Lead Acid Battery Monitoring using Multiple Linear Regression Method

Article Sidebar

PDF No.6
Published: May 30, 2023
DOI: https://doi.org/10.14456/nuej.2023.6
Keywords:
Battery Monitoring System, Multiple Linear Regression, Internet of Things

Main Article Content

Somporn Ruangsinchaiwanich
Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, Thailand
Thiraphong Banchong
Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, Thailand
Sumet Sittisrijan
Department of Electrical and Computer Engineering, Faculty of Engineering, Naresuan University, Phitsanulok, Thailand

Abstract

This paper is an electric motor battery monitoring system using Multiple Linear Regression. This proposed system can display battery parameters based on internet of things that afford its values of the voltage, current, and the remaining charge capacity in a real-time scenario. Also designed electronic hardwires and data storage system are illustrated. This article concerns an electric car battery status system with Multiple Linear Regression. The prototype consists of a microcontroller, current sensor module, voltage divider circuit, and MCP3008. The data of power batteries can be displayed on a smartphone and stored in the cloud server database. Eventually, this system can also be used to study battery characteristics throughout its lifespan.

Article Details

How to Cite
Ruangsinchaiwanich, S., Banchong, T., & Sittisrijan, S. (2023). Lead Acid Battery Monitoring using Multiple Linear Regression Method. Naresuan University Engineering Journal, 18(1), 42–46. https://doi.org/10.14456/nuej.2023.6
Issue
Vol. 18 No. 1 (2023): January-June 2023
Section
Research Paper
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Abdullah, N. F., Rashid, N. E. A., Othman, K. A., & Musirin, I. (2014). Vehicles classification using Z-score and modelling neural network for forward scattering radar. 15th International Radar Symposium (IRS) (pp. 1-4).

Badawy, A., El-Habrouk, M., Ragi Ali Rifaat Hamdy, R.A.R., Karim Hassan Youssef, K. H. (2020). Online monitoring and fault diagnosis and isolation of Valve Regulated Lead Acid batteries in Uninterruptible Power Supplies using decision trees. 11th International Renewable Energy Congress (IREC).

Feng, Y., & Wang, S. (2017). A forecast for bicycle rental demand based on random forests and multiple linear regression. IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (pp. 101–105).

Hegde, V, & Pallavi, S. (2015). Descriptive analytical approach to analyze the student performance by comparative study using Z score factor through R language. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1-4).

Jung, J., Zhang, L., & Zhang, J. (Eds.). (2015). Lead-Acid Battery Technologies: Fundamentals, Materials, and Applications CRC Press.

Kale, S., Chaudhari, B.N. (2022), IoT Based Battery Monitoring System. International Conference on Advances in Computing, Communication and Materials (ICACCM).

Ochkov, V. F., & Tikhonov, A. I. (2022) Jupyter Notebook, JupyterLab – Integrated Environment for STEM Education. International Conference on Information Technologies in Engineering Education (Inforino) (pp. 1-5).

Pavlov, D. (2011). Processes After Formation of the Plates and During Battery Storage. Lead-Acid Batteries: Science and Technology, Elsevier, 2011, pp. 535-566.

Zhang, Z., Li, Y., Li, L., Zhu, L., & Liu, S. (2019). Multiple linear regression for high efficiency video intra coding. ICASSP 2019–2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) (pp. 1832-1836).