Battery health prediction under generalized conditions using a Gaussian process transition model

Journal of Energy Storage - Tập 23 - Trang 320-328 - 2019
Robert R. Richardson1, Michael A. Osborne1, David A. Howey1
1Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, United Kingdom

Tóm tắt

Từ khóa


Tài liệu tham khảo

He, 2018, An intertemporal decision framework for electrochemical energy storage management, Nat. Energy, 3, 404, 10.1038/s41560-018-0129-9

Wankmueller, 2017, Impact of battery degradation on energy arbitrage revenue of grid-level energy storage, J. Energy Storage, 10, 56, 10.1016/j.est.2016.12.004

Birkl, 2017, Degradation diagnostics for lithium ion cells, J. Power Sources, 341, 373, 10.1016/j.jpowsour.2016.12.011

Ruetschi, 2004, Aging mechanisms and service life of lead-acid batteries, J. Power Sources, 127, 33, 10.1016/j.jpowsour.2003.09.052

Farmann, 2015, Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles, J. Power Sources, 281, 114, 10.1016/j.jpowsour.2015.01.129

Schimpe, 2018, Comprehensive modeling of temperature-dependent degradation mechanisms in lithium iron phosphate batteries, J. Electrochem. Soc., 165, A181, 10.1149/2.1181714jes

Wang, 2011, Cycle-life model for graphite-LiFePO4 cells, J. Power Sources, 196, 3942, 10.1016/j.jpowsour.2010.11.134

Schmalstieg, 2014, A holistic aging model for Li (NiMnCo) O2 based 18650 lithium-ion batteries, J. Power Sources, 257, 325, 10.1016/j.jpowsour.2014.02.012

Ecker, 2012, Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data, J. Power Sources, 215, 248, 10.1016/j.jpowsour.2012.05.012

Dufo-López, 2014, Comparison of different lead-acid battery lifetime prediction models for use in simulation of stand-alone photovoltaic systems, Appl. Energy, 115, 242, 10.1016/j.apenergy.2013.11.021

Kupper, 2017, Multi-scale thermo-electrochemical modeling of performance and aging of a LiFePO4/graphite lithium-ion cell, J. Electrochem. Soc., 164, A304, 10.1149/2.0761702jes

Pinson, 2013, Theory of SEI formation in rechargeable batteries: capacity fade, accelerated aging and lifetime prediction, J. Electrochem. Soc., 160, A243, 10.1149/2.044302jes

Yang, 2017, Modeling of lithium plating induced aging of lithium-ion batteries: transition from linear to nonlinear aging, J. Power Sources, 360, 28, 10.1016/j.jpowsour.2017.05.110

Deshpande, 2017, Modeling Solid-Electrolyte Interphase (SEI) fracture: coupled mechanical/chemical degradation of the lithium ion battery, J. Electrochem. Soc., 164, A461, 10.1149/2.0841702jes

Yan, 2019, A battery management system with a Lebesgue-sampling-based extended Kalman filter, IEEE Trans. Ind. Electron., 66, 3227, 10.1109/TIE.2018.2842782

Zhang, 2018, An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction, Microelectron. Reliab., 81, 288, 10.1016/j.microrel.2017.12.036

Hu, 2016, Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling, IEEE Trans. Ind. Electron., 63, 2645

Patil, 2015, A novel multistage Support Vector Machine based approach for Li-ion battery remaining useful life estimation, Appl. Energy, 159, 285, 10.1016/j.apenergy.2015.08.119

Wang, 2013, Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model, J. Power Sources, 239, 253, 10.1016/j.jpowsour.2013.03.129

Nuhic, 2013, Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods, J. Power Sources, 239, 680, 10.1016/j.jpowsour.2012.11.146

Goebel, 2008, Prognostics in battery health management, IEEE Instrum. Meas. Mag., 11, 33, 10.1109/MIM.2008.4579269

Saha, 2008, Uncertainty management for diagnostics and prognostics of batteries using Bayesian techniques, IEEE Aerospace Conference, 1

He, 2011, Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method, J. Power Sources, 196, 10314, 10.1016/j.jpowsour.2011.08.040

Liu, 2012, Data-driven prognostics for lithium-ion battery based on gaussian process regression, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing), 1

Richardson, 2017, Gaussian process regression for forecasting battery state of health, J. Power Sources, 357, 209, 10.1016/j.jpowsour.2017.05.004

Birkl, 2017

Bole, 2014, Randomized battery usage data set, NASA AMES Prognostics Data Repository

Bole, 2014, Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use, 1

Nickson, 2015

Wilson, 2015

Rasmussen, 2006

Murphy, 2012

Nowotarski, 2017, Recent advances in electricity price forecasting: a review of probabilistic forecasting, Renew. Sustain. Energy Rev., 81, 1548, 10.1016/j.rser.2017.05.234

Taieb, 2014, A gradient boosting approach to the Kaggle load forecasting competition, Int. J. Forecast., 30, 382, 10.1016/j.ijforecast.2013.07.005