Permanent magnet temperature estimation of high power density permanent magnet synchronous machines by considering magnetic saturation

Gao, Jian; Li, Chengxu; Zhang, Wenjuan; Huang, Shoudao

doi:10.1007/s43236-021-00314-9

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Gao et al developed an efficient method to estimate the temperature of the PM in high power density PMSM by considering magnetic saturation. Compared with the conventional method, this method adds saturation coefficients for analysis, which greatly improves the accuracy of the proposed method, and the validity of the revised method is verified by using the magneto-thermal coupled finite element model [7]. Wan et al designed a new motor with heat tube for new Energy Vehicles, aiming at reducing the end winding temperature, and simulated its temperature field by using FLUENT software, the innovative design shows that the heat pipe can transfer the heat from windings to the end cover, thus reducing the temperature of the motor and ensuring the operating efficiency [8].…”

Section: Introductionmentioning

confidence: 99%

Temperature rise characteristics of permanent magnet synchronous motor considering boiling heat transfer

He,

Feng,

Zhang

et al. 2023

Electr Eng

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The temperature rise characteristics of PMSM (permanent magnet synchronous motor) during operation were studied under the condition of BHT (boiling heat transfer) in cooling water. The subcooling boiling heat transfer model, RPI (Rensselaer polytechnic institute), was used to calculate and improve the computational accuracy of simulation results. The experiments have shown that, when the motor was tested and simulated after preheating, the temperature rise characteristics were similar to test data because the boiling heat transfer phenomenon of the motor was taken into account, with an error of 1.7 %, which was more accurate than the error of 7.5 % without boiling. Therefore, from the results obtained from the experiments, the phenomenon of boiling heat transfer in cooling system has a significant effect on the temperature rise characteristic of the motor, which should be regarded as an important factor. Due to the influence of two-phase flow boiling heat transfer, the temperature rise of the motor in the plain areas is 4-5 ℃ higher than that in the high altitude areas, Consequently, if the motor is operated in high altitude areas, its temperature rise characteristic has better effect compared with plain area. Additionally, when BHT in cooling water is considered, the temperature of the motor drops as the rate of flow increases. In the meantime, the temperature difference between the minimum flow rate and the maximum flow rate is about 1 ℃, which means that the increase of rate of flow can enhance the boiling heat transfer effect. KeywordsPermanent magnet • Synchronous motor • Temperature field • Boiling heat transfer • Two-phase flow • Plateau environment

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Section: Introductionmentioning

confidence: 99%

Temperature rise characteristics of permanent magnet synchronous motor considering boiling heat transfer

He,

Feng,

Zhang

et al. 2023

Electr Eng

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“…Then the basic parameters of the model were determined by the optimization of the particle swarm algorithm. [ 17 ] The remaining capacity can also be precisely estimated by RVM, which is based on the health indicators extracted from charging health data, and various operating conditions have been verified. [ 18 ] Under the circumstances of the rapid development of machine learning algorithms, data‐driven prediction methods show better prognosis performance.…”

Section: Introductionmentioning

confidence: 99%

“…[13] The multiple Gaussian process regression (GPR) models which were based on three indirect health indicators (HIs) were raised for RUL prediction of batteries. [14] The typical machine learning (ML) methods include neural network (NN), [15] support vector machine (SVM), [16,17] relevant vector machine (RVM), [18,19] etc. A feedforward neural network (FFNN) with battery terminal voltage as input was applied to implement the battery RUL prediction.…”

mentioning

confidence: 99%

Prediction of Lithium‐Ion Battery Remaining Useful Life via Empirical Mode Decomposition‐ Autoregressive Integrated Moving Average and Regularized Particle Filter Algorithm

Cai

Zheng

et al. 2023

Energy Tech

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This article focuses on improving the prediction accuracy of lithium‐ion battery's remaining useful life (RUL) by combining empirical mode decomposition (EMD), autoregressive integrated moving average (ARIMA), and regularized particle filter (RPF). First, to obtain detailed information about the battery capacity degradation, the monitored capacity data are decoupled by EMD. Second, the long‐term predicted model is constructed by ARIMA for the decoupled components. Finally, the long‐term prediction results are utilized as the measurement equation of the RPF prediction framework. In the proposed novel hybrid framework combining EMD‐ARIMA and RPF, the capacity prediction values are corrected and updated during the iteration of the regularized particle filter. With the estimated capacity data of every cycle, it can be detected whether the batteries reach their service life threshold. To validate the performance of the aforementioned method, the comparative experiments which are based on the NASA Prognostic Center of Excellence battery data sets are performed. According to the analysis results, higher prediction accuracy has been obtained with the proposed method in the lithium‐ion battery RUL, compared with the other three methods.

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Junction Temperature Estimation of Direct-Drive GaN HEMTs in Two-Level Inverters for Driving PMSM Through Power Loss Analysis

Lee,

Park,

Cho

et al. 2023

J. Electr. Eng. Technol.

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Permanent magnet temperature estimation of high power density permanent magnet synchronous machines by considering magnetic saturation

Cited by 6 publications

References 17 publications

Temperature rise characteristics of permanent magnet synchronous motor considering boiling heat transfer

Temperature rise characteristics of permanent magnet synchronous motor considering boiling heat transfer

Prediction of Lithium‐Ion Battery Remaining Useful Life via Empirical Mode Decomposition‐ Autoregressive Integrated Moving Average and Regularized Particle Filter Algorithm

Junction Temperature Estimation of Direct-Drive GaN HEMTs in Two-Level Inverters for Driving PMSM Through Power Loss Analysis

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