Aging study of a lead-acid storage bank in a multi-source hybrid system

EL MEHDI LAADISSI, Jaouad KHALFI, Chouaib Ennawaoui, Fouad Belhora, Abdessamad El Ballouti

Abstract


Autonomous and grid-connected systems play an important role in the massive integration of renewable energy sources necessary for the global development of a sustainable society. In this regard, the analysis of the behavior of electrochemical storage devices such as lead-acid batteries installed on hybrid energy systems and microgrids in terms of lifespan and economic profitability is an important research subject. The purpose of this article is to present a methodology for calculating the aging rate of a storage battery inserted in a hybrid multisource system. The approach consists in first knowing the solicitations of the battery during a year knowing at every moment its state of charge. This curve is obtained from a dynamic simulator taking into account the intermittences of the sources and the load. The second step is to determine the number of cycles and the depth of discharge of each from the stat of charge. Finally, based on the battery life characteristic given by the manufacturer (cycle number vs. discharge depth), the aging rate of the battery for one year of operation is determined.


Keywords


Battery Aging Rate ; Hybrid System multi-sources; Renewable Energies; Diesel Generator

References


T. M. Layadi, M. Mostefai, G. Champenois, and D. Abbes, “Dimensioning a hybrid electrification system (PV/WT/DG+ battery) using a dynamic simulation,” in 2013 International Conference on Electrical Engineering and Software Applications, 2013, pp. 1–6.

R. Dufo-López and J. L. Bernal-Agustín, “Design and control strategies of PV-Diesel systems using genetic algorithms,” Sol. Energy, vol. 79, no. 1, pp. 33–46, Jul. 2005.

E. Koutroulis, D. Kolokotsa, A. Potirakis, and K. Kalaitzakis, “Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms,” Sol. Energy, vol. 80, no. 9, pp. 1072–1088, Sep. 2006.

D. B. Nelson, M. H. Nehrir, and C. Wang, “Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems,” Renew. Energy, vol. 31, no. 10, pp. 1641–1656, 2006.

R. Belfkira, L. Zhang, and G. Barakat, “Optimal sizing study of hybrid wind/PV/diesel power generation unit,” Sol. Energy, vol. 85, no. 1, pp. 100–110, Jan. 2011.

D. Abbes, A. Martinez, and G. Champenois, “Eco-design optimisation of an autonomous hybrid wind–photovoltaic system with battery storage,” IET Renew. Power Gener., vol. 6, no. 5, pp. 358–371, Sep. 2012.

D. Abbes, A. Martinez, G. Champenois, and J.-P. Gaubert, “Étude d’un système hybride éolien photovoltaïque avec stockage: Dimensionnement et analyse du cycle de vie,” Eur. J. Electr. Eng. EJEE Vol155, pp. 479–497, 2012.

D. Abbes, A. Martinez, and G. Champenois, “Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems,” Math. Comput. Simul., vol. 98, pp. 46–62, Apr. 2014.

E. M. LAADISSI, E. F. ANAS, and M. ZAZI, “NONLINEAR BLACK BOX MODELING OF A LEAD ACID BATTERY USING HAMMERSTEIN-WIENER MODEL.,” J. Theor. Appl. Inf. Technol., vol. 89, no. 2, 2016.

B. Bogno et al., “Improvement of safety, longevity and performance of lead acid battery in off-grid PV systems,” Int. J. Hydrog. Energy, vol. 42, no. 5, pp. 3466–3478, 2017.

M. S. Rahmanifar, “Enhancing the cycle life of Lead-Acid batteries by modifying negative grid surface,” Electrochimica Acta, vol. 235, pp. 10–18, 2017.

Lambert DW, Greenwood PH, Reed MC. Advances in gelled-electrolyte technology for valve-regulated lead-acid batteries. Journal of power sources. 2002; 107(2): 173-179.

A. H. Anbuky and P. E. Pascoe, “VRLA battery state-of-charge estimation in telecommunication power systems,” IEEE Trans. Ind. Electron., vol. 47, no. 3, pp. 565–573, Jun. 2000.

N. Khera, S. A. Khan, and O. Rahman, “Valve regulated lead acid battery diagnostic system based on infrared thermal imaging and fuzzy algorithm,” Int. J. Syst. Assur. Eng. Manag., Feb. 2020.

Catherino HA. Complexity in battery systems: Thermal runaway in VRLA batteries. Journal of PowerSources. 2006; 158(2): 977-986.

A. Zainuri, U. Wibawa, M. Rusli, R. N. Hasanah, and R. A. Harahap, “VRLA battery state of health estimation based on charging time,” Telkomnika, vol. 17, no. 3, 2019.

S. D. Downing and D. F. Socie, “Simple rainflow counting algorithms,” Int. J. Fatigue, vol. 4, no. 1, pp. 31–40, 1982.

A. Gheiratmand, R. Effatnejad, and M. Hedayati, “Technical and economic evaluation of hybrid wind/PV/battery systems for off-grid areas using HOMER software,” Int. J. Power Electron. Drive Syst., vol. 7, no. 1, p. 134, 2016.

W. Tiezhou, C. Quan, L. Lunan, X. Qing, and W. Xieyang, “Research on the fast charging of VRLA,”TELKOMNIKA Indones. J. Electr. Eng., vol. 10, no. 7, pp. 1660–1666, 2012.

Chan CC, Lo EW, Weixiang S. The available capacity computation model based on artificial neuralnetwork for lead-acid batteries in electric vehicles. J. Power Sources. 2000; 87: 201-204.

Bhangu BS, Bentley P, Stone DA, Bingham CM. Nonlinear observers for predicting state-of-chargeand state-of-health of lead-acid batteries for hybrid-electric vehicles. IEEE Trans. Veh. Tech. 2005;54: 783-794.

Kim IS. A technique for estimating the state of health of lithium batteries through a dual-sliding-modeobserver. IEEE Trans. Power Electronics. 2010; 25: 1013-1022.

Haifeng D, Xuezhe W, Zechang S. A new SOH prediction concept for the power lithium-ion batteryused on HEVs. Vehicle Power and Propulsion Conference. Dearborn. 2009.1649-1653.

Shahriari M, Farrokhi M. State of health estimation of VRLA batteries using fuzzy logic. 18th IranianConference on Electrical Engineering. Isfahan. 2010: 629-634.

Lin HT, Liang TJ, Chen SM. Estimation of Battery State of Health Using Probabilistic Neural Network.IEEE Transactions on Industrial Informatics. 2013; 9(2): 679-685.

Kim T, Qiao W, Qu L. Online SOC and SOH estimation for multicell lithium-ion batteries based on anadaptive hybrid battery model and sliding-mode observer. IEEE Energy Conversion Congress andExposition. Denver. 2013; 292-298.

Qiuting W. State of Health Estimation for Lithium-ion Battery Based on D-UKF. International Journalof Hybrid Information Technology. 2015; 8(7): 55-70.

Moura JS, et.al. Adaptive Partial Differential Equation Observer for Battery State of charge/State ofHealth Estimation via an Electrochemical Model. Journal of Dynamic System, Measurement andControl, Transaction ASME. 2014; 136: 1-11.




DOI: http://doi.org/10.11591/ijeecs.v20.i3.pp%25p
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