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Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes

Kobelco Research Institute Inc., Kobe 6512271, Japan
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Batteries 2019, 5(3), 54; https://doi.org/10.3390/batteries5030054
Received: 29 March 2019 / Revised: 19 July 2019 / Accepted: 23 July 2019 / Published: 1 August 2019
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. View Full-Text
Keywords: Li-ion battery; electrode; porous structure; virtual structure; machine learning; simulation; physico-chemical model; optimization Li-ion battery; electrode; porous structure; virtual structure; machine learning; simulation; physico-chemical model; optimization
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MDPI and ACS Style

Takagishi, Y.; Yamanaka, T.; Yamaue, T. Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes. Batteries 2019, 5, 54.

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