State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles
Authors:
- Steffen BockrathSBSteffen Bockrath
- Vincent LorentzVLVincent Lorentz
- Marco PrucknerMPMarco Pruckner
Open Access
- 21 November 2022
- journal article
- research article
- Published by Elsevier in Applied Energy
- Vol. 329
Abstract
No abstract availableKeywords
Funding Information
- European Commission
- Bundesministerium für Forschung, Technologie und Raumfahrt
- Horizon 2020 Framework Programme
- Horizon 2020
- Electronic Components and Systems for European Leadership
- Horizon 2020 (826060, 963522, 963560)
This publication has 55 references indexed in Scilit:
- Gaussian process regression for forecasting battery state of healthJournal of Power Sources, 2017
- Degradation diagnostics for lithium ion cellsJournal of Power Sources, 2017
- A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimationApplied Energy, 2015
- A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operationJournal of Power Sources, 2014
- Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteriesJournal of Power Sources, 2014
- On the use of cross-validation for time series predictor evaluationInformation Sciences, 2012
- A survey of cross-validation procedures for model selectionStatistics Surveys, 2010
- Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteriesApplied Energy, 2009
- Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packsJournal of Power Sources, 2006
- Extended Kalman filtering for battery management systems of LiPB-based HEV battery packsJournal of Power Sources, 2004