Discovering Governing Equations of Li-ion Batteries Pertaining State of Charge Using Input-Output Data
Author:
Affiliation:
1. Temple University,Dynamical Systems Lab (DSLab),Department of Mechanical Engineering,Philadelphia,PA,USA
2. Ford Motor Company,Dearborn,MI,USA
Funder
Office of Naval Research
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10155646/10155787/10156114.pdf?arnumber=10156114
Reference30 articles.
1. Modeling of Li-ion batteries for real-time analysis and control: A data-driven approach
2. Enhanced physics-based models for state estimation of li-ion batteries;luder;Proc Comsol Conf,2020
3. A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery
4. Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE);chen,2021
5. A Physics-Inspired Machine Learning Nonlinear Model of Li-ion Batteries
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3. A data-driven framework for learning governing equations of Li-ion batteries and co-estimating voltage and state-of-charge;Journal of Energy Storage;2024-04
4. Data-Driven Discovery of Lithium-Ion Battery State of Charge Dynamics;Journal of Dynamic Systems, Measurement, and Control;2023-11-23
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