Cyber-physical system based optimization framework for intelligent powertrain control

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dc.contributor.authorLv, Chen
dc.contributor.authorWang, Hong
dc.contributor.authorZhao, Bolin
dc.contributor.authorCao, Dongpu
dc.contributor.authorHuaji, Wang
dc.contributor.authorZhang, Junzhi
dc.contributor.authorLi, Yutong
dc.contributor.authorYuan, Ye
dc.date.accessioned2017-06-05T09:30:39Z
dc.date.available2017-06-05T09:30:39Z
dc.date.issued2017-03-28
dc.description.abstractThe interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented. Simulation-based parameter optimizations are carried out according to the objective functions. Simulation results show that an electric powertrain with intelligent controller can perform its tasks well under sport, eco, and normal driving modes. The vehicle further improves overall performance in vehicle dynamics, ride comfort, and energy efficiency. The results validate the feasibility and effectiveness of the proposed CPS-based optimization framework, and demonstrate its advantages over a baseline benchmark.en_UK
dc.identifier.citationLv C, Wang H, Zhao B, et al., (2017) Cyber-physical system based optimization framework for intelligent powertrain control. SAE International Journal of Commercial Vehicles, Volume 10, Issue 1, pp. 254-264, SAE TP 2017-01-0426en_UK
dc.identifier.issn1946-391X
dc.identifier.urihttp://papers.sae.org/2017-01-0426/
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/11967
dc.language.isoenen_UK
dc.publisherSociety of Automotive Engineersen_UK
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleCyber-physical system based optimization framework for intelligent powertrain controlen_UK
dc.typeArticleen_UK

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