Experimental Estimation of the State of Health of Lithium-ion Batteries under Different Conditions

Ilies, Adelina Ioana; Chindris, G.; Pitica, Dan; Jano, Rajmond

doi:10.1109/siitme53254.2021.9663419

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…The aging pattern and remaining capacity can be determined from the reconstructed OCV curve. To investigate how the performance of lithium‐ion cells changes over time after long‐term storage in certain conditions, a series of tests were performed to measure their capacity and internal resistance 13 …”

Section: Introductionmentioning

confidence: 99%

“…To investigate how the performance of lithium-ion cells changes over time after long-term storage in certain conditions, a series of tests were performed to measure their capacity and internal resistance. 13 Model-based methods mainly include adaptive filtering techniques and data-driven techniques. Xiong et al 14 simplified the computational electrochemical model, used a genetic algorithm for parameter identification, and extracted five characteristic parameters for estimation by regression.…”

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confidence: 99%

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Bayesian optimization algorithm‐based Gaussian process regression for in situ state of health prediction of minorly deformed lithium‐ion battery

Liu,

Bao,

Guo

et al. 2024

Energy Science & Engineering

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Accurate on‐board state‐of‐health (SOH) prediction is crucial for lithium‐ion battery applications. This study presents an in situ prediction technique for minorly deformed battery SOH, utilizing a Gaussian process regression (GPR) model tuned by a Bayesian optimization algorithm. Unlike previous methods that interpret voltage–time data as incremental capacitance curves, our approach directly operates on raw voltage–time data. We apply gray relational analysis to select feature variables as inputs and train the Bayesian Gaussian process regression (BGPR) model using experimental data from batteries under different working conditions. To demonstrate the performance of the BGPR model, we compare it with stepwise linear regression, neural network, and Bayesian support vector machine (BSVM) models. The performance of these four models is evaluated using different performance indicators: mean absolute percentage error (MAPE), root‐mean‐squared percentage error (RMSPE), and coefficient of determination (R²). The results demonstrate that the BGPR model exhibits superior prediction performance with the lowest MAPE (0.11%), RMSPE (0.12%), and the highest R² (0.9915) for minorly deformed batteries. Furthermore, the BGPR model exhibits excellent robustness for SOH prediction of normal batteries under different conditions. This study provides an effective and robust method for accurate on‐board SOH prediction in lithium‐ion battery applications.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%