ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER MENGGUNAKAN BACK PROPAGATION NEURAL NETWORK
Abstract
The battery is an important component in the context of implementing renewable energy. The type of battery that has a density in energy storage is lithium polymer. The parameter in the battery that must be considered is the State of Charge (SOC) estimation. In general, the SOC battery estimation uses the coloumb counting method because the difficulty level is low. However, there are weaknesses in the dependence on the utility of the current sensor which is used as an accumulation of the integral of the incoming and outgoing currents over time. In this study presents Back Propagation Neural Network (BPNN) as an algorithm for estimating SOC based on OCV-SOC characteristic curves. The OCV-SOC characteristic curve of the battery is obtained from the battery pulse test. Battery voltage, current and discharging time are used as the first BPNN input layer for the estimation of Open Circuit Voltage (OCV). OCV will be learned as the second BPNN input layer for estimating battery SOC. The results of SOC estimation simulations obtained an average error of 0.479% against the real SOC based on the characteristic curve of OCV - SOC.
Downloads
References
A. H. Budiman, P. Teknologi, K. Dan, K. Energi, B. Pengkajian, and D. A. N. Penerapan, “Pengembangan Energy Storage,” Kaji. Roadmap Pengemb. Energy Storage untuk Smart Grid Syst., p. 46, 2013.
G. J. May, A. Davidson, and B. Monahov, “Lead batteries for utility energy storage: A review,” J. Energy Storage, vol. 15, pp. 145–157, 2018.
L. Long, S. Wang, M. Xiao, and Y. Meng, “Polymer electrolytes for lithium polymer batteries,” J. Mater. Chem. A, vol. 4, no. 26, pp. 10038–10039, 2016.
C. Huai, P. Liu, and X. Jia, “Measurement and analysis for lithium battery of high-rate discharge performance,” Procedia Eng., vol. 15, pp. 2619–2623, 2011.
J. Kim, S. Lee, and B. H. Cho, “Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction,” IEEE Trans. Power Electron., vol. 27, no. 1, pp. 436–451, 2012.
J. Klee Barillas, J. Li, C. Günther, and M. A. Danzer, “A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems,” Appl. Energy, vol. 155, pp. 455–462, 2015.
Y. Su, “Switching-based state-of-charge estimation of lithium-ion batteries Switching-Based State-of-Charge Estimation of Lithium-Ion Batteries,” 2011.
L. W. Kang, X. Zhao, and J. Ma, “A new neural network model for the state-of-charge estimation in the battery degradation process,” Appl. Energy, vol. 121, pp. 20–27, 2014.
F. Yang, Y. Xing, D. Wang, and K. L. Tsui, “A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile,” Appl. Energy, vol. 164, pp. 387–399, 2016.
S. Jeon, J. J. Yun, and S. Bae, “Comparative study on the battery state-of-charge estimation method,” Indian J. Sci. Technol., vol. 8, no. 26, pp. 1–6, 2015.
B. Scrosati and J. Garche, “Lithium batteries: Status, prospects and future,” J. Power Sources, vol. 195, no. 9, pp. 2419–2430, 2010.
R. Ahmed, “Modeling and State of Charge Estimation of Electric Vehicle,” Dr. Diss., 2014.
J. Xie, J. Ma, and K. Bai, “Enhanced coulomb counting method for state-of-charge estimation of lithium-ion batteries based on peukert’s law and coulombic efficiency,” J. Power Electron., vol. 18, no. 3, pp. 910–922, 2018.
W.-Y. Chang, “The State of Charge Estimating Methods for Battery: A Review,” ISRN Appl. Math., vol. 2013, no. 1, pp. 1–7, 2013.
C. Dong and G. Wang, “Estimation of power battery SOC based on improved BP neural network,” 2014 IEEE Int. Conf. Mechatronics Autom. IEEE ICMA 2014, pp. 2022–2027, 2014.
L. Xiao, X. Chen, and X. Zhang, “A joint optimization of momentum item and levenberg-marquardt algorithm to level Up the BPNN’s generalization ability,” Math. Probl. Eng., vol. 2014, no. November 2015, 2014.
L. Fausett, “Fundamentals Of Neural Network Architectures, Algorithms, and Applications,” Inc.,New Jersey, vol. 116, no. 0, pp. 1–476, 1994.
S. Tong, J. H. Lacap, and J. W. Park, “Battery state of charge estimation using a load-classifying neural network,” J. Energy Storage, vol. 7, pp. 236–243, 2016.
