# Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis

arXiv: https://arxiv.org/abs/2104.07576 · HTML: https://mlsanity.com/paper/2104.07576/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 15 Apr 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)
- [Factoring the Cycle Aging Cost of Batteries Participating in Electricity Markets](https://mlsanity.com/paper/1707.04567/) (2017)

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

- [A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions](https://mlsanity.com/paper/2401.04351/) (2024)
- [Feature selection algorithm based on incremental mutual information and cockroach swarm optimization](https://mlsanity.com/paper/2302.10522/) (2023)

## Abstract (arXiv)

The complex nature of lithium-ion battery degradation has led to many machine learning based approaches to health forecasting being proposed in literature. However, machine learning can be computationally intensive. Linear approaches are faster but have previously been too inflexible for successful prognosis. For both techniques, the choice and quality of the inputs is a limiting factor of performance. Piecewise-linear models, combined with automated feature selection, offer a fast and flexible alternative without being as computationally intensive as machine learning. Here, a piecewise-linear approach to battery health forecasting was compared to a Gaussian process regression tool and found to perform equally well. The input feature selection process demonstrated the benefit of limiting the correlation between inputs. Further trials found that the piecewise-linear approach was robust to changing input size and availability of training data.