Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter

Xiaopeng TANG, Kailong LIU, Jingyi LU*, Boyang LIU, Xin WANG, Furong GAO

*Corresponding author for this work

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

47 Citations (Scopus)

Abstract

Incremental capacity analysis is a popular tool for the evaluation of state-of-health in battery management. In digital systems, the incremental capacity is generally approximated with the ratio of the capacity difference to voltage difference (ΔQ∕ΔV), which unavoidably amplifies measurement noises. To enhance its resilience against noises and improve the estimation accuracy, a two-dimensional filter is designed by employing historical information from both time and batch (cycle) directions inspired by batch-wise repetitiveness of the incremental capacity trajectories. Specifically, in the batch direction, a Luenberger observer is utilised to provide a batch-to-batch smoothing at the beginning of each charging cycle, while in the time direction, a bias-corrected Gaussian moving average filter is applied to smooth the incremental capacity value with respect to the voltage at every sampling time. Experimental results show that the root-mean-square-error of the proposed filter is 50% lower than the benchmark algorithms, and the noise sensitivity is significantly reduced by 93%. When using incremental capacity peaks extracted from the proposed filter for state-of-health modelling, the width of the 99% confidence interval would be narrowed by 45%. Moreover, the model-free nature of the proposed method enables its application to different batteries, paving a reliable way for effective battery health assessment.

Original languageEnglish
Article number115895
JournalApplied Energy
Volume280
Early online date1 Oct 2020
DOIs
Publication statusPublished - 15 Dec 2020
Externally publishedYes

Bibliographical note

Funding Information:
The first and the fourth authors would like to thank the Guangzhou HKUST Fok Ying Tung Research Institute for the continuing support during the Hong Kong's unrest and 2019-nCoV's outbreak. This work is supported partly by the Guangdong Provincial Science and Technology Planning Project (2017B010120002), partly by Guangdong Provincial Science and Technology Planning Project-Guangdong, Hong Kong and Macao joint Innovation Areas (2019A050516002), partly by Guangzhou Development Zone International Science and Technology Cooperation Project (2018GH13) and partly by Hong Kong Research Grant Council (Grant No. 16207717 and 16233316).

Publisher Copyright:
© 2020 Elsevier Ltd

Keywords

  • Electric vehicle
  • Incremental capacity analysis
  • Lithium-ion battery management
  • State of health
  • Two-dimensional filtering

Fingerprint

Dive into the research topics of 'Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter'. Together they form a unique fingerprint.

Cite this