FPGA-Based battery management system for real-time monitoring and instantaneous SOC prediction

Abstract

Battery management systems (BMS) are becoming essential for all types of electric vehicles using battery packs. Various factors, such as battery temperature and balance, directly affect the life, safety, and efficiency of batteries used in vehicles. For security and robustness, these factors should be monitored and adjusted instantly. Today, battery management systems are constantly being developed using different production methods and algorithms. In the studies, calculations are made by measuring parameters such as temperature, current, current balance, load status, and health status of the battery cells, and the control of the battery group is provided with these calculations. Instant and continuous measurement and processing of all these data and the creation of a control algorithm according to the calculation result are possible with the use of powerful processors. FPGA is a processor that can provide the speed and functionality required for BMS. In the battery management system, the FPGA is responsible for receiving and processing all signals from the battery cells and producing results. It instantly processes the data from temperature, current, and voltage sensors and applies the control stage required for balancing. In addition, the charge and discharge capacity of the battery is calculated by instantly measuring the state of charge (SOC). SOC is of great importance in the battery management system to ensure the safety of the battery pack. Therefore, the SOC needs to be estimated accurately and in real-time. Thanks to its parallel processing capability, the FPGA can simultaneously read data from the sensors and perform related calculations. In this study, a versatile system design with real-time, high computational speed for BMS was carried out on FPGA. The voltage and current of an experimental battery based on the embedded system were monitored in real time in a simulation environment. Experimental results show that the instantaneous SOC estimation is successful, and the system returns instant results to the incoming sensor data. The use of FPGA as a management unit will provide significant advantages in BMS with its high operating speed, real-time monitoring, low power consumption, and re-programmability.

Keywords

• BMS
• Battery Management System
• BEV
• Electric Vehicle
• FPGA
• SoC

References

1. [1] Kumar B, Khare N, Chaturvedi P. FPGA-based design of advanced BMS implementing SoC/SoH estimators. Microelectronics Reliability. 2018;84:66-74.
2. [2] Madsen AK, Trimboli MS, Perera DG, editors. An optimised FPGA-based hardware accelerator for physics-based EKF for battery cell management. 2020 IEEE International Symposium on Circuits and Systems (ISCAS); 2020: IEEE.
3. [3] Ren H, Zhao Y, Chen S, Wang T. Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation. Energy. 2019;166:908-17.
4. [4] Lipu MH, Hannan M, Karim TF, Hussain A, Saad MHM, Ayob A, et al. Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. Journal of Cleaner Production. 2021;292:126044.
5. [5] Dinesh P, Teja KK, Singh S, Selvan M, Moorthi S, editors. FPGA Based SoC Estimator and Constant Current Charging/Discharging Controller for Lead–Acid Battery. 2018 15th IEEE India Council International Conference (INDICON); 2018: IEEE.
6. [6] Kim M-J, Chae S-H, Moon Y-K, editors. Adaptive Battery State-of-Charge Estimation Method for Electric Vehicle Battery Management System. 2020 International SoC Design Conference (ISOCC); 2020: IEEE.
7. [7] Divakar B, Cheng KWE, Wu H, Xu J, Ma H, Ting W, et al., editors. Battery management system and control strategy for hybrid and electric vehicle. 2009 3rd international conference on power electronics systems and applications (PESA); 2009: IEEE.
8. [8] Zhang R, Xia B, Li B, Cao L, Lai Y, Zheng W, et al. State of the art of lithium-ion battery SOC estimation for electrical vehicles. Energies. 2018;11(7):1820.

9. [9] Lelie M, Braun T, Knips M, Nordmann H, Ringbeck F, Zappen H, et al. Battery management system hardware concepts: An overview. Applied Sciences. 2018;8(4):534.
10. [10] Kassim MRM, Jamil WAW, Sabri RM, editors. State-of-Charge (SOC) and State-of-Health (SOH) Estimation Methods in Battery Management Systems for Electric Vehicles. 2021 IEEE International Conference on Computing (ICOCO); 2021: IEEE.
11. [11] Zhan H, Wu H, Muhammad M, Lambert S, Pickert V. Combining electric vehicle battery charging and battery cell equalisation in one circuit. IET Electrical Systems in Transportation. 2021;11(4):377-90.
12. [12] Le Gall G, Montavont N, Papadopoulos GZ. IoT Network Management within the Electric Vehicle Battery Management System. Journal of Signal Processing Systems. 2022;94(1):27-44.
13. [13] Duraisamy T, Kaliyaperumal D. Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System. IEEE Access. 2021;9:132846-61.
14. [14] Liu K, Li K, Peng Q, Zhang C. A brief review on key technologies in the battery management system of electric vehicles. Frontiers of mechanical engineering. 2019;14(1):47-64.
15. [15] Lee Y-L, Lin C-H, Farooqui SA, Liu H-D, Ahmad J. Validation of a balancing model based on master-slave battery management system architecture. Electric Power Systems Research. 2023;214:108835.
16. [16] Gabbar HA, Othman AM, Abdussami MR. Review of battery management systems (BMS) development and industrial standards. Technologies. 2021;9(2):28.
17. [17] Shen M, Gao Q. A review on battery management system from the modeling efforts to its multiapplication and integration. International Journal of Energy Research. 2019;43(10):5042-75.
18. [18] Kashmiri M. Current sensing techniques: Principles and readouts. Next-Generation ADCs, High-Performance Power Management, and Technology Considerations for Advanced Integrated Circuits: Springer; 2020. p. 143-65.
19. [19] Maniar K. Comparing shunt-and hall-based isolated current-sensing solutions in HEV/EV. 2018.
20. [20] Xu D, Wang L, Yang J, editors. Research on li-ion battery management system. 2010 International conference on electrical and control engineering; 2010: IEEE.