Reduced-Order Model of Lithium-Iron Phosphate Battery Dynamics: A POD-Galerkin Approach
Author:
Affiliation:
1. Energy Science and Engineering, Stanford University, Mountain View, CA, USA
2. Data Modeling Algorithm Team, Battery R&D, LG Energy Solution, Seoul, South Korea
Funder
LG Energy Solution through the Global Innovation Contest Award
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Control and Optimization,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/7782633/9828546/09991824.pdf?arnumber=9991824
Reference20 articles.
1. Enhanced state-of-charge estimation of LiFePO4 batteries using an augmented physics-based model
2. Reduction of an Electrochemistry-Based Li-Ion Battery Model via Quasi-Linearization and Padé Approximation
3. Model-reduction techniques for PDE models with Turing type electrochemical phase formation dynamics
4. Reduction of Model Order Based on Proper Orthogonal Decomposition for Lithium-Ion Battery Simulations
5. Nonlinear Model Order Reduction via Dynamic Mode Decomposition
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1. Comparative Analysis of Numerical Methods for Lithium-Ion Battery Electrochemical Modeling;Journal of The Electrochemical Society;2023-12-01
2. Development of a Hardware-in-the-Loop Testbed for Distributed Electric Propulsion System in All-Electric Aircraft;2023 IEEE Energy Conversion Congress and Exposition (ECCE);2023-10-29
3. Comparative Evaluation of PRR and PODA Methods for Model Order Reduction in Electrical Circuits;Advances in Information and Communication Technology;2023
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