Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries

TL;DR

This paper introduces a hybrid modeling approach combining physics-based models and machine learning to improve the accuracy of lithium-ion battery behavior predictions, especially at high C-rates.

Contribution

It presents a novel integration method that informs machine learning models with physical model states, enhancing battery modeling precision.

Findings

High predictive accuracy at high C-rates

Effective physics-informed learning models

Parsimony in model structure

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.