State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries Based on Dual Fractional-Order Extended Kalman Filter and Online Parameter Identification

Ling, Liuyi; Wei, Ying

doi:10.1109/access.2021.3068813

Cited by 105 publications

(42 citation statements)

References 35 publications

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“…One of the solutions traditionally approached to deal with nonlinear ones, for SoC estimation, are those based on adaptive systems, in particular Kalman filter (KF) and its variants [ 18 , 19 , 20 ]. In this method, the battery is seen as a power system and is described as an equivalent circuit.…”

Section: Related Workmentioning

confidence: 99%

“…Methods based on Artificial Intelligence, such as Artificial Neural Networks (ANNs) and Fuzzy Neural Networks (FNNs), have been used in the estimation of the SoC, as well as in the estimation of current and temperature parameters [ 20 , 22 , 23 ]. These methods can be applied in almost all types of batteries and present an excellent non-linear performance.…”

Section: Related Workmentioning

confidence: 99%

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State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

Vieira

Villanueva

Flores

et al. 2022

Sensors

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Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA’s research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA’s research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

Vieira

Villanueva

Flores

et al. 2022

Sensors

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“…Despite the 1-RC model, with its variants, as a good choice for online SOP estimation, the lack of model accuracy in a broad-frequency band leads to a compromised performance in a lengthy prediction window. In recent years, fractional order models have gained popularity in battery state estimation, owing to their better interpretation of battery electrochemical processes from fast dynamic on the solid electrolyte interface at high frequency regions to charge transfer process and diffusion phenomenon at mid-low frequency regions [76][77][78]. However, they have not been extensively employed in SOP estimation, and the relevant research is still few.…”

Section: Equivalent-circuit Modelmentioning

confidence: 99%

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

Guo

Shen

2021

Vehicles

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With rapid transportation electrification worldwide, lithium-ion batteries have gained much attention for energy storage in electric vehicles (EVs). State of power (SOP) is one of the key states of lithium-ion batteries for EVs to optimise power flow, thereby requiring accurate online estimation. Equivalent circuit model (ECM)-based methods are considered as the mainstream technique for online SOP estimation. They primarily vary in their basic principle, technical contribution, and validation approach, which have not been systematically reviewed. This paper provides an overview of the improvements on ECM-based online SOP estimation methods in the past decade. Firstly, online SOP estimation methods are briefed, in terms of different operation modes, and their main pros and cons are also analysed accordingly. Secondly, technical contributions are reviewed from three aspects: battery modelling, online parameters identification, and SOP estimation. Thirdly, SOP testing methods are discussed, according to their accuracy and efficiency. Finally, the challenges and outlooks are presented to inspire researchers in this field for further developments in the future.

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“…The model parameters are updated while estimating the SOC, which accurately reflects the relationship between SOC and model parameters. 15 The fractional-order algorithm introduces the previous data, which increases the complexity and noise interference of the algorithm. None of the above methods considers how to reduce the complexity and noise interference of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

An adaptive fractional‐order extended Kalman filtering for state of charge estimation of high‐capacity lithium‐ion battery

Peng

Wang

et al. 2021

Intl J of Energy Research

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The state of charge (SOC) estimation of lithium-ion battery is a crucial portion of the battery management system (BMS). The high-precision estimation is the foundation of BMS safety and efficiency. To that extent, a fractional-order algorithm with time-varying parameters model is proposed to ensure the accuracy of the SOC. Since the battery state changes slowly and is related to the state in the past, this study proposes a memory factor M containing the battery state in the past to estimate the SOC. Moreover, by comparing the experimental results of different orders, the most appropriate fractional order is determined. In order to eliminate the influence of noises introduced into historical data processing, an adaptive noise factor is added to the algorithm. The experimental results confirm that the maximum error of the adaptive fractional-order extended Kalman (AFEKF) estimation is less than 2%, which indicates that the estimation method provides a higher accuracy than the extended Kalman filter.

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State-of-Charge and State-of-Health Estimation for Lithium-Ion Batteries Based on Dual Fractional-Order Extended Kalman Filter and Online Parameter Identification

Cited by 105 publications

References 35 publications

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

A Review of Equivalent Circuit Model Based Online State of Power Estimation for Lithium-Ion Batteries in Electric Vehicles

An adaptive fractional‐order extended Kalman filtering for state of charge estimation of high‐capacity lithium‐ion battery

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