IoT-Enabled Battery Health Monitoring and Data Logging System for Electric Vehicles

Author

Dr. M.Venkatesh, Teki Sri Tarunkumar, Suggu Iswarya, Losu Shanmukha Priya

Abstract

 The reliable operation of an electric vehicle (EV) depends on the electrical and thermal condition of its battery pack. Voltage, current and temperature vary with load, charge–discharge cycling and ageing; therefore, continuous monitoring and dependable data retention are important for assessing battery operation. This paper presents an IoT-enabled battery monitoring and black-box data logging prototype based on an ESP32 microcontroller. The proposed arrangement combines an INA219 voltage/current sensor, a DS18B20 temperature sensor, a DS3231 real-time clock (RTC), a microSD storage module and Wi-Fi connectivity. The ESP32 acquires and validates sensor readings, applies configurable threshold checks, updates local indicators and stores timestamped measurements and fault events on the microSD card. When a network connection is available, selected data can be transmitted to a cloud dashboard for remote monitoring and trend visualisation. State of charge (SOC) and state of health (SOH) are considered derived indicators rather than direct sensor outputs; their accuracy depends on the estimation method, calibration and validation procedure. The paper details the system architecture, methodology, component roles, data flow and proposed validation plan. Since a complete experimental dataset and measured performance results were not supplied, no quantitative accuracy or reliability claims are made. The design is presented as a prototype monitoring aid and not as a replacement for an independently protected, vehicle-grade battery management system.

Keywords

Electric vehicles, battery monitoring, Internet of Things, ESP32, data logging, black-box system, state of charge, state of health, lithium-ion battery.

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References

[1] M. S. Aarthi, A. C., M. A. and S. V., “IoT-Based Lithium-Ion Battery Monitoring System in EV,” 2025 International Conference on Emerging Technologies in Engineering Applications (ICETEA), Puducherry, India, 2025, pp. 1–4, doi: 10.1109/ICETEA64585.2025.11099881.

[2] G. Ramkumar, S. Kannan and V. Mohanavel, “A Smart Battery Management System (BMS) for Electric Vehicles Using Internet of Things Technology,” 2025 10th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2025, pp. 208–214, doi: 10.1109/ICCES67310.2025.11337202.

[3] M. Bharathiraja et al., “IoT-Driven EV Battery Management System with Real-Time Monitoring and Fire Safety,” 2025 Global Conference in Emerging Technology (GINOTECH), Pune, India, 2025, pp. 1–6, doi: 10.1109/GINOTECH63460.2025.11076898.

[4] R. R. Kumar et al., “Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications,” IEEE Access, vol. 11, pp. 105761–105809, 2023, doi: 10.1109/ACCESS.2023.3318121.

[5] S. Jiao, G. Zhang, M. Zhou and G. Li, “A Comprehensive Review of Research Hotspots on Battery Management Systems for UAVs,” IEEE Access, vol. 11, pp. 84636–84650, 2023, doi: 10.1109/ACCESS.2023.3301989.

[6] S. Murlidharan, V. Ravulakole, J. Karnati and H. Malik, “Battery Management System: Threat Modeling, Vulnerability Analysis, and Cybersecurity Strategy,” IEEE Access, vol. 13, pp. 37198–37220, 2025, doi: 10.1109/ACCESS.2025.3543249.

[7] M. O. Qays, Y. Buswig, M. L. Hossain and A. Abu-Siada, “Recent progress and future trends on the state of charge estimation methods to improve battery-storage efficiency: A review,” CSEE Journal of Power and Energy Systems, vol. 8, no. 1, pp. 105–114, 2022, doi: 10.17775/CSEEJPES.2019.03060.

[8] R. Xiong, J. Cao, Q. Yu, H. He and F. Sun, “Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles,” IEEE Access, vol. 6, pp. 1832–1843, 2018, doi: 10.1109/ACCESS.2017.2780258.

[9] W. Xu, J. Xu, J. Lang and X. Yan, “A Multi-Timescale Estimator for Lithium-Ion Battery State of Charge and State of Energy Estimation Using Dual H Infinity Filter,” IEEE Access, vol. 7, pp. 181229–181241, 2019, doi: 10.1109/ACCESS.2019.2959396.

[10] X. Feng, Y. Zhang, R. Xiong and A. Tang, “Estimating battery state of health with 10-min relaxation voltage across various charging states of charge,” iEnergy, vol. 2, no. 4, pp. 308–313, 2023, doi: 10.23919/IEN.2023.0034.

[11] S. Rahimifard, V. Ravulakole, J. Karnati and H. Malik, “Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/Power Estimation,” IEEE Access, vol. 9, pp. 109875–109888, 2021, doi: 10.1109/ACCESS.2021.3102607.

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