A comprehensive review of the lithium-ion battery state of health prognosis methods combining aging mechanism analysis
暂无分享,去创建一个
[1] Chi-Guhn Lee,et al. A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries , 2022, IEEE Transactions on Industrial Informatics.
[2] K. M. Begam,et al. Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review , 2022, Journal of Energy Storage.
[3] R. Hu,et al. Estimation of remaining capacity of lithium-ion batteries based on X-ray computed tomography , 2022, Journal of Energy Storage.
[4] M. Saad,et al. SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements , 2022, Batteries.
[5] Yan Zhang,et al. A novel hybrid data-driven method based on uncertainty quantification to predict the remaining useful life of lithium battery , 2022, Journal of Energy Storage.
[6] Chaolong Zhang,et al. Lithium-ion battery capacity and remaining useful life prediction using board learning system and long short-term memory neural network , 2022, Journal of Energy Storage.
[7] Lili Zheng,et al. Effect of High-Rate Cycle Aging and Over-Discharge on NCM811 (LiNi0.8Co0.1Mn0.1O2) Batteries , 2022, Energies.
[8] Xiaosong Hu,et al. Battery health estimation with degradation pattern recognition and transfer learning , 2022, Journal of Power Sources.
[9] Youssef A. Fahmy,et al. A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles , 2022, Energies.
[10] R. Teodorescu,et al. Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction , 2021, Electronics.
[11] Yew Chai Paw,et al. Efficient linear predictive model with short term features for lithium-ion batteries state of health estimation , 2021, Journal of Energy Storage.
[12] Andrew Ball,et al. A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles , 2021 .
[13] Yujie Wang,et al. Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries , 2021, iScience.
[14] Shunli Wang,et al. A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries , 2021, Energy Reports.
[15] Shen Xiaoyu,et al. State-of-health estimation based on real data of electric vehicles concerning user behavior , 2021 .
[16] Yanqiu Xiao,et al. A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods , 2021, World Electric Vehicle Journal.
[17] Shunli Wang,et al. A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods , 2021, Frontiers in Mechanical Engineering.
[18] Eric J. Dufek,et al. Rapid failure mode classification and quantification in batteries: A deep learning modeling framework , 2021, Energy Storage Materials.
[19] Anna G. Stefanopoulou,et al. The challenge and opportunity of battery lifetime prediction from field data , 2021, Joule.
[20] Tiezhou Wu,et al. Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm , 2021, Journal of Energy Storage.
[21] Xiaosong Hu,et al. General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries , 2021, IEEE/ASME Transactions on Mechatronics.
[22] Xingxing Jiang,et al. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries , 2021 .
[23] Xianzhong Sun,et al. Electrochemical impedance spectroscopy study of lithium-ion capacitors: Modeling and capacity fading mechanism , 2021 .
[24] Matthieu Dubarry,et al. Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis , 2020 .
[25] Xu Guo,et al. A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis , 2020 .
[26] Yong Guan,et al. The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach , 2020 .
[27] Heath Hofmann,et al. Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest , 2020, Journal of Energy Storage.
[28] Rui Xiong,et al. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives , 2020 .
[29] Kun Li,et al. A review of the state of health for lithium-ion batteries: Research status and suggestions , 2020 .
[30] Ke Yang,et al. Online estimation of state of health for the airborne Li-ion battery using adaptive DEKF-based fuzzy inference system , 2020, Soft Computing.
[31] Penghua Li,et al. State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network , 2020, Journal of Power Sources.
[32] Yao Lei,et al. A novel intelligent method for fault diagnosis of electric vehicle battery system based on wavelet neural network , 2020 .
[33] Xiaosong Hu,et al. Battery Lifetime Prognostics , 2020 .
[34] Yang Gao,et al. Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method , 2019 .
[35] Kaike Wang,et al. Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks , 2019 .
[36] Yan Xu,et al. A Comprehensive Review of Health Indicators of Li-ion Battery for Online State of Health Estimation , 2019, 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2).
[37] Mattia Ricco,et al. A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model , 2019, IEEE Transactions on Industrial Electronics.
[38] Joeri Van Mierlo,et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.
[39] Chao Hu,et al. A deep learning method for online capacity estimation of lithium-ion batteries , 2019, Journal of Energy Storage.
[40] Zhe Li,et al. A review on the key issues of the lithium ion battery degradation among the whole life cycle , 2019, eTransportation.
[41] Xiao-Sheng Si,et al. State-of-Health Estimation for Lithium-Ion Batteries Based on Wiener Process With Modeling the Relaxation Effect , 2019, IEEE Access.
[42] Zhenpo Wang,et al. Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression , 2019, Journal of Power Sources.
[43] Kristen A. Severson,et al. Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.
[44] Jaber A. Abu Qahouq,et al. Adaptive and Fast State of Health Estimation Method for Lithium-ion Batteries Using Online Complex Impedance and Artificial Neural Network , 2019, 2019 IEEE Applied Power Electronics Conference and Exposition (APEC).
[45] Zhaohua Yang,et al. A Review of Lithium-Ion Battery for Electric Vehicle Applications and Beyond , 2019, Energy Procedia.
[46] David Anseán,et al. Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis , 2019, IEEE Transactions on Industry Applications.
[47] M. A. Hannan,et al. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations , 2018, Journal of Cleaner Production.
[48] Michael A. Osborne,et al. Battery health prediction under generalized conditions using a Gaussian process transition model , 2018, Journal of Energy Storage.
[49] M. Carvalho,et al. The lithium-ion battery: State of the art and future perspectives , 2018, Renewable and Sustainable Energy Reviews.
[50] Dylan Dah-Chuan Lu,et al. Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries , 2018 .
[51] Andreas Kamilaris,et al. Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..
[52] P. Ajayan,et al. High-temperature solid electrolyte interphases (SEI) in graphite electrodes , 2018 .
[53] Lijun Zhang,et al. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Exponential Model and Particle Filter , 2018, IEEE Access.
[54] Hongwen He,et al. Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2018, IEEE Transactions on Vehicular Technology.
[55] Pham Luu Trung Duong,et al. Heuristic Kalman optimized particle filter for remaining useful life prediction of lithium-ion battery , 2018, Microelectron. Reliab..
[56] Chen Lu,et al. A review of stochastic battery models and health management , 2017 .
[57] Andreas Jossen,et al. Comprehensive Modeling of Temperature-Dependent Degradation Mechanisms in Lithium Iron Phosphate Batteries , 2017 .
[58] W. D. Widanage,et al. A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Battery Management Systems , 2017 .
[59] Yang Gao,et al. Lithium-ion battery aging mechanisms and life model under different charging stresses , 2017 .
[60] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[61] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[62] Mihai V. Micea,et al. Battery state of health estimation: a structured review of models, methods and commercial devices , 2017 .
[63] D. Finegan,et al. Investigating lithium-ion battery materials during overcharge-induced thermal runaway: an operando and multi-scale X-ray CT study. , 2016, Physical chemistry chemical physics : PCCP.
[64] Tulga Ersal,et al. Battery state of health monitoring by estimation of the number of cyclable Li-ions , 2016 .
[65] Oyas Wahyunggoro,et al. State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter , 2016, 2016 2nd International Conference on Science and Technology-Computer (ICST).
[66] F. Marone,et al. Quantifying microstructural dynamics and electrochemical activity of graphite and silicon-graphite lithium ion battery anodes , 2016, Nature Communications.
[67] Pengjian Zuo,et al. The effect of elevated temperature on the accelerated aging of LiCoO2/mesocarbon microbeads batteries , 2016 .
[68] Wei Qi,et al. D-UKF based state of health estimation for 18650 type lithium battery , 2016, 2016 IEEE International Conference on Mechatronics and Automation.
[69] Hicham Jamouli,et al. Lithium-ion Battery Degradation Assessment and Remaining Useful Life Estimation in Hybrid Electric Vehicle , 2016 .
[70] Debasish Mohanty,et al. The state of understanding of the lithium-ion-battery graphite solid electrolyte interphase (SEI) and its relationship to formation cycling , 2016 .
[71] Maitane Berecibar,et al. State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application , 2016 .
[72] Michael S. Lew,et al. Deep learning for visual understanding: A review , 2016, Neurocomputing.
[73] Pan Chaofeng,et al. On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis , 2016 .
[74] I. Villarreal,et al. Critical review of state of health estimation methods of Li-ion batteries for real applications , 2016 .
[75] Simona Onori,et al. Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery , 2016, IEEE Transactions on Control Systems Technology.
[76] E. Sarasketa-Zabala,et al. Realistic lifetime prediction approach for Li-ion batteries , 2016 .
[77] K. Jalkanen,et al. Cycle aging of commercial NMC/graphite pouch cells at different temperatures , 2015 .
[78] Matteo Galeotti,et al. Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy , 2015 .
[79] Shengkui Zeng,et al. Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model , 2015, Microelectron. Reliab..
[80] Wenwei Wang,et al. A Review of SOH Estimation Methods in Lithium-ion Batteries for Electric Vehicle Applications , 2015 .
[81] James B. Robinson,et al. In-operando high-speed tomography of lithium-ion batteries during thermal runaway , 2015, Nature Communications.
[82] J. Heinzel,et al. Impact of high rate discharge on the aging of lithium nickel cobalt aluminum oxide batteries , 2015 .
[83] Kuo-Hsin Tseng,et al. Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries , 2015 .
[84] Jianqiu Li,et al. Online estimation of lithium-ion battery remaining discharge capacity through differential voltage analysis , 2015 .
[85] M. Wohlfahrt‐Mehrens,et al. Interaction of cyclic ageing at high-rate and low temperatures and safety in lithium-ion batteries , 2015 .
[86] Chao Lyu,et al. Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter , 2014 .
[87] Yuanyuan Liu,et al. Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks , 2014 .
[88] Yanling Shen,et al. A new SOH prediction model for lithium-ion battery for electric vehicles , 2014, 2014 17th International Conference on Electrical Machines and Systems (ICEMS).
[89] M. Wohlfahrt‐Mehrens,et al. Temperature dependent ageing mechanisms in Lithium-ion batteries – A Post-Mortem study , 2014 .
[90] Masahiro Kinoshita,et al. Capacity fading of LiAlyNi1−x−yCoxO2 cathode for lithium-ion batteries during accelerated calendar and cycle life tests (effect of depth of discharge in charge–discharge cycling on the suppression of the micro-crack generation of LiAlyNi1−x−yCoxO2 particle) , 2014 .
[91] Jay Lee,et al. Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .
[92] Zafer Sahinoglu,et al. Fast UD factorization-based RLS online parameter identification for model-based condition monitoring of lithium-ion batteries , 2014, 2014 American Control Conference.
[93] Zhe Li,et al. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification , 2014 .
[94] Zhao Ping Chen,et al. The Application of UKF Algorithm for 18650-type Lithium Battery SOH Estimation , 2014, CIT 2014.
[95] Peng Lu,et al. Chemistry, Impedance, and Morphology Evolution in Solid Electrolyte Interphase Films during Formation in Lithium Ion Batteries , 2014 .
[96] Simona Onori,et al. Model-based state of charge estimation and observability analysis of a composite electrode lithium-ion battery , 2013, 52nd IEEE Conference on Decision and Control.
[97] Delphine Riu,et al. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications , 2013 .
[98] M. Winter,et al. Parametrisation of the influence of different cycling conditions on the capacity fade and the internal resistance increase for lithium nickel manganese cobalt oxide/graphite cells , 2013 .
[99] Qiang Miao,et al. Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .
[100] Tsorng-Juu Liang,et al. Estimation of Battery State of Health Using Probabilistic Neural Network , 2013, IEEE Transactions on Industrial Informatics.
[101] Mengyun Nie,et al. Lithium Ion Battery Graphite Solid Electrolyte Interphase Revealed by Microscopy and Spectroscopy , 2013 .
[102] Hosam K. Fathy,et al. A survey of long-term health modeling, estimation, and control of Lithium-ion batteries: Challenges and opportunities , 2012, 2012 American Control Conference (ACC).
[103] Simona Onori,et al. A new life estimation method for lithium-ion batteries in plug-in hybrid electric vehicles applications , 2012 .
[104] Olfa Kanoun,et al. Use of stochastic methods for robust parameter extraction from impedance spectra , 2011 .
[105] Bo-Suk Yang,et al. Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..
[106] Jay Lee,et al. A review on prognostics and health monitoring of Li-ion battery , 2011 .
[107] Michael Buchholz,et al. State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation , 2011 .
[108] M. Dubarry,et al. Identifying battery aging mechanisms in large format Li ion cells , 2011 .
[109] J. Apt,et al. Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization , 2010 .
[110] Nigel P. Brandon,et al. Characterization of the 3-dimensional microstructure of a graphite negative electrode from a Li-ion battery , 2010 .
[111] Sun Zechang,et al. A new SOH prediction concept for the power lithium-ion battery used on HEVs , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.
[112] C. Moo,et al. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .
[113] T. Guena,et al. How Depth of Discharge Affects the Cycle Life of Lithium-Metal-Polymer Batteries , 2006, INTELEC 06 - Twenty-Eighth International Telecommunications Energy Conference.
[114] M. Dubarry,et al. Incremental Capacity Analysis and Close-to-Equilibrium OCV Measurements to Quantify Capacity Fade in Commercial Rechargeable Lithium Batteries , 2006 .
[115] Ralph E. White,et al. Analysis of capacity fade in a lithium ion battery , 2005 .
[116] I. Bloom,et al. Differential voltage analyses of high-power, lithium-ion cells: 1. Technique and application , 2005 .
[117] Ralph E. White,et al. Development of First Principles Capacity Fade Model for Li-Ion Cells , 2004 .
[118] Ralph E. White,et al. Mathematical modeling of the capacity fade of Li-ion cells , 2003 .
[119] Georges Caillon,et al. Thin and flexible lithium-ion batteries: investigation of polymer electrolytes , 2003 .
[120] Gan Ning,et al. Capacity fade study of lithium-ion batteries cycled at high discharge rates , 2003 .
[121] J. D. Kozlowski. Electrochemical cell prognostics using online impedance measurements and model-based data fusion techniques , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).
[122] M. Doyle,et al. Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell , 1993 .
[123] Xiuze Zhou,et al. Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2022, IEEE Access.
[124] Song Ci,et al. Data-Driven Methods for Battery SOH Estimation: Survey and a Critical Analysis , 2021, IEEE Access.
[125] G. Rizzoni,et al. Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health , 2021 .
[126] Peggy Zwolinski,et al. Review on State of Health estimation methodologies for lithium-ion batteries in the context of circular economy , 2021 .
[127] Jiuchun Jiang,et al. Lifetime Rapid Evaluation Method for Lithium-Ion Battery with Li(NiMnCo)O2 Cathode , 2019, Journal of The Electrochemical Society.
[128] Simon J. L. Billinge,et al. X-Ray Diffraction Computed Tomography for Structural Analysis of Electrode Materials in Batteries , 2015 .
[129] Shengbo Eben Li,et al. Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles , 2015 .
[130] Rui Xiong,et al. A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles , 2014 .
[131] Mohammad Farrokhi,et al. Online State-of-Health Estimation of VRLA Batteries Using State of Charge , 2013, IEEE Transactions on Industrial Electronics.
[132] L. Castro,et al. Aging Mechanisms of LiFePO4 // Graphite Cells Studied by XPS: Redox Reaction and Electrode/Electrolyte Interfaces , 2012 .
[133] T. Abe,et al. Spectroscopic Characterization of Surface Films Formed on Edge Plane Graphite in Ethylene Carbonate-Based Electrolytes Containing Film-Forming Additives , 2012 .
[134] M. Doyle,et al. Simulation and Optimization of the Dual Lithium Ion Insertion Cell , 1994 .