A Comprehensive Review of Parameter Estimation Methods for Photovoltaic Cells via Optimization Techniques

Accurate estimation of Photovoltaic (PV) cell and module parameters is essential for reliable modeling, simulation, and optimization. The present study offers a comprehensive overview of the main methodologies used for PV parameter estimation, including analytical, numerical, and metaheuristic approaches. Analytical methods offer fast and interpretable solutions but are often limited in accuracy under complex or noisy conditions. Numerical methods improve precision through iterative refinement, though a good initial guess is required and can be sensitive to local minima. Metaheuristic algorithms, encompassing classical evolutionary techniques and recently enhanced variants, demonstrate robust performance in complex, nonlinear, and multimodal problems, often achieving lower Root Mean Square Error (RMSE) values and improved convergence stability. This study highlights the strengths, limitations, and comparative performance of each approach, identifies existing research gaps, and discusses promising directions such as hybrid strategies, adaptive algorithms, and the integration of machine learning and surrogate models to enhance accuracy and computational efficiency. By synthesizing these insights, this review aims to guide researchers and practitioners in selecting and applying effective parameter estimation techniques for PV systems.