Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles
Energy management strategies and optimal power source sizing for fuel cell/battery/super capacitor hybrid electric vehicles (HEVs) are critical for power splitting and cost-effective sizing to meet power demand for a good drive range, less energy loss and consumption, and minimal fuel cell and batte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723000410 |
| _version_ | 1797781911584636928 |
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| author | Adem Siraj Mohammed Samson Mekbib Atnaw Ayodeji Olalekan Salau Joy Nnenna Eneh |
| author_facet | Adem Siraj Mohammed Samson Mekbib Atnaw Ayodeji Olalekan Salau Joy Nnenna Eneh |
| author_sort | Adem Siraj Mohammed |
| collection | DOAJ |
| description | Energy management strategies and optimal power source sizing for fuel cell/battery/super capacitor hybrid electric vehicles (HEVs) are critical for power splitting and cost-effective sizing to meet power demand for a good drive range, less energy loss and consumption, and minimal fuel cell and battery degradation for hybrid power sources. This paper presents a comprehensive review of the energy management techniques and their integration with energy source sizing, mainly for fuel cell/battery/supercapacitor hybrid electric vehicles. The paper discussed the benefits of integrating an energy management strategy (EMS) and the sizing of hybrid energy sources. Predictive based energy management strategies such as Artificial Neural Network (ANN), Reinforcement Learning (RL), and Model Predictive Control (MPC) were briefly examined. In addition, the paper reviewed hybrid algorithms or techniques for energy management strategies, that could be the combination of rule-based with a predictive, rule based with real-time and predictive with real-time, and predictive with learning based algorithms to give a good energy management strategy for fuel cell/battery/supercapacitor HEVs to achieve the optimal objective functions. The results show, that in terms of the size of the fuel cell, the evaluation of power demand-based and state of charge (SoC)-based methods used for large capacity batteries and smaller capacity batteries, reveals that the SoC-based method is appropriate for real-time energy management, while small capacity batteries have higher degradation. Fuel economy was improved with RL for battery engine hybrid vehicles than when Dynamic Programming (DP) was used. When the EMS was compared using dynamic programming (DP), Pontryagin’s minimum principle (PMP), and Equivalent Consumption Minimization Strategy (ECMS), the results show that ECMS is more efficient for online optimization than PMP and DP. Further results show that RL-based EMSs help to reduce energy losses and also increases the system efficiency, and help to reduce battery degradation as compared to when rule-based EMSs are used. |
| first_indexed | 2024-03-13T00:03:38Z |
| format | Article |
| id | doaj.art-26ceb3e15f93470199d1743e94bb38be |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T00:03:38Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-26ceb3e15f93470199d1743e94bb38be2023-07-13T05:29:13ZengElsevierEnergy Reports2352-48472023-12-01922132228Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehiclesAdem Siraj Mohammed0Samson Mekbib Atnaw1Ayodeji Olalekan Salau2Joy Nnenna Eneh3Department of Mechanical Engineering, Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa, EthiopiaDepartment of Mechanical Engineering, Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa, EthiopiaDepartment of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria; Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu 600124, India; Corresponding author at: Department of Electronic Engineering, University of Nigeria, Nsukka, Nigeria.Department of Electronic Engineering, University of Nigeria, Nsukka, NigeriaEnergy management strategies and optimal power source sizing for fuel cell/battery/super capacitor hybrid electric vehicles (HEVs) are critical for power splitting and cost-effective sizing to meet power demand for a good drive range, less energy loss and consumption, and minimal fuel cell and battery degradation for hybrid power sources. This paper presents a comprehensive review of the energy management techniques and their integration with energy source sizing, mainly for fuel cell/battery/supercapacitor hybrid electric vehicles. The paper discussed the benefits of integrating an energy management strategy (EMS) and the sizing of hybrid energy sources. Predictive based energy management strategies such as Artificial Neural Network (ANN), Reinforcement Learning (RL), and Model Predictive Control (MPC) were briefly examined. In addition, the paper reviewed hybrid algorithms or techniques for energy management strategies, that could be the combination of rule-based with a predictive, rule based with real-time and predictive with real-time, and predictive with learning based algorithms to give a good energy management strategy for fuel cell/battery/supercapacitor HEVs to achieve the optimal objective functions. The results show, that in terms of the size of the fuel cell, the evaluation of power demand-based and state of charge (SoC)-based methods used for large capacity batteries and smaller capacity batteries, reveals that the SoC-based method is appropriate for real-time energy management, while small capacity batteries have higher degradation. Fuel economy was improved with RL for battery engine hybrid vehicles than when Dynamic Programming (DP) was used. When the EMS was compared using dynamic programming (DP), Pontryagin’s minimum principle (PMP), and Equivalent Consumption Minimization Strategy (ECMS), the results show that ECMS is more efficient for online optimization than PMP and DP. Further results show that RL-based EMSs help to reduce energy losses and also increases the system efficiency, and help to reduce battery degradation as compared to when rule-based EMSs are used.http://www.sciencedirect.com/science/article/pii/S2352484723000410Energy management systemSizingHybrid electric vehicleFuel cellBatterySupercapacitor |
| spellingShingle | Adem Siraj Mohammed Samson Mekbib Atnaw Ayodeji Olalekan Salau Joy Nnenna Eneh Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles Energy Reports Energy management system Sizing Hybrid electric vehicle Fuel cell Battery Supercapacitor |
| title | Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles |
| title_full | Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles |
| title_fullStr | Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles |
| title_full_unstemmed | Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles |
| title_short | Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles |
| title_sort | review of optimal sizing and power management strategies for fuel cell battery super capacitor hybrid electric vehicles |
| topic | Energy management system Sizing Hybrid electric vehicle Fuel cell Battery Supercapacitor |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723000410 |
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