| Project/Area Number |
16K12832
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Social systems engineering/Safety system
|
|---|
| Research Institution | National Institute of Informatics |
|---|
Principal Investigator |
Suenaga Shunichiro 国立情報学研究所, 先端ソフトウェア工学・国際研究センター, 特任研究員 (20530976)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
本位田 真一 国立情報学研究所, 大学共同利用機関等の部局等, 教授 (70332153)
|
|---|
| Project Period (FY) |
2016-04-01 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
|---|
| Keywords | 電気自動車 / EVM / プロジェクトマネジメント / Earned Value Management / 運行支援 |
|---|
| Outline of Final Research Achievements |
It is well known that the limited driving range of electric cars raises range anxieties of drivers. Drivers generally want to arrive at destinations within a planned time and consumed energy; however, the planned time and energy consumption cannot always be maintained. Violations of the planned energy are especially critical for electric cars because violations increase the possibility of running out of stored energy. Therefore, driving support systems that analyze reachability and suggest actions to drivers are required. Applying naive techniques to reachability analysis introduces a problem that cannot distinguish two different statuses because naive techniques do not take into consideration the weight of energy consumed for the traveled distance. There are two adverse effects in this problem: unnecessary actions are performed, and necessary actions are not performed. We propose a method of analysis that applies earned value management and open data to overcome this problem.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究では，プロジェクトマネジメントの手法を電気自動車に転用し，予測時間と予測電力を算出する手法を確立した．そのうえで，予測時間内，予測電力内での到達可否を分析する手法を提案した．提案した分析手法の有効性を確認するために，時間・電力を調整する他技術と併用し，その効果をシミュレーション環境で検証した。
|
