Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries

Zeyu Chen, Jiahuan Lu, Bo Liu, Nan Zhou and Shijie Li
Additional contact information
Zeyu Chen: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Jiahuan Lu: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Bo Liu: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Nan Zhou: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Shijie Li: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China

Energies, 2020, vol. 13, issue 10, 1-15

Abstract: The performance of lithium-ion batteries will inevitably degrade during the high frequently charging/discharging load applied in electric vehicles. For hybrid electric vehicles, battery aging not only declines the performance and reliability of the battery itself, but it also affects the whole energy efficiency of the vehicle since the engine has to participate more. Therefore, the energy management strategy is required to be adjusted during the entire lifespan of lithium-ion batteries to maintain the optimality of energy economy. In this study, tests of the battery performances under thirteen different aging stages are involved and a parameters-varying battery model that represents the battery degradation is established. The influences of battery aging on energy consumption of a given plug-in hybrid electric vehicle (PHEV) are analyzed quantitatively. The results indicate that the variations of capacity and internal resistance are the main factors while the polarization and open circuit voltage (OCV) have a minor effect on the energy consumption. Based on the above efforts, the optimal energy management strategy is proposed for optimizing the energy efficiency concerning both the fresh and aging batteries in PHEV. The presented strategy is evaluated by a simulation study with different driving cycles, illustrating that it can balance out some of the harmful effects that battery aging can have on energy efficiency. The energy consumption is reduced by up to 2.24% compared with that under the optimal strategy without considering the battery aging.

Keywords: battery aging; plug-in electric vehicles; energy management; global optimization; state of health; particle swarm algorithm; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/10/2543/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/10/2543/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:10:p:2543-:d:359293

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().