Neural Network Predictive Control for Vanadium Redox Flow Battery
The vanadium redox flow battery (VRB) is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/538237 |
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| _version_ | 1832563973595070464 |
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| author | Hai-Feng Shen Xin-Jian Zhu Meng Shao Hong-fei Cao |
| author_facet | Hai-Feng Shen Xin-Jian Zhu Meng Shao Hong-fei Cao |
| author_sort | Hai-Feng Shen |
| collection | DOAJ |
| description | The vanadium redox flow battery (VRB) is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF) network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA) is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge. |
| format | Article |
| id | doaj-art-2ae7df7171c34bfb9311fdcea549fbcb |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-2ae7df7171c34bfb9311fdcea549fbcb2025-02-03T01:12:10ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/538237538237Neural Network Predictive Control for Vanadium Redox Flow BatteryHai-Feng Shen0Xin-Jian Zhu1Meng Shao2Hong-fei Cao3Automation Department, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaInstitute of Fuel Cell, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaInstitute of Fuel Cell, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaInstitute of Fuel Cell, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaThe vanadium redox flow battery (VRB) is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF) network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA) is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.http://dx.doi.org/10.1155/2013/538237 |
| spellingShingle | Hai-Feng Shen Xin-Jian Zhu Meng Shao Hong-fei Cao Neural Network Predictive Control for Vanadium Redox Flow Battery Journal of Applied Mathematics |
| title | Neural Network Predictive Control for Vanadium Redox Flow Battery |
| title_full | Neural Network Predictive Control for Vanadium Redox Flow Battery |
| title_fullStr | Neural Network Predictive Control for Vanadium Redox Flow Battery |
| title_full_unstemmed | Neural Network Predictive Control for Vanadium Redox Flow Battery |
| title_short | Neural Network Predictive Control for Vanadium Redox Flow Battery |
| title_sort | neural network predictive control for vanadium redox flow battery |
| url | http://dx.doi.org/10.1155/2013/538237 |
| work_keys_str_mv | AT haifengshen neuralnetworkpredictivecontrolforvanadiumredoxflowbattery AT xinjianzhu neuralnetworkpredictivecontrolforvanadiumredoxflowbattery AT mengshao neuralnetworkpredictivecontrolforvanadiumredoxflowbattery AT hongfeicao neuralnetworkpredictivecontrolforvanadiumredoxflowbattery |