Data-Driven State of Health Estimation Method of Lithium-ion Batteries for Partial Charging Curves
Artikel i vetenskaplig tidskrift, 2024

State of health (SOH) is one of the most important performance indicators of lithium-ion batteries (LIBs). Accurate estimation of SOH is a prerequisite for the safe and reliable operation of LIBs. Traditional SOH estimation methods predominantly rely on complete charging cycle data acquired through laboratory testing. However, in practical application, the charging behaviors of electric vehicle users are random and unpredictable, making the partial charging curves difficult to utilize the traditional methods. This work introduces a novel data-driven approach to estimating a battery's SOH for partial charging cases. Firstly, a curve fitting method is proposed to extract health indicators (HIs) from partial charging voltage data, where novel HIs based on the energy-voltage curve are extracted. A composite Gaussian process regression-based data-driven method is proposed to achieve highly accurate SOH estimation. The method's adaptability to real-world partial charging habits is evaluated through three representative scenarios derived from extensive charging behavior reports of EV users. The impact of partial charging on HI extraction is analyzed based on the three identified scenarios. The proposed method is verified using a combination of our laboratory testing data and the Oxford open dataset. The results show that the proposed framework demonstrates the ability to estimate SOH accurately and strong robustness to various partial charging behaviors.

partial charging

Fading channels


Lithium-ion battery




Discharges (electric)

Integrated circuit modeling

state of health estimation

health indicator

data-driven method


Jinrui Tang

Hubei Province New Energy Power Battery Engineering Research Center

Yang Li

Chalmers, Elektroteknik, System- och reglerteknik

Shaojin Wang

Wuhan University of Technology

Binyu Xiong

Hubei Province New Energy Power Battery Engineering Research Center

Xiangjun Li

Energy Storage System Integration and Configuration Technology Research Lab. at the China E

Jinxuan Pan

State Grid Yichun Power Supply Company

Qihong Chen

Wuhan University of Technology

Peng Wang

School of Electrical and Electronic Engineering

IEEE Transactions on Energy Conversion

0885-8969 (ISSN) 15580059 (eISSN)

Vol. In Press



Sannolikhetsteori och statistik

Annan elektroteknik och elektronik



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