State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm

Liu, Gengfeng; Zhang, Xiangwen; Liu, Zhiming

doi:10.1016/j.energy.2022.124851

Cited by 45 publications

(5 citation statements)

References 42 publications

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“…For the combination of different data-driven methods, building upon the multi-feature fusion models using a stacking mechanism, reference [126] proposes a method to estimate the health state of power batteries, in which SVR and LSTM are integrated to estimate battery health state by combining various feature parameters. The outcomes demonstrate that this novel approach enhances multi-feature fusion performance and improves estimation accuracy.…”

Section: Overview Of the Hybrid Methodsmentioning

confidence: 99%

Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

Xiong,

Wang,

Takyi-Aninakwa

et al. 2024

Prot. Control Mod. Power Syst.

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Section: Overview Of the Hybrid Methodsmentioning

confidence: 99%

Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

Xiong,

Wang,

Takyi-Aninakwa

et al. 2024

Prot. Control Mod. Power Syst.

View full text Add to dashboard Cite

“…Currently, some ensemble methods have been proposed for SOH estimation. [24][25][26] The model structure, feature source, and estimation accuracy are compared and shown in Table 8.…”

Section: Comparison With Other Ensemble Methodsmentioning

confidence: 99%

“…A reliable decision‐making rule‐based ensemble learning method is developed to improve SOH prediction accuracy, while only the extreme learning machine (ELM) is used as the base learner 24 . Liu et al 25 used SVR and LSTM to construct a stacking ensemble learning model, features from the current profiles of the constant voltage charging phase were extracted to map the aging process. Guo et al selected the cumulative voltage and charging capacity as the HFs, then an ensemble SVR model is designed for SOH estimation 26 .…”

Section: Introductionmentioning

confidence: 99%

State‐of‐health estimation for lithium‐ion batteries based on partial charging segment and stacking model fusion

Liu

Zhang

et al. 2022

Energy Science & Engineering

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State-of-health (SOH) estimation is essential for evaluating the aging process of lithium-ion batteries, which can effectively guarantee the steady application of the battery system. Most existing prediction approaches apply a single model or a single feature to achieve SOH estimation based on the entire charging curve. In this paper, a multifeature-based stacked ensemble learning framework is proposed for SOH prediction using partial charging curves. Firstly, combined with the range of state-of-charge (SOC) commonly used in the actual operation of vehicles, the charging segment is effectively intercepted through the mapping correlation between the SOC and the terminal voltage.Then, five relevant features characterizing the battery health status are extracted from multiple data, such as temperature, voltage, and incremental capacity profiles. Finally, a two-level stacking ensemble framework is developed to fuse several individual estimation methods for higher SOH accuracy. To validate the performance of the proposed method, the Oxford University data set and the NASA data set are deployed for comparison experiments, and the results reveal the superior precision and robustness of the developed model in SOH estimation.

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“…These strategies offer robust support for investigating the sample completion mechanism in the modeling of the MSWI process. To enhance multisource information representation and model interpretability, diverse methods have been introduced, including multifeature information fusion [192], multimodal deep learning [193], visual data depth modeling [194], Bayesian data-driven T-S fuzzy [195], and deep forest regression [66,196]. These serve as the theoretical foundation for exploring intelligent reduction in multisource features and constructing interpretable models in the MSWI process.…”

Section: Operational Indices Modelingmentioning

confidence: 99%

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

Tang,

Wang,

Xia

et al. 2024

Sustainability

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Artificial intelligence (AI) has found widespread application across diverse domains, including residential life and product manufacturing. Municipal solid waste incineration (MSWI) represents a significant avenue for realizing waste-to-energy (WTE) objectives, emphasizing resource reuse and sustainability. Theoretically, AI holds the potential to facilitate optimal control of the MSWI process in terms of achieving minimal pollution emissions and maximal energy efficiency. However, a noticeable shortage exists in the current research of the review literature concerning AI in the field of WTE, particularly MSWI, hindering a focused understanding of future development directions. Consequently, this study conducts an exhaustive survey of AI applications for optimal control, categorizing them into four fundamental aspects: modeling, control, optimization, and maintenance. Timeline diagrams depicting the evolution of AI technologies in the MSWI process are presented to offer an intuitive visual representation. Each category undergoes meticulous classification and description, elucidating the shortcomings and challenges inherent in current research. Furthermore, the study articulates the future development trajectory of AI applications within the four fundamental categories, underscoring the contribution it makes to the field of MSWI and WTE.

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State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm

Cited by 45 publications

References 42 publications

Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

State‐of‐health estimation for lithium‐ion batteries based on partial charging segment and stacking model fusion

An Overview of Artificial Intelligence Application for Optimal Control of Municipal Solid Waste Incineration Process

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