Analysing the Temperature Influence on State of Charge Estimation for an Electric Vehicle

Mohite, S.; Shadab, S.; Sheikh, A.; Bhil, S.

doi:10.1109/icpes47639.2019.9105419

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The third variable that will compose the model is the temperature. In [ 25 ], SoC is defined as the present charge of the battery proportional to its capacity. It indicates the remaining percentages of the battery of an EV.…”

Section: Database—national Aeronautics Ans Space Administration (Nasa)mentioning

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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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: Database—national Aeronautics Ans Space Administration (Nasa)mentioning

confidence: 99%

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

Vieira

Villanueva

Flores

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Performance enhancement of Battery Management System using Unscented Kalman Filter Approach

Mohite¹,

Shadab²,

Sheikh³

et al. 2020

2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)

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Enhancing the Performance Index of Battery Management System Using Nonlinear Approach

Mohite¹,

Shadab²,

Sheikh³

et al. 2020

2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)

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Analysing the Temperature Influence on State of Charge Estimation for an Electric Vehicle

Cited by 4 publications

References 13 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

Performance enhancement of Battery Management System using Unscented Kalman Filter Approach

Enhancing the Performance Index of Battery Management System Using Nonlinear Approach

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