Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control
In this paper, an electromagnetic energy-regenerative suspension system is proposed to achieve active control and vibration energy harvesting. For this system, a PID controller based on BP neural network algorithm is designed and vehicle dynamic performances are studied. Based on the traditional ene...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/4609754 |
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| _version_ | 1849691425293205504 |
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| author | Jiang Liu Xinjun Li Xilong Zhang Xiufeng Chen |
| author_facet | Jiang Liu Xinjun Li Xilong Zhang Xiufeng Chen |
| author_sort | Jiang Liu |
| collection | DOAJ |
| description | In this paper, an electromagnetic energy-regenerative suspension system is proposed to achieve active control and vibration energy harvesting. For this system, a PID controller based on BP neural network algorithm is designed and vehicle dynamic performances are studied. Based on the traditional energy-regenerative efficiency calculation, a novel self-supply energy efficiency concept is proposed to evaluate the utilization effect of the recycled energy for this dual-functional suspension. Simulations are carried out, and the results show that the vehicle dynamic performances are effectively improved under different input conditions, including road surfaces and vehicle speeds. Furthermore, the energy-regenerative suspension can recover part of vibration energy, where the self-supply energy efficiency is about 55% and the energy-regenerative efficiency is about 16%. Meanwhile, the BP-PID algorithm also enables the suspension system’s self-adaptability and stability characteristics on its energy recovery capability. |
| format | Article |
| id | doaj-art-e7b5cd5ebc28454ba6e3f140a02aac1f |
| institution | DOAJ |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-e7b5cd5ebc28454ba6e3f140a02aac1f2025-08-20T03:21:02ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/46097544609754Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID ControlJiang Liu0Xinjun Li1Xilong Zhang2Xiufeng Chen3School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaSchool of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, ChinaIn this paper, an electromagnetic energy-regenerative suspension system is proposed to achieve active control and vibration energy harvesting. For this system, a PID controller based on BP neural network algorithm is designed and vehicle dynamic performances are studied. Based on the traditional energy-regenerative efficiency calculation, a novel self-supply energy efficiency concept is proposed to evaluate the utilization effect of the recycled energy for this dual-functional suspension. Simulations are carried out, and the results show that the vehicle dynamic performances are effectively improved under different input conditions, including road surfaces and vehicle speeds. Furthermore, the energy-regenerative suspension can recover part of vibration energy, where the self-supply energy efficiency is about 55% and the energy-regenerative efficiency is about 16%. Meanwhile, the BP-PID algorithm also enables the suspension system’s self-adaptability and stability characteristics on its energy recovery capability.http://dx.doi.org/10.1155/2019/4609754 |
| spellingShingle | Jiang Liu Xinjun Li Xilong Zhang Xiufeng Chen Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control Shock and Vibration |
| title | Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control |
| title_full | Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control |
| title_fullStr | Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control |
| title_full_unstemmed | Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control |
| title_short | Modeling and Simulation of Energy-Regenerative Active Suspension Based on BP Neural Network PID Control |
| title_sort | modeling and simulation of energy regenerative active suspension based on bp neural network pid control |
| url | http://dx.doi.org/10.1155/2019/4609754 |
| work_keys_str_mv | AT jiangliu modelingandsimulationofenergyregenerativeactivesuspensionbasedonbpneuralnetworkpidcontrol AT xinjunli modelingandsimulationofenergyregenerativeactivesuspensionbasedonbpneuralnetworkpidcontrol AT xilongzhang modelingandsimulationofenergyregenerativeactivesuspensionbasedonbpneuralnetworkpidcontrol AT xiufengchen modelingandsimulationofenergyregenerativeactivesuspensionbasedonbpneuralnetworkpidcontrol |