A novel charging methodology for enhancing the reliability of solar gel batteries in renewable energy systems

Raja Yahmadi, Kais Brik

Abstract


The solar gel batteries are widely used in Renewable Energy (RE) systems. However, harsh climatic and operational conditions pose major challenges in terms of reliability, safety, and performance. Improving battery reliability and performance requires a detailed understanding of the physical, electrochemical, and chemical processes occurring during charging and discharging cycles. In this study, the aging mechanisms of solar gel batteries are analyzed using Failure Mode and Effects Analysis (FMEA) to identify critical failure modes and their causes. Common charging profiles, including Constant Current (CC), Constant Voltage (CV), Constant Current-Constant Voltage CC–CV, Constant Current-Constant Voltage- Constant Current CC–CV–CC, and Pulse Current (PC), are first analyzed based on the battery voltage response to assess their influence on performance and charging time. A fuzzy logic based approach is used to compute the Fuzzy Risk Priority Number (FRPN) using Occurrence (O), Severity (S), and Non-detection (ND) as input parameters. The charging profiles are then evaluated by combining reliability factor (FRPN) and charging time using multi-criteria methods including Combined score, TOPSIS, and Pareto analysis. The CC-CV-CC profile shows the best compromise among the conventional profiles with a Combined score of 0.465 and a TOPSIS score of 0.986. In addition, a new charging profile derived from PC and CC-CV-CC is proposed to mitigate aging mechanisms while optimizing charging time, achieving the highest TOPSIS score (0.988) and lying on the Pareto front. The results demonstrate an improved balance between charging efficiency and battery reliability for RE systems.

 

Received: 25 January 2026

Accepted: 12 April 2026

Published: 22 April 2026


Keywords


Battery degradation; Charging profiles; Failure Mode and Effect Analysis; Fuzzy logic method; multi-criteria decision analysis; Solar gel battery.

Full Text:

PDF

References


R. Yahmadi, K. Brik, and F. Ben Ammar, “Improvement of energy management system for maximum battery reliability in standalone photovoltaic applications,” Journal of Electrical Engineering, vol. 18, no. 2, pp. 81–91, 2018.

W. Jing, C. H. Lai, D. K. X. Ling, W. S. H. Wong, and M. L. D. Wong, “Battery lifetime enhancement via smart hybrid energy storage plug-in module in standalone photovoltaic power system,” Journal of Energy Storage, vol. 21, pp. 586–598, Feb. 2019, doi: https://doi.org/10.1016/j.est.2018.12.007.

M. T. Yeshalem and B. Khan, “Design of an off-grid hybrid PV/wind power system for remote mobile base station: A case study,” AIMS Energy, vol. 5, no. 1, pp. 96–112, 2017, doi: https://doi.org/10.3934/energy.2017.1.96.

R. Yahmadi, K. Brik, and F. Ben Ammar, “Research of critical causes and improvement of energy storage system reliability in power electronic applications,” International Journal of Hydrogen Energy, vol. 42, no. 13, pp. 8765–8776, Mar. 2017, doi: https://doi.org/10.1016/j.ijhydene.2016.10.034.

R. Yahmadi, K. Brik, and F. ben Ammar, “Fuzzy risk priority number assessment for solar gel battery manufacturing defects,” Engineering Failure Analysis, vol. 124, 2021, doi: https://doi.org/10.1016/j.engfailanal.2021.105327.

J. Balaraju, M. Govinda Raj, and C. S. Murthy, “Fuzzy-FMEA risk evaluation approach for LHD machine-A case study,” Journal of Sustainable Mining, vol. 18, no. 4, pp. 257–268, Nov. 2019, doi: https://doi.org/10.1016/j.jsm.2019.08.002.

E. Freeman, D. Occello, and F. Barnes, “Energy storage for electrical systems in the USA,” AIMS Energy, vol. 4, no. 6, pp. 856–875, 2016, doi: https://doi.org/10.3934/energy.2016.6.856.

A. M. S. M. H. S. Attanayaka, J. P. Karunadasa, and K. T. M. U. Hemapala, “Estimation of state of charge for lithium-ion batteries - A Review,” AIMS Energy, vol. 7, no. 2, pp. 186–210, 2019, doi: https://doi.org/10.3934/energy.2019.2.186.

H. Bizhani, S. Kamal, H. Rezazadeh, and S. Muyeen, “A Comprehensive Comparison of a Lead-Acid Battery Electro-Thermal Performance Considering Different Charging Profiles,” 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Sep. 2021, doi: https://doi.org/10.1109/gucon50781.2021.9573724.

M. James, J. Grummett, M. Rowan, and J. Newman, “Application of pulse charging techniques to submarine lead-acid batteries,” Journal of Power Sources, vol. 162, no. 2, pp. 878–883, Nov. 2006, doi: https://doi.org/10.1016/j.jpowsour.2005.02.018.

S. Lavety, R. K. Keshri, and M. A. Chaudhari, “Evaluation of Charging Strategies for Valve Regulated Lead-Acid Battery,” IEEE Access, vol. 8, pp. 164747–164761, 2020, doi: https://doi.org/10.1109/access.2020.3022235.

R. K. Sharma, D. Kumar, and P. Kumar, “Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling,” International Journal of Quality & Reliability Management, vol. 22, no. 9, pp. 986–1004, Dec. 2005, doi: https://doi.org/10.1108/02656710510625248.

R. R. Elbanna, M. H. ElMessmary, H. Diab, and M. Abdelsalam, “A smart hybrid optimization model for DSSE in renewable energy-powered distribution networks,” Renewable Energy and Sustainable Development, vol. 11, no. 2, p. 314, Sep. 2025, doi: https://doi.org/10.21622/resd.2025.11.2.1271.

P. S. Kumar, C. Chandrika, P. K. Rao, P. K. Rao, and S. K. Oruganti, “Interpretable hybrid machine learning models for renewable-powered smart grid stability prediction,” Renewable Energy and Sustainable Development, vol. 11, no. 2, p. 397, Oct. 2025, doi: https://doi.org/10.21622/resd.2025.11.2.1509.

R. Yahmadi, K. Brik, and F. Ben Ammar, “Sizing and improving performances of a photovoltaic water pumping system for irrigation in Jerid Tunisia,” 2019 10th International Renewable Energy Congress (IREC), pp. 1–6, Mar. 2019, doi: https://doi.org/10.1109/irec.2019.8754534.

S. Boral, I. Howard, S. K. Chaturvedi, K. McKee, and V. N. A. Naikan, “An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA,” Engineering Failure Analysis, vol. 108, p. 104195, Jan. 2020, doi: https://doi.org/10.1016/j.engfailanal.2019.104195.

W. Song, X. Ming, Z. Wu, and B. Zhu, “A rough TOPSIS Approach for Failure Mode and Effects Analysis in Uncertain Environments,” Quality and Reliability Engineering International, vol. 30, no. 4, pp. 473–486, Feb. 2013, doi: https://doi.org/10.1002/qre.1500.

A. A. Supciller and N. Abali, “Occupational Health and Safety Within the Scope of Risk Analysis with Fuzzy Proportional Risk Assessment Technique (Fuzzy Prat),” Quality and Reliability Engineering International, vol. 31, no. 7, pp. 1137–1150, Oct. 2015, doi: https://doi.org/10.1002/qre.1908.

G. Di Bona, A. Silvestri, A. Forcina, and A. Petrillo, “Total efficient risk priority number (TERPN): a new method for risk assessment,” Journal of Risk Research, vol. 21, no. 11, pp. 1384–1408, Apr. 2017, doi: https://doi.org/10.1080/13669877.2017.1307260.

N. G. Mutlu and S. Altuntas, “Risk analysis for occupational safety and health in the textile industry: Integration of FMEA, FTA, and BIFPET methods,” International Journal of Industrial Ergonomics, vol. 72, pp. 222–240, Jul. 2019, doi: https://doi.org/10.1016/j.ergon.2019.05.013.

C.-F. Chi, D. Sigmund, and M. O. Astardi, “Classification Scheme for Root Cause and Failure Modes and Effects Analysis (FMEA) of Passenger Vehicle Recalls,” Reliability Engineering & System Safety, p. 106929, Mar. 2020, doi: https://doi.org/10.1016/j.ress.2020.106929.

K. Jenab, R. M. Blecher, and S. Moslehpour, “SRB Field Joints Failure Analysis Using Fuzzy FMEA,” International Journal of Physics and Astronomy , vol. 3, no. 1, 2015, doi: https://doi.org/10.15640/ijpa.v3n1a1.

H.-C. Liu, X. Fan, P. Li, and Y. Chen, “Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment,” Engineering Applications of Artificial Intelligence, vol. 34, pp. 168–177, Sep. 2014, doi: https://doi.org/10.1016/j.engappai.2014.04.011.

F. Tamtam and A. Tourabi, “Innovative fuzzy VIKOR approach for green hydrogen technologies assessment: implications for sustainable energy development,” Renewable Energy and Sustainable Development, vol. 11, no. 2, p. 229, Aug. 2025, doi: https://doi.org/10.21622/resd.2025.11.2.1283.

R. T. Moyo, M. Dewa, H. F. M. Romero, V. A. Gómez, J. I. M. Aragonés, and L. Hernández-Callejo, “An adaptive neuro-fuzzy inference scheme for defect detection and classification of solar PV cells,” Renewable Energy and Sustainable Development, vol. 10, no. 2, p. 218, Sep. 2024, doi: https://doi.org/10.21622/resd.2024.10.2.929.

M. Kumru and P. Y. Kumru, “Fuzzy FMEA application to improve purchasing process in a public hospital,” Applied Soft Computing, vol. 13, no. 1, pp. 721–733, Jan. 2013, doi: https://doi.org/10.1016/j.asoc.2012.08.007.

M. Braglia, M. Frosolini, and R. Montanari, “Fuzzy criticality assessment model for failure modes and effects analysis,” International Journal of Quality & Reliability Management, vol. 20, no. 4, pp. 503–524, Jun. 2003, doi: https://doi.org/10.1108/02656710310468687.

H. Shirouyehzad, M. Badakhshian, R. Dabestani, and H. Panjehfouladgaran, “Fuzzy FMEA Analysis For Identification And Control Of Failure Preferences In ERP Implementation,” Journal of Mathematics and Computer Science, vol. 01, no. 04, pp. 366–376, Dec. 2010, doi: https://doi.org/10.22436/jmcs.001.04.14.

S. Vinodh, S. Aravindraj, R. Sathya Narayanan, and N. Yogeshwaran, “Fuzzy assessment of FMEA for rotary switches: a case study,” The TQM Journal, vol. 24, no. 5, pp. 461–475, Aug. 2012, doi: https://doi.org/10.1108/17542731211261601.

L. Song and J. W. Evans, “Electrochemical-Thermal Model of Lithium Polymer Batteries,” Journal of The Electrochemical Society, vol. 147, no. 6, pp. 2086–2095, 2000, doi: https://doi.org/10.1149/1.1393490.

M. Chen and G. A. Rincon-Mora, “Accurate Electrical Battery Model Capable of Predicting Runtime and I–V Performance,” IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 504–511, Jun. 2006, doi: https://doi.org/10.1109/tec.2006.874229.

R. Islam, S.-Y. Park, and B. Balasingam, “Unification of Internal Resistance Estimation Methods for Li-Ion Batteries Using Hysteresis-Free Equivalent Circuit Models,” Batteries, vol. 6, no. 2, p. 32, Jun. 2020, doi: https://doi.org/10.3390/batteries6020032.

K. Brik and F. ben Ammar, “Causal tree analysis of depth degradation of the lead acid battery,” Journal of Power Sources, vol. 228, pp. 39–46, Apr. 2013, doi: https://doi.org/10.1016/j.jpowsour.2012.10.088.

S. Madani, E. Schaltz, and S. Knudsen Kær, “An Electrical Equivalent Circuit Model of a Lithium Titanate Oxide Battery,” Batteries, vol. 5, no. 1, p. 31, Mar. 2019, doi: https://doi.org/10.3390/batteries5010031.

Y. Yu et al., “Constructing Accurate Equivalent Electrical Circuit Models of Lithium Iron Phosphate and Lead–Acid Battery Cells for Solar Home System Applications,” Energies, vol. 11, no. 9, p. 2305, Sep. 2018, doi: https://doi.org/10.3390/en11092305.

R. Yahmadi, K. Brik, and F. ben Ammar, “Causal tree analysis for quality control of the lead acid battery manufacturing process,” International Journal of Energy Research, vol. 42, no. 4, pp. 1738–1759, Jan. 2018, doi: https://doi.org/10.1002/er.3987.

R. Yahmadi, K. Brik, and F. Ammar, “Analysis Approach of the Formation Current Profiles Impact on the Lead Acid Battery Manufacturing,” Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), vol. 9, no. 3, pp. 231–240, Feb. 2017, doi: https://doi.org/10.2174/2352096509666161110105048.

M. James, J. Grummett, M. Rowan, and J. Newman, “Application of pulse charging techniques to submarine lead-acid batteries,” Journal of Power Sources, vol. 162, no. 2, pp. 878–883, Nov. 2006, doi: https://doi.org/10.1016/j.jpowsour.2005.02.018.

N. Chanamool and T. Naenna, “Fuzzy FMEA application to improve decision-making process in an emergency department,” Applied Soft Computing, vol. 43, no. 4, pp. 441–453, 2016, doi: https://doi.org/10.1016/j.asoc.2016.01.007.

W. Song, X. Ming, Z. Wu, and B. Zhu, “A rough TOPSIS Approach for Failure Mode and Effects Analysis in Uncertain Environments,” Quality and Reliability Engineering International, vol. 30, no. 4, pp. 473–486, Feb. 2013, doi: https://doi.org/10.1002/qre.1500.

H. Taherdoost and M. Madanchian, “A Comprehensive Survey and Literature Review on TOPSIS,” International Journal of Service Science, Management, Engineering, and Technology, vol. 15, no. 1, pp. 1–65, Aug. 2024, doi: https://doi.org/10.4018/ijssmet.347947.




DOI: https://dx.doi.org/10.21622/resd.2026.12.1.1915

Refbacks



Copyright (c) 2026 Raja Yahmadi, Kais Brik

Renewable Energy and Sustainable Development

E-ISSN: 2356-8569

P-ISSN: 2356-8518

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

resd@aast.edu