A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging

Xiaopeng TANG, Yujie WANG*, Changfu ZOU, Ke YAO, Yongxiao XIA, Furong GAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

145 Citations (Scopus)

Abstract

Temperature and cell aging are two major factors that influence the reliability and safety of Li-ion batteries. A general battery model considering both temperature and degradation is often difficult to develop, given the fact that there are many different types of cells with different shapes and/or internal chemical components. In response, a migration-based framework is proposed in this paper for battery modeling, in which the effects of temperature and aging are treated as uncertainties. An accurate model for a fresh cell is established first and then migrated to the degraded batteries through a Bayes Monte Carlo method. Experiments are carried out on both LiFePO4 batteries and Li(Ni1/3Co1/3Mn1/3) O2 batteries under various ambient temperatures and aging levels. The results indicate that the typical voltage prediction error can be limited within ±20 mV, for the cases of temperature change up to 40 °C, and capacity degradation up to 20%. The proposed method paves ways to an effective battery management and energy control for electric vehicles or micro grid applications.

Original languageEnglish
Pages (from-to)162-170
Number of pages9
JournalEnergy Conversion and Management
Volume180
Early online date6 Nov 2018
DOIs
Publication statusPublished - 15 Jan 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported partly by the National Natural Science Foundation of China (Grant No. 61433005 and 61803359 ), partly by Guangdong Scientific and Technological Project (Grant No. 2017B010120002 ), and partly by CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellow (Grant No. 2017LH007 ).

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

  • Battery management system
  • Bayes Monte Carlo method
  • Lithium-ion batteries
  • Model migration

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