Accelerated state of health estimation of second life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine learning techniques

[1]  W. D. Widanage,et al.  Operando Electrochemical Impedance Spectroscopy and its Application to Commercial Lithium-Ion Batteries , 2022, SSRN Electronic Journal.

[2]  Zhiwu Huang,et al.  State-of-health estimation of lithium-ion batteries based on improved long short-term memory algorithm , 2022, Journal of Energy Storage.

[3]  D. Sauer,et al.  Battery degradation diagnosis with field data, impedance-based modeling and artificial intelligence , 2022, Energy Storage Materials.

[4]  Xinbo Chen,et al.  Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles , 2022, Energy.

[5]  Tonni Agustiono Kurniawan,et al.  Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction , 2022, Journal of Energy Storage.

[6]  Kailong Liu,et al.  Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model , 2022, IEEE Transactions on Industrial Electronics.

[7]  Mohammad Amin Alipour,et al.  Improved Battery Cycle Life Prediction Using a Hybrid Data‐Driven Model Incorporating Linear Support Vector Regression and Gaussian , 2022, Chemphyschem : a European journal of chemical physics and physical chemistry.

[8]  Saeid Habibi,et al.  Estimating battery state of health using electrochemical impedance spectroscopy and the relaxation effect , 2021, Journal of Energy Storage.

[9]  Yanqiu Xiao,et al.  A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods , 2021, World Electric Vehicle Journal.

[10]  Afshin Rezaei-Zare,et al.  State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach , 2021 .

[11]  J. Kowal,et al.  On-line state-of-health estimation of Lithium-ion battery cells using frequency excitation , 2020 .

[12]  J. A. Abu Qahouq,et al.  Evaluation of Parameter Variations of Equivalent Circuit Model of Lithium-ion Battery under Different SOH Conditions , 2020, 2020 IEEE Energy Conversion Congress and Exposition (ECCE).

[13]  Yue Liu,et al.  Machine learning assisted materials design and discovery for rechargeable batteries , 2020, Energy Storage Materials.

[14]  M. Ozkan,et al.  State‐of‐health prediction for lithium‐ion batteries via electrochemical impedance spectroscopy and artificial neural networks , 2020, Energy Storage.

[15]  E. Komaroff.  Relationships Between p-values and Pearson Correlation Coefficients, Type 1 Errors and Effect Size Errors, Under a True Null Hypothesis , 2020, Journal of Statistical Theory and Practice.

[16]  U. Stimming,et al.  Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning , 2020, Nature Communications.

[17]  Yi Li,et al.  Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries , 2019, IEEE Transactions on Transportation Electrification.

[18]  James Marco,et al.  Critical review of non-invasive diagnosis techniques for quantification of degradation modes in lithium-ion batteries , 2019, Renewable and Sustainable Energy Reviews.

[19]  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).

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

[21]  Lin Chen,et al.  Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine , 2018, Energy.

[22]  Zonghai Chen,et al.  A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve , 2018 .

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

[24]  Pedro Rodriguez,et al.  Technical Viability of Battery Second Life: A Study From the Ageing Perspective , 2018, IEEE Transactions on Industry Applications.

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

[26]  Joeri Van Mierlo,et al.  Lithium-ion batteries: Comprehensive technical analysis of second-life batteries for smart grid applications , 2017, 2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe).

[27]  Weige Zhang,et al.  Recognition of battery aging variations for LiFePO 4 batteries in 2nd use applications combining incremental capacity analysis and statistical approaches , 2017 .

[28]  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 .

[29]  N. Gogtay,et al.  Principles of Correlation Analysis. , 2017, The Journal of the Association of Physicians of India.

[30]  Jinho Kim,et al.  Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model , 2017, IEEE Transactions on Control Systems Technology.

[31]  Mihai V. Micea,et al.  Battery state of health estimation: a structured review of models, methods and commercial devices , 2017 .

[32]  Julien Bernard,et al.  Novel state-of-health diagnostic method for Li-ion battery in service , 2016 .

[33]  James Marco,et al.  Identification and quantification of ageing mechanisms in Lithium-ion batteries using the EIS technique , 2016, 2016 IEEE Transportation Electrification Conference and Expo (ITEC).

[34]  Maitane Berecibar,et al.  State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application , 2016 .

[35]  Alex Densmore,et al.  Determining battery SoC using Electrochemical Impedance Spectroscopy and the Extreme Learning Machine , 2015, 2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC).

[36]  Andrew McGordon,et al.  A study on the impact of lithium-ion cell relaxation on electrochemical impedance spectroscopy , 2015 .

[37]  R. Teodorescu,et al.  Diagnosis of lithium-ion batteries state-of-health based on electrochemical impedance spectroscopy technique , 2014, 2014 IEEE Energy Conversion Congress and Exposition (ECCE).

[38]  A. Burke,et al.  Life cycle testing of lithium batteries for fast charging and second-use applications , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[39]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[40]  Julien Bernard,et al.  EIS Measurements for Determining the SoC and SoH of Li-Ion Batteries , 2011 .

[41]  Tadayoshi Fushiki,et al.  Estimation of prediction error by using K-fold cross-validation , 2011, Stat. Comput..

[42]  H. Razik,et al.  Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[43]  Carl E. Rasmussen,et al.  Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..

[44]  Xiaosong Hu,et al.  State of health prognostics for series battery packs: A universal deep learning method , 2022 .

[45]  Lin Chen,et al.  Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation , 2021 .

[46]  Siva Priya Thiagarajah,et al.  Feasibility of utilising second life EV batteries: Applications, lifespan, economics, environmental impact, assessment, and challenges , 2021 .

[47]  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 .

[48]  Amit Gupta,et al.  Effect of Relaxation Periods over Cycling Performance of a Li-Ion Battery , 2015 .

[49]  M. Krstić,et al.  Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model , 2014 .