| Project/Area Number |
16K06193
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent mechanics/Mechanical systems
|
|---|
| Research Institution | Tokyo Denki University |
|---|
Principal Investigator |
HIDAKA Koichi 東京電機大学, 工学部, 教授 (10321407)
|
|---|
| Research Collaborator |
TAKAHASHI Yuta
YAHAGI Hokuto
|
|---|
| Project Period (FY) |
2016-04-01 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2016: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
|
|---|
| Keywords | ハイブリット電気自動車 / モデル予測制御 / 線形時変システム / 充電率(SOC) / 駆動部制御 / LPVシステム / 燃費最適化 / ドライバー予測 / HEV駆動部モデル / MPC / モデル化誤差 / 人間機械システム / 可変トルク制御装置 / 燃費最適アルゴリズム |
|---|
| Outline of Final Research Achievements |
This study proposes to design a controller to improve the fuel economy of a hybrid electric vehicle using model predictive control with linear parameter-varying systems. The power-split configuration combines the advantages of both series and parallel hybrid vehicle, and can drive a generator to charge a battery by dividing engine power via a planetary gear set. Rule-based and dynamic programming controllers have been proposed on the basis of a certain driving cycle to improve the fuel economy of hybrid electric vehicles, however, the performance of the methods changes depending on a certain driving cycle that is used for the designs. Therefore, we utilize model predictive control to improve the fuel economy of hybrid electric vehicles. A dynamic model of the drive system of the hybrid electric vehicle is adopted a linear parameter-varying system, which depends on vehicle speed. Furthermore, an optimal fuel map is devised from test data performed by driving cycles of an actual vehicle.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究ではハイブリット電気自動車(HEV)の駆動部の制御方法にモデル予測制御法(MPC)を応用することで、運転者の要求する速度に対して燃料消費の効率がよいエンジン回転数領域では要求速度が出せない場合でもモーター補助を加えることで走行可能となる駆動方法を提案した。この方法の特徴としては、エンジン回転数と効率を表すマップデータを利用し運転者の要求速度が予測できたと仮定した時、電池残量を考慮しながらモータアシストを加えて効率的なエンジン回転数を決定する点である。
|
