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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65133
Volume:
10 |
6 |
Jun 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electrical and Electronics Engineering
Government College of Engineering, Erode
Electrical and Electronics Engineering
Government College of Engineering, Erode
Electrical and Electronics Engineering
Government College of Engineering, Erode
The reliability and operational safety of modern lithium-based energy storage systems depend heavily on the continuous observation of core physical parameters to mitigate thermal runaway and accelerated degradation. However, conventional monitoring solutions often rely on high-cost dedicated integrated circuits or computationally restrictive topologies that limit localized deployment. To resolve these constraints, this paper presents the design and implementation of a low-cost, multi-parametric Internet of Things (IoT) edge node built on a 32-bit ESP32 microcontroller architecture. The developed system establishes an integrated hardware framework dedicated to the real-time acquisition of critical cell metrics: terminal voltage via a calibrated resistive attenuation network, load utilizing an ACS712 Hall-effect sensor, and ambient surface temperature through a 1-Wire DS18B20 digital sensor probe. Analog-to- digital conversion noise inherent to the microcontroller's edge channels is suppressed in firmware using a localized digital moving-average filter. Experimental evaluation demonstrates that the monitoring node achieves robust tracking across dynamic load profiles, maintaining a peak voltage measurement error within ±0.47%, a current tracking deviation below ±2.00%, and a thermal accuracy of ±0.1℃. The resulting edge architecture provides a highly accurate, economically viable solution suitable for decentralized battery diagnostics and data logging applications.
KEYWORDS
This paper successfully presented the design, implementation, and validation of a low-cost, multi-parametric Internet of Things (IoT) edge node for real-time lithium-ion battery monitoring. By transitioning mathematical estimation and safety logic from high-cost proprietary ASICs to an optimized 32-bit ESP32 microcontroller, the proposed architecture significantly reduced hardware overhead while maintaining industrial-grade precision. The implementation of a localized firmware-level moving-average digital filter effectively suppressed analog-to-digital conversion noise. Experimental validation confirmed the system's high fidelity, demonstrating a peak voltage measurement error of less than ±0.47%, a current tracking deviation below ±2.00%, and a strict thermal accuracy of ±0.1°C under dynamic load conditions. Furthermore, the seamless integration with a decentralized IoT cloud broker ensures reliable, continuous data logging without latency.
1. Espressif Systems, "ESP32 Series Datasheet," v4.3, 2023. [Online]. Available: https://www.espressif.com. 2. Allegro MicroSystems, "ACS712: Fully Integrated, Hall Effect-Based Linear Current Sensor IC," Datasheet, Rev.15. 3. Maxim Integrated, "DS18B20 Programmable Resolution 1-Wire Digital Thermometer," Datasheet, Rev. 042208. 4. Rahimi-Eichi, H., Ojha, U., Baronti, F., & Chow, M. Y., "Battery management system: An overview of its application in the smart grid and electric vehicles," IEEE Industrial Electronics Magazine, vol.7, no.2, pp. 4-16, 2013. 5. Piller, S., Perrin, M., & Jossen, A., "Methods for state-of charge determination and their applications," Journal of Power Sources, vol. 96, no. 1, pp. 113-120, 2001. 6. Kim, J., Lee, S., & Cho, B. H., "Accurate state-of-charge estimation for Li-ion batteries using an enhanced Coulomb counting method," IEEE Transactions on Industrial Electronics, vol. 62, no. 9, pp. 5537-5545, 2015. 7. Hannan, M. A., Hoque, M. M., Hussain, A., Yusof, Y., & Ker, P. J., "State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applications: Issues and recommendations," IEEE Access, vol. 6, pp. 19362-19378, 2018.
Publication Date
6/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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