# Evaluating feasibility of batteries for second-life applications using machine learning

arXiv: https://arxiv.org/abs/2203.04249 · HTML: https://mlsanity.com/paper/2203.04249/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Machine learning pipeline for battery state of health estimation](https://mlsanity.com/paper/2102.00837/) (2021)
- [Gaussian process regression for forecasting battery state of health](https://mlsanity.com/paper/1703.05687/) (2017)
- [Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life](https://mlsanity.com/paper/2101.04440/) (2021)
- [Predicting battery end of life from solar off-grid system field data using machine learning](https://mlsanity.com/paper/2107.13856/) (2021)

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

- [PulseBat: A field-accessible dataset for second-life battery diagnostics from realistic histories using multidimensional rapid pulse test](https://mlsanity.com/paper/2502.16848/) (2025)
- [Taking Second-life Batteries from Exhausted to Empowered using Experiments, Data Analysis, and Health Estimation](https://mlsanity.com/paper/2402.18859/) (2024)
- [Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling](https://mlsanity.com/paper/2310.03750/) (2023)

## Abstract (arXiv)

This paper presents a combination of machine learning techniques to enable prompt evaluation of retired electric vehicle batteries as to either retain those batteries for a second-life application and extend their operation beyond the original and first intent or send them to recycle facilities. The proposed algorithm generates features from available battery current and voltage measurements with simple statistics, selects and ranks the features using correlation analysis, and employs Gaussian Process Regression enhanced with bagging. This approach is validated over publicly available aging datasets of more than 200 cells with slow and fast charging, with different cathode chemistries, and for diverse operating conditions. Promising results are observed based on multiple training-test partitions, wherein the mean of Root Mean Squared Percent Error and Mean Percent Error performance errors are found to be less than 1.48% and 1.29%, respectively, in the worst-case scenarios.