Abstract
The importance of energy storage continues to grow, whether in power generation, consumer electronics, aviation, or other systems. Therefore, energy management in batteries is becoming an increasingly crucial aspect of optimizing the overall system and must be done properly. Very few works have been found in the literature proposing the implementation of algorithms such as Extended Kalman Filter (EKF) to predict the State of Charge (SOC) in small systems such as mobile robots, where in some applications the computational power is severely lacking. To this end, this work proposes an implementation of the two algorithms mainly reported in the literature for SOC estimation, in an ATMEGA328P microcontroller-based BMS. This embedded system is designed taking into consideration the criteria already defined for such a system and adding the aspect of flexibility and ease of implementation with an average error of 5% and an energy efficiency of 94%. One of the implemented algorithms performs the prediction while the other will be responsible for the monitoring.
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References
Marwedel, P.: Embedded Systems Foundations of Cyber-Physical Systems, and the Internet of Things. Springer Nature (2021)
Park, K.-H., Kim, C.-H., Cho, H.-K., Seo, J.-K.: Design considerations of a lithium ion battery management system (BMS) for the STSAT-3 satellite. J. Power Electron. 10(2), 210–217 (2010)
Megnafi, H., Chellal, A.-A., Benhanifia, A.: Flexible and automated watering system using solar energy. In: International Conference in Artificial Intelligence in Renewable Energetic Systems, pp. 747–755. Springer, Cham, Tipaza (2020). https://doi.org/10.1007/978-3-030-63846-7_71
**a, B., Lao, Z., Zhang, R., et al.: Online parameter identification and state of charge estimation of lithium-ion batteries based on forgetting factor recursive least squares and nonlinear Kalman filter. Energies 11(1), 3 (2018)
Hannan, M.A., Lipu, M.H., Hussain, A., et al.: Toward enhanced State of charge estimation of Lithium-ion Batteries Using optimized Machine Learning techniques. Sci. Rep. 10(1), 1–15 (2020)
Thomas, B.-R.: Linden’s Handbook of Batteries, 4th edn. McGraw-Hill Education, New York (2011)
Taborelli, C., Onori, S., Maes, S., et al.: Advanced battery management system design for SOC/SOH estimation for e-bikes applications. Int. J. Powertrains 5(4), 325–357 (2016)
Mouna, A., Abdelilah, B., M’Sirdi, N.-K.: Estimation of the state of charge of the battery using EKF and sliding mode observer in Matlab-Arduino/LabView. In: 4th International Conference on Optimization and Applications, pp. 1–6. IEEE, Morocco (2018)
Sanguino, T.-D.-J.-M., Ramos, J.-E.-G.: Smart host microcontroller for optimal battery charging in a solar-powered robotic vehicle. IEEE/ASME Trans. Mechatron. 18(3), 1039–1049 (2012)
REC-BMS.: Battery Management System 4–15S. REC, Control your power, Slovenia (2017)
Robote, Q.: BMS10x0, B40/60V, 100 Amps Management System for Lithium Ion Batteries. RoboteQ, USA (2018)
Kim, I.-S.: A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer. IEEE Trans. Power Electron. 25(4), 1013–1022 (2009)
Mastali, M., Vazquez-Arenas, J., Fraser, R.: Battery state of the charge estimation using Kalman filtering. J. Power Sources 239, 294–307 (2013)
Campestrini, C., Heil, T., Kosch, S., Jossen, A.: A comparative study and review of different Kalman filters by applying an enhanced validation method. J. Energ. Storage 8, 142–159 (2016)
Bishop, G., Welch, G.: An introduction to the kalman filter. In: Proceedings of SIGGRAPH, Course. Proceedings of SIGGRAPH, vol. 41, pp. 27599–23175 (2001)
Campestrini, C., Horsche, M.-F., Zilberman, I.: Validation and benchmark methods for battery management system functionalities: state of charge estimation algorithms. J. Energ. Storage 7, 38–51 (2016)
Yuan, S., Wu, H., Yin, C.: State of charge estimation using the extended Kalman filter for battery management systems based on the ARX battery model. Energies 6(1), 444–470 (2013)
Marian, N., Ma, Y.: Translation of simulink models to component-based software models. In: 8th International Workshop on Research and Education in Mechatronics, pp. 262–267. Citeseer, Location (2007)
Hu, T., Zanchi, B., Zhao, J.: Determining battery parameters by simple algebraic method. In: Proceedings of the 2011 American Control Conference, pp. 3090–3095. IEEE, San Francisco (2011)
Xu, Y., Hu, M., Zhou, A., et al.: State of charge estimation for lithium-ion batteries based on adaptive dual Kalman filter. Appl. Math. Modell. 77(5), 1255–1272 (2020)
Mazidi, M.-A., Naimi, S., Naimi, S.: AVR Microcontroller and Embedded Systems. Pearson, India (2010)
ABLIC INC.: S-8254A Series Battery Protection IC for 3-Serial- or 4-Serial-CELL Pack REV.5.2. ABLIC, Japan (2016)
Chatzakis, J., Kalaitzakis, K., Voulgaris, C., Manias, N.-S.: Designing a new generalized battery management system. IEEE Trans. Ind. Electron. 50(5), 990–999 (2003)
Chen, L., Xu, L., Wang, R.: State of charge estimation for lithium-ion battery by using dual square root cubature kalman filter. Mathematical Problems in Engineering 2017 (2017)
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Chellal, A.A., Lima, J., Gonçalves, J., Megnafi, H. (2021). Dual Coulomb Counting Extended Kalman Filter for Battery SOC Determination. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_16
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DOI: https://doi.org/10.1007/978-3-030-91885-9_16
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