###### Abstract
The single diode model with capacitance [10], drift-diffusion model [11], three-diode model [12; 1], multi-dimension diode model [13], and modified two diode models are some examples of different improved representations of PV cells. An improved model was applied for organic solar cells in [14]. Reference [15] reviews the methods that were developed for finding the optimal parameters of various solar cell models. As the need of finding the best model is of interest, recently, a modified two diode model was presented to cover the research gap of modeling the behavior of MCS solar cells, especially under low illumination conditions. That resulted in a need for more accurate models; for that reason, the modified DDM (MDDM) was proposed [14]. Here a modified TDM (MTDM) is explored to describe the physics of this type of PV cells more precisely. On the other hand, these methods were sought to arouse more accurate estimation techniques for the calculation of the parameters of each model. The main reason for this its large effect on the efficiency and maximum power point tracking computations. These estimation methods are split into three main categories, namely, analytical approaches, meta-heuristic optimization techniques, and hybrid methods from the two previous categories [15]. Finding the parameters of these models is of interest to many researchers. These algorithms have been applied for optimizing the PV cells parameters of SDM and DDM but there is a small number of works in this field for TDM and smaller for MDDM and almost no one for MTDM. The recent advanced methods for parameter estimation of PV cells can be summarized as follows:  
* In [16], the authors estimate the DDM, MDDM, and TDM parameters by using the moth-flame optimizer. * In [17], the bacterial foraging (BF) algorithm was developed to estimate the parameters for two operating conditions (normal and shading conditions);
* In [18], the generalized oppositional teaching-learning optimizer was employed to estimate the parameters and speed up the conversion process. In [19], two methods are used to estimate the PV cell parameters based on differential evolution accomplished with an iteratively updated mutation effect and the other one is a hybrid evolutionary algorithm. It was noticed that these two methods have good performance and high accuracy. * In [20], another method called the flower pollination algorithm was proposed for low solar irradiance levels. The estimated and experimental results are almost identical. * The artificial bee colony algorithm was employed in [21] to identify solar cell parameters. * In [22], the artificial bee swarm optimizer was used for parameter identification based on the PV cell single- and double- diode models. * A harmony search-based algorithm was introduced to estimate the unidentified solar cell parameters with the usage of single- and double-diode models in [23];
* A simulated annealing algorithm [24] estimated the parameters of different solar cell models, namely single-, double-diode models and photovoltaic module. They help to verify the proposed approach outcomes. * The pattern search [25] and genetic algorithms [26] estimated the parameters of SDM and DDM. * Bird mating optimizer [27] estimates the parameters of a PV solar array that is modeled by SDM. * In [6], a modified DDM analysis of MCSSC at low illumination levels was presented. * In [28], a procedure based on three points approach was developed for PV parameters estimation. * In [29], the PV crystalline-silicon modules are represented by various parameters with a single exponential model. * In [30], another optimization algorithm called the moth-search optimizer was developed for the parameter estimation of three junction PV panels. * In [31], an improved shuffled complex evolution algorithm was developed for estimating the parameters of solar cells. * In [32], the coyote optimization algorithm is proposed for parameter estimation of the three-diode model of solar cell and models.