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[email protected] +91 9840521421 Scholarly Open Access, Peer-reviewed, Refereed Journal E-ISSN 3139-8138 Since 2024
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Original Article

Intelligent Battery Management Systems for Electric Vehicles Using AI and Internet of Things Technologies

  • Paper ID 46
  • Volume 2, Issue 3
  • Pages: 24-27
  • Published Online: September-December 2025
  • 302 Views
Open Access Peer Reviewed

Authors

  • Chitti Bandari1
  • Bukkawar Akhila2
  • Chilla Hemanth3
  • Thippireddy Srikanth Reddy4
  • P. Damayanthi5

1 2 3 4 UG Scholar, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India. 5 Assistant Professor, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India.

Article Information

DOI
No Doi
Published Online
September-December 2025
Pages
24-27
Article Type
Original Article
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Abstract

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The rapid growth of electric vehicles (EVs) has increased the demand for advanced battery management systems capable of ensuring safety, reliability, efficiency, and extended battery lifespan. Batteries represent one of the most critical and expensive components of electric vehicles, making their effective monitoring and management essential for optimal vehicle performance. Conventional Battery Management Systems (BMS) primarily rely on predefined control algorithms and limited monitoring capabilities, which often struggle to address complex battery behaviors under varying operating conditions. Recent advancements in Artificial Intelligence (AI) and Internet of Things (IoT) technologies have created opportunities for developing intelligent battery management solutions that provide real-time monitoring, predictive analytics, and adaptive control. This study proposes an intelligent Battery Management System integrating AI algorithms and IoT-based communication technologies for electric vehicle applications. The framework utilizes smart sensors, cloud computing platforms, machine learning models, and wireless communication networks to continuously monitor battery parameters such as voltage, current, temperature, state of charge, and state of health. AI-driven predictive analytics enable accurate battery condition assessment, fault diagnosis, and remaining useful life estimation. IoT technologies facilitate real-time data transmission, remote monitoring, and centralized battery management. Performance evaluation demonstrates significant improvements in battery efficiency, charging optimization, fault detection accuracy, and battery lifespan prediction compared with traditional BMS approaches. The proposed framework supports safer and more efficient EV operation while reducing maintenance costs and improving energy utilization. The findings highlight the potential of AI and IoT technologies in enabling next-generation intelligent battery management systems for sustainable electric mobility.

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  • ISSN 3139-8138
  • Open Access
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