Application domain extension of incremental capacity-based battery SoH indicators

[1]  Joeri Van Mierlo,et al.  Random forest regression for online capacity estimation of lithium-ion batteries , 2018, Applied Energy.

[2]  Jonghoon Kim,et al.  Review of state-of-the-art battery state estimation technologies for battery management systems of stationary energy storage systems , 2020, Journal of Power Electronics.

[3]  Ping Fu,et al.  Research on state of health prediction model for lithium batteries based on actual diverse data , 2021 .

[4]  Jiuchun Jiang,et al.  State of health estimation of second-life LiFePO4 batteries for energy storage applications , 2018, Journal of Cleaner Production.

[5]  Guang Li,et al.  A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization , 2020 .

[6]  Kristen A. Severson,et al.  Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.

[7]  Michael A. Osborne,et al.  Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries , 2017, IEEE Transactions on Industrial Informatics.

[8]  Jinpeng Tian,et al.  Towards a smarter battery management system: A critical review on battery state of health monitoring methods , 2018, Journal of Power Sources.

[9]  Wenhu Qin,et al.  Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Optimal Time Series Health Indicator , 2020, IEEE Access.

[10]  Jonghyun Park,et al.  A Single Particle Model with Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation , 2018 .

[11]  Valérie Sauvant-Moynot,et al.  Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime , 2016 .

[12]  Olympia Colizoli,et al.  Task-evoked pupil responses reflect internal belief states , 2018, Scientific Reports.

[13]  Jia Tang,et al.  A Health Monitoring Method Based on Multiple Indicators to Eliminate Influences of Estimation Dispersion for Lithium-Ion Batteries , 2019, IEEE Access.

[14]  Furong Gao,et al.  A fast estimation algorithm for lithium-ion battery state of health , 2018, Journal of Power Sources.

[15]  Zhenpo Wang,et al.  State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression , 2020 .

[16]  Xuning Feng,et al.  Low temperature aging mechanism identification and lithium deposition in a large format lithium iron phosphate battery for different charge profiles , 2015 .

[17]  Yu Peng,et al.  An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter , 2018, IEEE Access.

[18]  Jay Lee,et al.  Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .

[19]  Mohammad Ali Abdelkareem,et al.  Critical review of energy storage systems , 2021 .

[20]  Zhenpo Wang,et al.  State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression , 2020, Energy.

[21]  Ashley Fly,et al.  Rate dependency of incremental capacity analysis (dQ/dV) as a diagnostic tool for lithium-ion batteries , 2020, Journal of Energy Storage.

[22]  Yann Bultel,et al.  Innovative Incremental Capacity Analysis Implementation for C/LiFePO4 Cell State-of-Health Estimation in Electrical Vehicles , 2019, Batteries.

[23]  Matthieu Dubarry,et al.  Synthesize battery degradation modes via a diagnostic and prognostic model , 2012 .

[24]  Weixiong Wu,et al.  Low‐temperature reversible capacity loss and aging mechanism in lithium‐ion batteries for different discharge profiles , 2018, International Journal of Energy Research.

[25]  Joeri Van Mierlo,et al.  A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter , 2018 .

[26]  Pascal Venet,et al.  LiFePO4 Battery State of Health Online Estimation Using Electric Vehicle Embedded Incremental Capacity Analysis , 2015, 2015 IEEE Vehicle Power and Propulsion Conference (VPPC).

[27]  Ronald W. Schafer,et al.  What Is a Savitzky-Golay Filter? [Lecture Notes] , 2011, IEEE Signal Processing Magazine.

[28]  Matteo Galeotti,et al.  Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy , 2015 .

[29]  Yao Lei,et al.  An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine , 2021, Energy.

[30]  Wei Yi,et al.  Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter , 2020 .

[31]  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 .

[32]  Christoph R. Birkl,et al.  Diagnosis and prognosis of degradation in lithium-ion batteries , 2017 .

[33]  Christoph R. Birkl,et al.  Oxford Battery Degradation Dataset 1 , 2017 .

[34]  Akhil Garg,et al.  A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles , 2019, Journal of Electrochemical Energy Conversion and Storage.

[35]  Jingwen Wei,et al.  A multi-timescale framework for state monitoring and lifetime prognosis of lithium-ion batteries , 2021 .

[36]  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 .

[37]  Rui Xiong,et al.  A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles , 2014 .

[38]  P. Venet,et al.  A method to estimate battery SOH indicators based on vehicle operating data only , 2021 .

[39]  Jianqiang Kang,et al.  Correlation between capacity loss and measurable parameters of lithium-ion batteries , 2019, International Journal of Electrical Power & Energy Systems.

[40]  Shengkui Zeng,et al.  State of health estimation of lithium-ion batteries based on the constant voltage charging curve , 2019, Energy.

[41]  W. D. Widanage,et al.  A study of the influence of measurement timescale on internal resistance characterisation methodologies for lithium-ion cells , 2018, Scientific Reports.

[42]  I. Bloom,et al.  Differential voltage analyses of high-power, lithium-ion cells: 1. Technique and application , 2005 .

[43]  Xueying Zheng,et al.  State-of-Health Prediction For Lithium-Ion Batteries With Multiple Gaussian Process Regression Model , 2019, IEEE Access.

[44]  Zonghai Chen,et al.  A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems , 2020 .

[45]  D. Sauer,et al.  Lithium titanate oxide battery cells for high-power automotive applications – Electro-thermal properties, aging behavior and cost considerations , 2020 .

[46]  James Marco,et al.  On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system , 2017 .

[47]  Brian Ospina Agudelo,et al.  Experimental Analysis of Capacity Degradation in Lithium-ion Battery Cells with Different Rest Times , 2020, 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES).

[48]  Loic Boulon,et al.  A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges , 2020, World Electric Vehicle Journal.

[49]  Kwok-Leung Tsui,et al.  Early prediction of battery lifetime via a machine learning based framework , 2021, Energy.

[50]  Yi-Jun He,et al.  A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach , 2017 .