A Microstructure-resolved Electrochemical Modeling and Machine Learning Framework for Li-ion Battery Cathodes under Mechanical Deformation

We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

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Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries

RSC Advances ◽

10.1039/c8ra07112h ◽

2018 ◽

Vol 8 (69) ◽

pp. 39414-39420 ◽

Cited By ~ 11

Author(s):

Omar Allam ◽

Byung Woo Cho ◽

Ki Chul Kim ◽

Seung Soon Jang

Keyword(s):

Machine Learning ◽

Density Functional Theory ◽

High Throughput Screening ◽

Density Functional ◽

Electrode Materials ◽

Screening Method ◽

Li Ion Batteries ◽

Functional Theory ◽

Learning Framework ◽

Li Ion

In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials.

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Contactless Li-Ion Battery Voltage Detection by Using Walabot and Machine Learning

Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications ◽

10.1115/detc2019-97668 ◽

2019 ◽

Author(s):

Yanan Wang ◽

Haoyu Niu ◽

Tiebiao Zhao ◽

Xiaozhong Liao ◽

Lei Dong ◽

...

Keyword(s):

Machine Learning ◽

Gradient Descent ◽

Learning Algorithm ◽

Three Dimensional ◽

Lithium Ion ◽

Principal Component ◽

Stochastic Gradient Descent ◽

Li Ion Battery ◽

Linear Discriminant ◽

Li Ion

Abstract This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.

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An End-to-End Learning Framework for Battery Capacity-Fade Trajectory Prediction Using Early Life Data

Annual Conference of the PHM Society ◽

10.36001/phmconf.2021.v13i1.3053 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Jinqiang Liu ◽

Adam Thelen ◽

Chao Hu ◽

Xiao-Guang Yang

Keyword(s):

Machine Learning ◽

Early Life ◽

Life Prediction ◽

Capacity Fade ◽

Learning Problem ◽

Learning Framework ◽

Life Data ◽

Machine Learning Model ◽

Li Ion ◽

End To End

Predicting the capacity-fade trajectory of a lithium-ion (Li-ion) battery cell is a critical task given its broad utility throughout the battery product life cycle. Even more useful is estimating a battery cell’s capacity-fade trajectory when this cell has not exhibited any noticeable capacity degradation. Accurately predicting the entire capacity-fade trajectory using early life data enables more efficient cell design, operation, maintenance, and evaluation for second-life use. To accomplish this challenging task, we propose an end-to-end learning framework combining empirical capacity fade models and data-driven machine learning models, in which the two types of models are closely coupled. First, we evaluate the accuracy of a library of relevant empirical models which have been shown to model the observed capacity fade of Li-ion cells with reasonable accuracy. After selecting a model, we formulate an end-to-end learning problem that simultaneously fits the chosen empirical model to estimate the capacity fade curve and trains a machine learning model to estimate the best-fit parameters of the empirical model. By solving this end-to-end learning problem, rather than sequentially executing the separate tasks of fitting the capacity fade model and training the machine learning model, we achieve a more optimal solution which is shown to better balance these two objectives. Our proposed end-to-end learning framework is evaluated using a publicly available battery dataset consisting of 124 lithium-iron-phosphate/graphite cells charged with various fast-charging protocols. This dataset was split into training, primary test, and secondary test datasets. Our method performs on par with existing early prediction methods in terms of cycle life prediction, attaining root-mean-square errors of 84 cycles and 169 cycles for primary and secondary test datasets, respectively. In addition to the cycle life prediction, our method possesses a unique ability to predict the entire capacity-fade trajectory.

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