical Evaluation of Exponentially Weighted Moving Averages for Simple Linear Thermal Modeling of Permanent Magnet Synchronous Machines

Cited by 30 publications

References 15 publications

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

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