References

Bahrami, M. and Khashroum, Z., 2023. Review of machine learning techniques for power electronics control and optimization. arXiv preprint arXiv:2310.04699.’

Bonnie Baker, 2021. What makes SiC and GaN suitable for high-power designs. https://www.edn.com/what-makes-sic-and-gan-suitable-for-high-power-designs/

Daniel Gebbran, Alberto Barragan-Moreno, Pere Izquierdo Gómez, and Ramadhani Kurniawan Subroto, 2022. Cloud & Edge Computing for Smart Management of Power Electronic Converter Fleets: A Key Connective Fabric to Enable the Green Transition. https://doi.org/10.1109/MIE.2022.3211125

David Williams, 2017. Understanding, Calculating, and Measuring Total Harmonic Distortion (THD). https://www.allaboutcircuits.com/technical-articles/the-importance-of-total-harmonic-distortion/

Dilip Kumar, 2025. Support Vector Machine (SVM) classification model. https://dilipkumar.medium.com/support-vector-machine-svm-classification-model-b5048923778a

Eskilson, T. and Ho, C.N.M., 2021, October. Design of a Low-latency Power Electronics-based Power-HIL System for an EV Motor Controller. In 2021 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 4719-4723). IEEE.

Fatma Yıldırım, YunusYalman, Kamil ÇağatayBayındır and ErmanTerciyanlı, 2025. Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications. https://doi.org/10.3390/app15084592

Grandviewresearch, 2025. Electric Vehicle Market (2025 - 2030). Available at: https://www.grandviewresearch.com/industry-analysis/electric-vehicles-ev-market [Accessed 12th, February, 2026]

Ibrahim, M.S., Dong, W. and Yang, Q., 2020. Machine learning driven smart electric power systems: Current trends and new perspectives. Applied Energy, 272, p.115237.

Johansson, E., 2020. High-Efficiency Power Conversion in Electric Vehicles: Challenges and Solutions. American Journal Of Power Electronics And Power Systems, 1(3), pp.1-8.

Lalitha S D, Chaitanya Danamaraju, Prabhu Raj Manickam, and Dinesh Kumar, 2024. AI-Driven Control Strategies for Power Electronics Converters. https://doi.org/10.1109/ICONSTEM60960.2024.10568766

Leite, D., Martins Jr, A., Rativa, D., De Oliveira, J.F. and Maciel, A.M., 2022. An automated machine learning approach for real-time fault detection and diagnosis. Sensors, 22(16), p.6138.

Luo, R., 2025. Application of High-Efficiency Wide-Bandgap Semiconductor Devices in Power Electronics. Development of Energy Science, 8(1).

Md. Abbas, and Beh L. Wei, 2025. Wide-Bandgap Semiconductor-Based Power Converters: Enhancing Efficiency in Modern Electrical Machines. https://doi.org/10.17051/NJEMPC/01.04.01

Ponnambalam, R. and Vairavasundaram, I., 2025. GaN-Based DC-DC Converters for EV Fast Charging: A Review of Wide Bandgap Devices Technology. Results in Engineering, p.107548.

Reddy, B.N., Jyothi, B., Rao, G.S., Janaki, N., Swathi, P. and Reddy, S.V.R., 2025. State-of-the-art DC–DC converters for electric mobility and renewable integration: trends, challenges, and future directions. Discover Applied Sciences.

Ruan, G., Zhong, H., Zhang, G., He, Y., Wang, X. and Pu, T., 2020. Review of learning-assisted power system optimization. CSEE Journal of Power and Energy Systems, 7(2), pp.221-231.

Shiyang Liang, ShuangshuangJin,  andYousu Chen, 2023. A Review of Edge Computing Technology and Its Applications in Power Systems.https://doi.org/10.3390/en17133230

Singh, A.R., Rathore, R.S., Jiang, W., Thakare, A., Kumar, R.S., Khadse, C.B. and Addis, H.K., 2025. A scalable cloud-integrated AI platform for real-time optimization of EV charging and resilient microgrid energy management. Scientific Reports, 15(1), p.37692.

Tao, Y., Qiu, J. and Lai, S., 2021. A hybrid cloud and edge control strategy for demand responses using deep reinforcement learning and transfer learning. IEEE Transactions on Cloud Computing, 10(1), pp.56-71.

Ullah, Z., Kotb, K.M., Elkadeem, M.R., Yan, L., Khan, T., Qazi, H.S., Badshah, F. and Abido, M.A., 2025. Integrating electric vehicles and renewable energy in modern power systems: A review of EV charging station design, control strategies, and emerging trends. Ain Shams Engineering Journal, 16(10), p.103592.

Yıldırım, F., Yalman, Y., Bayındır, K.Ç. and Terciyanlı, E., 2025. Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications. Applied Sciences (2076-3417), 15(8).

Yuan, L., Zhang, J., Liang, Z., Hu, M., Chen, G. and Lu, W., 2023. EMI challenges in modern power electronic-based converters: recent advances and mitigation techniques. Frontiers in Electronics, 4, p.1274258.

Cite this Article as: Gayathri M & Lavanya S (2026), “Machine learning driven cloud edge adaptive control for high-frequency wide-bandgap EV converters”, International Journal of Advances in Engineering & Scientific Research,  Volume 13, Issue 1, 2026, pp 01-08.  DOI : https://doi.org/ 10.5281/zenodo.18637851

References

Bahrami, M. and Khashroum, Z., 2023. Review of machine learning techniques for power electronics control and optimization. arXiv preprint arXiv:2310.04699.’

Bonnie Baker, 2021. What makes SiC and GaN suitable for high-power designs. https://www.edn.com/what-makes-sic-and-gan-suitable-for-high-power-designs/

Daniel Gebbran, Alberto Barragan-Moreno, Pere Izquierdo Gómez, and Ramadhani Kurniawan Subroto, 2022. Cloud & Edge Computing for Smart Management of Power Electronic Converter Fleets: A Key Connective Fabric to Enable the Green Transition. https://doi.org/10.1109/MIE.2022.3211125

David Williams, 2017. Understanding, Calculating, and Measuring Total Harmonic Distortion (THD). https://www.allaboutcircuits.com/technical-articles/the-importance-of-total-harmonic-distortion/

Dilip Kumar, 2025. Support Vector Machine (SVM) classification model. https://dilipkumar.medium.com/support-vector-machine-svm-classification-model-b5048923778a

Eskilson, T. and Ho, C.N.M., 2021, October. Design of a Low-latency Power Electronics-based Power-HIL System for an EV Motor Controller. In 2021 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 4719-4723). IEEE.

Fatma Yıldırım, YunusYalman, Kamil ÇağatayBayındır and ErmanTerciyanlı, 2025. Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications. https://doi.org/10.3390/app15084592

Grandviewresearch, 2025. Electric Vehicle Market (2025 - 2030). Available at: https://www.grandviewresearch.com/industry-analysis/electric-vehicles-ev-market [Accessed 12th, February, 2026]

Ibrahim, M.S., Dong, W. and Yang, Q., 2020. Machine learning driven smart electric power systems: Current trends and new perspectives. Applied Energy, 272, p.115237.

Johansson, E., 2020. High-Efficiency Power Conversion in Electric Vehicles: Challenges and Solutions. American Journal Of Power Electronics And Power Systems, 1(3), pp.1-8.

Lalitha S D, Chaitanya Danamaraju, Prabhu Raj Manickam, and Dinesh Kumar, 2024. AI-Driven Control Strategies for Power Electronics Converters. https://doi.org/10.1109/ICONSTEM60960.2024.10568766

Leite, D., Martins Jr, A., Rativa, D., De Oliveira, J.F. and Maciel, A.M., 2022. An automated machine learning approach for real-time fault detection and diagnosis. Sensors, 22(16), p.6138.

Luo, R., 2025. Application of High-Efficiency Wide-Bandgap Semiconductor Devices in Power Electronics. Development of Energy Science, 8(1).

Md. Abbas, and Beh L. Wei, 2025. Wide-Bandgap Semiconductor-Based Power Converters: Enhancing Efficiency in Modern Electrical Machines. https://doi.org/10.17051/NJEMPC/01.04.01

Ponnambalam, R. and Vairavasundaram, I., 2025. GaN-Based DC-DC Converters for EV Fast Charging: A Review of Wide Bandgap Devices Technology. Results in Engineering, p.107548.

Reddy, B.N., Jyothi, B., Rao, G.S., Janaki, N., Swathi, P. and Reddy, S.V.R., 2025. State-of-the-art DC–DC converters for electric mobility and renewable integration: trends, challenges, and future directions. Discover Applied Sciences.

Ruan, G., Zhong, H., Zhang, G., He, Y., Wang, X. and Pu, T., 2020. Review of learning-assisted power system optimization. CSEE Journal of Power and Energy Systems, 7(2), pp.221-231.

Shiyang Liang, ShuangshuangJin,  andYousu Chen, 2023. A Review of Edge Computing Technology and Its Applications in Power Systems.https://doi.org/10.3390/en17133230

Singh, A.R., Rathore, R.S., Jiang, W., Thakare, A., Kumar, R.S., Khadse, C.B. and Addis, H.K., 2025. A scalable cloud-integrated AI platform for real-time optimization of EV charging and resilient microgrid energy management. Scientific Reports, 15(1), p.37692.

Tao, Y., Qiu, J. and Lai, S., 2021. A hybrid cloud and edge control strategy for demand responses using deep reinforcement learning and transfer learning. IEEE Transactions on Cloud Computing, 10(1), pp.56-71.

Ullah, Z., Kotb, K.M., Elkadeem, M.R., Yan, L., Khan, T., Qazi, H.S., Badshah, F. and Abido, M.A., 2025. Integrating electric vehicles and renewable energy in modern power systems: A review of EV charging station design, control strategies, and emerging trends. Ain Shams Engineering Journal, 16(10), p.103592.

Yıldırım, F., Yalman, Y., Bayındır, K.Ç. and Terciyanlı, E., 2025. Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications. Applied Sciences (2076-3417), 15(8).

Yuan, L., Zhang, J., Liang, Z., Hu, M., Chen, G. and Lu, W., 2023. EMI challenges in modern power electronic-based converters: recent advances and mitigation techniques. Frontiers in Electronics, 4, p.1274258.