# Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries

arXiv: https://arxiv.org/abs/2112.12979 · HTML: https://mlsanity.com/paper/2112.12979/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Universal Battery Performance and Degradation Model for Electric Aircraft](https://mlsanity.com/paper/2008.01527/) (2020)
- [Health-aware and user-involved battery charging management for electric vehicles: linear quadratic strategies](https://mlsanity.com/paper/1507.01898/) (2015)
- [Nonlinear Double-Capacitor Model for Rechargeable Batteries: Modeling, Identification and Validation](https://mlsanity.com/paper/1906.04150/) (2019)
- [Real-Time Optimal Lithium-Ion Battery Charging Based on Explicit Model Predictive Control](https://mlsanity.com/paper/2004.14321/) (2020)
- [Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries](https://mlsanity.com/paper/2103.11580/) (2021)

## Built on by (most cited first, 8 of 8)

- [PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model](https://mlsanity.com/paper/2312.17329/) (2023)
- [Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data](https://mlsanity.com/paper/2406.19015/) (2024)
- [Diagnostic-free onboard battery health assessment](https://mlsanity.com/paper/2503.07383/) (2025)
- [Toward High-Performance Energy and Power Battery Cells with Machine Learning-based Optimization of Electrode Manufacturing](https://mlsanity.com/paper/2307.05521/) (2023)
- [Machine Learning in Proton Exchange Membrane Water Electrolysis -- Part I: A Knowledge-Integrated Framework](https://mlsanity.com/paper/2404.03660/) (2024)
- [Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction](https://mlsanity.com/paper/2507.00353/) (2025)
- [Set-Valued Transformer Network for High-Emission Mobile Source Identification](https://mlsanity.com/paper/2508.11976/) (2025)
- [From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark](https://mlsanity.com/paper/2602.18551/) (2026)

## Abstract (arXiv)

Mathematical modeling of lithium-ion batteries (LiBs) is a primary challenge in advanced battery management. This paper proposes two new frameworks to integrate physics-based models with machine learning to achieve high-precision modeling for LiBs. The frameworks are characterized by informing the machine learning model of the state information of the physical model, enabling a deep integration between physics and machine learning. Based on the frameworks, a series of hybrid models are constructed, through combining an electrochemical model and an equivalent circuit model, respectively, with a feedforward neural network. The hybrid models are relatively parsimonious in structure and can provide considerable voltage predictive accuracy under a broad range of C-rates, as shown by extensive simulations and experiments. The study further expands to conduct aging-aware hybrid modeling, leading to the design of a hybrid model conscious of the state-of-health to make prediction. The experiments show that the model has high voltage predictive accuracy throughout a LiB's cycle life.