Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

IEEE Access, Год журнала: 2024, Номер 12, С. 77903 - 77931

Опубликована: Янв. 1, 2024

Lithium-ion (Li-ion) batteries are being used in electric vehicles to reduce the reliance on fossil fuels due their high energy density, design flexibility, and efficiency compared other battery technologies. However, they undergo complex nonlinear degradation performance declines when abused, making reliability crucial for effective vehicle performance. This survey paper presents a comprehensive review of state-of-the-art assessments vehicles. First, operating principle Li-ion batteries, patterns, models briefly discussed. Afterwards, detailed qualitative quantitative approaches. The approach encompasses failure modes mechanisms effects analysis, X-ray computed tomography, scanning electron microscopy. In contrast, approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity differential voltage machine learning, transfer learning. Each technique is examined terms its principles, advantages, limitations, applicability Comparative analysis reveals that methods primarily early stages assess potential risks post-mortem laboratory, while techniques such as learning offer real-time prognostic health management anomaly prevention. Also, tend be more cost-effective counterparts. consolidating through standardization testing protocols, real-world data integration, controller area network use, policy regulation highlighted guide further research.

Язык: Английский

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A hybrid battery degradation model combining arrhenius equation and neural network for capacity prediction under time-varying operating conditions

Reliability Engineering & System Safety, Год журнала: 2024, Номер 252, С. 110471 - 110471

Опубликована: Авг. 27, 2024

Язык: Английский

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A health assessment method with attribute importance modeling for complex systems using belief rule base

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110387 - 110387

Опубликована: Июль 29, 2024

Язык: Английский

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SOH estimation method for lithium-ion batteries under low temperature conditions with nonlinear correction

Journal of Energy Storage, Год журнала: 2023, Номер 75, С. 109690 - 109690

Опубликована: Ноя. 19, 2023

Язык: Английский

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Towards an intelligent battery management system for electric vehicle applications: Dataset considerations, algorithmic approaches, and future trends

Journal of Energy Storage, Год журнала: 2024, Номер 101, С. 113827 - 113827

Опубликована: Сен. 25, 2024

Язык: Английский

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Lithium-Ion Battery State of Health and Failure Analysis with Mixture Weibull and Equivalent Circuit Model

iScience, Год журнала: 2024, Номер 27(6), С. 109980 - 109980

Опубликована: Май 20, 2024

Existing methods for interpreting Electrochemical Impedance Spectroscopy data involve various models, which face significant challenges in parameterization and physical interpretation fail to comprehensively reflect the electrochemical behavior within batteries. To address these issues, this study proposes a Temperature-Controlled Second-Order R-CPE Equivalent Circuit Model capture non-ideal capacitive characteristics of electrode surfaces. Additionally, employs Copula based Joint Mixture Weibull multi-output Gaussian Process Regression enhance precision capturing distribution battery parameters predict SoH curves. Experimental validation shows that model used article has an average RMSE error 8.5%, prediction curve after 100th cycle can achieve 9.2%. These findings provide insightful implications understanding complexities parameter interdependencies aging process, offering robust framework future research diagnostics.

Язык: Английский

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Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110395 - 110395

Опубликована: Июль 23, 2024

Язык: Английский

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State of Health Estimation of Lithium-Ion Batteries Based on Hybrid Neural Networks with Residual Connections

Journal of The Electrochemical Society, Год журнала: 2025, Номер 172(2), С. 020503 - 020503

Опубликована: Янв. 23, 2025

Accurately estimating the state of health (SOH) lithium-ion batteries is essential for ensuring stability and safety battery. Although hybrid neural network model demonstrates strong performance in SOH, degradation becomes a significant issue as depth increases, potentially undermining accuracy estimation. This paper presents estimation with residual connections to address degradation. First, utilizes combination convolutional networks an attention mechanism automatically extract feature information highly correlated SOH from partial charging data batteries. Subsequently, multi-layer gated recurrent unit (GRU) employed capture temporal within extracted features. To that arises stacking multiple layers networks, are incorporated into GRUs, mitigating accumulation errors deep networks. Finally, three distinct datasets validate proposed model. The experimental results demonstrate exhibits average mean absolute error root square less than 1.8% on both these datasets.

Язык: Английский

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A comprehensive review of pre-treatment discharge of the spent lithium-ion cells

Journal of Energy Storage, Год журнала: 2025, Номер 112, С. 115497 - 115497

Опубликована: Янв. 24, 2025

Язык: Английский

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Early prediction of battery lifetime for lithium-ion batteries based on a hybrid clustered CNN model

Energy, Год журнала: 2025, Номер unknown, С. 134992 - 134992

Опубликована: Фев. 1, 2025

Язык: Английский

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