A review of battery energy storage systems and advanced battery management system for different applications: Challenges and recommendations

Journal of Energy Storage, Год журнала: 2024, Номер 86, С. 111179 - 111179

Опубликована: Март 7, 2024

Язык: Английский

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State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges

Applied Energy, Год журнала: 2024, Номер 369, С. 123542 - 123542

Опубликована: Май 31, 2024

Язык: Английский

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Sustainable Vehicles for Decarbonizing the Transport Sector: A Comparison of Biofuel, Electric, Fuel Cell and Solar-Powered Vehicles

World Electric Vehicle Journal, Год журнала: 2024, Номер 15(3), С. 93 - 93

Опубликована: Март 1, 2024

Climate change necessitates urgent action to decarbonize the transport sector. Sustainable vehicles represent crucial alternatives traditional combustion engines. This study comprehensively compares four prominent sustainable vehicle technologies: biofuel-powered (BPVs), fuel cell (FCVs), electric (EVs), and solar vehicles. We examine each technology’s history, development, classification, key components, operational principles. Furthermore, we assess their sustainability through technical factors, environmental impacts, cost considerations, policy dimensions. Moreover, discussion section addresses challenges opportunities associated with technology assesses social impact, including public perception adoption. Each offers promise for transportation but faces unique challenges. Policymakers, industry stakeholders, researchers must collaborate address these accelerate transition toward a decarbonized future. Potential future research areas are identified guide advancements in technologies.

Язык: Английский

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Lithium battery prognostics and health management for electric vehicle application – A perspective review

Sustainable Energy Technologies and Assessments, Год журнала: 2024, Номер 65, С. 103766 - 103766

Опубликована: Март 25, 2024

Язык: Английский

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Joint estimation of State of Charge (SOC) and State of Health (SOH) for lithium ion batteries using Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Sort Term Memory Network (LSTM) models

International Journal of Electrochemical Science, Год журнала: 2024, Номер 19(9), С. 100747 - 100747

Опубликована: Июль 31, 2024

This paper proposes a data-driven method for jointly estimating the State of Charge (SOC) and Health (SOH) batteries, addressing impact battery aging on SOC estimation. Initially, Support Vector Machine (SVM) is employed to estimate SOH battery, using constant voltage charging time discharging lithium-ion batteries as inputs, output. By training SVM model, accurate estimation achieved. Subsequently, rated capacity adjusted based estimated obtain current maximum available capacity. adjustment allows coupling SOC, resulting in that accounts factors. Leveraging advantages Convolutional Neural Networks (CNN) feature extraction Long Short-Term Memory (LSTM) neural networks handling long-term sequential data, CNN-LSTM model utilized The proposed utilizes Oxford Battery Dataset (Cells 1–8) NASA (B0005–B0007) estimation, (Cell 8) (B0007) results demonstrate Root Mean Square Error (RMSE) less than 0.81 % Absolute (MAE) 0.65 Cells 1–8, while B0005 B0007, RMSE 1.81 MAE 1.29 %. For show average over entire lifecycle Cell 8 0.3923 0.3339 %, whereas 0.6123 0.4976

Язык: Английский

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Capacity prediction of lithium-ion batteries with fusing aging information

Energy, Год журнала: 2024, Номер 293, С. 130743 - 130743

Опубликована: Фев. 19, 2024

Язык: Английский

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Fundamentals of the recycling of spent lithium-ion batteries

Chemical Society Reviews, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Fundamentals of battery recycling play a vital role in addressing the challenges posed by spent lithium-ion batteries providing theoretical foundation and technical tools necessary for efficient LIBs.

Язык: Английский

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A framework for joint SOC and SOH estimation of lithium-ion battery: Eliminating the dependency on initial states

Applied Energy, Год журнала: 2024, Номер 377, С. 124624 - 124624

Опубликована: Окт. 16, 2024

Язык: Английский

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Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

Journal of Dynamics Monitoring and Diagnostics, Год журнала: 2024, Номер unknown

Опубликована: Янв. 8, 2024

With the dramatic increase in electric vehicles (EVs) globally, demand for lithium-ion batteries has grown dramatically, resulting many being retired future. Developing a rapid and robust capacity estimation method is challenging work to recognize battery ageing level on service provide regroup strategy of retied secondary use. There are still limitations current methods, such as direct internal resistance (DCIR) electrochemical impedance spectroscopy (EIS), terms efficiency robustness. To address challenges, this paper proposes an improved version DCIR, named pulse technique (PIT), with more First, PIT carried out based transient excitation dynamic voltage measurement using high sampling frequency, which coherence analysis used guide selection reliable frequency band. The can be extracted wide range bands compared traditional DCIR method, obtains information evaluation. Second, various statistical variables extract features Pearson correlation applied determine highly correlated features. Then linear regression model developed map relationship between capacity. validate performance proposed experimental system designed conduct comparative studies EIS two 18650 connected series. results reveal that indicators EIS, contributes higher accuracy than technology lower time cost. Conflict Interest Statement authors declare no conflicts interest.

Язык: Английский

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State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries

Energy, Год журнала: 2024, Номер unknown, С. 134293 - 134293

Опубликована: Дек. 1, 2024

Язык: Английский

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