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Year 2025, Volume: 18 Issue: 3, 876 - 891

Abstract

Project Number

sCAPE2018

References

  • [1] Patil, S. D., Dharan, S. R., MR, D. & SR, B. S. (2023). Battery Management System (BMS80) to Improve Battery Life in Electric Vehicles. Computational Learning & Intelligence Computational Learning & Intelligence, 2(2), Accessed: Nov. 13, 2024. [Online]. Available: https://milestoneresearch.in/JOURNALS/index.php/IJCLI/article/view/82
  • [2] Bhuvaneswari, S., Mohan, A. & Sundaram, A. (2024). Improved BMS: A Smart Electric Vehicle Design based on an Intelligent Battery Management System. 2024 International Conference on Inventive Computation Technologies (ICICT), IEEE, 1999-2006.
  • [3] Liu, Q. & Chen, G. (2023). Design of Electric Vehicle Battery Management System. 2023 International Conference on Power, Grid and Energy Storage, Journal of Physics: Conference Series, IOP Publishing, 2614, 012001.
  • [4] Pradhan, S. K. & Chakraborty, B. (2022). Battery management strategies: An essential review for battery state of health monitoring techniques. Journal of Energy Storage, 51, 104427.
  • [5] Raghavan, A., Kiesel, P., Sommer, L. W., Schwartz, J., Lochbaum, A., Hegyi, A., Schuh, A., Arakaki, K., Saha, B., Ganguli, A., Kim, K. H., Kim, C., Hah, H. J., Kim, S., Hwang, G.- O., Chung, G.-C., Choi, B. & Alamgir, M. (2017). Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 1: Cell embedding method and performance. Journal of Power Sources, 341, 466-473A.
  • [6] Lin, Q., Wang, J., Xiong, R., Shen, W. & He, H. (2019). Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries. Energy, Elsevier, 183(C), 220-234.
  • [7] Raoofi, T. & Yildiz, M. (2023). Comprehensive review of battery state estimation strategies using machine learning for battery Management Systems of Aircraft Propulsion Batteries. Journal of Energy Storage, 59, 106486.
  • [8] How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J. & Muttaqi, K. M. (2020). State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach. IEEE Transactions on Industry Applications, 56(5), 5565-5574.
  • [9] Gabbar, H. A., Othman, A. M. & Abdussami, M. R. (2021). Review of Battery Management Systems (BMS) Development and Industrial Standards. Technologies, 9(2), 28.
  • [10] Hannan, M. A., Hoque, M. M., Mohamed, A. & Ayob, A. (2017). Review of energy storage systems for electric vehicle applications: Issues and challenges. Renewable and Sustainable Energy Reviews, 69, 771-789.
  • [11] Ouyang, Q., Han, W., Zou, C., Xu, G. & Wang, Z. (2020). Cell Balancing Control for Lithium-Ion Battery Packs: A Hierarchical Optimal Approach. IEEE Transactions on Industrial Informatics, 16(8), 5065-5075.
  • [12] Zhang, J., Zhang, L., Sun, F. & Wang, Z. (2018). An Overview on Thermal Safety Issues of Lithium-ion Batteries for Electric Vehicle Application. IEEE Access, 6, 23848-23863.
  • [13] Yu, C. & Chau, K. T. (2009). Thermoelectric automotive waste heat energy recovery using maximum power point tracking. Energy Conversion and Management, 50 (6), 1506-1512
  • [14] Li, D., Zhang, Z., Liu, P., Wang Z. & Zhang, L. (2021). Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model. IEEE Transactions on Power Electronics, 36(2), 1303-1315.
  • [15] Song, Y., Peng, Y. & Liu, D. (2021). Model-Based Health Diagnosis for Lithium-Ion Battery Pack in Space Applications. IEEE Transactions on Industrial Electronics, 68(12), 12375-12384.
  • [16] Xing, Y., Ma, E. W. M., Tsui, K. L. & Pecht, M. (2011). Battery Management Systems in Electric and Hybrid Vehicles. Energies, 4(11), 1840-1857.
  • [17] Lelie, M., Braun, T., Knips, M., Nordmann, H., Ringbeck, F., Zappen, H. & Sauer, D. U. (2018). Battery Management System Hardware Concepts: An Overview. Applied Sciences, 8(4), 534.
  • [18] Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M. & Chen, Z. (2020). A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, 110015.
  • [19] Xing, Y., He, W., Pecht, M. & Tsui, K. L. (2014). State of charge estimation of lithium- ion batteries using the open-circuit voltage at various ambient temperatures. Applied Energy, 113, 106-115.
  • [20] Laadjal, K. & Cardoso, A. J. M. (2021). Estimation of Lithium-Ion Batteries State- Condition in Electric Vehicle Applications: Issues and State of the Art. Electronics, 10(13), 1588.
  • [21] Moulik, B., Dubey, A. K. & Ali A. M. (2023). A Battery Modeling Technique Based on Fusion of Hybrid and Adaptive Algorithms for Real-Time Applications in Pure EVs. IEEE Transactions on Intelligent Transportation Systems, 24(3), 2760-2771.
  • [22] Polat, A. O., Celtek, S. A., Kul, S., Balci, S., Rawat, N., Oubelaid, A. & Bajaj, M. (2024). Zero Energy Vehicle Concept: L6e Electric Vehicle Design for Sustainable Urban Transportation. E3S Web of Conferences (ICPGRES-2024), 564, 02001.
  • [23] Inan, R., Güçkiran, M., Altinişik, Y. E., Tek, S. E. & Potuk, M. (2023). Real-time implementation of battery management system designed with improved passive balancing technique for electric vehicles. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(3), 1757-1768.
  • [24] Saleh, Y. B. & Kürüm, H. (2021). Design of Energy Management System Base on lithium-ion Battery. European Journal of Science and Technology, 28, 1144-1151.
  • [25] Wu, T.-H. & Chen, P.-Y. (2024). Battery Management System for Electric Garbage Compactor Trucks. IEEE Access, 12, 88596–88607.
  • [26] Waseem, M., Ahmad, M., Parveen, A. & Suhaib, M. (2023). Battery technologies and functionality of battery management system for EVs: Current status, key challenges, and future prospectives. Journal of Power Sources, 580, 233349.
  • [27] Thangavel, S., Mohanraj, D., Girijaprasanna, T., Raju, S., Dhanamjayulu, C. & Muyeen, S. M. (2023). A Comprehensive Review on Electric Vehicle: Battery Management System, Charging Station, Traction Motors. IEEE Access, 11, 20994-21019.
  • [28] Krishna, T. N. V., Kumar, V. S. V. P. D., Srinivasa Rao, S. & Chang, L. (2024). "Powering the Future: Advanced Battery Management Systems (BMS) for Electric Vehicles. Energies, 17(14), 3360.
  • [29] Kumar, R. R., Bharatiraja, C., Udhayakumar, K., Devakirubakaran, S., Sekar, K. S. & Mihet-Popa, L. (2023). Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications. IEEE Access, 11, 105761-105809.
  • [30] Omariba, Z. B., Zhang, L. & Sun, D. (2019). Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles. IEEE Access, 7, 129335-129352.
  • [31] Manas, M., Yadav, R. & Dubey, R. K. (2023). Designing a battery Management system for electric vehicles: A congregated approach. Journal of Energy Storage, 74 (A).
  • [32] Hannan, M. A., Hoque, M. M., Hussain, A., Yusof, Y. & Ker, P. J. (2018). State-of-the- Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations. IEEE Access, 19362-19378.
  • [33] Marques, T. M. B., dos Santos, J. L. F., Castanho, D. S., Ferreira, M. B., Stevan, S. L., Jr., Illa Font, C. H., Antonini Alves, T., Piekarski, C. M., Siqueira, H. V. & Corrêa, F. C. (2023). An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles. Energies, 16(13), 5050.
  • [34] He, L. & Guo, D. (2019). An Improved Coulomb Counting Approach Based on Numerical Iteration for SOC Estimation with Real-Time Error Correction Ability. IEEE Access, 7, 74274- 74282.
  • [35] Emami, A., Akbarizadeh, G. & Mahmoudi, A. (2024). A Novel Approach for Real-Time Estimation of State of Charge in Li-Ion Battery Through Hybrid Methodology. IEEE Access, 12, 148979-148989.
  • [36] Kadem, O. & Kim, J. (2023). Real-Time State of Charge-Open Circuit Voltage Curve Construction for Battery State of Charge Estimation. IEEE Transactions on Vehicular Technology, 72 (7), 8613- 8622

Design and Evaluation of a Cost-Effective BMS for CERYAN Using Nuvoton

Year 2025, Volume: 18 Issue: 3, 876 - 891

Abstract

The development of Electric Vehicles (EVs) has also accelerated the advancement of battery technology. A Battery Management System (BMS) is essential to use batteries in EVs safely and efficiently. The basic functions of a BMS include measurement, monitoring, and balancing. In the rapidly growing field of micromobility, achieving cost-effective balancing requires high-accuracy measurements. In this study, a BMS design was made with a Nuvoton NUC131 microcontroller for CERYAN, an L6e light electric vehicle, and was evaluated in terms of measurement, performance, and cost. As a result, Since the unit cost of the Nuvoton microcontroller is approximately 30% cheaper compared to STM32Fx, one of the most used microcontrollers, the unit cost of the designed BMS prototype is 27% cheaper. In addition, the ADC measurement error is approximately 0.7 mV, which allows accurate measurement of cell voltages and efficient balancing.

Ethical Statement

There are no ethical issues regarding the publication of this study.

Supporting Institution

This research was supported by the KOSGEB R&D Innovation Project (Project Number: sCAPE2018)

Project Number

sCAPE2018

References

  • [1] Patil, S. D., Dharan, S. R., MR, D. & SR, B. S. (2023). Battery Management System (BMS80) to Improve Battery Life in Electric Vehicles. Computational Learning & Intelligence Computational Learning & Intelligence, 2(2), Accessed: Nov. 13, 2024. [Online]. Available: https://milestoneresearch.in/JOURNALS/index.php/IJCLI/article/view/82
  • [2] Bhuvaneswari, S., Mohan, A. & Sundaram, A. (2024). Improved BMS: A Smart Electric Vehicle Design based on an Intelligent Battery Management System. 2024 International Conference on Inventive Computation Technologies (ICICT), IEEE, 1999-2006.
  • [3] Liu, Q. & Chen, G. (2023). Design of Electric Vehicle Battery Management System. 2023 International Conference on Power, Grid and Energy Storage, Journal of Physics: Conference Series, IOP Publishing, 2614, 012001.
  • [4] Pradhan, S. K. & Chakraborty, B. (2022). Battery management strategies: An essential review for battery state of health monitoring techniques. Journal of Energy Storage, 51, 104427.
  • [5] Raghavan, A., Kiesel, P., Sommer, L. W., Schwartz, J., Lochbaum, A., Hegyi, A., Schuh, A., Arakaki, K., Saha, B., Ganguli, A., Kim, K. H., Kim, C., Hah, H. J., Kim, S., Hwang, G.- O., Chung, G.-C., Choi, B. & Alamgir, M. (2017). Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 1: Cell embedding method and performance. Journal of Power Sources, 341, 466-473A.
  • [6] Lin, Q., Wang, J., Xiong, R., Shen, W. & He, H. (2019). Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries. Energy, Elsevier, 183(C), 220-234.
  • [7] Raoofi, T. & Yildiz, M. (2023). Comprehensive review of battery state estimation strategies using machine learning for battery Management Systems of Aircraft Propulsion Batteries. Journal of Energy Storage, 59, 106486.
  • [8] How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J. & Muttaqi, K. M. (2020). State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach. IEEE Transactions on Industry Applications, 56(5), 5565-5574.
  • [9] Gabbar, H. A., Othman, A. M. & Abdussami, M. R. (2021). Review of Battery Management Systems (BMS) Development and Industrial Standards. Technologies, 9(2), 28.
  • [10] Hannan, M. A., Hoque, M. M., Mohamed, A. & Ayob, A. (2017). Review of energy storage systems for electric vehicle applications: Issues and challenges. Renewable and Sustainable Energy Reviews, 69, 771-789.
  • [11] Ouyang, Q., Han, W., Zou, C., Xu, G. & Wang, Z. (2020). Cell Balancing Control for Lithium-Ion Battery Packs: A Hierarchical Optimal Approach. IEEE Transactions on Industrial Informatics, 16(8), 5065-5075.
  • [12] Zhang, J., Zhang, L., Sun, F. & Wang, Z. (2018). An Overview on Thermal Safety Issues of Lithium-ion Batteries for Electric Vehicle Application. IEEE Access, 6, 23848-23863.
  • [13] Yu, C. & Chau, K. T. (2009). Thermoelectric automotive waste heat energy recovery using maximum power point tracking. Energy Conversion and Management, 50 (6), 1506-1512
  • [14] Li, D., Zhang, Z., Liu, P., Wang Z. & Zhang, L. (2021). Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model. IEEE Transactions on Power Electronics, 36(2), 1303-1315.
  • [15] Song, Y., Peng, Y. & Liu, D. (2021). Model-Based Health Diagnosis for Lithium-Ion Battery Pack in Space Applications. IEEE Transactions on Industrial Electronics, 68(12), 12375-12384.
  • [16] Xing, Y., Ma, E. W. M., Tsui, K. L. & Pecht, M. (2011). Battery Management Systems in Electric and Hybrid Vehicles. Energies, 4(11), 1840-1857.
  • [17] Lelie, M., Braun, T., Knips, M., Nordmann, H., Ringbeck, F., Zappen, H. & Sauer, D. U. (2018). Battery Management System Hardware Concepts: An Overview. Applied Sciences, 8(4), 534.
  • [18] Wang, Y., Tian, J., Sun, Z., Wang, L., Xu, R., Li, M. & Chen, Z. (2020). A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 131, 110015.
  • [19] Xing, Y., He, W., Pecht, M. & Tsui, K. L. (2014). State of charge estimation of lithium- ion batteries using the open-circuit voltage at various ambient temperatures. Applied Energy, 113, 106-115.
  • [20] Laadjal, K. & Cardoso, A. J. M. (2021). Estimation of Lithium-Ion Batteries State- Condition in Electric Vehicle Applications: Issues and State of the Art. Electronics, 10(13), 1588.
  • [21] Moulik, B., Dubey, A. K. & Ali A. M. (2023). A Battery Modeling Technique Based on Fusion of Hybrid and Adaptive Algorithms for Real-Time Applications in Pure EVs. IEEE Transactions on Intelligent Transportation Systems, 24(3), 2760-2771.
  • [22] Polat, A. O., Celtek, S. A., Kul, S., Balci, S., Rawat, N., Oubelaid, A. & Bajaj, M. (2024). Zero Energy Vehicle Concept: L6e Electric Vehicle Design for Sustainable Urban Transportation. E3S Web of Conferences (ICPGRES-2024), 564, 02001.
  • [23] Inan, R., Güçkiran, M., Altinişik, Y. E., Tek, S. E. & Potuk, M. (2023). Real-time implementation of battery management system designed with improved passive balancing technique for electric vehicles. Journal of the Faculty of Engineering and Architecture of Gazi University, 38(3), 1757-1768.
  • [24] Saleh, Y. B. & Kürüm, H. (2021). Design of Energy Management System Base on lithium-ion Battery. European Journal of Science and Technology, 28, 1144-1151.
  • [25] Wu, T.-H. & Chen, P.-Y. (2024). Battery Management System for Electric Garbage Compactor Trucks. IEEE Access, 12, 88596–88607.
  • [26] Waseem, M., Ahmad, M., Parveen, A. & Suhaib, M. (2023). Battery technologies and functionality of battery management system for EVs: Current status, key challenges, and future prospectives. Journal of Power Sources, 580, 233349.
  • [27] Thangavel, S., Mohanraj, D., Girijaprasanna, T., Raju, S., Dhanamjayulu, C. & Muyeen, S. M. (2023). A Comprehensive Review on Electric Vehicle: Battery Management System, Charging Station, Traction Motors. IEEE Access, 11, 20994-21019.
  • [28] Krishna, T. N. V., Kumar, V. S. V. P. D., Srinivasa Rao, S. & Chang, L. (2024). "Powering the Future: Advanced Battery Management Systems (BMS) for Electric Vehicles. Energies, 17(14), 3360.
  • [29] Kumar, R. R., Bharatiraja, C., Udhayakumar, K., Devakirubakaran, S., Sekar, K. S. & Mihet-Popa, L. (2023). Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications. IEEE Access, 11, 105761-105809.
  • [30] Omariba, Z. B., Zhang, L. & Sun, D. (2019). Review of Battery Cell Balancing Methodologies for Optimizing Battery Pack Performance in Electric Vehicles. IEEE Access, 7, 129335-129352.
  • [31] Manas, M., Yadav, R. & Dubey, R. K. (2023). Designing a battery Management system for electric vehicles: A congregated approach. Journal of Energy Storage, 74 (A).
  • [32] Hannan, M. A., Hoque, M. M., Hussain, A., Yusof, Y. & Ker, P. J. (2018). State-of-the- Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations. IEEE Access, 19362-19378.
  • [33] Marques, T. M. B., dos Santos, J. L. F., Castanho, D. S., Ferreira, M. B., Stevan, S. L., Jr., Illa Font, C. H., Antonini Alves, T., Piekarski, C. M., Siqueira, H. V. & Corrêa, F. C. (2023). An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles. Energies, 16(13), 5050.
  • [34] He, L. & Guo, D. (2019). An Improved Coulomb Counting Approach Based on Numerical Iteration for SOC Estimation with Real-Time Error Correction Ability. IEEE Access, 7, 74274- 74282.
  • [35] Emami, A., Akbarizadeh, G. & Mahmoudi, A. (2024). A Novel Approach for Real-Time Estimation of State of Charge in Li-Ion Battery Through Hybrid Methodology. IEEE Access, 12, 148979-148989.
  • [36] Kadem, O. & Kim, J. (2023). Real-Time State of Charge-Open Circuit Voltage Curve Construction for Battery State of Charge Estimation. IEEE Transactions on Vehicular Technology, 72 (7), 8613- 8622
There are 36 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Makaleler
Authors

Seda Kul 0000-0001-8278-4723

Abdurrahman Özgür Polat 0000-0002-4922-6567

Şevval Yorulmaz Atay 0000-0001-5785-2667

Seyit Alperen Çeltek 0000-0002-7097-2521

Project Number sCAPE2018
Early Pub Date October 30, 2025
Publication Date November 7, 2025
Submission Date November 17, 2024
Acceptance Date July 29, 2025
Published in Issue Year 2025 Volume: 18 Issue: 3

Cite

APA Kul, S., Polat, A. Ö., Yorulmaz Atay, Ş., Çeltek, S. A. (2025). Design and Evaluation of a Cost-Effective BMS for CERYAN Using Nuvoton. Erzincan University Journal of Science and Technology, 18(3), 876-891.