A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle
In this paper, energy management strategy (EMS) model based on deep recurrent neural network (DRNN) is presented to learn optimal torque distribution for the single-axle parallel hybrid electric vehicle. The model has two distinguishing properties: 1) because the EMS is formulated as a time series p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8717634/ |
| _version_ | 1818641740451545088 |
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| author | Huifang Kong Yao Fang Lei Fan Hai Wang Xiaoxue Zhang Jie Hu |
| author_facet | Huifang Kong Yao Fang Lei Fan Hai Wang Xiaoxue Zhang Jie Hu |
| author_sort | Huifang Kong |
| collection | DOAJ |
| description | In this paper, energy management strategy (EMS) model based on deep recurrent neural network (DRNN) is presented to learn optimal torque distribution for the single-axle parallel hybrid electric vehicle. The model has two distinguishing properties: 1) because the EMS is formulated as a time series prediction problem, taking historical data as input of the EMS model captures the input-and-output dynamic characteristics and enhances the prediction capability and 2) the EMS model based on end-to-end framework directly generates torque distribution results without extracting features of driving cycles and other artificial interference. The extensive simulations are conducted to demonstrate the accuracy and generalization capability of the EMS model in public platform TensorFlow. Comparing with other energy management strategies, our proposed model yields better performance in terms of fuel economy and accuracy. The simulation results show that our proposed EMS model provides a novel way to study the energy management strategy. |
| first_indexed | 2024-12-16T23:31:58Z |
| format | Article |
| id | doaj.art-159b941f34bd42ccbe4ce36dabfcb635 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:31:58Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-159b941f34bd42ccbe4ce36dabfcb6352022-12-21T22:11:51ZengIEEEIEEE Access2169-35362019-01-017651746518510.1109/ACCESS.2019.29175458717634A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric VehicleHuifang Kong0Yao Fang1https://orcid.org/0000-0002-4830-4682Lei Fan2https://orcid.org/0000-0001-9472-7152Hai Wang3Xiaoxue Zhang4Jie Hu5School of Electric Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electric Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electric Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electric Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electric Engineering and Automation, Hefei University of Technology, Hefei, ChinaSchool of Electric Engineering and Automation, Hefei University of Technology, Hefei, ChinaIn this paper, energy management strategy (EMS) model based on deep recurrent neural network (DRNN) is presented to learn optimal torque distribution for the single-axle parallel hybrid electric vehicle. The model has two distinguishing properties: 1) because the EMS is formulated as a time series prediction problem, taking historical data as input of the EMS model captures the input-and-output dynamic characteristics and enhances the prediction capability and 2) the EMS model based on end-to-end framework directly generates torque distribution results without extracting features of driving cycles and other artificial interference. The extensive simulations are conducted to demonstrate the accuracy and generalization capability of the EMS model in public platform TensorFlow. Comparing with other energy management strategies, our proposed model yields better performance in terms of fuel economy and accuracy. The simulation results show that our proposed EMS model provides a novel way to study the energy management strategy.https://ieeexplore.ieee.org/document/8717634/Torque distributionenergy management strategyhybrid electric vehicledeep recurrent neural network |
| spellingShingle | Huifang Kong Yao Fang Lei Fan Hai Wang Xiaoxue Zhang Jie Hu A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle IEEE Access Torque distribution energy management strategy hybrid electric vehicle deep recurrent neural network |
| title | A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle |
| title_full | A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle |
| title_fullStr | A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle |
| title_full_unstemmed | A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle |
| title_short | A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle |
| title_sort | novel torque distribution strategy based on deep recurrent neural network for parallel hybrid electric vehicle |
| topic | Torque distribution energy management strategy hybrid electric vehicle deep recurrent neural network |
| url | https://ieeexplore.ieee.org/document/8717634/ |
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