EXPERIMENTAL STUDY OF SOLAR LEAD-ACID BATTERY CHARGING EFFICIENCY UNDER VARYING CONDITIONS
Keywords:
solar, battery charging, atmospheric conditions, battery relaxation
Abstract
This study presents an in-depth experimental investigation into the charging performance of 12-volt photovoltaic (PV) solar panels with lead-acid batteries under diverse atmospheric conditions. The research aims to evaluate how variations in solar irradiance, temperature, and cloud cover influence the charging efficiency and long-term stability of both functional and non-functional 12 V 130 Ah lead-acid batteries. A solar charging system was assembled using four 100 W panels connected in parallel and monitored under real outdoor conditions. Voltage data were recorded at thirty-minute intervals for both types of batteries, enabling a comparative assessment of their performance in clear and cloudy weather. The results demonstrated that the functional battery exhibited steady voltage growth and reached its target charge within two hours under clear skies, while cloudy conditions extended the charging duration to four hours. Conversely, the non-functional battery showed irregular voltage fluctuations and a pronounced decline in charging capacity, confirming its inability to store energy effectively. Mathematical models were formulated to describe the voltage–time relationships for each scenario, revealing charging rates of 0.644 V/h and 0.405 V/h under clear and cloudy skies, respectively. Additionally, post-charging relaxation analysis showed that the functional battery maintained voltage stability, whereas the non-functional battery experienced a continuous drop of 0.342 V/h, indicating internal degradation. The findings underscore the strong dependency of solar charging performance on meteorological conditions and battery health, highlighting the importance of effective charge control strategies and battery maintenance in optimizing solar energy storage and extending battery lifespan.
References
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26. A. Fernando, M. Kuipers, G. Angenendt, K.-P. Kairies, and M. Dubarry, ‘Voltage relaxation characterization methods in lithium-ion batteries’, Measurement: Energy, vol. 3, p. 100013, Sep. 2024, http://dx.doi.org/10.1016/j.meaene.2024.100013.
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28. N. Achaibou, M. Haddadi, and A. Malek, ‘Modeling of Lead Acid Batteries in PV Systems’, Energy Procedia, vol. 18, pp. 538–544, Jan. 2012, http://dx.doi.org/10.1016/j.egypro.2012.05.065.
29. O. Badran, Modeling and Simulation of LeadAcid Storage Batteries within Photovoltaic Power Systems. 2012.
30. A. Li, S. Pelissier, P. Venet, and P. Gyan, ‘Fast Characterization Method for Modeling Battery Relaxation Voltage’, Batteries, vol. 2, no. 2, Art. no. 2, Jun. 2016, http://dx.org/10.3390/batteries2020007.
31. W. C. Anggundari, A. Dewantoro, B. B. Louhenapessy, R. D. Susmiarni, Y. Yopi, and N. T. E. Darmayanti, ‘Analysis of the Quality Parameter Requirements of National Standard Development for Battery Swap in Electric Vehicles’, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 9, no. 1, Art. no. 1, Jan. 2021, http://dx.org/10.52549/ijeei.v9i1.2702.
32. P. Manoharan, M. R, K. K, and S. R, ‘SoC Estimation and Monitoring of Li-ion Cell using Kalman-Filter Algorithm’, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 6, no. 4, Art. no. 4, Dec. 2018, http://dx.org/10.52549/ijeei.v6i4.548.
33. M. Souaihia, B. Belmadani, and R. Taleb, ‘State of charge estimation based on adaptive algorithm for Lead-Acid battery’, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 7, no. 2, Art. no. 2, May 2019, http://dx.org/10.52549/ijeei.v7i2.1002.
2. M. Arrinda et al., ‘Application Dependent End-of-Life Threshold Definition Methodology for Batteries in Electric Vehicles’, Batteries, vol. 7, no. 1, Art. no. 1, Mar. 2021, http://dx.doi.org/10.3390/batteries7010012.
3. M. P. T., V. Pushpak, and L. S. R. K., ‘Design and mathematical modeling of solar grass cutter’, Materials Today: Proceedings, Mar. 2023, http://dx.doi.org/10.1016/j.matpr.2023.02.270.
4. J. Schnabel and S. Valkealahti, ‘Energy Storage Requirements for PV Power Ramp Rate Control in Northern Europe’, International Journal of Photoenergy, vol. 2016, no. 1, p. 2863479, 2016 ,
http://dx.doi.org/10.1155/2016/2863479.
5. F. Peprah, S. Gyamfi, E. Effah-Donyina, and M. Amo-Boateng, ‘Evaluation of reactive power support in solar PV prosumer grid’, e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 2, p. 100057, Jan. 2022, http://dx.doi.org/10.1016/j.prime.2022.100057.
6. A. Bonkaney, S. Madougou, and R. Adamou, ‘Impacts of Cloud Cover and Dust on the Performance of Photovoltaic Module in Niamey’, Journal of Renewable Energy, vol. 2017, Jan. 2017, Accessed: Oct. 10, 2024. [Online]. Available: https://go.gale.com/ps/i.do?p=HRCA&sw=w&issn=23144386&v=2.1&it=r&id=GALE%7CA553628003&sid=googleScholar&linkaccess=abs
7. Y. Parvini and A. Vahidi, ‘Maximizing charging efficiency of lithium-ion and lead-acid batteries using optimal control theory’, 2015 American Control Conference (ACC), pp. 317–322, Jul. 2015, http://dx.doi.org/10.1109/ACC.2015.7170755.
8. M. Child, C. Kemfert, D. Bogdanov, and C. Breyer, ‘Flexible electricity generation, grid exchange and storage for the transition to a 100% renewable energy system in Europe’, Renewable Energy, vol. 139, pp. 80–101, Aug. 2019, http://dx.doi.org/10.1016/j.renene.2019.02.077.
9. M. El Haj Assad, A. Khosravi, M. Malekan, M. A. Rosen, and M. A. Nazari, ‘Chapter 14 - Energy storage’, in Design and Performance Optimization of Renewable Energy Systems, M. E. H. Assad and M. A. Rosen, Eds., Academic Press, 2021, pp. 205–219. http://dx.doi.org/10.1016/B978-0-12-821602-6.00016-X.
10. M. Wu et al., ‘A sustainable chitosan-zinc electrolyte for high-rate zinc-metal batteries’, Matter, vol. 5, no. 10, pp. 3402–3416, Oct. 2022, http://dx.doi.org/10.1016/j.matt.2022.07.015.
11. ‘BU-808: How to Prolong Lithium-based Batteries’, Battery University. Accessed: Oct. 16, 2024. [Online]. Available: https://batteryuniversity.com/article/bu-808-how-to-prolong-lithium-based-batteries
12. S. Dorel, M. Gmal Osman, C.-V. Strejoiu, and G. Lazaroiu, ‘Exploring Optimal Charging Strategies for Off-Grid Solar Photovoltaic Systems: A Comparative Study on Battery Storage Techniques’, Batteries, vol. 9, no. 9, Art. no. 9, Sep. 2023, http://dx.doi.org/10.3390/batteries9090470.
13. K. B. Hueso, M. Armand, and T. Rojo, ‘High temperature sodium batteries: status, challenges and future trends’, Energy Environ. Sci., vol. 6, no. 3, pp. 734–749, Feb. 2013, doi: 10.1039/C3EE24086J.
14. Y. Zhang, J. Xu, S. Yang, C. Deng, F. Chen, and F. Lei, ‘Battery module capacity fade model based on cell voltage inconsistency and probability distribution’, Advances in Mechanical Engineering, vol. 9, no. 9, p. 1687814017730757, Sep. 2017, http://dx.doi.org/10.1177/1687814017730757.
15. E. Altuğ, Ö. F. Akyünci, and E. Özgül, ‘Virtual battery electric vehicle development via 1D tools’, Advances in Mechanical Engineering, vol. 15, no. 10, p. 16878132231177663, Oct. 2023, http://dx.doi.org/10.1177/16878132231177663.
16. E. Nasri, T. Jarou, S. Benchikh, and Y. Elkoudia, ‘Reliable energy supply and voltage control for hybrid microgrid by pid controlled with integrating of an EV charging station’, http://dx.doi.org/10.29354/diag/174145.
17. J. Monieta and M. Nowicki, ‘Experimental manners of a interference reduction of chosen measuring signals of a generating set in conditions of a marine power plants’, http://dx.doi.org/10.29354/diag/82975.
18. M. Ekiert, P. Mróz, P. Górny, and T. Uhl, ‘An estimation of lithium-ion battery state of health – ageing-included modelling and experimental studies’, Accessed: Oct. 16, 2024. [Online]. Available: http://www.diagnostyka.net.pl/An-estimation-of-lithium-ion-battery-state-of-health-ageing-included-modelling-and,63001,0,2.html
19. B. G. Ghule, Z. P. Shaikh, and R. S. Mane, ‘Chapter 7 - Ferrites for Batteries’, in Spinel Ferrite Nanostructures for Energy Storage Devices, R. S. Mane and V. V. Jadhav, Eds., in Micro and Nano Technologies. , Elsevier, 2020, pp. 147–172. http://dx.doi.org/10.1016/B978-0-12-819237-5.00007-9.
20. Y. Tang, S. Zheng, Y. Xu, X. Xiao, H. Xue, and H. Pang, ‘Advanced batteries based on manganese dioxide and its composites’, Energy Storage Materials, vol. 12, pp. 284–309, May 2018, http://dx.doi.org/10.1016/j.ensm.2018.02.010.
21. Y.-M. Chen, C.-Y. Tang, and Y.-T. Chen, ‘PV Power System with Multi-Mode Operation and Low-Voltage Ride-Through Capability’, IEEE Transactions on Industrial Electronics, vol. 62, pp. 1–1, Dec. 2015, http://dx.doi.org/10.1109/TIE.2015.2449777.
22. H. Catherino, P. Shi, A. Rusek, and F. Feres, ‘Self-Discharging of Lead-Acid Batteries’, SAE TECHNICAL PAPER SERIES, vol. 2000-01–0305, pp. 776–4841, Jan. 2002, http://dx.doi.org/10.4271/2000-01-0305.
23. M. Etxandi-Santolaya, L. Canals Casals, T. Montes, and C. Corchero, ‘Are electric vehicle batteries being underused? A review of current practices and sources of circularity’, Journal of Environmental Management, vol. 338, p. 117814, Jul. 2023, http://dx.doi.org/10.1016/j.jenvman.2023.117814.
24. D. Theuerkauf and L. Swan, ‘Characteristics of Open Circuit Voltage Relaxation in Lithium-Ion Batteries for the Purpose of State of Charge and State of Health Analysis’, Batteries, vol. 8, no. 8, Art. no. 8, Aug. 2022, http://dx.doi.org/10.3390/batteries8080077.
25. C.-J. Ko and K.-C. Chen, ‘Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries’, Applied Energy, vol. 357, p. 122488, Mar. 2024, http://dx.doi.org/10.1016/j.apenergy.2023.122488.
26. A. Fernando, M. Kuipers, G. Angenendt, K.-P. Kairies, and M. Dubarry, ‘Voltage relaxation characterization methods in lithium-ion batteries’, Measurement: Energy, vol. 3, p. 100013, Sep. 2024, http://dx.doi.org/10.1016/j.meaene.2024.100013.
27. S. E. A. Yousif, A. Fotouhi, D. J. Auger, and K. Propp, ‘Self-Discharge Effects in Lithium-Sulfur Equivalent Circuit Networks for State Estimation’, J. Electrochem. Soc., vol. 165, no. 1, p. A6081, Nov. 2017, doi: 10.1149/2.0121801jes.
28. N. Achaibou, M. Haddadi, and A. Malek, ‘Modeling of Lead Acid Batteries in PV Systems’, Energy Procedia, vol. 18, pp. 538–544, Jan. 2012, http://dx.doi.org/10.1016/j.egypro.2012.05.065.
29. O. Badran, Modeling and Simulation of LeadAcid Storage Batteries within Photovoltaic Power Systems. 2012.
30. A. Li, S. Pelissier, P. Venet, and P. Gyan, ‘Fast Characterization Method for Modeling Battery Relaxation Voltage’, Batteries, vol. 2, no. 2, Art. no. 2, Jun. 2016, http://dx.org/10.3390/batteries2020007.
31. W. C. Anggundari, A. Dewantoro, B. B. Louhenapessy, R. D. Susmiarni, Y. Yopi, and N. T. E. Darmayanti, ‘Analysis of the Quality Parameter Requirements of National Standard Development for Battery Swap in Electric Vehicles’, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 9, no. 1, Art. no. 1, Jan. 2021, http://dx.org/10.52549/ijeei.v9i1.2702.
32. P. Manoharan, M. R, K. K, and S. R, ‘SoC Estimation and Monitoring of Li-ion Cell using Kalman-Filter Algorithm’, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 6, no. 4, Art. no. 4, Dec. 2018, http://dx.org/10.52549/ijeei.v6i4.548.
33. M. Souaihia, B. Belmadani, and R. Taleb, ‘State of charge estimation based on adaptive algorithm for Lead-Acid battery’, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 7, no. 2, Art. no. 2, May 2019, http://dx.org/10.52549/ijeei.v7i2.1002.
Published
2026-02-02
How to Cite
1.
Mohammed Bouzidi, Ben AtallahM, Nasri A, Ouledali O. EXPERIMENTAL STUDY OF SOLAR LEAD-ACID BATTERY CHARGING EFFICIENCY UNDER VARYING CONDITIONS. MatTech [Internet]. 2026Feb.2 [cited 2026Sep.8];60(1):23–29. Available from: https://mater-tehnol.si/index.php/MatTech/article/view/1417
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