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Design Space Exploration for Powertrain Electrification using Gaussian Processes
(Center for Automotive Research, College of Engineering, The Ohio State University, OH, USA)ORCID iD: 0000-0003-0808-052X
(Center for Automotive Research, College of Engineering, The Ohio State University, OH, USA)
(Center for Automotive Research, College of Engineering, The Ohio State University, OH, USA)
2018 (English)In: 2018 Annual American Control Conference (ACC), 2018, p. 846-851Conference paper, Published paper (Refereed)
Abstract [en]

Design space exploration of hybrid electric vehicles is an important multi-objective global optimization problem. One of the main objectives is to minimize fuel consumption while maintaining satisfactory driveability performance and vehicle cost. The design problem often includes multiple design options, including different driveline architectures and component sizes, where different candidates have various trade-offs between different, in many cases contradictory, performance requirements. Thus, there is no global optimum but a set of Pareto-optimal solutions to be explored. The objective functions can be expensive to evaluate, due to time-consuming simulations, which requires careful selection of which candidates to evaluate. A design space exploration algorithm is proposed for finding the set of Pareto-optimal solutions when the design search space includes multiple design options. As a case study, powertrain optimization is performed for a medium-sized series hybrid electric delivery truck.

Place, publisher, year, edition, pages
2018. p. 846-851
Keywords [en]
Gaussian processes;hybrid electric vehicles;Pareto optimisation;power transmission (mechanical);hybrid electric vehicles;multiobjective global optimization problem;satisfactory driveability performance;vehicle cost;Pareto-optimal solutions;powertrain optimization;medium-sized series hybrid electric delivery truck;powertrain electrification;Linear programming;Space exploration;Mechanical power transmission;Gaussian processes;Optimization;Hybrid electric vehicles;Fuels
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:liu:diva-151301DOI: 10.23919/ACC.2018.8430899OAI: oai:DiVA.org:liu-151301DiVA, id: diva2:1248572
Conference
American Control Conference
Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2018-09-17

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Jung, Daniel

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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