Abstract
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.
Highlights
With the development of electric vehicles, the technologies related to energy management systems have been of extreme importance in recent years
The performance of the adaptive control algorithm RN A Maximum Correntropy Criterion (MCC) is evaluated through simulation tests, whose objective is to identify the state of charge
An auto-regressive artificial neural network has been proposed to estimate the state of charge of Lithium-ion batteries
Summary
With the development of electric vehicles, the technologies related to energy management systems have been of extreme importance in recent years. It is believed that the wide use of this technique was only possible due to certain peculiar characteristics of artificial neural networks, such as: Potential to model complex dynamics such as those usually presented by nonlinear systems; Artificial neural networks can be trained (when compared to other techniques), using historical data from the process under study; Are applied to multivariable systems; Have the ability to infer general rules and extract typical patterns from specific examples, and recognize input-output mapping from multi-dimensional complex, multidimensional field data In this context, in adaptive systems used to process identification, most of the works found in the literature adopt the Mean Squared Error (MSE) as a cost function both for parameter tuning and for performance analysis of the designed models [13], as shown in the diagram of Figure 2. In this Figure, ym is the neural network signal, y is the reference model signal, and e is the error model signal
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