Intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario

Jianfei Cao, Hongwen He and Dong Wei

Applied Energy, 2021, vol. 281, issue C, No S0306261920313994

Abstract: At present, plug-in hybrid electric vehicle has got widely concerned and studied for its special advantages of fuel-saving and emission-reduction potential. In order to improve the electric energy efficiency in power battery and meet the practical requirements of energy management strategies, an intelligent SOC-consumption allocation of commercial plug-in hybrid electric vehicles in variable scenario is proposed in this paper. Generated by random combination of the selected driving cycle units, the variable scene could reflect the driving situation of commercial vehicles performing transportation tasks at some extent. The strategy is trained and generated by a reinforcement learning framework. Firstly, a power-flow analysis model is constructed. Then, a dynamic programming description that could solve the optimal power-flow is defined. Furthermore, the optimizations for different initial conditions were performed repeatedly. With the solution results, an energy-conversion model is generated to describes the conversion of fuel and electricity, and reward in reinforcement learning was defined and used to guide the agent to improve and enhance the required SOC-allocation strategy. The trained strategy performed a reasonable SOC-allocation according to the cycle unit and residual SOC; and fuel consumption under average condition is significantly lower than that of two normally used SOC-allocation strategies.

Keywords: SOC-consumption allocation; Reinforcement learning; Variable scenario; Dynamic programming; Power-flow model (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920313994
Full text for ScienceDirect subscribers only

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:eee:appene:v:281:y:2021:i:c:s0306261920313994

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.115942

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().