Artificial Intelligence-Powered Electric Vehicle's Battery Management System with IoT

Authors

  • Nageswara Rao Gali Department of Information Technology, Aditya Institute of Technology and Management,Tekkali, K Kotturu, Andhra Pradesh 532201,India
  • Singamaneni Krishnapriya Department of Computer science / Data science, VNR Vignana Jyothi institute of Engineering and technology, Hyderabad, Telangana 500090,India.
  • G. Kirubasri Department of Computer Science and Engineering ,Sona College of Technology, Salem-5.
  • Mahendra T. Jagtap Department of Computer Engineering , PVG's College of Engg & SSDIOM Nashik (SPPU Pune University), Nashik, Maharashtra 422004,India.
  • A. V. G. A. Marthanda Department of Electrical and Electronics Engineering, LakiReddy Bali Reddy college of Engineering, Mylavaram, Andhra Pradesh 521230. India
  • Mohammad Shahid Department of Electrical Engineering. Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh 201310,India,

Keywords:

Battery management system (BMS), embedded system, IoT, notification, messaging protocol

Abstract

As a key part of electric vehicles, batteries are the maximum important parts of electric vehicles because of their charging and discharging functions.They supply the electricity that drives the vehicle's motor. A vehicle powered by electricity could not function without batteries. The vehicle fails to operate smoothly if the batteries aren't functioning properly. The current and voltage variations affect the battery system.So we cannot predict the accurate voltage and current measurement. The objective of this study is to observe and optimise the efficiency of battery energy management systems (BEMS) by using the Internet of Things (IoT) and Artificial Intelligence (AI). Additionally, the research aims to investigate strategies for effectively managing batteries in electric cars. Lithium-ion battery used in this system because of greater energy density compared to other conventional batteries. The costliness of batteries in electric vehicles offers significant opportunity for the enhancement of battery State of Health (SOH) and State of Charge (SOC) predictions via the use of AI-Powered Cloud Services. This improvement aims to enhance cost-effectiveness and durability. A system driven by artificial intelligence and hosted on a cloud platform has the capability to adapt to evolving changes in battery health resulting from operational conditions. It then provides updated information to the battery management system, enabling it to make continually improved management choices. The neural network algorithm is built using a Python script. Node-RED designed the user interface and login for the web server. Concerning embedded devices, sensors, and mobile apps, the Internet of Things plays a significant role. MQTT is a reasonably lightweight messaging protocol.

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Published

27.10.2023

How to Cite

Gali, N. R. ., Krishnapriya, S. ., Kirubasri, G. ., Jagtap, M. T. ., Marthanda, A. V. G. A. ., & Shahid, M. . (2023). Artificial Intelligence-Powered Electric Vehicle’s Battery Management System with IoT. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 511–518. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3651

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

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