Rancang Bangun Sistem Balancing Empat Sel Baterai Lithium-Ion Berbasis Selective Charge Balancing dan Polynomial Regression
DOI:
https://doi.org/10.21009/JEVET.0091.05Keywords:
battery balancing, lithium-ion battery, selective charge balancing, polynomial regressionAbstract
Abstrak
Penelitian ini membahas rancang bangun sistem balancing empat sel baterai lithium-ion berbasis selective charge balancing dan polynomial regression. Permasalahan ketidakseimbangan tegangan antarsel pada baterai multi-sel dapat menurunkan performa, efisiensi, serta mempercepat degradasi baterai. Sistem dirancang menggunakan mikrokontroler ESP32, modul charging individual TP5100, sensor INA219, relay, serta rangkaian corrective discharge balancing. Metode utama balancing dilakukan dengan pengisian selektif pada masing-masing sel hingga mencapai tegangan 4,0 V, kemudian dilanjutkan dengan tahap rest verification selama 30 menit untuk memperoleh kondisi tegangan yang lebih stabil. Apabila masih terdapat selisih tegangan pada rentang tertentu, sistem akan mengaktifkan corrective pulse discharge balancing atau selective recharge balancing. Selain itu, metode polynomial regression digunakan untuk merepresentasikan hubungan nonlinier antara kapasitas muatan (mAh) dan Open Circuit Voltage (OCV) baterai lithium-ion. Hasil pengujian menunjukkan bahwa sistem mampu menurunkan selisih tegangan antarsel hingga mendekati batas toleransi balancing serta menghasilkan representasi karakteristik SOC–OCV yang sesuai dengan perilaku baterai selama proses charging berlangsung.
Abstract
This research discusses the design and implementation of a four-cell lithium-ion battery balancing system based on selective charge balancing and polynomial regression. Voltage imbalance between cells in multi-cell battery systems can reduce performance, efficiency, and battery lifespan. The proposed system was developed using an ESP32 microcontroller, TP5100 individual charging modules, INA219 sensors, relays, and a corrective discharge balancing circuit. The main balancing method was carried out through selective charging on each cell until reaching a balancing voltage of 4.0 V, followed by a 30-minute rest verification stage to obtain more stable voltage conditions. If voltage differences still existed within a certain range, the system activated corrective pulse discharge balancing or selective recharge balancing. In addition, the polynomial regression method was used to represent the nonlinear relationship between charge capacity (mAh) and the battery’s Open Circuit Voltage (OCV). The experimental results showed that the system was able to reduce voltage differences between cells close to the balancing tolerance limit and successfully represent the SOC–OCV characteristics according to lithium-ion battery behavior during the charging process.
References
Abadi, A. (2025). OF INTELLIGENT CONTROL AND OPTIMIZATION Design of a Battery Pack for a Solar Power System at the Farmers Group Hut in Guo Village , Padang City. 2(2), 31–37.
Ashraf, A., Ali, B., Sunjury, A., & Tricoli, P. (2025). A Comprehensive Review of the Art of Cell Balancing Techniques and Trade-Offs in Battery Management Systems. Energies, 18(13), 3321–3321. https://doi.org/10.3390/en18133321
Bhawna, B., Phogat, P., Shreya, S., Jha, R., & Singh, S. (2025). Advancements and challenges in lithium-ion and lithium-polymer batteries: towards sustainable energy storage solutions. Ionics, 31(6). https://doi.org/10.1007/s11581-025-06309-x
Chavan, S. L., Kanawade, M. A., & Ankushe, R. S. (2025). An effective passive cell balancing technique for lithium-ion battery. Next Energy, 8, 1–12. https://doi.org/10.1016/j.nxener.2025.100258
Chen, J., Zhou, Z., Zhou, Z., Wang, X., & Liaw, B. (2022). Impact of Battery Cell Imbalance on Electric Vehicle Range. Green Energy and Intelligent Transportation, December. https://doi.org/https://doi.org/10.1016/j.geits.2022.100025
Emre Eyímaya, S., & Altin, N. (2026). Design of Multi-Agent-Based Energy Management System for DC Microgrids. IEEE Access, 14, 39120–39134. https://doi.org/10.1109/access.2026.3672618
Gu, X., See, K. W., Liu, Y., Arshad, B., Zhao, L., & Wang, Y. (2023). A time-series Wasserstein GAN method for state-of-charge estimation of lithium-ion batteries. Journal of Power Sources, 581(July), 233472. https://doi.org/10.1016/j.jpowsour.2023.233472
Karmakar, S., Bohre, A. K., & Bera, T. K. (2025). Recent Advancements in Cell Balancing Techniques of BMS for EVs: A Critical Review. IEEE Transactions on Industry Applications, 61(2), 1–17. https://doi.org/10.1109/tia.2025.3531822
Khan, N., Ai, C., Alturki, A., & Amir, M. (2024). Review article A critical review of battery cell balancing techniques , optimal design , converter topologies , and performance evaluation for optimizing storage system in electric vehicles. Energy Reports, 11(December 2023), 4999–5032. https://doi.org/10.1016/j.egyr.2024.04.041
Kurniawan, R. D., Hsb, M. P., & Sitompul, R. A. (2025). Studi Literatur Sistem Penyimpanan Energi Berbasis Baterai: Jenis, Kinerja dan Aplikasinya di Sistem Energi Terbarukan. Perwira Journal of Science & Engineering, 5(2), 165–169.
Marlow, M. N., Chen, J., & Wu, B. (2024). Degradation in parallel-connected lithium-ion battery packs under thermal gradients. Communications Engineering, 3(1), 1–15. https://doi.org/10.1038/s44172-023-00153-5
Mikhak-Beyranvand, M., Salehi, M., & Mohammadkhani, M. A. (2026). Assessing the impact of open-circuit voltage estimation methods on UKF performance for lithium-ion battery SOC and SOH estimation. Scientific Reports, 16(1). https://doi.org/10.1038/s41598-026-38846-4
Oh, J., Lee, M., Ko, E., Kim, K. M., & Kim, J. (2025). Comprehensive understanding of the effects of imbalanced cell via battery module tests for further usage of cycled batteries. Journal of Power Sources, 631, 236282. https://doi.org/10.1016/j.jpowsour.2025.236282
Safari, A., Sorouri, H., Oshnoei, A., & Blaabjerg, F. (2025). A state-of-the-art review on battery cell balancing strategies.
Theuerkauf, D., & Swan, L. (2022). Characteristics of Open Circuit Voltage Relaxation in Lithium-Ion Batteries for the Purpose of State of Charge and State of Health Analysis.
Timur, O., & Ustunel, H. Y. (2025). Design and Experimental Validation of a Novel Energy Storage-Based Method for Optimal Operation Capability of Variable Frequency Drives. Arabian Journal for Science and Engineering, 51(8), 10685–10709. https://doi.org/10.1007/s13369-025-10816-4
Wang, X., Gong, R., Yang, Z., & Kang, L. (2024). State of Charge Estimation of Lithium-ion Batteries Based on Online OCV Curve Construction.
Wu, X., Yan, C., Li, Y., Wang, L., Wang, J., Gao, G., Wang, X., Du, J., Yuan, G., & Fan, Y. (2025). A multifeature fusion approach for Lithium-ion battery state of charge estimation based on mechanical stress via the BiMamba-X model. Journal of Energy Storage, 125, 116976–116976. https://doi.org/10.1016/j.est.2025.116976
Zhang, C., Wang, J., Zhang, L., Zhang, W., Zhu, T., Yang, X.-G., & Cruden, A. (2025). Decoding battery aging in fast-charging electric vehicles: An advanced SOH estimation framework using real-world field data. Energy Storage Materials, 78, 104236. https://doi.org/10.1016/j.ensm.2025.104236
Downloads
Published
Issue
Section
License
Copyright (c) 2026 ridho sani

This work is licensed under a Creative Commons Attribution 4.0 International License.





