A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS
The driving characteristics of a hybrid propulsion system (HPS) depend on operational management strategies. However, the differences in output and response of various types of power sources make real-time management of operating conditions complex. In this study, an operational management strategy,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723008879 |
| _version_ | 1827582164736147456 |
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| author | Yifei Zhang Lijun Diao Zheming Jin Haoying Pei Chunmei Xu |
| author_facet | Yifei Zhang Lijun Diao Zheming Jin Haoying Pei Chunmei Xu |
| author_sort | Yifei Zhang |
| collection | DOAJ |
| description | The driving characteristics of a hybrid propulsion system (HPS) depend on operational management strategies. However, the differences in output and response of various types of power sources make real-time management of operating conditions complex. In this study, an operational management strategy, which combines adaptive equivalent consumption minimization strategy (AECMS) and general regression neural network (GRNN), is proposed for optimizing power controls within hybrid power sources. Under this strategy, the optimal function of the AECMS is adaptively calculated by the load profile changes based on GRNN’s prediction results, and it is more suitable for the HPS. Furthermore, the proposed GRNN-AECMS method can minimize fuel consumption and meet requirements for power tracking. From simulation results, fuel consumption under GRNN-AECMS method can be reduced by 2.715 kg, or 31.8%. |
| first_indexed | 2024-03-08T22:46:36Z |
| format | Article |
| id | doaj.art-c448a04ff3e6472cb6826fb966507d84 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T22:46:36Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-c448a04ff3e6472cb6826fb966507d842023-12-17T06:39:02ZengElsevierEnergy Reports2352-48472023-10-019662670A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMSYifei Zhang0Lijun Diao1Zheming Jin2Haoying Pei3Chunmei Xu4School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaCorresponding author.; School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, ChinaThe driving characteristics of a hybrid propulsion system (HPS) depend on operational management strategies. However, the differences in output and response of various types of power sources make real-time management of operating conditions complex. In this study, an operational management strategy, which combines adaptive equivalent consumption minimization strategy (AECMS) and general regression neural network (GRNN), is proposed for optimizing power controls within hybrid power sources. Under this strategy, the optimal function of the AECMS is adaptively calculated by the load profile changes based on GRNN’s prediction results, and it is more suitable for the HPS. Furthermore, the proposed GRNN-AECMS method can minimize fuel consumption and meet requirements for power tracking. From simulation results, fuel consumption under GRNN-AECMS method can be reduced by 2.715 kg, or 31.8%.http://www.sciencedirect.com/science/article/pii/S2352484723008879Hybrid propulsion systemsOperational managementAdaptive equivalent consumption minimization strategyGeneral regression neural networkHybrid energy storage system |
| spellingShingle | Yifei Zhang Lijun Diao Zheming Jin Haoying Pei Chunmei Xu A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS Energy Reports Hybrid propulsion systems Operational management Adaptive equivalent consumption minimization strategy General regression neural network Hybrid energy storage system |
| title | A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS |
| title_full | A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS |
| title_fullStr | A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS |
| title_full_unstemmed | A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS |
| title_short | A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS |
| title_sort | real time operational management strategy for vehicular hybrid propulsion system based on grnn aecms |
| topic | Hybrid propulsion systems Operational management Adaptive equivalent consumption minimization strategy General regression neural network Hybrid energy storage system |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723008879 |
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