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 :
- Thiruvonasundari Duraisamy, Deepa Kaliyaperumal, “Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System”, IEEE Access, September, 2021.
- 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.
- 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.
- 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.
- 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.
- 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.