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