# Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics

arXiv: https://arxiv.org/abs/2411.12935 · HTML: https://mlsanity.com/paper/2411.12935/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Contemporary Symbolic Regression Methods and their Relative Performance](https://mlsanity.com/paper/2107.14351/) (2021)
- [Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization](https://mlsanity.com/paper/2405.10750/) (2024)
- [Integrating Electrochemical Modeling with Machine Learning for Lithium-Ion Batteries](https://mlsanity.com/paper/2103.11580/) (2021)

## Built on by (most cited first, 3 of 3)

- [Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model](https://mlsanity.com/paper/2504.15578/) (2025)
- [Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability](https://mlsanity.com/paper/2603.24475/) (2026)
- [Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction](https://mlsanity.com/paper/2507.00353/) (2025)

## Abstract (arXiv)

Accurate modeling of lithium ion (li-ion) batteries is essential for enhancing the safety, and efficiency of electric vehicles and renewable energy systems. This paper presents a data-inspired approach for improving the fidelity of reduced-order li-ion battery models. The proposed method combines a Genetic Algorithm with Sequentially Thresholded Ridge Regression (GA-STRidge) to identify and compensate for discrepancies between a low-fidelity model (LFM) and data generated either from testing or a high-fidelity model (HFM). The hybrid model, combining physics-based and data-driven methods, is tested across different driving cycles to demonstrate the ability to significantly reduce the voltage prediction error compared to the baseline LFM, while preserving computational efficiency. The model robustness is also evaluated under various operating conditions, showing low prediction errors and high Pearson correlation coefficients for terminal voltage in unseen environments.