Vol. 5 No. 1 (2025): Issue 5
Articles

Ridge Regression-based Approach for Estimate of Remaining Life Prediction of Lithium Battery

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  • Olof Andersson,
  • Linnea Gustafsson,
  • Erik Nilsson
Olof Andersson
Department of Sustainable Energy Systems, Mälardalen University, Västerås, SE-721 23, Sweden
Linnea Gustafsson
Center for Battery Innovation and Energy Technologies, University of Skövde, Skövde, SE-541 28, Sweden
Erik Nilsson
Institute of Advanced Materials and Energy Technologies, Örebro University, Örebro, SE-701 82, Sweden
DOI: https://doi.org/10.71070/oaml.v5i1.128

Published 2025-01-13

Keywords

  • Lithium Battery,
  • Performance Prediction,
  • Remaining Life Estimation,
  • Ridge Regression,
  • Machine Learning Techniques

How to Cite

Andersson, O., Gustafsson, L., & Nilsson, E. (2025). Ridge Regression-based Approach for Estimate of Remaining Life Prediction of Lithium Battery. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.128
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

In the context of lithium battery performance prediction, this paper addresses the critical need for accurately estimating the remaining life of the battery to optimize its utilization. Despite existing research efforts, challenges persist in achieving precise predictions due to factors like non-linear degradation mechanisms and limited data availability. To overcome these obstacles, our study proposes a novel Ridge Regression-based approach that integrates machine learning techniques with physics-based models. This innovative method not only improves prediction accuracy but also enhances model interpretability. By combining empirical data with theoretical insights, our research contributes to advancing the field of lithium battery prognostics.

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