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Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption

Saboohi, Y., Farzaneh, H.·2009·Applied Energy

Abstract

Additional consumption of fuel in an intense traffic condition is inevitable. Excess fuel consumption may be avoided, if an optimal driving strategy is implemented subject to the surrounding condition of a vehicle and existing constraints. Development of an optimal driving strategy has been the subject of eco-driving. A model of optimal driving strategy has been developed and it has been applied for assessment of eco-driving rules. The model may be categorized as an optimal control and the objective function is minimization of fuel consumption in a given route. Vehicle speed and gear ratio are identified as control variables. The effect of working load has been considered according three engine running processes of Idle, part-load and wide open throttle. The model has then been applied to identify the optimal driving strategy of a vehicle in different traffic congestion based on eco-driving rules.

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Crossref · 10.1016/j.apenergy.2008.12.017

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Metadata

Authors
Saboohi, Y., & Farzaneh, H.
Venue
Applied Energy
Publisher
Elsevier BV
Published
2009-10
Volume
86
Issue
10
Pages
1925-1932
ISSN
0306-2619 (ISSN-L)
OpenAlex
W2016985433
Cited by
232
References
0
FWCI
26.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. https://app.cochat.ai/papers/10.1016/j.apenergy.2008.12.017
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Saboohi, Y., & Farzaneh, H. (2009). Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption. Applied Energy. https://doi.org/10.1016/j.apenergy.2008.12.017

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.