Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries

• URL: http://arxiv.org/abs/2103.11580v2
• Date: Tue, 23 Mar 2021 02:45:03 GMT
• Title: Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries
• Authors: Hao Tu, Scott Moura, Huazhen Fang
• Abstract summary: This paper presents a new approach to integrate a physics-based model with machine learning to achieve high-precision modeling for lithium-ion batteries (LiBs) We propose two hybrid physics-machine learning models based on the approach, which blend a single particle model with thermal dynamics (SPMT) with a feedforward neural network (FNN) to perform physics-informed learning of a LiB's dynamic behavior.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge in advanced battery management. This paper presents a new approach to integrate a physics-based model with machine learning to achieve high-precision modeling for LiBs. This approach uniquely proposes to inform the machine learning model of the dynamic state of the physical model, enabling a deep integration between physics and machine learning. We propose two hybrid physics-machine learning models based on the approach, which blend a single particle model with thermal dynamics (SPMT) with a feedforward neural network (FNN) to perform physics-informed learning of a LiB's dynamic behavior. The proposed models are relatively parsimonious in structure and can provide considerable predictive accuracy even at high C-rates, as shown by extensive simulations.

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