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Improvement of fuel efficiency and drivability using simple prediction for gear changing
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0002-3626-6367
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0001-5703-5923
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0002-7550-3134
2013 (English)In: IFAC Proceedings Volumes (IFAC-PapersOnline), 2013, no PART 1, 518-523 p.Conference paper (Refereed)
Abstract [en]

Decreasing fuel consumption and emissions in automobiles has been an active research topic in recent years. A promising technology is the hybridization of powertrain. The main focus in this area is usually on the development of optimal power management control methods. For parallel HEVs (hybrid electric vehicle), the primary control variable is the torque split between the internal combustion engine and the electric motor but gear number can also be considered as a control parameter. ECMS (equivalent consumption minimization strategy) is one of the well-known real time power management strategies and has been used extensively in different works; however, using ECMS for controlling gearbox cannot always lead to optimal fuel consumption and drivability. The slow dynamics of gearbox might introduce unnecessary gear changing, which leads to suboptimal fuel efficiency and degraded drivability. In this paper, a simple prediction strategy is implemented to improve fuel efficiency and drivability. The presented prediction method does not use any information from the environment and does not need any extra sensor. The strategy is not computationally heavy compared to other predictive methods. The simplicity of the method makes it suitable for implementations.

Place, publisher, year, edition, pages
2013. no PART 1, 518-523 p.
, IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670 ; 7
Keyword [en]
Drivability, Fuel efficiency, Hybrid electric vehicle, Power management, Prediction, Control parameters, Optimal fuel consumption, Power managements, Prediction methods, Predictive methods, Real-time power managements, Efficiency, Energy management, Forecasting, Fuel consumption, Fuels, Hybrid vehicles, Internal combustion engines, Optimization, Electric power measurement
National Category
Mechanical Engineering
URN: urn:nbn:se:kth:diva-140021DOI: 10.3182/20130904-4-JP-2042.00050ScopusID: 2-s2.0-84885923857ISBN: 9783902823434OAI: diva2:689786
7th IFAC Symposium on Advances in Automotive Control, AAC 2013, 4 September 2013 through 7 September 2013, Tokyo

QC 20140121

Available from: 2014-01-21 Created: 2014-01-16 Last updated: 2014-01-21Bibliographically approved

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