To make batteries in electric vehicles and energy storage systems last longer, work better, and be safer, they need a Smart Battery Management System (BMS). The main goal of this project is to find out the battery's State of Charge (SOC) and State of Health (SOH) right away. SOC reflects the remaining usable capacity of the battery, while SOH indicates the overall condition and degree of aging of the battery. To optimize battery utilization and ensure reliable system performance, it is crucial to accurately estimate these parameters. The system continuously monitors critical battery parameters including voltage, current, temperature, and charge/discharge cycles through integrated sensors. These inputs are processed by embedded algorithms running on a microcontroller platform to deliver accurate, real-time SOC and SOH estimations. Advanced filtering and estimation techniques are employed to improve accuracy under varying operating conditions. The microcontroller also manages data acquisition and provides a user interface for monitoring. By preventing overcharging, deep discharging, and overheating, this smart BMS significantly improves battery safety and operational efficiency. Real-time monitoring facilitates informed decisions related to battery usage and maintenance schedules. The system that was designed is affordable, dependable, and flexible enough to be used in a wide range of situations, including electric cars, storing renewable energy, and portable electronics. The system supports scalability and can be integrated with IoT-based monitoring frameworks for remote diagnostics.

Keywords - Lithium-ion batteries, embedded algorithms, battery monitoring, thermal management, smart BMS, SOC and SOH estimation, real-time data acquisition, electric vehicles, and energy storage systems.

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1. Rubio-Domingo G, Linares P. The future investment costs of offshore wind: An estimation based on auction results. Renewable and Sustainable Energy Reviews. 2021 Sep 1;148:111324.
2. Hwang HS, Lee EC. Non-contact respiration measurement method based on RGB camera using 1D convolutional neural networks. Sensors. 2021 May 15;21(10):3456.A. Kumar and R. Singh, “Smart battery management system with fault detection using IoT,” Journal of Energy Storage, vol. 45, 2022. Available: https://doi.org/10.1016/j.est.2021.103703
3. Habib MM, Siddiqui AW, Syed MI, Hussain S. IoT Based Smart Battery Monitoring System Using NODEMCU and ThingSpeak. Frontiers in Engineering Science and Technology. 2022 Oct 20;1(1):2201-11.
4. Erd A, Stoklosa J. Main design guidelines for battery management systems for traction purposes. In2018 XI International Science-Technical Conference Automotive Safety 2018 Apr 18 (pp. 1-5). IEEE.
5. Hu X, Deng Z, Lin X, Xie Y, Teodorescu R. Research directions for next-generation battery management solutions in automotive applications. Renewable and Sustainable Energy Reviews. 2021 Dec 1;152:111695.
6. Nizam M, Maghfiroh H, Rosadi RA, Kusumaputri KD. Battery management system design (BMS) for lithium ion batteries. InAIP Conference Proceedings 2020 Apr 13 (Vol. 2217, No. 1, p. 030157). AIP Publishing LLC.
7. Dai H, Jiang B, Hu X, Lin X, Wei X, Pecht M. Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends. Renewable and Sustainable Energy Reviews. 2021 Mar 1;138:110480.
8. How DN, Hannan MA, Lipu MH, Ker PJ. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review. Ieee Access. 2019 Sep 18;7:136116-36.
9. Tan X, Tan Y, Zhan D, Yu Z, Fan Y, Qiu J, Li J. Real-time state-of-health estimation of lithium-ion batteries based on the equivalent internal resistance. Ieee Access. 2020 Mar 9;8:56811-22.
10. Liu Z, Dang X, Jing B. A novel open circuit voltage based state of charge estimation for lithium-ion battery by multi-innovation Kalman filter. IEEE Access. 2019 Apr 12;7:49432-47.
11. Chen B, Liu Y. Data driven-based health prognostics and charge estimation for lithium-ion batteries under varying discharging patterns. Energy. 2025 Aug 14:137918.

To make batteries in electric vehicles and energy storage systems last longer, work better, and be safer, they need a Smart Battery Management System (BMS). The main goal of this project is to find out the battery's State of Charge (SOC) and State of Health (SOH) right away. SOC reflects the remaining usable capacity of the battery, while SOH indicates the overall condition and degree of aging of the battery. To optimize battery utilization and ensure reliable system performance, it is crucial to accurately estimate these parameters. The system continuously monitors critical battery parameters including voltage, current, temperature, and charge/discharge cycles through integrated sensors. These inputs are processed by embedded algorithms running on a microcontroller platform to deliver accurate, real-time SOC and SOH estimations. Advanced filtering and estimation techniques are employed to improve accuracy under varying operating conditions. The microcontroller also manages data acquisition and provides a user interface for monitoring. By preventing overcharging, deep discharging, and overheating, this smart BMS significantly improves battery safety and operational efficiency. Real-time monitoring facilitates informed decisions related to battery usage and maintenance schedules. The system that was designed is affordable, dependable, and flexible enough to be used in a wide range of situations, including electric cars, storing renewable energy, and portable electronics. The system supports scalability and can be integrated with IoT-based monitoring frameworks for remote diagnostics.

Keywords - Lithium-ion batteries, embedded algorithms, battery monitoring, thermal management, smart BMS, SOC and SOH estimation, real-time data acquisition, electric vehicles, and energy storage systems.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Rubio-Domingo G, Linares P. The future investment costs of offshore wind: An estimation based on auction results. Renewable and Sustainable Energy Reviews. 2021 Sep 1;148:111324.
2. Hwang HS, Lee EC. Non-contact respiration measurement method based on RGB camera using 1D convolutional neural networks. Sensors. 2021 May 15;21(10):3456.A. Kumar and R. Singh, “Smart battery management system with fault detection using IoT,” Journal of Energy Storage, vol. 45, 2022. Available: https://doi.org/10.1016/j.est.2021.103703
3. Habib MM, Siddiqui AW, Syed MI, Hussain S. IoT Based Smart Battery Monitoring System Using NODEMCU and ThingSpeak. Frontiers in Engineering Science and Technology. 2022 Oct 20;1(1):2201-11.
4. Erd A, Stoklosa J. Main design guidelines for battery management systems for traction purposes. In2018 XI International Science-Technical Conference Automotive Safety 2018 Apr 18 (pp. 1-5). IEEE.
5. Hu X, Deng Z, Lin X, Xie Y, Teodorescu R. Research directions for next-generation battery management solutions in automotive applications. Renewable and Sustainable Energy Reviews. 2021 Dec 1;152:111695.
6. Nizam M, Maghfiroh H, Rosadi RA, Kusumaputri KD. Battery management system design (BMS) for lithium ion batteries. InAIP Conference Proceedings 2020 Apr 13 (Vol. 2217, No. 1, p. 030157). AIP Publishing LLC.
7. Dai H, Jiang B, Hu X, Lin X, Wei X, Pecht M. Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends. Renewable and Sustainable Energy Reviews. 2021 Mar 1;138:110480.
8. How DN, Hannan MA, Lipu MH, Ker PJ. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: A review. Ieee Access. 2019 Sep 18;7:136116-36.
9. Tan X, Tan Y, Zhan D, Yu Z, Fan Y, Qiu J, Li J. Real-time state-of-health estimation of lithium-ion batteries based on the equivalent internal resistance. Ieee Access. 2020 Mar 9;8:56811-22.
10. Liu Z, Dang X, Jing B. A novel open circuit voltage based state of charge estimation for lithium-ion battery by multi-innovation Kalman filter. IEEE Access. 2019 Apr 12;7:49432-47.
11. Chen B, Liu Y. Data driven-based health prognostics and charge estimation for lithium-ion batteries under varying discharging patterns. Energy. 2025 Aug 14:137918.