# Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries

arXiv: https://arxiv.org/abs/2103.11580 · HTML: https://mlsanity.com/paper/2103.11580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 28 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Battery health prediction under generalized conditions using a Gaussian process transition model](https://mlsanity.com/paper/1807.06350/) (2018)
- [Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation](https://mlsanity.com/paper/1906.04150/) (2019)

## Built on by (most cited first, 2 of 2)

- [Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries](https://mlsanity.com/paper/2112.12979/) (2021)
- [Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics](https://mlsanity.com/paper/2411.12935/) (2024)

## Abstract (arXiv)

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.