Interpretable machine learning prediction for li-ion battery's state of health based on electrochemical impedance spectroscopy and temporal features

Bao, Minghan; Liu, Dexun; Wu, Yuyao; Wang, Zhengying; Yang, Jing; Lan, Lin; Ru, Qiang

doi:10.1016/j.electacta.2024.144449

Electrochimica Acta

2024

DOI: 10.1016/j.electacta.2024.144449

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Interpretable machine learning prediction for li-ion battery's state of health based on electrochemical impedance spectroscopy and temporal features

Minghan Bao,

Dexun Liu,

Yuyao Wu

et al.

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2024

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Cited by 5 publications

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Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm

Yang,

Wang,

Zhou

et al. 2024

Journal of Energy Storage

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Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm

Yang,

Wang,

Zhou

et al. 2024

Journal of Energy Storage

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Magnetic recyclable g-C3N4/CuFe2O4/MnO2 activated peroxymonosulfate process via dual Z-scheme heterojunction for photodegradation of ciprofloxacin

Zeng,

Gao,

et al. 2024

Journal of Environmental Chemical Engineering

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Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics

Hammou,

Tala-Ighil,

Makany

et al. 2024

Batteries

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The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell’s temperature to predict the cell’s capacity. The features are extracted from experimental measurements of the cell’s surface temperature and selected based on Spearman correlation analysis. The prediction results were evaluated and compared considering three different percentages of the training dataset: 60%, 70%, and 80%. Training and testing data were generated experimentally based on accelerated ageing cycling tests. During these experiments, four Nickel Manganese Cobalt/Graphite (NMC) cells were cycled under a controlled temperature environment based on a dynamic current profile extracted from the Worldwide Harmonized Light Vehicles Test Cycles.

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Interpretable machine learning prediction for li-ion battery's state of health based on electrochemical impedance spectroscopy and temporal features

Cited by 5 publications

References 51 publications

Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm

Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm

Magnetic recyclable g-C3N4/CuFe2O4/MnO2 activated peroxymonosulfate process via dual Z-scheme heterojunction for photodegradation of ciprofloxacin

Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics

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