State-of-health estimation for lithium-ion batteries based on historical dependency of charging data and ensemble SVR
Authors:
- Yongfang GuoYGYongfang Guo
- Kai HuangKai Huang
- Xiangyuan YuXYXiangyuan Yu
- Yashuang WangYWYashuang Wang
- 1 August 2022
- journal article
- research article
- Published by Elsevier in Electrochimica Acta
- Vol. 428
Abstract
No abstract availableKeywords
Funding Information
- Natural Science Foundation of Hebei Province (E2019202328)
This publication has 47 references indexed in Scilit:
- Re-synthesis of LiCoO 2 extracted from spent Li-ion batteries with low and high state of healthMaterials Chemistry and Physics, 2017
- State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak trackingApplied Energy, 2016
- Cycle life testing and modeling of graphite/LiCoO2 cells under different state of charge rangesJournal of Power Sources, 2016
- Critical review of state of health estimation methods of Li-ion batteries for real applicationsRenewable and Sustainable Energy Reviews, 2016
- A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and PrognosticsIEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015
- On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regressionJournal of Power Sources, 2013
- State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimationJournal of Power Sources, 2011
- Identifying battery aging mechanisms in large format Li ion cellsJournal of Power Sources, 2011
- LIBSVMACM Transactions on Intelligent Systems and Technology, 2011
- Bagging for Gaussian process regressionNeurocomputing, 2009