Analyzing Fuel Cell Vehicles Through Intelligent Battery Management Systems (BMS)-Reference-Cited by-同舟云学术

Analyzing Fuel Cell Vehicles Through Intelligent Battery Management Systems (BMS)

Published:2024-05-16 Issue: Volume: Page:335-355
ISSN:2328-2762
Container-title:Advances in Web Technologies and Engineering
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Author:

Reddy Putchakayala Yanna¹,Madan Balpreet Singh²,Anandaram Harishchander³^ORCID,Rathod Praveen⁴,Vasanthaseelan S.⁵^ORCID,Boopathi S.⁶

Affiliation:

1. Department of Electrical & Electronics Engineering Department, Bharath Institute of Engineering and Technology, India

2. Department of Art and Design, School of Design, Architecture, and Planning, Sharda University, Greater Noida, India

3. Department of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India

4. Department of Mechanical Engineering, Vishwakarma Institute of Information Technology, Pune, India

5. Department of Mechanical Engineering, Sri Krishna College of Technology, Coimbatore, India

6. Muthayammal Engineering College, India

Abstract

Integrating artificial intelligence (AI), internet of things (IoT), and machine learning (ML) technologies into fuel cell systems offers numerous benefits, applications, and opportunities for advancement across various sectors. This chapter explores the synergistic potential of AI, IoT, and ML in fuel cell integration, outlining their advantages, applications, challenges, and potential solutions. By leveraging AI for predictive maintenance, optimizing operating conditions through IoT sensors, and employing ML algorithms for efficiency enhancements, fuel cell systems can achieve higher performance and reliability. Real-world case studies and examples demonstrate successful integration in sectors such as transportation, energy production, and manufacturing. Moreover, this chapter discusses future prospects, including advancements in data analytics, system optimization, and scalability, driving innovation in fuel cell technology integration with AI, IoT, and ML.

Publisher

IGI Global

Reference52 articles.

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

5. Advancements in Machine Learning and AI for Intelligent Systems in Drone Applications for Smart City Developments

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