Power Allocation Optimization In Hybrid Energy Storage System Based On Low Pass Filter And Artificial Neural Network
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Abstract
This paper presents optimizing power allocation in hybrid energy storage systems (HESS) by combining Low Pass Filter (LPF) and Artificial Neural Network (ANN) techniques. Conventional LPF-based strategies employ fixed cut-off frequencies that cannot adapt to dynamic operating conditions, resulting in suboptimal performance and reduced battery lifespan. This research proposes optimization framework that determines optimal cut-off frequencies through three approaches: cost-based, stress-based, and hybrid optimization. Spectral analysis of requested power confirmed that energy content is predominantly concentrated in low-frequency ranges (10^-6 to 10^-3 Hz), justifying the frequency-based power allocation strategy. The hybrid optimization method yielded an optimal cut-off frequency of 0.0013 Hz, balancing economic and technical considerations with a 15% reduction in battery stress compared to cost-based approaches while increasing system costs by only 8%. The implemented ANN model achieved predictive capability (R² = 0.9728) in determining optimal cut-off frequencies across various operating conditions. Performance evaluations demonstrated that the ANN-based approach produces nearly identical power allocation patterns compared to conventional LPF methods while executing approximately 120 times faster, enabling real-time adaptation to changing system conditions. The combined methodology effectively separates low-frequency power components (87%) to batteries and high-frequency components (13%) to supercapacitors, optimizing component utilization based on their intrinsic characteristics while maintaining system efficiency above 88%.
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References
R. M. Elavarasan et al., “A Comprehensive Review on Renewable Energy Development, Challenges, and Policies of Leading Indian States with an International Perspective,” IEEE Access, vol. 8, no. May, pp. 74432–74457, 2020, doi: 10.1109/ACCESS.2020.2988011.
W. Jing, C. H. Lai, S. H. W. Wong, and M. L. D. Wong, “Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: Areview,” IET Renew. Power Gener., vol. 11, no. 4, pp. 461–469, 2017, doi: 10.1049/iet-rpg.2016.0500.
S. Farrag, M. Numair, D. E. A. Mansour, and S. M. Dawoud, “Dynamic Control of Li-Ion Battery-Supercapacitor Hybrid Energy Stora ge System Power Sharing Through Low Pass Filter’s time constant,” 22nd Int. Middle East Power Syst. Conf. MEPCON 2021 - Proc., pp. 509–514, 2021, doi: 10.1109/MEPCON50283.2021.9686234.
M. A. Zdiri et al., “Design and Analysis of Sliding-Mode Artificial Neural Network Control Strategy for Hybrid PV-Battery-Supercapacitor System,” Energies, vol. 15, no. 11, 2022, doi: 10.3390/en15114099.
C. Vidal et al., “Hybrid Energy Storage System State-Of-Charge Estimation Using Artificial Neural Network for Micro-Hybrid Applications,” 2018 IEEE Transp. Electrif. Conf. Expo, ITEC 2018, pp. 868–873, 2018, doi: 10.1109/ITEC.2018.8450251.
T. Wu, W. Yu, L. Wang, L. Guo, and Z. Tang, “Power Distribution Strategy of Microgrid Hybrid Energy Storage System Based on Improved Hierarchial Control,” energi, vol. 12, 2019.
F. A. Yusvianti, V. Lystianingrum, and M. F. Romlie, “Neural-Network Based Energy Management System for Battery-Supercapacitor Hybrid Storage,” Proc. - 2024 IEEE 6th Glob. Power, Energy Commun. Conf. GPECOM 2024, pp. 411–416, 2024, doi: 10.1109/GPECOM61896.2024.10582642.
Y. Wu et al., “An improved feed-forward load compensation method for hybrid energy storage systems,” 2019 IEEE Energy Convers. Congr. Expo. ECCE 2019, pp. 5753–5758, 2019, doi: 10.1109/ECCE.2019.8913248.
W. Li, R. Jin, X. Ma, and G. Zhang, “Capacity Optimal Allocation Method and Frequency Division Energy Management for Hybrid Energy Storage System Considering Grid-Connected Requirements in Photovoltaic System,” Energies, vol. 16, no. 10, 2023, doi: 10.3390/en16104154.
J. Zhang, T. Zhang, P. Cheng, D. Yang, J. Yan, and X. Tian, “Optimal Allocation of Hybrid Energy Storage System Based on Smoothing Wind Power Fluctuation and Improved Scenario Clustering Algorithm,” Processes, vol. 11, no. 12, 2023, doi: 10.3390/pr11123407.
L. W. Chong, Y. W. Wong, R. K. Rajkumar, and D. Isa, “An adaptive learning control strategy for standalone PV system with battery-supercapacitor hybrid energy storage system,” J. Power Sources, vol. 394, no. February, pp. 35–49, 2018, doi: 10.1016/j.jpowsour.2018.05.041.