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GRAB-ECO for Minimal Fuel Consumption Estimation of Parallel Hybrid Electric Vehicles
IFP Energies Nouvelles, France.
IFP Energies Nouvelles, France.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-8646-8998
2017 (English)In: Oil & gas science and technology, ISSN 1294-4475, E-ISSN 1953-8189, Vol. 72, no 6, article id 39Article in journal (Refereed) Published
Abstract [en]

As a promising solution to the reduction of fuel consumption and CO2 emissions in road transport sector, hybrid electric powertrains are confronted with complex control techniques for the evaluation of the minimal fuel consumption, particularly the excessively long computation time of the design-parameter optimization in the powertrains early design stage. In this work, a novel and simple GRaphical-Analysis-Based method of fuel Energy Consumption Optimization (GRAB-ECO) is developed to estimate the minimal fuel consumption for parallel hybrid electric powertrains in light-and heavy-duty application. Based on the power ratio between powertrains power demand and the most efficient engine power, GRAB-ECO maximizes the average operating efficiency of the internal combustion engine by shifting operating points to the most efficient conditions, or by eliminating the engine operation from poorly efficient operating points to pure electric vehicle operation. A turning point is found to meet the requirement of the final state of energy of the battery, which is charge-sustaining mode in this study. The GRAB-ECO was tested with both light- and heavy-duty parallel hybrid electric vehicles, and validated in terms of the minimal fuel consumption and the computation time. Results show that GRAB-ECO accurately approximates the minimal fuel consumption with less than 6% of errors for both light-and heavy-duty parallel hybrid electric powertrains. Meanwhile, GRAB-ECO reduces computation time by orders of magnitude compared with PMP-based (Pontryagins Minimum Principle) approaches.

Place, publisher, year, edition, pages
EDP SCIENCES S A , 2017. Vol. 72, no 6, article id 39
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-143989DOI: 10.2516/ogst/2017035ISI: 000418072600001OAI: oai:DiVA.org:liu-143989DiVA, id: diva2:1170228
Available from: 2018-01-02 Created: 2018-01-02 Last updated: 2018-01-30

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