A machine-learning prediction method of lithium-ion battery life based on charge process for different applications
Author:
- Yixin YangYYYixin Yang
- 1 June 2021
- journal article
- research article
- Published by Elsevier in Applied Energy
- Vol. 292
Abstract
No abstract availableKeywords
This publication has 36 references indexed in Scilit:
- An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity predictionReliability Engineering & System Safety, 2015
- Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO–SVR modelMicroelectronics Reliability, 2015
- Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysisJournal of Power Sources, 2015
- Calendar ageing analysis of a LiFePO4/graphite cell with dynamic model validations: Towards realistic lifetime predictionsJournal of Power Sources, 2014
- Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian NetworksJournal of Power Sources, 2014
- Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation modelJournal of Power Sources, 2013
- Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methodsJournal of Power Sources, 2013
- Remaining useful life prediction of lithium batteries in calendar ageing for automotive applicationsMicroelectronics Reliability, 2012
- Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo methodJournal of Power Sources, 2011
- An accelerated calendar and cycle life study of Li-ion cellsJournal of Power Sources, 2001