기계 학습을 활용한 구동 토크 예측 기반 차량 속도 프로파일 최적화open accessVehicle Speed Optimization Based on Predicted Traction Torque Using Machine Learning
- Other Titles
- Vehicle Speed Optimization Based on Predicted Traction Torque Using Machine Learning
- Authors
- 김병건; 김기훈; 안윤용; 성지훈; 최석훈; 전영호; 허건수
- Issue Date
- Jun-2022
- Publisher
- 한국자동차공학회
- Keywords
- Machine learning; Road-load; Optimal control; Dynamic programming; Electric vehicle; Eco drive; 기계 학습; 주행 저항; 최적 제어; 동적계획법; 전기 자동차; 에코 드라이브
- Citation
- 한국자동차공학회 논문집, v.30, no.6, pp.511 - 518
- Indexed
- SCOPUS
KCI
- Journal Title
- 한국자동차공학회 논문집
- Volume
- 30
- Number
- 6
- Start Page
- 511
- End Page
- 518
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/191149
- DOI
- 10.7467/KSAE.2022.30.6.511
- ISSN
- 1225-6382
- Abstract
- A number of studies have been proposed in order to obtain the optimal vehicle speed profile for a given route based on dynamic programming(DP). In general, solving optimization problems requires a vehicle dynamics model to accurately calculate energy consumption. However, this model cannot exactly reflect the real characteristics of various vehicles because of the nonlinearity of the rolling resistance, air resistance, and gradient resistance. Therefore, this study proposes vehicle speed optimization by using a machine learning network model that is trained from actual vehicle driving data. The performance of the proposed method is verified by simulation where the driving environment is duplicated corresponding to real driving conditions. The effectiveness of the proposed optimal speed profile is evaluated by comparing with conventional cruise control driving. As a result, driving with the optimal speed profile for a given route of 27.3 km significantly reduces battery energy consumption by 8.4 %.
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Collections - 서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles
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