A Review on the Hybridization of the LM Algorithm for PV Modeling and Parameter Estimation

The modeling of photovoltaic (PV) systems serves as a critical component in assessing their electrical performance and enhancing their integration into broader energy frameworks. To tackle the challenges stemming from the highly nonlinear characteristics of PV model parameter identification, this paper offers an in-depth review of hybrid strategies that integrate the Levenberg-Marquardt (LM) algorithm with global metaheuristic methods. This study examines the theoretical basis of hybrid approaches and their advantages over pure metaheuristics, motivating the use of techniques like GA-LM, SA-LM, PSO-LM, and ABC-LM. A classification framework for these hybrids is presented, including evolutionary algorithms and swarm-based methods. Simulation results for the single-diode PV equivalent circuit demonstrate enhanced performance, with RMSE improvements ranging from 10% to 33% for hybrids like ABC-LM and PSO-LM compared to pure ABC and PSO, particularly under varying irradiances (600, 800, and 1000 W/m²). Hybrids also maintain low computational costs relative to high-CPU pure methods, offering better compromise scores. Finally, key challenges related to scalability, computational complexity, and irradiance-dependent accuracy are addressed, while highlighting emerging trends toward multi-objective and adaptive optimization frameworks. This review thus provides practical guidance for developing robust hybrid optimization methods tailored to PV system modeling and parameter identification.