AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles
The increasing adoption of electric vehicles (EVs) is driving the need for efficient and sustainable energy sources, such as fuel cells, to enhance vehicle range and performance. This paper explores the application of AI-driven optimization techniques to improve the performance of fuel cells in elec...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/121/e3sconf_icrera2024_04003.pdf |
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| author | Badhoutiya Arti R Srisainath Shirbavikar K.A. Bhuvaneshwari P. Al-Farouni Mohammed Narkhede Jitendra Kumar N Anil |
| author_facet | Badhoutiya Arti R Srisainath Shirbavikar K.A. Bhuvaneshwari P. Al-Farouni Mohammed Narkhede Jitendra Kumar N Anil |
| author_sort | Badhoutiya Arti |
| collection | DOAJ |
| description | The increasing adoption of electric vehicles (EVs) is driving the need for efficient and sustainable energy sources, such as fuel cells, to enhance vehicle range and performance. This paper explores the application of AI-driven optimization techniques to improve the performance of fuel cells in electric vehicles. By leveraging machine learning algorithms, particularly reinforcement learning and predictive modeling, the system can optimize key parameters such as temperature, pressure, and hydrogen consumption in real-time, thereby maximizing efficiency and extending the operational lifetime of the fuel cells. The study demonstrates that AI-based approaches can significantly enhance energy output and fuel utilization while adapting to dynamic driving conditions. This research provides a promising pathway for improving fuel cell performance, thus promoting the broader adoption of hydrogen-based electric vehicles as a viable alternative to traditional internal combustion engines. |
| format | Article |
| id | doaj-art-e6f0ea444c9f4c5b9a7d2fdd3c8d227b |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-e6f0ea444c9f4c5b9a7d2fdd3c8d227b2024-11-21T11:32:00ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015910400310.1051/e3sconf/202459104003e3sconf_icrera2024_04003AI-Driven Optimization of Fuel Cell Performance in Electric VehiclesBadhoutiya Arti0R Srisainath1Shirbavikar K.A.2Bhuvaneshwari P.3Al-Farouni Mohammed4Narkhede Jitendra5Kumar N Anil6Department of Electrical Engineering, GLA UniversityAssistant Professor,Department of MECH,Prince Shri Venkateshwara Padmavathy Engineering CollegeMechanical Department,Vishwakarma Institute of TechnologyAsst Professor,Department of CSE,New Prince Shri Bhavani College of Engineering and TechnologyDepartment of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq Department of computers Techniques engineering, College of technical engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq Department of computers Techniques engineering, College of technical engineering, The Islamic University of BabylonDepartment of Mechanical engineering, Dr. D. Y. Patil Institute of TechologyAssistant Professor, Department of Electronics and Communication Engineering, School of Engineering, Mohan Babu UniversityThe increasing adoption of electric vehicles (EVs) is driving the need for efficient and sustainable energy sources, such as fuel cells, to enhance vehicle range and performance. This paper explores the application of AI-driven optimization techniques to improve the performance of fuel cells in electric vehicles. By leveraging machine learning algorithms, particularly reinforcement learning and predictive modeling, the system can optimize key parameters such as temperature, pressure, and hydrogen consumption in real-time, thereby maximizing efficiency and extending the operational lifetime of the fuel cells. The study demonstrates that AI-based approaches can significantly enhance energy output and fuel utilization while adapting to dynamic driving conditions. This research provides a promising pathway for improving fuel cell performance, thus promoting the broader adoption of hydrogen-based electric vehicles as a viable alternative to traditional internal combustion engines.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/121/e3sconf_icrera2024_04003.pdfai-driven optimizationfuel cellselectric vehiclesmachine learninghydrogen economy |
| spellingShingle | Badhoutiya Arti R Srisainath Shirbavikar K.A. Bhuvaneshwari P. Al-Farouni Mohammed Narkhede Jitendra Kumar N Anil AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles E3S Web of Conferences ai-driven optimization fuel cells electric vehicles machine learning hydrogen economy |
| title | AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles |
| title_full | AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles |
| title_fullStr | AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles |
| title_full_unstemmed | AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles |
| title_short | AI-Driven Optimization of Fuel Cell Performance in Electric Vehicles |
| title_sort | ai driven optimization of fuel cell performance in electric vehicles |
| topic | ai-driven optimization fuel cells electric vehicles machine learning hydrogen economy |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/121/e3sconf_icrera2024_04003.pdf |
| work_keys_str_mv | AT badhoutiyaarti aidrivenoptimizationoffuelcellperformanceinelectricvehicles AT rsrisainath aidrivenoptimizationoffuelcellperformanceinelectricvehicles AT shirbavikarka aidrivenoptimizationoffuelcellperformanceinelectricvehicles AT bhuvaneshwarip aidrivenoptimizationoffuelcellperformanceinelectricvehicles AT alfarounimohammed aidrivenoptimizationoffuelcellperformanceinelectricvehicles AT narkhedejitendra aidrivenoptimizationoffuelcellperformanceinelectricvehicles AT kumarnanil aidrivenoptimizationoffuelcellperformanceinelectricvehicles |