A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition
Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed....
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/2/696 |
| _version_ | 1819025896330231808 |
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| author | Qi Zhang Xiaoling Fu |
| author_facet | Qi Zhang Xiaoling Fu |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles. |
| first_indexed | 2024-12-21T05:17:58Z |
| format | Article |
| id | doaj.art-1dbdb793dc6d4eb99ddf3a26177f9aeb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-21T05:17:58Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1dbdb793dc6d4eb99ddf3a26177f9aeb2022-12-21T19:14:53ZengMDPI AGApplied Sciences2076-34172020-01-0110269610.3390/app10020696app10020696A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle RecognitionQi Zhang0Xiaoling Fu1State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, ChinaDepartment of Physics, Changji College, Changji 831100, ChinaAiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.https://www.mdpi.com/2076-3417/10/2/696hybrid electric vehicleenergy management strategydriving cycle recognitionneural network fuzzy |
| spellingShingle | Qi Zhang Xiaoling Fu A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition Applied Sciences hybrid electric vehicle energy management strategy driving cycle recognition neural network fuzzy |
| title | A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition |
| title_full | A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition |
| title_fullStr | A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition |
| title_full_unstemmed | A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition |
| title_short | A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition |
| title_sort | neural network fuzzy energy management strategy for hybrid electric vehicles based on driving cycle recognition |
| topic | hybrid electric vehicle energy management strategy driving cycle recognition neural network fuzzy |
| url | https://www.mdpi.com/2076-3417/10/2/696 |
| work_keys_str_mv | AT qizhang aneuralnetworkfuzzyenergymanagementstrategyforhybridelectricvehiclesbasedondrivingcyclerecognition AT xiaolingfu aneuralnetworkfuzzyenergymanagementstrategyforhybridelectricvehiclesbasedondrivingcyclerecognition AT qizhang neuralnetworkfuzzyenergymanagementstrategyforhybridelectricvehiclesbasedondrivingcyclerecognition AT xiaolingfu neuralnetworkfuzzyenergymanagementstrategyforhybridelectricvehiclesbasedondrivingcyclerecognition |