Parameter identification and state-of-charge estimation for Li-ion batteries using an improved tree seed algorithm

Weijie Chen, Ming Cai, Xiaojun Tan, Bo Wei

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Accurate estimation of the state-of-charge is a crucial need for the battery, which is the most important power source in electric vehicles. To achieve better estimation result, an accurate battery model with optimum parameters is required. In this paper, a gradient-free optimization technique, namely tree seed algorithm (TSA), is utilized to identify specific parameters of the battery model. In order to strengthen the search ability of TSA and obtain more quality results, the original algorithm is improved. On one hand, the DE/rand/2/bin mechanism is employed to maintain the colony diversity, by generating mutant individuals in each time step. On the other hand, the control parameter in the algorithm is adaptively updated during the searching process, to achieve a better balance between the exploitation and exploration capabilities. The battery state-of-charge can be estimated simultaneously by regarding it as one of the parameters. Experiments under different dynamic profiles show that the proposed method can provide reliable and accurate estimation results. The performance of conventional algorithms, such as genetic algorithm and extended Kalman filter, are also compared to demonstrate the superiority of the proposed method in terms of accuracy and robustness.

Original languageEnglish
Pages (from-to)1489-1497
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE102D
Issue number8
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • Differential evolution
  • Optimizing algorithm
  • Parameter identification
  • Swarm intelligence
  • Tree seed algorithm

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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