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 and Furong Gao

Energy, 2023, vol. 282, issue C

Abstract: 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.

Keywords: State of health; Voltage reconstruction; Neural network; Lithium-ion batteries; Electric vehicles (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023654

DOI: 10.1016/j.energy.2023.128971

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