Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing

Xin LAI, Ming YUAN, Xiaopeng TANG*, Yi YAO, Jiahui WENG, Furong GAO, Weiguo MA, Yuejiu ZHENG

*Corresponding author for this work

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

28 Citations (Scopus)


State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time under different battery ageing degrees. Then, the TLS algorithm is used to estimate the battery SOH to improve the prior estimation accuracy of SOC. Next, the SOC is calculated by the UKF algorithm, and finally, a more accurate SOH can be obtained according to the UKF-based SOC trajectory. The battery-in-the-loop experiments are utilized to verify the proposed algorithm. For the cases of temperature change up to 35 °C and capacity decay up to 10%, our joint estimator can achieve ultra-low errors, bounded by 2%, respectively, for SOH and SOC. The proposed method paves the way for the advancement of battery use in applications, such as electric vehicles and microgrid applications.

Original languageEnglish
Article number7416
Issue number19
Publication statusPublished - 9 Oct 2022
Externally publishedYes

Bibliographical note

Funding Information:
This research was funded by the National Natural Science Foundation of China (Grant Nos. 51977131 and 52277223), Natural Science Foundation of Shanghai (19ZR1435800), State Key Laboratory of Automotive Safety and Energy (KF2020), Shanghai Science and Technology Development Fund (19QA1406200) and the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2122-6S06).

Publisher Copyright:
© 2022 by the authors.


  • forgetting factor recursive least squares
  • joint SOC-SOH estimation
  • lithium-ion batteries
  • total least squares
  • unscented Kalman filter


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