Smart Battery Health Monitoring Using Digital Twin and AI/ML Technologies


Authors : Adithi M; Damodar G N; Jesmin K Joseph; Nayana S; Shivalingamurthy A G

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/7xmj5x56

Scribd : https://tinyurl.com/4724z3j9

DOI : https://doi.org/10.5281/zenodo.14471042

Abstract : This project involves integrating a sophisticated battery health monitoring system that leverages Digital Twin (DT) and AIML technologies in conjunction with an Arduino Uno. The system incorporates current, voltage, and temperature sensors to continuously track battery metrics, while employing machine learning algorithms to identify any irregularities. Furthermore, a DC load is utilized to mimic battery usage, and notifications are dispatched through LCD, GSM, and a buzzer. Ultimately, the system guarantees effective battery supervision and timely detection of potential failures.

Keywords : Digital Twin, Artificial Intelligence and Machine learning, GSM Model.

References :

  1. Thiruvonasundari Duraisamy, Deepa Kaliyaperumal, “Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System”, IEEE Access, September, 2021.
  2. Kailong Li, Yunlong Shang, Quan Ouyang, and Widanalage Dhammika Widanage, “A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery”, IEEE Transactions On Industrial Electronics, Vol. 68, No. 4, April 2021.
  3. Tsung-Wen Sun and Tsung-Heng Tsai, “A Battery Management System with Charge Balancing and Aging Detection Based on ANN”, IEEE International Symposium on Circuits andSystems, April, 2021.
  4. Yizhao Gao;Kailong Liu;Chong Zhu;Xi Zhang;Dong Zhang,“Co-Estimation of State-of-Charge and Stateof- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model”, IEEE Transactions on Industrial Electronics, March, 2021.
  5. Yuanliang Fan; Jing Wu; Zitao Chen; Han Wu; Jianye Huang, “Data-driven state-of-charge estimation of lithium-ion batteries”, International Conference on Power Electronics Systems and Application, February, 2021.
  6. Rasool M. Imran, Qiang Li, And Firas M. F. Flaih, “An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter”, IEEE Access, November, 2020.

This project involves integrating a sophisticated battery health monitoring system that leverages Digital Twin (DT) and AIML technologies in conjunction with an Arduino Uno. The system incorporates current, voltage, and temperature sensors to continuously track battery metrics, while employing machine learning algorithms to identify any irregularities. Furthermore, a DC load is utilized to mimic battery usage, and notifications are dispatched through LCD, GSM, and a buzzer. Ultimately, the system guarantees effective battery supervision and timely detection of potential failures.

Keywords : Digital Twin, Artificial Intelligence and Machine learning, GSM Model.

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