State of health prediction for lithium-ion battery using a gradient boosting-based data-driven method

Author:

Qin Pengliang,Zhao Linhui,Liu Zhiyuan

Funder

National Natural Science Foundation of China

Natural Science Foundation of Heilongjiang Province

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference69 articles.

1. Real-time model-based estimation of SOC and SOH for energy storage systems;Cacciato;IEEE Trans. Power Electron.,2016

2. State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method;Gou;IEEE Trans. Veh. Technol.,2020

3. A review of the state of health for lithium -ion batteries: research status and suggestions;Tian;J. Clean. Prod.,2020

4. Remaining useful life prediction of lithium-ion battery based on improved cuckoo search particle filter and a novel state of charge estimation method;Qiu;J. Power Sources,2020

5. SOC and SOH joint estimation of the power batteries based on fuzzy unscented kalman filtering algorithm;Zeng;Energies,2019

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