State of charge-state of health collaborative estimation for onboard lithium-ion batteries based on real-world applications from the northern and southern regions of China

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Impact Factor:5.6

DOI number:10.1016/j.measurement.2026.121578

Journal:Measurement

Key Words:Lithium-ion battery; State of charge-state of health; Collaborative estimation; Hybrid network; SOC changing threshold

Abstract:The low accuracy and weak robustness of the state of charge-state of health (SOC-SOH) collaborative estimation for onboard lithium-ion batteries lead to fire and explosion of electric vehicles. However, the internal electrochemical reaction of lithium-ion batteries is complex, and the external application scenarios are random and changeable, making SOC-SOH collaborative estimation challenging. This paper proposes a SOC-SOH collaborative estimation framework based on the hybrid network and the SOC changing threshold using real-world data. Firstly, a Convolutional Neural Network-Gated Recurrent Unit-Attention (CNN-GRU-Attention) hybrid network for SOC estimation is constructed. Then, a SOC changing threshold concept is introduced for online capacity estimation using discharging data, avoiding the difficulty of obtaining some features when using charging data for SOH estimation, due to the random charging start point and the charging end point. Finally, the capacity convergence correction and the temperature correction methods are developed to achieve accurate and robust SOC-SOH collaborative estimation results. The experimental results indicate that the proposed framework can reach ideal SOC-SOH collaborative estimation results in real-world applications, including the northern and southern regions of China. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of SOC estimation results are all below 0.95% and 1.10%, respectively. The MAE and RMSE of SOH estimation results are all below 0.95% and 1.05%, respectively.

Note:中科院2区Top

Co-author:Sihan Cheng,Siyi Liu,Yuan Chen,Heng Li

First Author:Muyao Wu

Indexed by:Journal paper

Correspondence Author:Li Wang

Document Code:121578

Discipline:Engineering

Document Type:J

ISSN No.:0263-2241

Translation or Not:no

Date of Publication:2026-04-26

Included Journals:SCI、EI