Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression

Xiaoyu Li, Zhenpo Wang*, Jinying Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

288 Citations (Scopus)

Abstract

Precisely battery state of health estimation and remaining useful lifetime prediction are crucial factors in ensuring the reliability and safety for system operation. This paper thus focuses on the short-term battery state of health estimation and long-term battery remaining useful lifetime prediction. A novel hybrid method by fusion of partial incremental capacity and Gaussian process regression is proposed and dual Gaussian process regression models are employed to forecast battery health conditions. First, the initial incremental capacity curves are filtered by using the advanced signal process technology. Second, the important health feature variables are extracted from partial incremental capacity curves using correlation analysis method. Third, the Gaussian process regression is applied to model the short-term battery SOH estimation using the feature variables. Forth, an autoregressive long-term battery remaining useful lifetime model is established using the results of battery SOH values and previous output. The predictive capability and effectiveness of two models are demonstrated by four battery datasets under different cycling test conditions. Otherwise, the robustness of the two models is verified using four datasets with different health levels. The experimental results show that the proposed method can provide accurate battery state of health estimation and remaining useful lifetime.

Original languageEnglish
Pages (from-to)56-67
Number of pages12
JournalJournal of Power Sources
Volume421
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Correlation coefficient
  • Gaussian regression process
  • Incremental capacity analysis
  • Lithium-ion batteries
  • State of health

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