A novel hybrid model for lithium-ion batteries lifespan prediction with high accuracy and interpretability-Reference-Cited by-同舟云学术

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A novel hybrid model for lithium-ion batteries lifespan prediction with high accuracy and interpretability

Published:2023-05 Issue: Volume:61 Page:106728
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Pang Xiaoxian,Yang Wei,Wang Chengyun,Fan Haosen,Wang Le,Li Junhao,Zhong Shi,Zheng Wenzhi,Zou Hanbo,Chen Shengzhou,Liu Quanbing

Publisher

Elsevier BV

Subject

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

Reference57 articles.

1. Predicting the state of charge and health of batteries using data-driven machine learning;Ng;Nat. Mach. Intell.,2020

2. A review on the prediction of health state and serving life of lithium-ion batteries;Pang;Chem. Rec.,2022

3. Ageing mechanisms in lithium-ion batteries;Vetter;J. Power Sources,2005

4. Solid-electrolyte interphase during battery cycling: theory of growth regimes;von Kolzenberg;Chemsuschem,2020

5. In-situ etching and ion exchange induced 2D–2D MXene@Co9S8/CoMo2S4 heterostructure for superior Na+ storage;Wang;Chem. Eng. J.,2023

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm;Energy;2023-12

2. A Data-Driven Approach to Rapidly Identify the Critical Current of Fast-Charging Lithium-Ion Batteries;Journal of The Electrochemical Society;2023-12-01

3. A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning;IEEE Access;2023

4. Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Based On Improved Grey Wolf Optimization Algorithm-Deep Extreme Learning Machine Algorithm;2023

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