Power management optimization in plug-in hybrid electric vehicles subject to uncertain driving cycles
Optimization of power management in plug-in hybrid electric vehicles (PHEVs) with dual-power-source plays a critical role in achieving higher fuel economy and less pollutant emissions. In this study, power management and optimal control strategies in PHEVs have been investigated subject to uncertain driving cycles of individual drivers for particular trips. First, a stochastic driving cycle is constructed to more accurately model the dynamic characteristics of the uncertain driving cycles, derived from the historic record of individual drivers. Finite-horizon stochastic dynamic programming is adapted to globally optimize the vehicle performance in stochastic sense. Simulation results show that the proposed strategy significantly improves fuel economy, indicating the present optimization approach is very effective in exploring the potential of the hybridization of power train. A higher discretization of (that is, with smaller step sizes in) vehicle dynamics state variables (vehicle velocity, power demand and battery state of charge) has a positive impact on the fuel economy while the limitation of driving operability actually degrades the fuel economy. The commuting time with doubly truncated normal distribution slightly enhances the fuel economy in comparison with uniform distribution. In addition, there exists a tradeoff between the fuel economy and the pollutant emissions. These results could be utilized as a guideline for the design of PHEVs with different objectives.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/25901168
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Supplemental Notes:
- © 2019 Elsevier B.V. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhang, Hongtao
- Qin, Yanzhou
- Li, Xianguo
- Liu, Xinzhi
- Yan, Jinyue
- Publication Date: 2020-2
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 100029
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Serial:
- eTransportation
- Volume: 3
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2590-1168
- Serial URL: https://www.sciencedirect.com/journal/etransportation
Subject/Index Terms
- TRT Terms: Commuting; Electric vehicles; Fuel consumption; Optimization; Plug-in hybrid vehicles; Stochastic processes
- Subject Areas: Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01840339
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 25 2022 12:36PM