Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios
Abstract Vehicle connectivity technologies has propelled integrated optimization of vehicle’s motion and power splitting becoming a hotspot in eco-driving control research. However, the security issues and power sources life loss of fuel cell-battery hybrid electric vehicle (FCHEV) are still challen...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01698-4 |
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| author | Longlong Zhu Fazhan Tao Zhumu Fu Mengyang Li Guoqu Deng |
| author_facet | Longlong Zhu Fazhan Tao Zhumu Fu Mengyang Li Guoqu Deng |
| author_sort | Longlong Zhu |
| collection | DOAJ |
| description | Abstract Vehicle connectivity technologies has propelled integrated optimization of vehicle’s motion and power splitting becoming a hotspot in eco-driving control research. However, the security issues and power sources life loss of fuel cell-battery hybrid electric vehicle (FCHEV) are still challenging due to disturbances and power sources degradation. To address these problems, in this paper, control barrier function (CBF) based multi-objective energy management strategy (EMS) for FCHEV in car-following process is proposed. Firstly, the state of health models of fuel cell and battery are established to reflect the relationship between power sources degradation and energy consumption. Secondly, multi-objective model predictive control (MPC) based EMS framework is developed by comprehensive considering tracking performance, comfort, fuel consumption and power sources life loss. Thirdly, to robustly cope with disturbances and uncertainties, discrete-time CBFs are designed to enforce safety-critical constraints related to safety issues of both vehicle dynamics and powertrain operation in MPC. Finally, comprehensive simulations in extreme and long driving cycle testing scenarios show the proposed strategy can prevent vehicles from entering unsafe states, while improving fuel economy by 9.95%, reducing power sources life loss by 6.53%. |
| format | Article |
| id | doaj-art-4fcbcc0c4b4e4b628a7ca31844cfb22b |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-4fcbcc0c4b4e4b628a7ca31844cfb22b2025-02-02T12:50:22ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111111310.1007/s40747-024-01698-4Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenariosLonglong Zhu0Fazhan Tao1Zhumu Fu2Mengyang Li3Guoqu Deng4School of Software, Henan University of Science and TechnologyCollege of Information Engineering, Henan University of Science and TechnologyCollege of Information Engineering, Henan University of Science and TechnologyCollege of Physics & Electronic Information, Luoyang Normal UniversityBusiness School and MBA Education Center, Henan University of Science and TechnologyAbstract Vehicle connectivity technologies has propelled integrated optimization of vehicle’s motion and power splitting becoming a hotspot in eco-driving control research. However, the security issues and power sources life loss of fuel cell-battery hybrid electric vehicle (FCHEV) are still challenging due to disturbances and power sources degradation. To address these problems, in this paper, control barrier function (CBF) based multi-objective energy management strategy (EMS) for FCHEV in car-following process is proposed. Firstly, the state of health models of fuel cell and battery are established to reflect the relationship between power sources degradation and energy consumption. Secondly, multi-objective model predictive control (MPC) based EMS framework is developed by comprehensive considering tracking performance, comfort, fuel consumption and power sources life loss. Thirdly, to robustly cope with disturbances and uncertainties, discrete-time CBFs are designed to enforce safety-critical constraints related to safety issues of both vehicle dynamics and powertrain operation in MPC. Finally, comprehensive simulations in extreme and long driving cycle testing scenarios show the proposed strategy can prevent vehicles from entering unsafe states, while improving fuel economy by 9.95%, reducing power sources life loss by 6.53%.https://doi.org/10.1007/s40747-024-01698-4Fuel cell-battery hybrid electric vehicleEco-driving controlEnergy managementControl barrier functionModel predictive control |
| spellingShingle | Longlong Zhu Fazhan Tao Zhumu Fu Mengyang Li Guoqu Deng Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios Complex & Intelligent Systems Fuel cell-battery hybrid electric vehicle Eco-driving control Energy management Control barrier function Model predictive control |
| title | Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios |
| title_full | Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios |
| title_fullStr | Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios |
| title_full_unstemmed | Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios |
| title_short | Safety-involved co-optimization of speed trajectory and energy management for fuel cell-battery electric vehicle in car-following scenarios |
| title_sort | safety involved co optimization of speed trajectory and energy management for fuel cell battery electric vehicle in car following scenarios |
| topic | Fuel cell-battery hybrid electric vehicle Eco-driving control Energy management Control barrier function Model predictive control |
| url | https://doi.org/10.1007/s40747-024-01698-4 |
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