Vol. 4 No. 1 (2024): Issue 4
Articles

Battery Degradation Analysis through Sparse Ridge Regression

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  • Weidong Huang,
  • Yiqun Cai,
  • Guojun Zhang
Weidong Huang
Upower Energy Technology (Guang Zhou) Co..Ltd., Guangzhou, 510000, CHINA
Yiqun Cai
Corresponding, University of Florida, Herbert Wertheim College, FL, 32608, USA
Guojun Zhang
Quadrant international inc., San Diego, 92121, USA
DOI: https://doi.org/10.71070/es.v4i1.65

Published 2024-12-10

Keywords

  • Battery Degradation,
  • Energy Storage Systems,
  • Degradation Models,
  • Sparse Ridge Regression,
  • Predictive Performance

How to Cite

Huang, W., Cai, Y., & Zhang, G. (2024). Battery Degradation Analysis through Sparse Ridge Regression. Energy & System, 4(1). https://doi.org/10.71070/es.v4i1.65

Abstract

Battery degradation is a critical issue in the field of energy storage systems due to its impact on system performance and lifespan. Understanding and accurately predicting battery degradation is essential for ensuring the optimal operation of energy storage systems. Current research in the field focuses on developing degradation models based on historical data, yet faces challenges in accurately capturing the complex degradation processes and optimizing predictive accuracy. In this paper, we propose a novel approach for battery degradation analysis using Sparse Ridge Regression, which combines the advantages of both sparse regression and ridge regression to enhance predictive performance while addressing model complexity. Our work aims to provide a more effective and efficient method for analyzing battery degradation, offering insights for improving the management and operation of energy storage systems.
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