State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy
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 contr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/1179 |
| _version_ | 1797484614014468096 |
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| author | Rômulo Navega Vieira Juan Moises Mauricio Villanueva Thommas Kevin Sales Flores Euler Cássio Tavares de Macêdo |
| author_facet | Rômulo Navega Vieira Juan Moises Mauricio Villanueva Thommas Kevin Sales Flores Euler Cássio Tavares de Macêdo |
| author_sort | Rômulo Navega Vieira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:06:58Z |
| format | Article |
| id | doaj.art-7ebc6a0d09da46f484e24c98ad0c23d3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:06:58Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7ebc6a0d09da46f484e24c98ad0c23d32023-11-23T17:51:48ZengMDPI AGSensors1424-82202022-02-01223117910.3390/s22031179State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with CorrentropyRômulo Navega Vieira0Juan Moises Mauricio Villanueva1Thommas Kevin Sales Flores2Euler Cássio Tavares de Macêdo3Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58051-900, BrazilRenewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58051-900, BrazilRenewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58051-900, BrazilRenewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), Joao Pessoa 58051-900, BrazilNowadays, 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.https://www.mdpi.com/1424-8220/22/3/1179estimationstate of chargebatteriescorrentropycost functionArtificial Neural Networks |
| spellingShingle | Rômulo Navega Vieira Juan Moises Mauricio Villanueva Thommas Kevin Sales Flores Euler Cássio Tavares de Macêdo State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy Sensors estimation state of charge batteries correntropy cost function Artificial Neural Networks |
| title | State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy |
| title_full | State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy |
| title_fullStr | State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy |
| title_full_unstemmed | State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy |
| title_short | State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy |
| title_sort | state of charge estimation of battery based on neural networks and adaptive strategies with correntropy |
| topic | estimation state of charge batteries correntropy cost function Artificial Neural Networks |
| url | https://www.mdpi.com/1424-8220/22/3/1179 |
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