Battery impedance spectrum prediction from partial charging voltage curve by machine learning

[1]  Y. Zhao,et al.  Machine Learning for Battery Research , 2022, SSRN Electronic Journal.

[2]  Yunhong Che,et al.  Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network , 2022, Applied Energy.

[3]  U. Stimming,et al.  Impedance-based forecasting of lithium-ion battery performance amid uneven usage , 2022, Nature Communications.

[4]  L. Gurevich,et al.  Understanding the mechanism of capacity increase during early cycling of commercial NMC/graphite lithium-ion batteries , 2022, Journal of Energy Chemistry.

[5]  C. Zinola,et al.  Identification and quantification of ageing mechanisms in Li-ion batteries by Electrochemical impedance spectroscopy. , 2022, Electrochimica Acta.

[6]  M. Zabara,et al.  Operando Investigations of the Interfacial Electrochemical Kinetics of Metallic Lithium Anodes via Temperature-Dependent Electrochemical Impedance Spectroscopy , 2022, The Journal of Physical Chemistry C.

[7]  Yuezhou Zhang,et al.  An odyssey of lithium metal anode in liquid lithium–sulfur batteries , 2021, Chinese Chemical Letters.

[8]  Hong‐Jie Peng,et al.  A generalizable, data-driven online approach to forecast capacity degradation trajectory of lithium batteries , 2021, Journal of Energy Chemistry.

[9]  Jaeyoung Lee,et al.  Rapid determination of lithium-ion battery degradation: High C-rate LAM and calculated limiting LLI , 2021, Journal of Energy Chemistry.

[10]  Xiaosong Hu,et al.  Lithium Plating Mechanism, Detection, and Mitigation in Lithium-Ion Batteries , 2021 .

[11]  Remus Teodorescu,et al.  A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery , 2021 .

[12]  W. Shen,et al.  Deep neural network battery impedance spectra prediction by only using constant-current curve , 2021 .

[13]  K. Ryan,et al.  Review—Use of Impedance Spectroscopy for the Estimation of Li-ion Battery State of Charge, State of Health and Internal Temperature , 2021, Journal of The Electrochemical Society.

[14]  Qiang Zhang,et al.  Applying Machine Learning in Rechargeable Batteries from Microscale to Macroscale. , 2021, Angewandte Chemie.

[15]  Haifeng Dai,et al.  Investigation of capacity fade for 18650-type lithium-ion batteries cycled in different state of charge (SoC) ranges , 2021 .

[16]  Didier Dumur,et al.  State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking , 2020, Journal of Power Sources.

[17]  Rui Xiong,et al.  Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives , 2020 .

[18]  Haiying Xia,et al.  Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction , 2020, Energies.

[19]  M. Kronforst,et al.  A shared genetic basis of mimicry across swallowtail butterflies points to ancestral co-option of doublesex , 2020, Nature Communications.

[20]  Wei Li,et al.  High-voltage electrochemical performance of LiNi0.5Co0.2Mn0.3O2 cathode material via the synergetic modification of the Zr/Ti elements , 2018, Electrochimica Acta.

[21]  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 .

[22]  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 .

[23]  Ji‐Guang Zhang,et al.  Optimized Operating Range for Large-Format LiFePO4/Graphite Batteries , 2014 .

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