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Life extension of a multi-unit energy storage system by optimizing the power distribution based on the degradation ratio

  • Xinjia Gao
  • , Xiaogang Wu*
  • , Yinlong Xia
  • , Yalun Li*
  • *Corresponding author for this work
  • Harbin University of Science and Technology
  • Tsinghua University

Research output: Contribution to journalArticlepeer-review

Abstract

Battery energy storage systems are widely used to absorb renewable energy. However, the difference in the initial state and operating conditions led to inconsistent degradation between the battery units. It is urgent to develop life extension algorithms to solve the problem. In this study, a calculation scheme is proposed for the power distribution toward an optimized cycle life. First, the degradation ratio between the energy storage units was calculated based on the Arrhenius degradation model validated by aging experiments. A decisive correlation was revealed between the current rate and the degradation ratios using Pearson correlation analysis. Next, a simplified calculation method was proposed toward various operating conditions, which proved to acceptable errors less than 3 %. It was inferred that the degradation ratio could be directly controlled by the current rate ratio. Based on this concept, the degradation path was optimized based on genetic algorithm, to obtain the optimized power distribution factor of the entire life. The results showed that the cycle life could be extended by 21.9 % after separately adjusting the power distribution with 4-stage optimization. The study has effectively extended the service life of energy storage, which helps to develop the on-line control strategy toward life extension.

Original languageEnglish
Article number129598
JournalEnergy
Volume286
DOIs
StatePublished - 1 Jan 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Battery energy storage systems
  • Degradation ratio
  • Genetic algorithm
  • Life extension
  • Pearson correlation analysis

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