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96 results on '"Aarniovuori, Lassi"'

9. Regularization-Theory-Based Fast Torque Tracking Method for Interior Permanent Magnet Synchronous Machines

Author

Qi, Xing, Aarniovuori, Lassi, and Cao, Wenping

Abstract

With the rapid growth of interior permanent magnet synchronous machines in electric vehicle applications, there is a need to generate torque tracking look-up tables that can both track the torque command and implement maximum torque per ampere (MTPA)/maximum torque per volt (MTPV). So far, most torque tracking methods require a large amount of test points, giving rise to long test time and workloads. This article proposes a fast torque tracking MTPA/MTPV look-up table generating method to improve the efficiency. The proposed method is based on a machine learning regularization theory, using an L1/L2 regularization to establish a data-driven torque tracking model. Then, a Lagrange dual principle is introduced to solve the unknown parameters, so that the look-up tables of optimal dq-axis currents are yielded by a global optimization solver. Experimental results show that the proposed method can generate the look-up tables with the same accuracy as classical methods, but requires less test points and testing time. As a result, the testing work loads are reduced, as the time cost is only 10%–15% of the classical methods.

Published
2023
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11. Reinforcement-Learning-Based Parameter Look-Up Table Generating Method for Optimal Torque Control of Induction Motors

Author

Qi, Xing, Cao, Wenping, Aarniovuori, Lassi, Lappeenrannan-Lahden teknillinen yliopisto LUT, Lappeenranta-Lahti University of Technology LUT, and fi=School of Energy Systems|en=School of Energy Systems

Subjects
reinforcement learning, parameters look-up table, optimal torque, Induction motor
Abstract

In the optimal control of induction motors, it is a challenging task to maintain the optimal torque over the varying operation conditions. This paper proposes a parameter look-up table generating method, that can achieve an optimal torque over a wide range of currents and speeds, even though the commands of current are not set correctly. Based on the motor’s testing data, this method uses a reinforcement-learning algorithm to generate parameter look-up tables iteratively. Experimental results show that the proposed method can learn appropriate parameters from the running data to output an optimal torque. The comparative studies show that the proposed method can generate 5%-25% more torque than traditional model-based parameter estimation methods, over a wide range of currents and speeds. Furthermore, the proposed method has a faster convergence feature and a higher identification resolution than many conventional search-based methods. Post-print / Final draft

Published
2022

13. Design and Manufacturing of a Modular Low-Voltage Multimegawatt High-Speed Solid-Rotor Induction Motor

Author

Kurvinen, Emil, primary, Di, Chong, additional, Petrov, Ilya, additional, Nerg, Janne, additional, Liukkonen, Olli, additional, Jastrzebski, Rafal Piotr, additional, Kepsu, Daria, additional, Jaatinen, Pekko, additional, Aarniovuori, Lassi, additional, Sikanen, Eerik, additional, Pyrhonen, Juha, additional, Sopanen, Jussi, additional, Pyrhonen, Olli, additional, Niemela, Markku, additional, and Kangasmaki, Toni, additional

Published
2021
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18. Experimental Investigation of a 75 kW Induction Motor Drive System Losses and Efficiency

Author

Aarniovuori, Lassi, Kärkkäinen, Hannu, Niemelä, Markku, Cai, Kewei, Pyrhönen, Juha, Cao, Wenping, Lappeenrannan-Lahden teknillinen yliopisto LUT, Lappeenranta-Lahti University of Technology LUT, and fi=School of Energy Systems|en=School of Energy Systems

Abstract

The frequency converter driven IM can be used as a reference for the efficiency level of a drive system. In here, the losses and efficiency of a 75 kW IE3 -rated modern squirrel-cage induction motor are investigated experimentally. First, the result of IEC loss segregation with sinusoidal supply is used to create a reference point. Secondly, five different commercially available voltage source converters are used to drive the same motor in 30 measurement points up to the rated speed and torque. The losses and efficiencies are analyzed with torque values of 10%, 25%, 50%, 75% and 100% of the rated torque of the motor and supply frequency values of 10%, 25%, 50%, 75%, 90% and 100% of the rated motor frequency. The results are presented as frequency - torque maps and the loss values are given as Table values. Three of these converters show very similar performance from the energy efficiency point of view. The input-output method is used to determine the losses of the motor and converter. Post-print / Final draft

Published
2019

45. Review of Electrical Motor Drives for Electric Vehicle Applications.

Author

WENPING CAO, BUKHARI, ABID ALI SHAH, and AARNIOVUORI, LASSI

Subjects
MOTOR vehicle driving, ELECTRIC motor buses, ELECTRIC vehicles, RELUCTANCE motors, INDUCTION machinery, VARIABLE speed drives, SQUIRREL cage motors
Abstract

EVs (Electric Vehicles) have been rejuvenated over the last decades while the motor drive technologies are still evolving. This paper provides a review of electrical motor drive technologies used in EV applications, with a performance comparison of candidate machines and their drive topologies. EV applications demand high efficiency, high torque density, high reliability, and wide speed range while reducing weight, complexity, total costs and environmental impact. In the literature, DC (Direct Current) motors, IMs (Induction Motors) and PM (Permanent Magnet) motors can be generally found in marketplace whilst RMs (Reluctance Motors) have been researched for some time and are nearing commercial availability. This paper evaluates the performance of these four main types of electrical motor drives for EV propulsion applications using analytical methods. PM motors may offer the best performance in terms of torque density and compactness but the cost is the highest (primarily dominated by rare-earth permanent magnets), limiting their widespread application in mass production EVs. DC motors have their own merits but suffer from limited power density and necessity for maintenance. Induction motor drives are a mature and proven technology. In particular, squirrel-cage IMs are robust, reliable and inexpensive, striking a balance between system cost and complexity, power density and extended speed range. Reluctance motors can provide a good torque density and cost effective EV drive solutions. Their drawbacks can also be overcome by the use of power electronic converters and advanced control strategies. Induction and reluctance motor drives are well suited for cost sensitive mass production EV applications. Looking to the future, increased hybridization may be a way forward in industry which combines attractive features of different electrical machines and control algorithms and still offer much promise in performance and total cost. At last, reliability study on EVs requires historical information and driving patterns, demanding research expertise in eco-sociology, human behaviors as well as human-machine interface. [ABSTRACT FROM AUTHOR]

Published
2019
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46. Multidisciplinary Design of a Permanent-Magnet Traction Motor for a Hybrid Bus Taking the Load Cycle into Account

Author

Lindh, Pia, primary, Tehrani, Mohammad Gerami, additional, Lindh, Tuomo, additional, Montonen, Jan-Henri, additional, Pyrhonen, Juha, additional, Sopanen, Jussi T., additional, Niemela, Markku, additional, Alexandrova, Yulia, additional, Immonen, Paula, additional, Aarniovuori, Lassi, additional, and Polikarpova, Maria, additional

Published
2016
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