Power Aware Tech Smart Systems For Precision Battery Health Management

Authors

  • K. Mohanappriya
  • N. Keerthivasan
  • M. Vaidhehi
  • C. Muthuselvi
  • S.Roselin Mary

Keywords:

Remaining Useful Life, SQLite, Machine Learning, Decision Tree Regressor, Mean Absolute Error, Mean Squared Error, R² Score

Abstract

This research presents a Battery Monitoring and Notification System that leverages machine learning to predict the Remaining Useful Life (RUL) of vehicle batteries. The system analyzes key indicators such as charging cycles, voltage, temperature, discharge patterns, and battery retention, in combination with real-time sensor inputs and historical performance data. A Decision Tree Regressor is employed to deliver accurate RUL predictions, with model performance evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² Score. The system features automated email notifications for critical battery conditions, including low voltage, overcharging, and performance degradation. Continuous voltage monitoring enables early detection of potential issues, supporting proactive maintenance. Data is efficiently collected and stored in an SQLite database, while a user-friendly Flask-based dashboard offers visual insights into battery trends, charging history, and predictive analytics. This integrated approach enhances maintenance planning, reduces operational downtime, and improves safety by enabling timely, data-driven decision-making.

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References

R. Xiong, Y. Pan, W. X. Shen, H. L. Li, and F. C. Sun, “Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: recent advances and perspectives,” Renewable and Sustainable Energy Reviews, vol. 131, pp. 110048, Oct. 2020.

J. Tian, R. Xiong, W. Shen and F. Sun, “Electrode Aging Estimation and Open-Circuit Voltage Reconstruction for Lithium-Ion Batteries”, Energy Storage Materials, vol. 37, pp. 283–295, May 2021.

X. Liu, Y. Jin, S. Zeng, X. Chen, Y. Feng, S. Liu and H. Liu. “Online identification of power battery parameters for electric vehicles using a decoupling multiple forgetting factors recursive least squares method”, CSEE Journal of Power and Energy Systems, vol. 6, no. 3, pp. 735–742, 2020.

Q. S. Wang, B. B. Mao, S. I. Stoliarov, and J. H. Sun, “A review of lithium ion battery failure mechanisms and fire prevention strategies,” Progress in Energy and Combustion Science, vol. 73, pp. 95–131, Jul. 2019.

X. N. Feng, M. G. Ouyang, X. Liu, L. G. Lu, Y. Xia, and X. M. He, “Thermal runaway mechanism of lithium ion battery for electric vehicles: a review,” Energy Storage Materials, vol. 10, pp. 246–267, Jan. 2018.

Z. D. Zhang, X. D. Kong, Y. J. Zheng, L. Zhou, and X. Lai, “Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters,” Energy, vol. 166, pp. 1013–1024, Jan. 2019.

W. K. Gao, Y. J. Zheng, M. G. Ouyang, J. Q. Li, X. Lai, and X. S. Hu, “Micro-short-circuit diagnosis for series-connected lithium-ion battery packs using mean-difference model,” IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 2132–2142, Mar. 2019.

M. B. Chen, F. F. Bai, S. L. Lin, W. J. Song, Y. Li, and Z. P. Feng, “Performance and safety protection of internal short circuit in lithiumion battery based on a multilayer electro-thermal coupling model,” Applied Thermal Engineering, vol. 146, pp. 775–784, Jan. 2019.

C. Kupper, S. Spitznagel, H. Doring, M. A. Danzer, C. Gutierrez, A. ¨ Kvasha, and W. G. Bessler, “Combined modeling and experimental study of the high-temperature behavior of a lithium-ion cell: Differential scanning calorimetry, accelerating rate calorimetry and external short circuit,” Electrochimica Acta, vol. 306, pp. 209–219, May 2019.

W. X. Wu, S, F. Wang, W. Wu, K. Chen, S. H. Hong, and Y. X. Lai, “A critical review of battery thermal performance and liquid based battery thermal management,” Energy Conversion and Management, vol. 182, pp. 262–281, Feb. 2019.

Palanisamy R, Gnana Kousalya C, Ramkumar R, Usha S, Thamizh Thentral TM, D Selvabharathi, Shanmugasundaram V,Analysing UPQC performance with dual NPC converters: Three-dimensional and two-dimensional space vector modulation, Results in Engineering,Volume25,2025,103611,ISSN25901230.

Chandrasekar, L.B., Ramkumar, R., Kiruba, S. et al. The Influence of Bi Doping Concentration on Structural, Dielectric and Optical Properties of Bi-doped ZnO thin Films. Semiconductors 59, 61–69 (2025). https://doi.org/10.1134/S1063782624602127

Geetha, A., Usha, S., Padmanabhan, J.B., Palanisamy, R., Alexander, A., Peter, G.,Ramkumar, R., Ganji, V.: Performance evaluation of coloured filters on PV panels in an outdoor environment. IET Renew. Power Gener. 1– 23 (2024). https://doi.org/10.1049/rpg2.13040

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Published

2025-05-31

How to Cite

1.
Mohanappriya K, Keerthivasan N, Vaidhehi M, Muthuselvi C, Mary S. Power Aware Tech Smart Systems For Precision Battery Health Management. J Neonatal Surg [Internet]. 2025May31 [cited 2025Oct.12];14(29S):623-32. Available from: https://jneonatalsurg.com/index.php/jns/article/view/6851

Issue

Vol. 14 No. 29S (2025): Journal of Neonatal Surgery

Section

Original Article

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