Model-free learning-based online management of hybrid electrical energy storage systems in electric vehicles

Siyu Yue, Yanzhi Wang, Qing Xie, Di Zhu, Massoud Pedram, Naehyuck Chang

Research output: Chapter in Book/Entry/PoemConference contribution

10 Scopus citations

Abstract

To improve the cycle efficiency and peak output power density of energy storage systems in electric vehicles (EVs), supercapacitors have been proposed as auxiliary energy storage elements to complement the mainstream Lithium-ion (Li-ion) batteries. The performance of such a hybrid electrical energy storage (HEES) system is highly dependent on the implemented management policy. This paper presents a model-free reinforcement learning-based approach to dynamically manage the current flows from and into the battery and supercapacitor banks under various scenarios (combinations of EV specs and driving patterns). Experimental results demonstrate that the proposed approach achieves up to 25% higher efficiency compared to a Li-ion battery only storage system and outperforms other online HEES system control policies in all test cases.

Original languageEnglish (US)
Title of host publicationProceedings, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3142-3148
Number of pages7
ISBN (Print)9781479940325
DOIs
StatePublished - Feb 24 2014
Externally publishedYes
Event40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014 - Dallas, United States
Duration: Oct 30 2014Nov 1 2014

Other

Other40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014
Country/TerritoryUnited States
CityDallas
Period10/30/1411/1/14

Keywords

  • Electric vehicle
  • Hybrid energy storage systems
  • Reinforcement learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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