Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery
Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Electrochem |
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| Online Access: | https://www.mdpi.com/2673-3293/3/1/3 |
| _version_ | 1797446740645773312 |
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| author | Devendrasinh Darbar Indranil Bhattacharya |
| author_facet | Devendrasinh Darbar Indranil Bhattacharya |
| author_sort | Devendrasinh Darbar |
| collection | DOAJ |
| description | Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na<sub>0.67</sub>Fe<sub>0.5</sub>Mn<sub>0.5</sub>O<sub>2</sub> (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na<sup>+,</sup> reaching the R<sup>2</sup> value of ~0.97–~0.99, ~0.99, and ~0.98, respectively. |
| first_indexed | 2024-03-09T13:44:55Z |
| format | Article |
| id | doaj.art-201c6cacd2d6428dbace2b214c782004 |
| institution | Directory Open Access Journal |
| issn | 2673-3293 |
| language | English |
| last_indexed | 2024-03-09T13:44:55Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electrochem |
| spelling | doaj.art-201c6cacd2d6428dbace2b214c7820042023-11-30T21:00:50ZengMDPI AGElectrochem2673-32932022-01-0131425710.3390/electrochem3010003Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion BatteryDevendrasinh Darbar0Indranil Bhattacharya1Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USADepartment of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN 38505, USAEstimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na<sub>0.67</sub>Fe<sub>0.5</sub>Mn<sub>0.5</sub>O<sub>2</sub> (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na<sup>+,</sup> reaching the R<sup>2</sup> value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.https://www.mdpi.com/2673-3293/3/1/3neural networkState of Charge (SOC) estimationsodium-ion batterydropout technique |
| spellingShingle | Devendrasinh Darbar Indranil Bhattacharya Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery Electrochem neural network State of Charge (SOC) estimation sodium-ion battery dropout technique |
| title | Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery |
| title_full | Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery |
| title_fullStr | Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery |
| title_full_unstemmed | Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery |
| title_short | Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery |
| title_sort | application of machine learning in battery state of charge estimation using feed forward neural network for sodium ion battery |
| topic | neural network State of Charge (SOC) estimation sodium-ion battery dropout technique |
| url | https://www.mdpi.com/2673-3293/3/1/3 |
| work_keys_str_mv | AT devendrasinhdarbar applicationofmachinelearninginbatterystateofchargeestimationusingfeedforwardneuralnetworkforsodiumionbattery AT indranilbhattacharya applicationofmachinelearninginbatterystateofchargeestimationusingfeedforwardneuralnetworkforsodiumionbattery |