Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions

Xin LAI, Yi YAO, Xiaopeng TANG, Yuejiu ZHENG, Yuanqiang ZHOU, Yuedong SUN, Furong GAO

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

5 Citations (Scopus)


The state of health (SOH) of batteries is an important but unmeasurable parameter closely related to battery safety and durability. However, most existing SOH estimation strategies rely on a specific load profile (e.g., constant current). To tackle this issue, we here report a method that first converts the dynamic voltage trajectories to the curves corresponding to the constant current profiles using a neural network model. Then, the aging characteristics are selected and the battery SOH is estimated accordingly from the converted voltage data using a Gaussian process regression model. Batteries with different aging degrees are tested with different working conditions to verify the proposed method. Numerically, the errors of the voltage reconstruction are bounded within 2 mV, while the SOH estimation errors under four dynamic working conditions remain within 2%. Our technical approach reduces the dependency of traditional SOH estimation methods on specific working conditions and shows strong potential for practical applications.
Original languageEnglish
Article number128971
Early online date2 Sept 2023
Publication statusPublished - Nov 2023

Bibliographical note

Funding Information:
This research is supported National Natural Science Foundation of China (NSFC) under Grant numbers 51977131 , 52277223 , and 52277222 , Hong Kong RGC Postdoctoral Fellowship Scheme ( PDFS2122-6S06 ), Shanghai Science and Technology Development Fund 22ZR1444500 and 19ZR1435800 .

Publisher Copyright:
© 2023 Elsevier Ltd


  • Electric vehicles
  • Lithium-ion batteries
  • Neural network
  • State of health
  • Voltage reconstruction


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