A generic model-free approach for lithium-ion battery health management

Bai, Guangxing; Wang, Pingfeng; Hu, Chao; Pecht, Michael

doi:10.1016/j.apenergy.2014.08.059

Cited by 110 publications

(24 citation statements)

References 35 publications

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“…The neural network is widely used as a machine learning method because it is easy to be realized and simulated nonlinearly, and these networks have been used previously in battery management applications [14,43,44], particularly for estimation of battery issues involving aging. For example, a neural network method could be used to estimate the battery's SOH [45,46], to estimate the battery's SOC during the degradation process [47] and to predict the remaining useful life of the battery [48].…”

Section: Charging Voltage Curve Analysis Based On Neural Networkmentioning

confidence: 99%

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

et al. 2019

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Lithium-ion (Li-ion) cells degrade after repeated cycling and the cell capacity fades while its resistance increases. Degradation of Li-ion cells is caused by a variety of physical and chemical mechanisms and it is strongly influenced by factors including the electrode materials used, the working conditions and the battery temperature. At present, charging voltage curve analysis methods are widely used in studies of battery characteristics and the constant current charging voltage curves can be used to analyze battery aging mechanisms and estimate a battery's state of health (SOH) via methods such as incremental capacity (IC) analysis. In this paper, a method to fit and analyze the charging voltage curve based on a neural network is proposed and is compared to the existing point counting method and the polynomial curve fitting method. The neuron parameters of the trained neural network model are used to analyze the battery capacity relative to the phase change reactions that occur inside the batteries. This method is suitable for different types of batteries and could be used in battery management systems for online battery modeling, analysis and diagnosis.

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Section: Charging Voltage Curve Analysis Based On Neural Networkmentioning

confidence: 99%

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

et al. 2019

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“…Prominent among these are artificial neural networks (ANN) [34][35][36] (reviewed in [5]) and Support Vector Machines (SVM). The SVM is one of the most popular machine learning algorithms, which is used in the pattern recognition community for classification tasks [37].…”

Section: Introductionmentioning

confidence: 99%

A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

et al. 2015

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“…Methods such as the AutoRegressive (AR) model [12], neural network [13][14][15][16][17][18], support vector machine (SVM) [19][20][21][22], and relevance vector machine (RVM) [23][24][25][26][27][28][29][30][31][32][33] are used.…”

Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

158

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Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

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A generic model-free approach for lithium-ion battery health management

Cited by 110 publications

References 35 publications

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

A Comparative Study of Charging Voltage Curve Analysis and State of Health Estimation of Lithium-ion Batteries in Electric Vehicle

A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

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