Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries
This work addresses battery management for energy storage applications, but it is incremental as it builds on existing physics and machine learning methods.
The paper tackles the challenge of modeling lithium-ion batteries by integrating a physics-based model with machine learning, resulting in hybrid models that achieve high predictive accuracy at high C-rates.
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.