Electric Systems

Modeling & optimization of power systems and electromagnetic machines.

Our research in this area includes flexible and resilient power system design and operational optimization; modeling and analysis of electric machines for applications such as fault detection of motors, power efficiency improvement and design complexity reduction.

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  • Researchers

  • Awards

    •  AWARD    Best paper award at PHMAP 2023
      Date: September 14, 2023
      Awarded to: Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith
      MERL Contacts: Abraham Goldsmith; Dehong Liu
      Research Areas: Electric Systems, Signal Processing
      Brief
      • MERL researchers Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith were awarded one of three best paper awards at Asia Pacific Conference of the Prognostics and Health Management Society 2023 (PHMAP23) held in Tokyo from September 11th to 14th, 2023, for their co-authored paper titled 'Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors.'

        PHMAP is a biennial international conference specialized in prognostics and health management. PHMAP23 attracted more than 300 attendees from worldwide and published more than 160 regular papers from academia and industry including aerospace, production, civil engineering, electronics, and so on.
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    •  AWARD    MERL receives 33rd ARIB Radio Achievement Award
      Date: June 28, 2022
      Awarded to: Yukimasa Nagai, Jianlin Guo, Shoichi Kitazawa, Kazuto Yano.
      MERL Contacts: Jianlin Guo; Philip V. Orlik
      Research Areas: Communications, Electric Systems
      Brief
      • Mitsubishi Electric Corporation (Yukimasa Nagai), MERL (Jianlin Guo), Muroran Institute of Technology (Shoichi Kitazawa) and Advanced Telecommunications Research Institute International (Kazuto Yano) jointly won the 33rd ARIB Radio Achievement Award with "IEEE 802.19.3 Standardization and Development for Sub-1 GHz Wireless Frequency Coexistence". The ARIB is an organization similar to the FCC in the U.S. It is responsible for setting standards for all radio communications in Japan at the request of the Ministry of Internal Affairs and Communications (MIC). In order to promote the effective use of radio waves, the "Radio Achievement Award" is given annually to an individual or organization that has made a special achievement in the effective use of radio waves. This award is the most prestigious award in the field of wireless communications in Japan.
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    •  AWARD    Best conference paper of IEEE PES-GM 2020
      Date: June 18, 2020
      Awarded to: Tong Huang, Hongbo Sun, K.J. Kim, Daniel Nikovski, Le Xie
      MERL Contacts: Daniel N. Nikovski; Hongbo Sun
      Research Areas: Data Analytics, Electric Systems, Optimization
      Brief
      • A paper on A Holistic Framework for Parameter Coordination of Interconnected Microgrids Against Natural Disasters, written by Tong Huang, a former MERL intern from Texas A&M University, has been selected as one of the Best Conference Papers at the 2020 Power and Energy Society General Meeting (PES-GM). IEEE PES-GM is the flagship conference for the IEEE Power and Energy Society. The work was done in collaboration with Hongbo Sun, K. J. Kim, and Daniel Nikovski from MERL, and Tong's advisor, Prof. Le Xie from Texas A&M University.
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  • News & Events


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  • Internships

    • EA0149: Internship - Electric Motor Design Optimization

      MERL is seeking a motivated and qualified individual to conduct research on physics informed neural network-based modeling for electric motor design optimization. Ideal candidates should be Ph.D. students with solid background and proven publication record in one or more of the following research areas: 2D/3D electromagnetic modeling and simulation, analytical modeling methods for electromagnetics and iron losses (e.g. magnetic equivalent circuit), and machine learning-based surrogate modeling. Strong coding skill with ANSYS or open-source FEM software and Python-based learning library is a must and prior experience with running jobs over cluster is a plus. Start date for this internship is flexible and the duration is 3-6 months.

      Required Specific Experience

      • Experience with modeling and simulations for motor design

    • EA0151: Internship - Physics-informed machine learning

      MERL is looking for a self-motivated intern to work on physics-informed machine learning with application to electric machine condition monitoring and predictive maintenance. The ideal candidate would be a Ph.D. student in electrical engineering or computer science with solid research background in electric machines, signal processing, and machine learning. Proficiency in Python and Matlab is required. The intern is expected to collaborate with MERL researchers to build machine learning model for multi-modal data analysis, prepare technical reports, and draft manuscripts for scientific publications. The total duration is anticipated to be 3-6 months. The start date is flexible. This internship requires work that can only be done at MERL.

    • EA0073: Internship - Fault Detection for Electric Machines

      MERL is seeking a motivated and qualified individual to conduct research on electric machine fault analysis and detection methods. Ideal candidates should be Ph.D. students with a solid background and publication record in one more research area on electric machines: electric and magnetic modeling, machine design and prototyping, harmonic analysis, fault detection, and predictive maintenance. Knowledge on data analysis and machine learning algorithms, and strong programming skills using Python/PyTorch are expected. Research experience on modeling and analysis of electric machines and fault diagnosis is desired. Senior Ph.D. students in related expertise, such as electrical engineering, mechanical engineering, and applied physics are encouraged to apply. Start date for this internship is flexible and the duration is 3 months.


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  • Openings


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  • Recent Publications

    •  Das, G., Wang, B., Lin, C., "Topology Optimization of Electric Motors using Mesh Projection", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.
      BibTeX TR2025-089 PDF
      • @inproceedings{Das2025jun2,
      • author = {Das, Ghanendra and Wang, Bingnan and Lin, Chungwei},
      • title = {{Topology Optimization of Electric Motors using Mesh Projection}},
      • booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-089}
      • }
    •  Sun, S., Wang, Y., Koike-Akino, T., Yamamoto, T., Sakamoto, Y., Wang, B., "Image-based Deep Learning Models for Electric Motors", International Conference on the Computation of Electromagnetic Fields (COMPUMAG), June 2025.
      BibTeX TR2025-088 PDF
      • @inproceedings{Sun2025jun,
      • author = {Sun, Siyuan and Wang, Ye and Koike-Akino, Toshiaki and Yamamoto, Tatsuya and Sakamoto, Yusuke and Wang, Bingnan},
      • title = {{Image-based Deep Learning Models for Electric Motors}},
      • booktitle = {International Conference on the Computation of Electromagnetic Fields (COMPUMAG)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-088}
      • }
    •  Ji, D.-Y., Wang, B., Inoue, H., Kanemaru, M., "Motor Fault Detection with a Hybrid Physics-based and Data-Driven Method", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.
      BibTeX TR2025-062 PDF
      • @inproceedings{Ji2025may,
      • author = {Ji, Dai-Yan and Wang, Bingnan and Inoue, Hiroshi and Kanemaru, Makoto},
      • title = {{Motor Fault Detection with a Hybrid Physics-based and Data-Driven Method}},
      • booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-062}
      • }
    •  Sun, S., Wang, Y., Koike-Akino, T., Yamamoto, T., Sakamoto, Y., Wang, B., "Electric Motor Cogging Torque Prediction with Vision Transformer Models", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.
      BibTeX TR2025-059 PDF
      • @inproceedings{Sun2025may,
      • author = {Sun, Siyuan and Wang, Ye and Koike-Akino, Toshiaki and Yamamoto, Tatsuya and Sakamoto, Yusuke and Wang, Bingnan},
      • title = {{Electric Motor Cogging Torque Prediction with Vision Transformer Models}},
      • booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-059}
      • }
    •  Wu, J., Goldsmith, A., Liu, D., Wang, B., Zhou, L., Wang, Y., "A Unified Observer for Smooth Speed-Sensorless Drive Control of Induction Machines at Full Speed Range", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.
      BibTeX TR2025-060 PDF
      • @inproceedings{Wu2025may,
      • author = {Wu, Jingjie and Goldsmith, Abraham and Liu, Dehong and Wang, Bingnan and Zhou, Lei and Wang, Yebin},
      • title = {{A Unified Observer for Smooth Speed-Sensorless Drive Control of Induction Machines at Full Speed Range}},
      • booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-060}
      • }
    •  Wu, J., Goldsmith, A., Zhou, L., Liu, D., Wang, B., Wang, Y., "A Novel High-Frequency Injection Method Towards Speed-Sensorless Drive Control of Induction Machines over Full Speed Range", IEEE International Electric Machines and Drives Conference (IEMDC), May 2025.
      BibTeX TR2025-061 PDF
      • @inproceedings{Wu2025may2,
      • author = {Wu, Jingjie and Goldsmith, Abraham and Zhou, Lei and Liu, Dehong and Wang, Bingnan and Wang, Yebin},
      • title = {{A Novel High-Frequency Injection Method Towards Speed-Sensorless Drive Control of Induction Machines over Full Speed Range}},
      • booktitle = {IEEE International Electric Machines and Drives Conference (IEMDC)},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-061}
      • }
    •  Lu, Z., Tu, H., Fang, H., Wang, Y., Mou, S., "Integrated Optimal Control for Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures", IEEE Transactions on Control Systems Technology, DOI: 10.1109/​TCST.2024.3498812, Vol. 33, No. 2, pp. 714-728, December 2024.
      BibTeX TR2025-005 PDF
      • @article{Lu2024dec,
      • author = {Lu, Zehui and Tu, Hao and Fang, Huazhen and Wang, Yebin and Mou, Shaoshuai},
      • title = {{Integrated Optimal Control for Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures}},
      • journal = {IEEE Transactions on Control Systems Technology},
      • year = 2024,
      • volume = 33,
      • number = 2,
      • pages = {714--728},
      • month = dec,
      • doi = {10.1109/TCST.2024.3498812},
      • url = {https://www.merl.com/publications/TR2025-005}
      • }
    •  Lu, Z., Zhang, T., Wang, Y., "Torque Constraint Modeling and Reference Shaping for Servo Systems", IEEE Control Systems Letters (L-CSS), DOI: 10.1109/​LCSYS.2024.3509495, Vol. 8, pp. 2637-2642, December 2024.
      BibTeX TR2025-006 PDF
      • @article{Lu2024dec2,
      • author = {Lu, Zehui and Zhang, Tianpeng and Wang, Yebin},
      • title = {{Torque Constraint Modeling and Reference Shaping for Servo Systems}},
      • journal = {IEEE Control Systems Letters (L-CSS)},
      • year = 2024,
      • volume = 8,
      • pages = {2637--2642},
      • month = dec,
      • doi = {10.1109/LCSYS.2024.3509495},
      • url = {https://www.merl.com/publications/TR2025-006}
      • }
    See All Publications for Electric Systems

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