Grey Wolf Optimization Algorithm-Based Hybrid Energy Storage System Controller Design for Electric Vehicles

Aydın Boyar; Yasin Kabalcı; Ersan Kabalcı

Makale detayları

Grey Wolf Optimization Algorithm-Based Hybrid Energy Storage System Controller Design for Electric Vehicles

Yazarlar:

Aydın Boyar | Yasin Kabalcı | Ersan Kabalcı

Yayıncı:

Gazi University

Ana Başlıklar:

Elektrik Makineleri ve Sürücüler

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 3, pp.428-439

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001328721

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 Electric vehicles EVs present several benefits over conventional internal combustion engine vehicles. They emit zero tailpipe emissions, thereby aiding in the reduction of air pollution and the mitigation of climate change. In addition, EVs tend to have lower operating expenses due to cheaper electricity compared to gasoline or diesel. They also provide a smoother and quieter driving experience. Furthermore, EVs help promote energy independence by decreasing dependence on fossil fuels. Overall, they represent a cleaner, more sustainable transportation option for the future. However, EVs encounter some important constraints such as inefficiency of energy consumption management, charging time, and battery range problems. To address these challenges, hybrid energy storage systems HESSs offer a solution by combining different energy storage technologies. These systems can improve energy efficiency, reduce charging times, and extend the driving range of EVs, making them more practical and appealing to consumers. In this study, a new controller design is realized using the grey wolf optimization GWO algorithm, and the energy consumption demands of EV HESS are optimized with the designed system. The performance results of the proposed system are compared with other energy management systems in the literature, and it is concluded from this study that the proposed system is much superior to previous methods and typically reduces energy consumption by 12.88.

 Electric vehicles EVs present several benefits over conventional internal combustion engine vehicles. They emit zero tailpipe emissions, thereby aiding in the reduction of air pollution and the mitigation of climate change. In addition, EVs tend to have lower operating expenses due to cheaper electricity compared to gasoline or diesel. They also provide a smoother and quieter driving experience. Furthermore, EVs help promote energy independence by decreasing dependence on fossil fuels. Overall, they represent a cleaner, more sustainable transportation option for the future. However, EVs encounter some important constraints such as inefficiency of energy consumption management, charging time, and battery range problems. To address these challenges, hybrid energy storage systems HESSs offer a solution by combining different energy storage technologies. These systems can improve energy efficiency, reduce charging times, and extend the driving range of EVs, making them more practical and appealing to consumers. In this study, a new controller design is realized using the grey wolf optimization GWO algorithm, and the energy consumption demands of EV HESS are optimized with the designed system. The performance results of the proposed system are compared with other energy management systems in the literature, and it is concluded from this study that the proposed system is much superior to previous methods and typically reduces energy consumption by 12.88.

 Electric vehicles EVs present several benefits over conventional internal combustion engine vehicles. They emit zero tailpipe emissions, thereby aiding in the reduction of air pollution and the mitigation of climate change. In addition, EVs tend to have lower operating expenses due to cheaper electricity compared to gasoline or diesel. They also provide a smoother and quieter driving experience. Furthermore, EVs help promote energy independence by decreasing dependence on fossil fuels. Overall, they represent a cleaner, more sustainable transportation option for the future. However, EVs encounter some important constraints such as inefficiency of energy consumption management, charging time, and battery range problems. To address these challenges, hybrid energy storage systems HESSs offer a solution by combining different energy storage technologies. These systems can improve energy efficiency, reduce charging times, and extend the driving range of EVs, making them more practical and appealing to consumers. In this study, a new controller design is realized using the grey wolf optimization GWO algorithm, and the energy consumption demands of EV HESS are optimized with the designed system. The performance results of the proposed system are compared with other energy management systems in the literature, and it is concluded from this study that the proposed system is much superior to previous methods and typically reduces energy consumption by 12.88.

 Electric vehicles EVs present several benefits over conventional internal combustion engine vehicles. They emit zero tailpipe emissions, thereby aiding in the reduction of air pollution and the mitigation of climate change. In addition, EVs tend to have lower operating expenses due to cheaper electricity compared to gasoline or diesel. They also provide a smoother and quieter driving experience. Furthermore, EVs help promote energy independence by decreasing dependence on fossil fuels. Overall, they represent a cleaner, more sustainable transportation option for the future. However, EVs encounter some important constraints such as inefficiency of energy consumption management, charging time, and battery range problems. To address these challenges, hybrid energy storage systems HESSs offer a solution by combining different energy storage technologies. These systems can improve energy efficiency, reduce charging times, and extend the driving range of EVs, making them more practical and appealing to consumers. In this study, a new controller design is realized using the grey wolf optimization GWO algorithm, and the energy consumption demands of EV HESS are optimized with the designed system. The performance results of the proposed system are compared with other energy management systems in the literature, and it is concluded from this study that the proposed system is much superior to previous methods and typically reduces energy consumption by 12.88.

Makale detayları

Grey Wolf Optimization Algorithm-Based Hybrid Energy Storage System Controller Design for Electric Vehicles

Yazarlar:

Aydın Boyar | Yasin Kabalcı | Ersan Kabalcı

Yayıncı:

Gazi University

Ana Başlıklar:

Elektrik Makineleri ve Sürücüler

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 3, pp.428-439

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001328721

pISSN:

2147-9526

Electric vehicles

hybrid energy storage systems

metaheuristic algorithm

grey wolf optimization

Sayı

Makale detayları

Grey Wolf Optimization Algorithm-Based Hybrid Energy Storage System Controller Design for Electric Vehicles

Yazarlar: Aydın Boyar | Yasin Kabalcı | Ersan Kabalcı

Yayıncı: Gazi University

Ana Başlıklar: Elektrik Makineleri ve Sürücüler

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 3, pp.428-439

Belge Tipi: Makale

Yayın Tarihi: 9/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001328721

pISSN: 2147-9526

Electric vehicles

hybrid energy storage systems

metaheuristic algorithm

grey wolf optimization

Sayı

Yazarlar:

Aydın Boyar | Yasin Kabalcı | Ersan Kabalcı

Yayıncı:

Gazi University

Ana Başlıklar:

Elektrik Makineleri ve Sürücüler

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.12 Issue 3, pp.428-439

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001328721

pISSN:

2147-9526