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Estimating the Health and State of Charge of Each Cell in a Second-Life Battery System from Field Data

Mart´ın Cornejo, Julian Meyer-Schwickerath

2024Englishbatteriesenergy storageelectrochemistrylithium-ion batteriessecond-life batteriesbattery health

Abstract

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Effective use of battery storage depends on reliable estimation of its state of health (SOH) and state of charge (SOC). This study presents a framework that jointly estimates these states and parameters for a second-life battery system using field operation data, leveraging Gaussian process regression to reconstruct the open-circuit voltage (OCV) curve. Applied to a real system comprising 27 modules and 324 cells, the methodology uncovers considerable SOH heterogeneity, a systematic SOC imbalance, and identifies two faulty cells, with validations against reference measurements. The study further aggregates the cell SOH and SOC data to module level and benchmarks these against a lumped-module model that does not incorporate individual cell voltages. The findings reveal that the lumped-module model can capture average behavior but fails to represent the limiting cells accurately, resulting in overestimations of SOH by up to 31% and SOC by up to 23%. This research highlights the importance of precise monitoring in second-life battery systems to enhance reliability and operational efficiency.

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Cite This Work

@article{0da2d821-a915-416b-a9dc-d739ff35e8d3,
  title={Estimating the Health and State of Charge of Each Cell in a Second-Life Battery System from Field Data},
  author={Mart´ın Cornejo and Julian Meyer-Schwickerath},
  year={2024},
  language={English}
}
TY  - JOUR
TI  - Estimating the Health and State of Charge of Each Cell in a Second-Life Battery System from Field Data
AU  - Mart´ın Cornejo
AU  - Julian Meyer-Schwickerath
PY  - 2024
LA  - English
ER  -

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