# Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries

arXiv: https://arxiv.org/abs/2409.14575 · HTML: https://mlsanity.com/paper/2409.14575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 68 references that are in the map.
- First posted on arXiv 22 Sep 2024.

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

- [Does deep learning always outperform simple linear regression in optical imaging?](https://mlsanity.com/paper/1911.00353/) (2019)
- [On-line Capacity Estimation for Lithium-ion Battery Cells via an Electrochemical Model-based Adaptive Interconnected Observer](https://mlsanity.com/paper/2008.10467/) (2020)

## Built on by (most cited first, 1 of 1)

- [Informed Machine Learning with Knowledge Landmarks](https://mlsanity.com/paper/2604.00256/) (2026)

## Abstract (arXiv)

Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5% .