A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks
This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/10/2548 |
| _version_ | 1827716648232026112 |
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| author | Angelo Bonfitto |
| author_facet | Angelo Bonfitto |
| author_sort | Angelo Bonfitto |
| collection | DOAJ |
| description | This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system. |
| first_indexed | 2024-03-10T19:45:28Z |
| format | Article |
| id | doaj.art-238f9c235f5d4ac0a667356b4f56bd56 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:45:28Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-238f9c235f5d4ac0a667356b4f56bd562023-11-20T00:48:32ZengMDPI AGEnergies1996-10732020-05-011310254810.3390/en13102548A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural NetworksAngelo Bonfitto0Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, ItalyThis paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system.https://www.mdpi.com/1996-1073/13/10/2548batterystate of chargestate of healthartificial intelligenceartificial neural networkshybrid vehicles |
| spellingShingle | Angelo Bonfitto A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks Energies battery state of charge state of health artificial intelligence artificial neural networks hybrid vehicles |
| title | A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks |
| title_full | A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks |
| title_fullStr | A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks |
| title_full_unstemmed | A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks |
| title_short | A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks |
| title_sort | method for the combined estimation of battery state of charge and state of health based on artificial neural networks |
| topic | battery state of charge state of health artificial intelligence artificial neural networks hybrid vehicles |
| url | https://www.mdpi.com/1996-1073/13/10/2548 |
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