Leveraging Fuel Cell Technology With AI and ML Integration for Next-Generation Vehicles-Reference-Cited by-同舟云学术

Leveraging Fuel Cell Technology With AI and ML Integration for Next-Generation Vehicles

Published:2024-06-21 Issue: Volume: Page:312-337
ISSN:2328-8205
Container-title:Advances in Mechatronics and Mechanical Engineering
language:
Short-container-title:

Author:

Muthukumar R.¹^ORCID,G. Pratheep V. G.²^ORCID,S. J. Sultanuddin³,Kunduru Krishnamohan Reddy⁴^ORCID,Kumar Praveen⁵^ORCID,Boopathi Sampath⁶^ORCID

Affiliation:

1. Department of Electrical and Electronics Engineering, Erode Sengunthar Engineering College, Erode, India

2. Department of Mechatronics Engineering, Kongu Engineering College, Perundurai, India

3. Department of AI&DS, Dhanalakshmi College of Engineering, Chennai, India

4. Department of Engineering Design, Overhead Door Corporation, Lewisville, USA

5. Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, India

6. Mechanical Engineering, Muthayammal Engineering College, Namakkal, India

Abstract

The integration of fuel cell technology with artificial intelligence and machine learning in electric vehicles (EVs) has the potential to enhance efficiency, performance, and reliability. Fuel cells, a clean alternative to traditional engines, produce electricity through electrochemical reactions between hydrogen and oxygen, with water vapor as the only byproduct. AI-driven algorithms analyze vast data from sensors and onboard systems, while ML algorithms enable early detection of potential system failures. AI-based driver assist systems can optimize driving behaviors using fuel cell data. However, integrating fuel cell technology with AI and ML faces challenges like data management, algorithm development, and interoperability with existing vehicle systems. Interdisciplinary collaboration between automotive engineers, data scientists, and AI specialists is needed to develop robust, scalable solutions.

Publisher

IGI Global

Reference66 articles.

1. Smart Precision Agriculture Using IoT and WSN

2. Arunprasad, R., & Boopathi, S. (2019). Chapter-4 Alternate Refrigerants for Minimization Environmental Impacts: A Review. In Advances in Engineering Technology (p. 75). AkiNik Publications New Delhi.

3. An investigation on gas emission concentration and relative emission rate of the near-dry wire-cut electrical discharge machining process

4. Boopathi, S. (2022b). Cryogenically treated and untreated stainless steel grade 317 in sustainable wire electrical discharge machining process: A comparative study. Springer :Environmental Science and Pollution Research, 1–10.

5. Energy Cascade Conversion System and Energy-Efficient Infrastructure

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. IoT and Blockchain-Based Smart Grid Energy Management;Advances in Civil and Industrial Engineering;2024-08-30

2. Synergizing Edge Computing With Energy Storage and Grid Integration in Electric Vehicles;Advances in Mechatronics and Mechanical Engineering;2024-07-26

3. Performance Improvements of Electric Vehicles Using Edge Computing and Machine Learning Technologies;Advances in Mechatronics and Mechanical Engineering;2024-07-26

4. Machine Learning Applications in Predictive Pest Modeling for Developing Pest-Resistant Crop Varieties;Advances in Environmental Engineering and Green Technologies;2024-06-28

5. Harnessing Digitalization and Internet of Things for Sustainable Energy;Practice, Progress, and Proficiency in Sustainability;2024-06-28