Electrothermal iterative learning control and optimization of electric vehicle operation
| Project/Area Number |
23K03906
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 21040:Control and system engineering-related
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| Research Institution | Kyushu University |
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Principal Investigator |
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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| Project Period (FY) |
2023-04-01 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2025: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2024: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 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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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The derived results so far are matched with the proposed timeline.
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| Strategy for Future Research Activity |
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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Report
(1 results)
Research Products
(4 results)
