State-of-health estimation for lithium-ion batteries using differential thermal voltammetry and Gaussian process regression

Wang, Ping; Peng, Xiangyuan; Cheng, Ze

doi:10.1007/s43236-022-00428-8

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Ref. [41] used differential thermal voltammetry and the SVR model to predict battery health. Furthermore, Ref.…”

Section: )Gaussian Process Regressionmentioning

confidence: 99%

Technologies for Energy Storage Power Stations Safety Operation: Battery State Evaluation Survey and a Critical Analysis

Xia,

Yue,

Guo

et al. 2024

IEEE Access

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As large-scale lithium-ion battery energy storage power facilities are built, the issues of safety operations become more complex. The existing difficulties revolve around effective battery health evaluation, cell-to-cell variation evaluation, circulation, and resonance suppression, and more. Based on this, this paper first reviews battery health evaluation methods based on various methods and summarizes the selection of existing health factors in data-driven methods. Secondly, the paper discusses the new research hotspots in the existing battery state evaluation technologies from the perspectives of state evaluation using data fragments and edge computing. Thirdly, we focus and discuss on the safety operation technologies of energy storage stations, including the issues of inconsistency, balancing, circulation, and resonance. To address these issues, we present an intelligent inspection robot, enabling real-time data interaction with the EMS and fulfilling rapid inspection and real-time diagnosis. Above all, we focus on the safety operation challenges for energy storage power stations and give our views and validate them with practical engineering applications, building the foundation of the next-generation techniques that support the development of new power systems.INDEX TERMS health factor, state of health, remaining useful life; cell-to-cell variation; entropy; safety operation

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“…Ref. [41] used differential thermal voltammetry and the SVR model to predict battery health. Furthermore, Ref.…”

Section: )Gaussian Process Regressionmentioning

confidence: 99%

Technologies for Energy Storage Power Stations Safety Operation: Battery State Evaluation Survey and a Critical Analysis

Xia,

Yue,

Guo

et al. 2024

IEEE Access

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“…This research contributes to improving the reliability and performance of BMSs in various applications. Wang et al [ 33 ] combined differential thermal voltammetry (DTV) and GPR to achieve SOH estimation. DTV measurements are taken at different temperatures to capture information about the battery's internal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

Qiang,

Tang,

et al. 2023

Energy Tech

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Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH estimation. Two aging features (AFs) are extracted through incremental capacity analysis from the partial charging process, and their efficacy is thoroughly validated on both battery cells and packs. To capture the relationship between AFs and capacity degradation, a robust DT model is established. Furthermore, Bayesian optimization is employed to optimize the model hyperparameters, enhancing the learning and generalization capabilities of the DT model. To validate the optimized DT model, a battery management system (BMS) is developed using a hybrid programming approach with LabVIEW and MATLAB, enabling online SOH estimation. The BMS undergoes rigorous evaluation using publicly available aging datasets of battery cells, as well as private datasets of battery packs. The results demonstrate that the optimized DT model outperforms traditional DT models, back propagation neural networks, and support vector machine approaches in accurately estimating battery SOH across diverse datasets. This research contributes to the advancement of BMS and holds profound implications for the efficient utilization of batteries in EVs.

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Analysis of uninterruptable power supply critical-to-quality factors

Neelamraju

Yellampalli

2023

J. Power Electron.

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State-of-health estimation for lithium-ion batteries using differential thermal voltammetry and Gaussian process regression

Cited by 8 publications

References 23 publications

Technologies for Energy Storage Power Stations Safety Operation: Battery State Evaluation Survey and a Critical Analysis

Technologies for Energy Storage Power Stations Safety Operation: Battery State Evaluation Survey and a Critical Analysis

Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

Analysis of uninterruptable power supply critical-to-quality factors

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