A Physics-Informed Machine Learning Approach for Estimating Lithium-Ion Battery Temperature
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
2. Department of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI, USA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09858911.pdf?arnumber=9858911
Reference45 articles.
1. A Physics-Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity
2. Prediction of porous media fluid flow using physics informed neural networks
3. Physics-informed machine learning
4. A Recurrent Neural Networks Approach for Estimating the Core Temperature in Lithium-Ion Batteries
5. Accurate Surface Temperature Estimation of Lithium-Ion Batteries Using Feedforward and Recurrent Artificial Neural Networks
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