| 研究課題/領域番号 |
23K03906
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分21040:制御およびシステム工学関連
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| 研究機関 | 九州大学 |
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研究代表者 |
Nguyen Hoa 九州大学, カーボンニュートラル・エネルギー国際研究所, 准教授 (00801086)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2025年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2024年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
2023年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
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| キーワード | iterative learning / control design / nonlinear optimization / electrothermal dynamics / Li-ion battery / electric vehicle / Battery dynamics / Electric vehicle / IILC / Optimization / Ancillary services |
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| 研究開始時の研究の概要 |
EVs are being strongly developed worldwide to gradually substitute for gasoline
vehicles as an approach to combat the severe climate changes caused by greenhouse gas emissions. Technologies for EVs unfortunately have not been mature as that for gasoline alternates, hence requiring much more effort to increase their energy efficiency, economy, and charging convenience for users. This project exploits the repetition on the daily driving of a large class of EV users to develop novel iterative learning control methods for the better electricity and thermal control and management of EVs.
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| 研究実績の概要 |
In the FY2023, a novel iterative learning control (ILC) algorithm based on a quadratic performance index with iteration-varying weighting matrices was derived. As a result, this ILC algorithm has iteration-varying learning gains. The superior performances of this algorithm for tracking iteration-varying references compared to that by conventional ILC laws with iteration-invariant learning gains were reported. This algorithm was then applied for the tracking of daily-varying state-of-charge profiles of electric vehicle batteries, whose results showed great potentials.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The derived results so far are matched with the proposed timeline.
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| 今後の研究の推進方策 |
In the next works, more complicated electrothermal models of Li-ion batteries will be taken into account in the design of iterative learning controllers. Additionally, the problem of employing electric vehicle Li-ion battery packs to provide ancillary services will be investigated. In the simulations, realistic and publicly available data will be utilized.
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