Feature Extraction from Charging Profiles for State of Health Estimation of Lithium-ion Battery

Zuolu Wang, Guojin Feng, Xiuquan Sun, Dong Zhen, Fengshou Gu, Andrew D. Ball

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance to ensure the reliability and safety of battery management systems (BMS). The difficulty of modelling the complex degradation mechanism has made the data-driven methods gain much attention in battery SOH prediction. To improve the estimation accuracy of battery SOH, extracting the suitable health indicators is still a challenging work. In this work, the health indication features are attracted from the charging voltage profile based on the experimental data measured under constant current charging mode. Subsequently, the Pearson correlation coefficient is used to evaluate the relationships between the extracted health features and battery capacity, thus selecting the most effective health features for establishing the prediction models. Finally, the battery SOH is estimated using a Gaussian process regression (GPR) method. The estimation results with R 2 of 1 and lower mean absolute error (MAE) and maximum error (MAX) provide higher accuracy based on the extracted health feature.

Original languageEnglish
Article number012024
Number of pages10
JournalJournal of Physics: Conference Series
Issue number1
Early online date16 May 2022
Publication statusPublished - 16 May 2022
Event14th International Conference on Damage Assessment of Structures - Virtual, Online
Duration: 29 Oct 20211 Nov 2021
Conference number: 14


Dive into the research topics of 'Feature Extraction from Charging Profiles for State of Health Estimation of Lithium-ion Battery'. Together they form a unique fingerprint.

Cite this