IoT-Based Battery Health Monitoring for Electric Vehicles Using Machine Learning

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 4 | 01 | Page :
    By

    Jay Bahadur Singh,

  • Ramniwas Yadav,

  1. Assistant Professor, Electrical Engineering Department, Bansal Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India
  2. Student, Electrical Engineering Department, Bansal Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

Abstract

With increasing utilization of the Electric Vehicles (EV)s in global scale, battery health management becomes a critical factor which has great impact on vehicle performance, safety and longevity. Battery materials, such as NMC LFP lithium-ion batteries and lithium-ion batteries, degrade over time from charging behaviour, heat stress, discharging voltage profiles and environmental limits. Conventional BMS only offer threshold based health diagnostics and cannot perform accurate degradation prediction. This work presents an IoT based Condition Monitoring System on Batteries with Machine Learning-based real-time estimation of the battery’s SoC, SoH, capacity fade and internal resistance growth as well as predicting the RUL. Real-time battery pack sensor data is sent off the edge to cloud platforms like Google Cloud IoT and AWS IoT Core using thin, IoT arms races protocols for ML-based health prediction. The experimental results show that the introduced system outperforms conventional BMS predictions for degradation state patterns indicating that IoT–ML can improve the reliability, safety and performance of EV.

Keywords: Electric Vehicles, battery management systems, ESP32 microcontroller. Support Vector Regression, IoT–ML framework

How to cite this article:
Jay Bahadur Singh, Ramniwas Yadav. IoT-Based Battery Health Monitoring for Electric Vehicles Using Machine Learning. International Journal of Machine Systems and Manufacturing Technology. 2026; 04(01):-.
How to cite this URL:
Jay Bahadur Singh, Ramniwas Yadav. IoT-Based Battery Health Monitoring for Electric Vehicles Using Machine Learning. International Journal of Machine Systems and Manufacturing Technology. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijmsmt/article=2026/view=245781


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Ahead of Print Subscription Original Research
Volume 04
01
Received 04/12/2025
Accepted 23/02/2026
Published 10/03/2026
Publication Time 96 Days
37

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