SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis &amp; Cubic Smoothing Spline

Lin, Chun Pang; Cabrera, Javier; Yu, Denis Y. W.; Yang, Fangfang; Tsui, Kwok Leung

doi:10.1149/1945-7111/ab8f56

Cited by 45 publications

(16 citation statements)

References 40 publications

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“…In fact, voltage as a function of capacity has been proposed and applied in previous work 5 . Note that linear interpolation is an alternative approach that also avoids the small additional error from the spline fit, but here we use the spline fits for consistency with Severson et al 3 Lin et al 32 suggest using a smoothing spline with a cross-validated smoothing parameter for sampling voltage-capacity curves, which is advantageous over manual tuning of filtering parameters. Alternatively, the data acquisition rate could be specified to match the desired basis vector.…”

Section: Matrix Representation Of Voltage-capacity Datamentioning

confidence: 99%

Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

Attia

Severson²,

Witmer³

2021

J. Electrochem. Soc.

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Data-driven methods for battery lifetime prediction are attracting increasing attention for applications in which the degradation mechanisms are poorly understood and suitable training sets are available. However, while advanced machine learning and deep learning methods promise high performance with minimal data preprocessing, simpler linear models with engineered features often achieve comparable performance, especially for small training sets, while also providing physical and statistical interpretability. In this work, we use a previously published dataset to develop simple, accurate, and interpretable data-driven models for battery lifetime prediction. We first present the “capacity matrix” concept as a compact representation of battery electrochemical cycling data, along with a series of feature representations. We then create a number of univariate and multivariate models, many of which achieve comparable performance to the highest-performing models previously published for this dataset; thus, our work can serve as a comprehensive benchmarking study for this dataset. These models also provide insights into the degradation of these cells. Our approaches can be used both to quickly train models for a new battery cycling dataset and to benchmark the performance of more advanced machine learning methods.

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Section: Matrix Representation Of Voltage-capacity Datamentioning

confidence: 99%

Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

Attia

Severson²,

Witmer³

2021

J. Electrochem. Soc.

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“…The SOHs of seven of these cells were determined based on the reference data obtained from one cell with less than one percent absolute error. Lin et al [16] first smoothed the data by means of a proposed cubic smoothing spline method, which was further compared to other filtering methods under different voltage window sizes. Subsequently, they demonstrated a SOH estimation method evaluating the IC peak height of LFP cells.…”

Section: Introductionmentioning

confidence: 99%

Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Krupp¹,

Ferg

Schuldt³

et al. 2020

Batteries

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Incremental capacity analysis (ICA) has proven to be an effective tool for determining the state of health (SOH) of Li-ion cells under laboratory conditions. This paper deals with an outstanding challenge of applying ICA in practice: the evaluation of battery series connections. The study uses experimental aging and characterization data of lithium iron phosphate (LFP) cells down to 53% SOH. The evaluability of battery series connections using ICA is confirmed by analytical and experimental considerations for cells of the same SOH. For cells of different SOH, a method for identifying non-uniform aging states on the modules’ IC curve is presented. The findings enable the classification of battery modules with series and parallel connections based on partial terminal data.

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“…Ding et al 11 introduced a machine learning method for the improvement of the efficiency of membrane electrode assembly design and experiment. Such data-driven methods focus on the relationships among the input and output features, and a key part of data-driven battery state estimation is the extraction of degradation features, which largely determines the estimation performance 12 – 14 .…”

Section: Introductionmentioning

confidence: 99%

Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

et al. 2022

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Accurate capacity estimation is crucial for the reliable and safe operation of lithium-ion batteries. In particular, exploiting the relaxation voltage curve features could enable battery capacity estimation without additional cycling information. Here, we report the study of three datasets comprising 130 commercial lithium-ion cells cycled under various conditions to evaluate the capacity estimation approach. One dataset is collected for model building from batteries with LiNi0.86Co0.11Al0.03O2-based positive electrodes. The other two datasets, used for validation, are obtained from batteries with LiNi0.83Co0.11Mn0.07O2-based positive electrodes and batteries with the blend of Li(NiCoMn)O2 - Li(NiCoAl)O2 positive electrodes. Base models that use machine learning methods are employed to estimate the battery capacity using features derived from the relaxation voltage profiles. The best model achieves a root-mean-square error of 1.1% for the dataset used for the model building. A transfer learning model is then developed by adding a featured linear transformation to the base model. This extended model achieves a root-mean-square error of less than 1.7% on the datasets used for the model validation, indicating the successful applicability of the capacity estimation approach utilizing cell voltage relaxation.

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SOH Estimation and SOC Recalibration of Lithium-Ion Battery with Incremental Capacity Analysis & Cubic Smoothing Spline

Cited by 45 publications

References 40 publications

Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

Statistical Learning for Accurate and Interpretable Battery Lifetime Prediction

Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

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