首页|Fast Remaining Capacity Estimation for Lithium-ion Batteries Based on Short-time Pulse Test and Gaussian Process Regression

Fast Remaining Capacity Estimation for Lithium-ion Batteries Based on Short-time Pulse Test and Gaussian Process Regression

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It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics,energy storage,and electric vehicles.Herein,by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm,an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100％of the state of health(SOH)to below 50％,reaching an average accuracy as high as 95％.Interestingly,the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80％compared with regular long charge/discharge tests.The short-term features of the current pulse test were selected for an optimal training process.Data at different voltage stages and state of charge(SOC)are collected and explored to find the most suitable estimation model.In particular,we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features,whereas Gaussian process regression with Matern kernel performs the best,providing guidance for future exploration.The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.

capacity estimationdata-driven methodGaussian process regressionlithium-ion batterypulse tests

Aihua Ran、Ming Cheng、Shuxiao Chen、Zheng Liang、Zihao Zhou、Guangmin Zhou、Feiyu Kang、Xuan Zhang、Baohua Li、Guodan Wei

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Tsinghua-Berkeley Shenzhen Institute(TBSI),Tsinghua University,Shenzhen 518055,China

Tsinghua Shenzhen International Graduate School,Tsinghua University,Shenzhen 518055,China

Shenzhen Municipal Development and Reform Commission深圳市科技计划Interdisciplinary Research and Innovation Fund of Tsinghua Shenzhen International Graduate SchoolShanghai Shun Feng Machinery Co.,Ltd

SDRC[2016]172KQTD20170810150821146

2023

10.1002/eem2.12386

能源与环境材料(英文)

能源与环境材料(英文)

CSCD
ISSN：
年,卷(期)：2023.6(3)
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