Energy Environment and Economy
Energy, Environment and Sustainable Sciences | online ISSN 3069-0935
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Volume 3 Number 1 2025
RESEARCH ARTICLE (Open Access)
Experimental Optimization of Photovoltaic Module Lamination Parameters Using Design of Experiments and Statistical Process Control
Ashok Kumar Chowdhury1*, Shipon Chandra Barman2
Energy Environment and Economy 3 (1) 1-9 https://doi.org/10.25163/energy.3110690
Submitted: 30 April 2025 Revised: 04 July 2025 Accepted: 11 July 2025 Published: 12 July 2025
Abstract
Photovoltaic (PV) technologies continue to expand as the global demand for sustainable energy accelerates. Yet, while advances in solar cell materials have improved conversion efficiency, the reliability and performance of photovoltaic modules still depend heavily on manufacturing precision. Among the most critical stages is the lamination process, where temperature, pressure, and curing time collectively determine structural bonding, electrical stability, and defect formation. This study explored how controlled adjustments in these parameters influence module performance during manufacturing. A full factorial Design of Experiments (DOE) framework was applied to examine lamination temperature (140–160 °C), pressure (0.70–0.90 MPa), and curing duration (12–18 min). Sixty experimental runs were conducted using monocrystalline silicon photovoltaic cells, while performance metrics—including power output, defect rate, adhesion strength, thermal stability, and electrical deviation—were systematically measured. Statistical Process Control (SPC) tools and process capability indices (Cp and Cpk) were used to evaluate production stability and reproducibility. Results suggest that incremental increases in lamination temperature, pressure, and curing duration generally improved module performance. Power output increased from 282.94 W to 287.62 W, while defect rates declined from 5.36% to 3.56%. Mechanical adhesion strength improved from 2.53 N/mm² to 2.95 N/mm², accompanied by enhanced thermal stability. Among the experimental runs, the optimal configuration—155 °C lamination temperature, 0.85 MPa pressure, and 18 min curing—produced 286.74 W output with a defect rate of only 2.83% and the highest observed adhesion strength of 3.01 N/mm². Overall, the findings highlight how carefully optimized lamination parameters can significantly enhance electrical efficiency, structural durability, and process reliability. These insights may contribute to more stable photovoltaic manufacturing systems and, perhaps more importantly, support the broader advancement of scalable and dependable solar energy technologies.
Keywords: Photovoltaic manufacturing; Lamination optimization; Design of experiments; Statistical process control; Solar module reliability
References
Abellan-Nebot, J. V., & Subirón, F. R. (2009). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology, 47(1–4), 237–257. https://doi.org/10.1007/s00170-009-2191-8
Alapatt, G. F., Singh, R., & Poole, K. F. (2012). Fundamental issues in manufacturing photovoltaic modules beyond the current generation of materials. Advances in OptoElectronics, 2012, 1–10. https://doi.org/10.1155/2012/782150
Alimi, O. A., Meyer, E. L., & Olayiwola, O. I. (2022). Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review. Energies, 15(16), 5964. https://doi.org/10.3390/en15165964
Al-Kharusi, G., Dunne, N. J., Little, S., & Levingstone, T. J. (2022). The role of machine learning and design of experiments in the advancement of biomaterial and tissue engineering research. Bioengineering, 9(10), 561. https://doi.org/10.3390/bioengineering9100561
Appiah, A. Y., Zhang, X., Ayawli, B. B. K., & Kyeremeh, F. (2019). Review and performance evaluation of photovoltaic array fault detection and diagnosis techniques. International Journal of Photoenergy, 2019, 1–19. https://doi.org/10.1155/2019/6953530
Bandaru, S. H., Becerra, V., Khanna, S., Radulovic, J., Hutchinson, D., & Khusainov, R. (2021). A Review of Photovoltaic Thermal (PVT) technology for Residential Applications: Performance Indicators, progress, and opportunities. Energies, 14(13), 3853. https://doi.org/10.3390/en14133853
Benato, A., Stoppato, A., De Vanna, F., & Schiro, F. (2021). Spraying Cooling System for PV modules: Experimental measurements for temperature trends assessment and system design feasibility. Designs, 5(2), 25. https://doi.org/10.3390/designs5020025
Bendato, I., Cassettari, L., Mosca, M., & Mosca, R. (2015). A design of experiments/response surface methodology approach to study the economic sustainability of a 1 MWe photovoltaic plant. Renewable and Sustainable Energy Reviews, 51, 1664–1679. https://doi.org/10.1016/j.rser.2015.07.074
Chupakhin, S., Klusemann, B., Huber, N., & Kashaev, N. (2019). Application of design of experiments for laser shock peening process optimization. The International Journal of Advanced Manufacturing Technology, 102(5–8), 1567–1581. https://doi.org/10.1007/s00170-018-3034-2
Djimtoingar, S. S., Derkyi, N. S. A., Kuranchie, F. A., & Yankyera, J. K. (2022). A review of response surface methodology for biogas process optimization. Cogent Engineering, 9(1). https://doi.org/10.1080/23311916.2022.2115283
Du, W., Yu, W., France, D. M., Singh, M., & Singh, D. (2022). Additive manufacturing and testing of a ceramic heat exchanger for high-temperature and high-pressure applications for concentrating solar power. Solar Energy, 236, 654–665. https://doi.org/10.1016/j.solener.2022.03.046
Haillant, O. (2010). Accelerated weathering testing principles to estimate the service life of organic PV modules. Solar Energy Materials and Solar Cells, 95(5), 1284–1292. https://doi.org/10.1016/j.solmat.2010.08.033
Harrou, F., Sun, Y., Taghezouit, B., Saidi, A., & Hamlati, M. (2017). Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches. Renewable Energy, 116, 22–37. https://doi.org/10.1016/j.renene.2017.09.048
Hirschl, C., Biebl–Rydlo, M., DeBiasio, M., Mühleisen, W., Neumaier, L., Scherf, W., Oreski, G., Eder, G., Chernev, B., Schwab, W., & Kraft, M. (2013). Determining the degree of crosslinking of ethylene vinyl acetate photovoltaic module encapsulants—A comparative study. Solar Energy Materials and Solar Cells, 116, 203–218. https://doi.org/10.1016/j.solmat.2013.04.022
Hoseinpour-Lonbar, M., Alavi, M. Z., & Palassi, M. (2020). Selection of asphalt mix with optimal fracture properties at intermediate temperature using Taguchi method for design of experiment. Construction and Building Materials, 262, 120601. https://doi.org/10.1016/j.conbuildmat.2020.120601
Karambeigi, M. S., Abbassi, R., Roayaei, E., & Emadi, M. A. (2015). Emulsion flooding for enhanced oil recovery: Interactive optimization of phase behavior, microvisual and core-flood experiments. Journal of Industrial and Engineering Chemistry, 29, 382–391. https://doi.org/10.1016/j.jiec.2015.04.019
Kessaissia, F. Z., Zegaoui, A., Aillerie, M., Arab, M., Boutoubat, M., & Fares, C. (2020). Factorial design and response surface optimization for modeling photovoltaic module parameters. Energy Reports, 6, 299–309. https://doi.org/10.1016/j.egyr.2019.11.016
Koronis, G., Silva, A., & Foong, S. (2017). Predicting the flexural performance of woven flax reinforced epoxy composites using design of experiments. Materials Today Communications, 13, 317–324. https://doi.org/10.1016/j.mtcomm.2017.10.019
Lamberti, F., Mazzariol, C., Spolaore, F., Ceccato, R., Salmaso, L., & Gross, S. (2022). Design of Experiment: a rational and still unexplored approach to inorganic materials’ synthesis. Sustainable Chemistry, 3(1), 114–130. https://doi.org/10.3390/suschem3010009
Masood, F., Nallagownden, P., Elamvazuthi, I., Akhter, J., & Alam, M. A. (2021). A new approach for design optimization and parametric analysis of symmetric compound parabolic concentrator for photovoltaic applications. Sustainability, 13(9), 4606. https://doi.org/10.3390/su13094606
Moseson, A. J., Moseson, D. E., & Barsoum, M. W. (2011). High volume limestone alkali-activated cement developed by design of experiment. Cement and Concrete Composites, 34(3), 328–336. https://doi.org/10.1016/j.cemconcomp.2011.11.004
Najafi, G., Ghobadian, B., Yusaf, T., Ardebili, S. M. S., & Mamat, R. (2015). Optimization of performance and exhaust emission parameters of a SI (spark ignition) engine with gasoline–ethanol blended fuels using response surface methodology. Energy, 90, 1815–1829. https://doi.org/10.1016/j.energy.2015.07.004
Najjar, M. K., Qualharini, E. L., Hammad, A. W. A., Boer, D., & Haddad, A. (2019). Framework for a systematic parametric analysis to maximize energy output of PV modules using an experimental design. Sustainability, 11(10), 2992. https://doi.org/10.3390/su11102992
Owen-Bellini, M., Hacke, P., Miller, D. C., Kempe, M. D., Spataru, S., Tanahashi, T., Mitterhofer, S., Jankovec, M., & Topic, M. (2020). Advancing reliability assessments of photovoltaic modules and materials using combined-accelerated stress testing. Progress in Photovoltaics Research and Applications, 29(1), 64–82. https://doi.org/10.1002/pip.3342
Prasad, A. G., Saravanan, S., Gijo, E., Dasari, S. M., Tatachar, R., & Suratkar, P. (2013). Six Sigma-based approach to optimise the diffusion process of crystalline silicon solar cell manufacturing. International Journal of Sustainable Energy, 35(2), 190–204. https://doi.org/10.1080/14786451.2013.861463
Prashar, A. (2016). A conceptual hybrid framework for industrial process improvement: integrating Taguchi methods, Shainin System and Six Sigma. Production Planning & Control, 27(16), 1389–1404. https://doi.org/10.1080/09537287.2016.1225999
Qin, Z., Wu, Y., Eizad, A., Lyu, S., & Lee, C. (2021). Advancement of mechanical engineering in extreme environments. International Journal of Precision Engineering and Manufacturing-Green Technology, 8(6), 1767–1782. https://doi.org/10.1007/s40684-020-00295-3
Rahul, Abhishek, K., Datta, S., Biswal, B. B., & Mahapatra, S. S. (2016). Machining performance optimization for electro-discharge machining of Inconel 601, 625, 718 and 825: an integrated optimization route combining satisfaction function, fuzzy inference system and Taguchi approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(9), 3499–3527. https://doi.org/10.1007/s40430-016-0659-7
Ramachandran, C. S., Balasubramanian, V., & Ananthapadmanabhan, P. V. (2010). Multiobjective optimization of atmospheric plasma spray process parameters to deposit YTTRIA-Stabilized zirconia coatings using response surface methodology. Journal of Thermal Spray Technology, 20(3), 590–607. https://doi.org/10.1007/s11666-010-9604-y
Sandrolini, L., Artioli, M., & Reggiani, U. (2009). Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Applied Energy, 87(2), 442–451. https://doi.org/10.1016/j.apenergy.2009.07.022
Sarmouk, M., Smaili, A., Fellouah, H., & Merabtine, A. (2022). Energy and economic assessment of a hybrid solar/gas heating system using a combined statistical-based multi-objective optimization method. Journal of Building Engineering, 59, 105095. https://doi.org/10.1016/j.jobe.2022.105095
Spataru, S., Sera, D., Kerekes, T., & Teodorescu, R. (2015). Diagnostic method for photovoltaic systems based on light I–V measurements. Solar Energy, 119, 29–44. https://doi.org/10.1016/j.solener.2015.06.020
Taherimakhsousi, N., Fievez, M., MacLeod, B. P., Booker, E. P., Fayard, E., Matheron, M., Manceau, M., Cros, S., Berson, S., & Berlinguette, C. P. (2021). A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics. Npj Computational Materials, 7(1). https://doi.org/10.1038/s41524-021-00657-8
Tippabhotla, S. K., Diesta, N. G., Zhang, X., Sridhara, S., Stan, C., Tamura, N., Tay, A. A., & Budiman, A. (2019). Thermomechanical residual stress evaluation in multi-crystalline silicon solar cells of photovoltaic modules with different encapsulation polymers using synchrotron X-ray microdiffraction. Solar Energy Materials and Solar Cells, 193, 387–402. https://doi.org/10.1016/j.solmat.2019.01.016
Vicente, A. T., Wojcik, P. J., Mendes, M. J., Águas, H., Fortunato, E., & Martins, R. (2017). A statistics modeling approach for the optimization of thin film photovoltaic devices. Solar Energy, 144, 232–243. https://doi.org/10.1016/j.solener.2017.01.029
Yang, Y., & Shao, C. (2021). Hybrid multi-task learning-based response surface modeling in manufacturing. Journal of Manufacturing Systems, 59, 607–616. https://doi.org/10.1016/j.jmsy.2021.04.012
Yazdani, H., & Yaghoubi, M. (2022). Dust deposition effect on photovoltaic modules performance and optimization of cleaning period: A combined experimental–numerical study. Sustainable Energy Technologies and Assessments, 51, 101946. https://doi.org/10.1016/j.seta.2021.101946
Zaman, U. K. U., Khan, U. A., Aziz, S., Baqai, A. A., Butt, S. U., Hussain, D., Siadat, A., & Jung, D. W. (2022). Optimization of Wire Electric Discharge Machining (WEDM) process parameters for AISI 1045 Medium Carbon Steel using Taguchi Design of Experiments. Materials, 15(21), 7846. https://doi.org/10.3390/ma15217846
Zarmai, M., Ekere, N., Oduoza, C., & Amalu, E. (2015). Optimization of thermo-mechanical reliability of solder joints in crystalline silicon solar cell assembly. Microelectronics Reliability, 59, 117–125. https://doi.org/10.1016/j.microrel.2015.12.031
Zhang, F., Wang, X., Wu, M., Hou, X., Han, C., & Liu, Z. (2021). Optimization design of uncertain parameters for improving the stability of photovoltaic system. Journal of Power Sources, 521, 230959. https://doi.org/10.1016/j.jpowsour.2021.230959
Zhang, Z., Liu, M., Wang, L., Chen, T., Zhao, L., Hu, Y., & Xu, C. (2021). Optimization of indium recovery from waste crystalline silicon heterojunction solar cells by acid leaching. Solar Energy Materials and Solar Cells, 230, 111218. https://doi.org/10.1016/j.solmat.2021.111218