Year 2025,
Volume: 18 Issue: 3, 876 - 891
Seda Kul
,
Abdurrahman Özgür Polat
,
Şevval Yorulmaz Atay
,
Seyit Alperen Çeltek
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
Seda Kul
,
Abdurrahman Özgür Polat
,
Şevval Yorulmaz Atay
,
Seyit Alperen Çeltek
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)
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