Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing

Muhammad Fikri Ardiansyah, Adha Imam Cahyadi, Oyas Wahyunggoro


Battery management system (BMS) has become an important research topic following the trend and development of the electric vehicle. Although research on Active Cell Balancing, SOC, and current estimation has been carried out, the previous work mostly focused on comparing and developing methods. In this research, we demonstrate the process of designing BMS hardware using a low-cost microcontroller and without using a current sensor. The SOC simulation results produce an RMSE of 0.0832% for the 100% -10% SOC-OCV curve, and the current estimation simulation produces an RMSE of 0.2576 A, while for testing using a 6-ohm pulse load, the RMSE error value is 0.3960 A. The Active Cell Balancing method was successfully performed in simulation with Simulink. Furthermore, our simulation and test results suggest that complex battery models and multiple SOC-OCV curves can be used for better current and OCV estimation results. Our experimental results are also useful to develop a guideline to design a microcontroller-based BMS.


Active Cell Balancing; Current Estimation; Low-cost Microcontroller; State of Charge

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Arendarik, S. & Radhostem, R. P. (2020). NXP Semiconductors," [Online]. Available: [Accessed 23 November 2020].

Dewangga, B. R. (2015). Estimasi Arus pada Battery Management System Berbasis Sensorless Current Menggunakan Model Baterai Sederhana. Yogyakarta.

Dewangga, B. R., Herdjunanto, S., & Cahyadi, A. I. (2018). Battery Current Estimation Based on Simple Model with Parameter Update Strategy Using Piecewise Linear SOC-OCV. International Conference on Science and Technology (ICST), Yogyakarta.

Fathoni, G. (2017). Comparison of State of Charge (SOC) Estimation Performance Based on Three Methods: Coulomb Counting, Open Circuit Voltage, and Karman Filter. Internation Conferention on Automation, Cognitive Science, Optics, Micro Electro-Mechanical Sytem, and Information Technology, Jakarta.

Ipek, E., Eren, M. K., & Yilmaz, M. (2019). State-of-Charge Estimation of Li-ion Battery Cell using Support Vector Regression and Gradient Boosting Techniques. International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), Istanbul.

Jalil, A. (2017). Active Cell Balancing Implementation to Lithium Polymer Battery Based on Pulse Width Modulation (PWM) Output Control. Yogyakarta.

Kupper, M., Brenneiser, J., Stark, O., Krebs, S., & Hohmann, S. (2018). Cascaded Fractional Kalman Filtering for State and Current Estimation of Large-Scale Lithium-Ion Battery Packs. Chinese Control and Decision Conference (CCDC), Shenyang.

Putra, W. S., Dewangga, B. R., Cahyadi, A. I., & Wahyunggoro, O. (2015). Current Estimation using Thevenin Battery Model. Joint International Conference on Electrical Vehicular and Industrial, Mechanical, Electrical and Chemical Engineering (ICEVT & IMECE).

Tesla, Tesla Powerwall, Tesla, [Online]. Available: [Accessed 23 November 2020].

Zhang, R., Xia, B., Li, B., Cao, L., Lai, Y., & Zheng, W. (2018). A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature. Energies, 11.



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