Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems

Energy, Год журнала: 2024, Номер 294, С. 130882 - 130882

Опубликована: Март 2, 2024

Язык: Английский

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Communications and Data Science for the Success of Vehicle-to-Grid Technologies: Current State and Future Trends

Electronics, Год журнала: 2024, Номер 13(10), С. 1940 - 1940

Опубликована: Май 15, 2024

Vehicle-to-grid (V2G) technology has emerged as a promising solution for enhancing the integration of electric vehicles (EVs) into grid, offering benefits, such distributed energy resource (DER) integration, grid stability support, and peak demand management, among others, well environmental advantages. This study provides comprehensive review V2G systems, with specific focus on role communication, they have been identified key enablers, challenges that must face. It begins by introducing fundamentals including their architecture, operation, description benefits different sectors. then delves communication technologies protocols in highlighting requirements achieving reliable efficient between EVs agents involved. A standards is described, main technologies, which are evaluated terms suitability applications. Furthermore, discusses implications technology, emphasizing importance addressing strong communications to maximize its potential benefits. Finally, future research directions solutions overcoming systems outlined, useful insights researchers, policymakers, administrations related industry stakeholders.

Язык: Английский

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Critical comparison of equivalent circuit and physics-based models for lithium-ion batteries: A graphite/lithium-iron-phosphate case study

Journal of Energy Storage, Год журнала: 2024, Номер 94, С. 112326 - 112326

Опубликована: Июнь 13, 2024

Язык: Английский

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Advancements in parameter estimation techniques for 1RC and 2RC equivalent circuit models of lithium-ion batteries: A comprehensive review

Journal of Energy Storage, Год журнала: 2025, Номер 113, С. 115581 - 115581

Опубликована: Фев. 6, 2025

Язык: Английский

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A comparative study of parameter identification methods for equivalent circuit models for lithium-ion batteries and their application to state of health estimation

Journal of Energy Storage, Год журнала: 2025, Номер 114, С. 115707 - 115707

Опубликована: Фев. 13, 2025

Язык: Английский

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Improved volumetric noise-adaptive H-infinity filtering for accurate state of power estimation of lithium-ion batteries with multi-parameter constraint considering low-temperature influence

Journal of Energy Storage, Год журнала: 2025, Номер 115, С. 115999 - 115999

Опубликована: Фев. 27, 2025

Язык: Английский

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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine

World Electric Vehicle Journal, Год журнала: 2025, Номер 16(4), С. 224 - 224

Опубликована: Апрель 9, 2025

An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality EIS data poses challenges an efficient analysis. This study proposes a novel method that combines with equivalent circuit model (ECM) distribution relaxation time (DRT) analysis to extract low-dimensional features from high-dimensional data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by Sparrow Search Algorithm (SSA), estimates battery SOH average mean absolute error (MAE) 1.37% root square (RMSE) 1.76%. In addition, compared support vector regression (SVR) Gaussian process (GPR), proposed reduces computational factors 4 30 lowers memory usage approximately 18%.

Язык: Английский

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A temporal convolution and gated recurrent unit network with attention for state of charge estimation of lithium-ion batteries

Journal of Energy Storage, Год журнала: 2023, Номер 72, С. 108774 - 108774

Опубликована: Авг. 28, 2023

Язык: Английский

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Dynamic internal resistance modeling and thermal characteristics of lithium-ion batteries for electric vehicles by considering state of health

Journal of Power Sources, Год журнала: 2024, Номер 612, С. 234806 - 234806

Опубликована: Июнь 3, 2024

Язык: Английский

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A Review on Lithium-Ion Battery Modeling from Mechanism-Based and Data-Driven Perspectives

Processes, Год журнала: 2024, Номер 12(9), С. 1871 - 1871

Опубликована: Сен. 1, 2024

As the low-carbon economy continues to advance, New Energy Vehicles (NEVs) have risen prominence in automotive industry. The design and utilization of lithium-ion batteries (LIBs), which are core component NEVs, directly related safety range performance electric vehicles. requirements for a refined battery electrode structures intelligent adjustment charging modes attracted extensive research from both academia LIB models can be divided into mechanism-based data-driven models; however, distinctions connections between these two kinds not been systematically reviewed as yet. Therefore, this work provides an overview perspectives on modeling perspectives. Meanwhile, potential fusion frameworks including mechanism information method also summarized. An introduction technologies is presented, along with current challenges opportunities. From perspective structure design, we further explore how morphology aging-related side reactions impact performance. Furthermore, within realm operation, that leverage machine learning techniques estimate health status investigated. bottlenecks state estimation, operational optimization LIBs prospects mechanism-data hybrid highlighted at end. This expected assist researchers engineers uncovering value operation data, thereby facilitating transformation industry towards energy conservation efficiency enhancement.

Язык: Английский

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