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Abstract

Predicting the state of health (SoH) of Lithium-ion battery cells is essential for optimizing their utilization. Although electrochemical degradation models and data-driven approaches are promising for SoH estimation, they often rely on experiments with repeated idealized cycles and extended relaxation periods, leading to non-robust models for dynamic real-world conditions. This study presents a novel investigation on how different cycling sequences (ageing history) influence future degradation rates and SoH estimation. In a unique experimental design, four identical automotive-grade NMC pouch cells were initially subjected to different accelerated cycling conditions, followed by common protocols until the degradation curve passed its knee point. A detailed analysis was conducted on capacity evolution, swelling and apparent capacity fade. Results indicate that ageing history must be considered in SoH modelling, to achieve robustness under dynamic conditions and improve system safety. Under some conditions, the knee point onset was delayed by at least 50%, while in others, it did not occur at all. Additionally, reversible short-term capacity fade is strongly dependent on the cell’s state, with fresh cells exhibiting a drop of approximately 15% which gradually decreases to around 1% by the end of the test campaign. These findings highlight key considerations for transitioning from laboratory-based to real-world applicable SoH models. Finally swelling shows strong correlation with SoH (R2
= 0.97), and is therefore a valuable alternative SoH indicator, especially in online in-service estimation techniques where controlled capacity checks under repeatable conditions are not available.
Original languageEnglish
Article number117690
JournalJournal of Energy Storage
Volume132
Issue numberPart B
Early online date23 Jul 2025
DOIs
Publication statusPublished - 10 Oct 2025

Bibliographical note

Publisher Copyright:
© 2025 The Authors

Data Availability Statement

Data will be made available on request.

Funding

The corresponding author would like to acknowledge the support from the EPSRC Centre for Doctoral Training in Advanced Automotive Propulsion Systems (AAPS), United Kingdom , under the project EP/S023364/1.

FundersFunder number
Engineering and Physical Sciences Research CouncilEP/S023364/1

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

  • Accelerated ageing methods
  • Apparent capacity fade
  • Path dependence
  • State of health estimation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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