令和8年電気学会全国大会で研究発表しました（D1 XUE, M1 林/ 松花/ YANG, B4 小松） Presantion at the 2026 Japan Institute of Electrical Engineers of Japan National Convention (D1 XUE, M1 Hayashi/Matsuhana/Yang, B4 Komatsu).
当研究室から5名の学生(D1:1名、M1:3名、B4:1名)が日ごろの研究成果を発表しました。
100人近い規模の会場もあり、質疑応答などではとても緊張したようですが、大学の先生・学生・企業の方々等、様々な研究者のみなさんとの議論の場は大変有意義な貴重な経験となりました。
電力・エネルギー分野においても、AIの活用がさらなる広がりを見せていることをあらためて感じました。
新たな知見を今後の研究活動に活かしていきたいと思います。
■From Natural Language to Optimal Schedules: An LLM Interface for EV Charging Sihui Xue
■マルチエージェント強化学習を用いた電力系統の安定化とユーザビリティを両立するEV群の充放電自律制御 林 弘辰
■深層強化学習による蓄電池制御に対する予測誤差の影響の定量分析 松花 正剛
■変調充電電流を用いたEV・充電器識別における計測パラメータの最適化と評価 YANG Jiseok
■蓄電池SoC指令追従制御における 通信遅延補正手法の実機検証 小松 悠太
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Five students from our laboratory (1 D1, 3 M1, and 1 B4) presented the results of their ongoing research.
Some venues had nearly 100 people, and the Q&A sessions seemed quite nerve-wracking, but the opportunity to discuss with various researchers, including university professors, students, and people from companies, was a very meaningful and valuable experience. I was once again struck by the expanding use of AI in the power and energy sector. We would like to utilize these new findings in my future research activities.
■From Natural Language to Optimal Schedules: An LLM Interface for EV Charging Sihui Xue
■Multi-Agent Reinforcement Learning-Based Autonomous Control of EV Charging and Discharging: Balancing Grid Stability and User Usability Koshin HAYASHI
■Evaluation of Prediction Errors Arising from the Application of Deep Reinforcement Learning to Battery Shogo MATSUHANA
■Optimization and Evaluation of Measurement Parameters for EV-Charger Identification Using Modulated Charging Current JISEOK YANG
■Experimental Evaluation of Communication-Delay Compensation for SoC Tracking Control of a Battery System Yuta KOMATSU
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Some venues had nearly 100 people, and the Q&A sessions seemed quite nerve-wracking, but the opportunity to discuss with various researchers, including university professors, students, and people from companies, was a very meaningful and valuable experience. I was once again struck by the expanding use of AI in the power and energy sector. We would like to utilize these new findings in my future research activities.
■From Natural Language to Optimal Schedules: An LLM Interface for EV Charging Sihui Xue
■Multi-Agent Reinforcement Learning-Based Autonomous Control of EV Charging and Discharging: Balancing Grid Stability and User Usability Koshin HAYASHI
■Evaluation of Prediction Errors Arising from the Application of Deep Reinforcement Learning to Battery Shogo MATSUHANA
■Optimization and Evaluation of Measurement Parameters for EV-Charger Identification Using Modulated Charging Current JISEOK YANG
■Experimental Evaluation of Communication-Delay Compensation for SoC Tracking Control of a Battery System Yuta KOMATSU
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