Increasing the efficiency of li-ion battery cycle life testing with a partial-machine learning based end of life prediction

[1]  A. Jossen,et al.  Experimental degradation study of a commercial lithium-ion battery , 2023, Journal of Power Sources.

[2]  E. Leland,et al.  An open access tool for exploring machine learning model choice for battery life cycle prediction , 2022, Frontiers in Energy Research.

[3]  L. Román-Ramírez,et al.  Design of experiments applied to lithium-ion batteries: A literature review , 2022, Applied Energy.

[4]  T. Wik,et al.  A machine learning-based framework for online prediction of battery ageing trajectory and lifetime using histogram data , 2022, Journal of Power Sources.

[5]  Zheng Chen,et al.  State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives , 2021, iScience.

[6]  Shunli Wang,et al.  A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods , 2021, Frontiers in Mechanical Engineering.

[7]  Yunlong Shang,et al.  A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery , 2021, IEEE Transactions on Industrial Electronics.

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

[9]  Joeri Van Mierlo,et al.  Battery lifetime prediction and performance assessment of different modeling approaches , 2021, iScience.

[10]  David A. Howey,et al.  Automated Feature Extraction and Selection for Data-Driven Models of Rapid Battery Capacity Fade and End of Life , 2021, IEEE Transactions on Industrial Informatics.

[11]  Kwok-Leung Tsui,et al.  Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model , 2020 .

[12]  Yunhong Che,et al.  Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning , 2020, IEEE Transactions on Transportation Electrification.

[13]  Daniel L. Campbell,et al.  Degradation of Commercial Lithium-Ion Cells as a Function of Chemistry and Cycling Conditions , 2020 .

[14]  Fu-Kwun Wang,et al.  Gradient boosted regression model for the degradation analysis of prismatic cells , 2020, Comput. Ind. Eng..

[15]  Jian Ma,et al.  Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method , 2020 .

[16]  Stefano Ermon,et al.  Closed-loop optimization of fast-charging protocols for batteries with machine learning , 2020, Nature.

[17]  M. Lienkamp,et al.  Accelerated Aging Characterization of Lithium-ion Cells: Using Sensitivity Analysis to Identify the Stress Factors Relevant to Cyclic Aging , 2020, Batteries.

[18]  Joeri Van Mierlo,et al.  Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.

[19]  Michael Pecht,et al.  Accelerated cycle life testing and capacity degradation modeling of LiCoO2-graphite cells , 2019, Journal of Power Sources.

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

[21]  Jian Liu,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Health Indicator and Gaussian Process Regression Model , 2019, IEEE Access.

[22]  Hongwen He,et al.  Lithium-Ion Battery Remaining Useful Life Prediction With Box–Cox Transformation and Monte Carlo Simulation , 2019, IEEE Transactions on Industrial Electronics.

[23]  Chengyi Song,et al.  Temperature effect and thermal impact in lithium-ion batteries: A review , 2018, Progress in Natural Science: Materials International.

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

[25]  Matthieu Dubarry,et al.  Battery durability and reliability under electric utility grid operations: Representative usage aging and calendar aging , 2018, Journal of Energy Storage.

[26]  Michael A. Osborne,et al.  Battery health prediction under generalized conditions using a Gaussian process transition model , 2018, Journal of Energy Storage.

[27]  Jun Lu,et al.  Batteries and fuel cells for emerging electric vehicle markets , 2018 .

[28]  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.

[29]  P. Gyan,et al.  D-optimal design of experiments applied to lithium battery for ageing model calibration , 2017 .

[30]  Zhenpo Wang,et al.  State-of-Health Estimation for Lithium-Ion Batteries Based on the Multi-Island Genetic Algorithm and the Gaussian Process Regression , 2017, IEEE Access.

[31]  Joeri Van Mierlo,et al.  Online state of health estimation on NMC cells based on predictive analytics , 2016 .

[32]  Serge Pelissier,et al.  Experimental protocols and first results of calendar and/or cycling aging study of lithium-ion batteries - the MOBICUS project , 2016 .

[33]  Nigel P. Brandon,et al.  Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries , 2016 .

[34]  Taejung Yeo,et al.  A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation , 2015 .

[35]  F. Marini,et al.  Validation of chemometric models - a tutorial. , 2015, Analytica chimica acta.

[36]  T. Baumhöfer,et al.  Production caused variation in capacity aging trend and correlation to initial cell performance , 2014 .

[37]  Huei Peng,et al.  On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression , 2013 .

[38]  Alexander Golbraikh,et al.  Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling? , 2012, J. Chem. Inf. Model..

[39]  Dirk Uwe Sauer,et al.  Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data , 2012 .

[40]  Moses O. Tadé,et al.  A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models , 2012 .

[41]  M. Dubarry,et al.  Identifying battery aging mechanisms in large format Li ion cells , 2011 .

[42]  Yi-Hwa Liu,et al.  Search for an Optimal Rapid-Charging Pattern for Li-Ion Batteries Using the Taguchi Approach , 2010, IEEE Transactions on Industrial Electronics.

[43]  Roberto Kawakami Harrop Galvão,et al.  A method for calibration and validation subset partitioning. , 2005, Talanta.

[44]  D. Massart,et al.  The Mahalanobis distance , 2000 .

[45]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[46]  J. Marcoc,et al.  Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data-Part B_ Cycling operation , 2020 .

[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]  Martin Cifrain,et al.  Design-of-Experiment and Statistical Modeling of a Large Scale Aging Experiment for Two Popular Lithium Ion Cell Chemistries , 2013 .