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Mission-based Design Space Exploration for Powertrain Electrification of Series Plugin Hybrid Electric Delivery Truck
(Center for Automotive Research, College of Engineering The Ohio State University)ORCID iD: 0000-0003-0808-052X
(Center for Automotive Research, College of Engineering The Ohio State University)
(Tsinghua University)
(Center for Automotive Research, College of Engineering The Ohio State University)
2018 (English)In: WCX World Congress Experience, SAE International , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions. The search algorithm is designed to carefully select the candidate in each iteration which is most likely to be Pareto-optimal, based on the results from previous candidates, to reduce computational time. The powertrain of a medium-sized series plugin hybrid electric delivery truck with a range extender is optimized for different driving missions. Three different powertrain architectures are included in the design space exploration and the fuel economy is evaluated using a simulation model of the powertrain and Dynamic Programming. Results from the analysis show which ranges of powertrain component sizes are recommended for the different types of driving scenarios.

Place, publisher, year, edition, pages
SAE International , 2018.
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:liu:diva-151300DOI: 10.4271/2018-01-1027OAI: oai:DiVA.org:liu-151300DiVA, id: diva2:1248571
Conference
SAE World Congress
Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2018-09-17

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Publisher's full texthttps://doi.org/10.4271/2018-01-1027

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Jung, Daniel

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf