Version 1
: Received: 4 June 2023 / Approved: 5 June 2023 / Online: 5 June 2023 (09:01:49 CEST)
How to cite:
Painter, R.; Parthasarathy, R.; Li, L.; Embry, I.; Sharpe, L.; Hargrove, S.K. A Deep Neural Network Regression of the Cahn–Hilliard Single-Particle Thermal Model for LiFePO4 Batteries. Preprints2023, 2023060283. https://doi.org/10.20944/preprints202306.0283.v1
Painter, R.; Parthasarathy, R.; Li, L.; Embry, I.; Sharpe, L.; Hargrove, S.K. A Deep Neural Network Regression of the Cahn–Hilliard Single-Particle Thermal Model for LiFePO4 Batteries. Preprints 2023, 2023060283. https://doi.org/10.20944/preprints202306.0283.v1
Painter, R.; Parthasarathy, R.; Li, L.; Embry, I.; Sharpe, L.; Hargrove, S.K. A Deep Neural Network Regression of the Cahn–Hilliard Single-Particle Thermal Model for LiFePO4 Batteries. Preprints2023, 2023060283. https://doi.org/10.20944/preprints202306.0283.v1
APA Style
Painter, R., Parthasarathy, R., Li, L., Embry, I., Sharpe, L., & Hargrove, S.K. (2023). A Deep Neural Network Regression of the Cahn–Hilliard Single-Particle Thermal Model for LiFePO<sub>4</sub> Batteries. Preprints. https://doi.org/10.20944/preprints202306.0283.v1
Chicago/Turabian Style
Painter, R., Lonnie Sharpe and S. Keith Hargrove. 2023 "A Deep Neural Network Regression of the Cahn–Hilliard Single-Particle Thermal Model for LiFePO<sub>4</sub> Batteries" Preprints. https://doi.org/10.20944/preprints202306.0283.v1
Abstract
Lithium-ion batteries serve as the primary sources of power for electric vehicles (EVs) and hybrid electric vehicles (HEVs). For vehicle applications, battery management systems (BMSs) are nec-essary to protect lithium-ion batteries from overheating and to ensure optimum vehicle perfor-mance. Our approach to developing a BMS was based on recent advances in the application of phase field models for lithium-ion batteries. In particular, our reduced-order model (ROM) uti-lized a dataset generated from the COMSOL® Multiphysics simulation of the Cahn–Hilliard equation for a single particle of a lithium iron phosphate (LiFePO4) cathode: an example of using a reduced-order model (ROM) based on a single-particle model (SPM). The main innovation of our ROM is that the SPM is fully coupled to a heat transfer model at the battery cell level. We utilized principal component analysis to identify a lower-order model that could reproduce the battery’s voltage and temperature response for ambient temperatures ranging from 253 to 298 K and for discharge rates ranging from 1 C to 20.5 C. The reduced-order dataset was then fitted to the ex-perimental data for an A123 Systems 26650 2.3 Ah cylindrical battery using deep neural network (DNN) regression.
Keywords
lithium-ion batteries; LIB; LiFePO4; electric vehicles; Cahn–Hilliard; principal component analysis; neural networks
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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