There are many different models to account for the physics driving the behavior of solar cells. We’ll start with the most basic first, and work our way toward increasing complexity.

Before we go too far, it’s worth specifying what we’re after in all of these models. Solar cell designers are inspecting the fine details of an I-V curve, which means they’re carrying out a non-linear optimization problem. Any parameters that we calculate have to match reality, be robust and repeatable, and give us similar measurements for slightly different data sets, all while taking a minimum time to compute.

There’s a trade-off between accuracy and simplicity, however. A simple model is what we started out with, but as we saw, for example, with the solar cell represented by a single diode, it couldn’t account for all the physical effects. The more complex the model, however, the more difficult it is to calculate, as well as to convey to other researchers.

Broadly speaking, there are 3 main approaches for determining the model parameters of a solar cell. The first is an analytical method, which uses the IV curve data to generate the parameters. Those types of methods are the ones we’ll be focusing on. They often involve approximations that can be inaccurate, or produce equations that are transcendental and extremely difficult to solve.

Another approach for determining model parameters involves parameter estimation and optimization. It is called a metaheuristic method because it tries to find the model parameters practically (i.e. heuristically or iteratively) while applying some intelligence and optimization (which adds the meta part). An example of a metaheuristic approach is a genetic algorithm modelling the solar cell as an artificial bee colony (don’t worry if this sounds very abstract—you’re not alone!). There are many, many other metaheuristic models.

The third approach to determining model parameters is a mixture of analytical and metaheuristic methods, trying to combine the best of both: the simplicity of the analytical methods with the power of the metaheuristic methods.

All told, there are a tremendous number of existing models (summarized in the table below), which are a testament to the creativity and ingenuity of the many researchers contributing to the field.

**ABCO** Artificial Bee Colony Optimization**ABSO** Artificial Bee Swarm Optimization**AE** Absolute Error**AGA** Adaptive Genetic Algorithm**AIS** Artificial Immune System**ANN** Artificial Neural Network**APE** Absolute Power Error**APSO** Particle Swarm Optimization with Adaptive Inertia Weight**APVE** Absolute Power and Voltage Error**BBO** Bio-Geography Based Optimization**BBO-M** Bio-Geography Based Optimization with Mutation**BFA** Bacterial Foraging Algorithm**BMO** Bird Mating Optimization**BPFPA** Bee Pollinated Flower Pollination Algorithm**CPSO** Chaos Particle Swarm Optimization**CPU** Central Processing Unit

Control**DDM** Double Diode Model**DE** Differential Evolution**DEIM** Differential Evolution with Integral Mutation**GA** Genetic Algorithm**GGHS** Grouping Based Global Harmony Search**GOTLBO** Generalized Oppositional Teaching Based Learning**HS** Harmony Search**IADE** Improved Adaptive DE**IAE** Individual Absolute Error**IBCPSO** PSO with Inverse Barrier Constraints**IGHS** Improved Global Harmony Search**IGHS** Innovative Global Harmony Search**IP** Interior Point**IPSO** Improved Particle Swarm Optimization

**ISDM** Improved Single Diode Model**JADE** Adaptive Differential Evolution**LM** Levenberg-Marquardt**LS** Least Square**MDDM** Modified Double Diode Model**MPCOA** Mutative-Scale Parallel Chaos Optimization**MSE** Mean Squared Error**N.E.** Not Extracting**NMS** Nelder-Mead Algorithm**NOCT** Nominal Operating Cell Temperature**NR** Newton-Raphson

Optimization**PDE** Penalty based DE**PS** Pattern Search**PSA** Parallel Swarm algorithm**R-JADE** Repaired Adaptive Differential Evolution**RADE** Repaired Adaptive Differential Evolution.**RMSE** Root Mean Squared Error**SA** Simulated Annealing**SBMO** Simplified Bird Mating Optimization**SDM** Single Diode Model**SIV** Suitability Index Variable**SSE** Sum of Squared Errors**STLBO** Simplified Teaching Learning Based Optimization

Strategies**TDM** Three Diode Model-STC: Standard Test Conditions**TLBO** Teaching Learning Based Optimization**TVIWAC-PSO** Particle Swarm Optimization with Time-Varying Inertia Weight and Acceleration Coefficients**VC-PSO** Particle Swarm Optimization with Velocity Clamping

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The following method is taken from Phang, Chan & Phillips’ 1984 paper entitled “Accurate analytical method for the extraction of solar cell model parameters,” published in Electronics Letters.
We start with the single-diode equation and include both a shunt and series resistance (we derived this earlier). However, we make the following definition to make the equation easier to write:

The diode equation becomes:

We can find the circuit parameters from the graph of the IV curve as shown below:

The following method is taken from Chan & Phang’s 1987 paper entitled “Analytical Methods for the Extraction of Solar-Cell Single- and Double-Diode Model Parameters from I- V Characteristics,” published in IEEE Transactions on Electron Devices. This model includes the physics of recombination as a second diode.
We start with the two-diode equation that includes both a shunt and series resistance (we derived this earlier). However, we make the following definition to make the equation easier to write:

The diode equation becomes:

The first approximate solution can be obtained from the following quadratic equation:

Where a, b and c are defined as

The series resistance can then be obtained by the solution to the quadratic equation:

Where A, B, C and D are defined as

The photocurrent varies as follows:

Where STC is the standard temperature condition, KI is the temperature coefficient for photocurrent, and G is irradiance.

The other solar cell parameters vary with temperature as follows:

Single-Diode Model with capacitance

Three Diode Model

Modified two-diode models

Drift-diffusion model

Multi-dimension diode model

Metaheuristic approaches to PV cell parameter estimation are basically different numerical optimisation methods. The undergoing optimisation is usually to minimize the error between a given fit to the parameters, and the original data. To put this into proper context, we will go through one example algorithm known as the Artificial Bee Colony (ABC) model, which takes inspiration from the behaviour of bees as they forage for food.

Any metaheuristic approach will define an objective function which it will then seek to optimize. In the case of the Artificial Bee Colony, it seeks to minimize the root mean square error, so its objective function is as follows:

Where Ii,exp is each experimental data point for the current, and Ii,est is each estimated data point for the current.

All the parameters that are available to adjust in order to match the data are gathered together into what’s called a decision vector. The ABC method can be applied to the single-diode model, which has the following decision vector:

The ABC algorithm starts with a population of food sources. Each of these “food sources” is one of the decision vectors described above. Their values are randomly and uniformly distributed between a set of defined boundaries.

The following three steps are then carried out until a “finish” criterion is met:

- Send out employed bees
- Choose food sources using onlooker bees
- Determine the scout bees

The employed bees represent solution attempts, so their number is the same as the number of food sources. The employed bees are hunting for the best food sources.

Each of the employed bees takes a random step through the decision vector (all while staying within the defined boundaries). The fitness parameter (our objective function, the RMSE above) is used to determine the quality, or nectar value, of a food source. If the employed bee has found a food source (solution) with a higher nectar value than its old one, then it forgets the old source and remembers the new source. In other words, it discards its old solution and keeps the new solution.

The role of onlooker bees is to watch from a distance and see which are the best food sources before joining the rank of employed bees. In practical terms, onlooker bees are then applied to the food sources using a probability function. The food sources with a higher nectar value have a higher probability of attracting onlooker bees. The onlooker bees then become employed bees, with new food sources randomly moved from the source they were initially attracted to.

Finally, we have the scout bees. The scout bees are there to watch and make sure the employed bees don’t waste their time pursuing poor-nectar food sources. These poor-nectar food sources are considered ones that can’t be improved through a given number of trials. If a nectar source’s fitness value (RMSE, above) doesn’t improve after a specified number of times,

While the above language sounds pretty far-removed from programming, it’s a useful way to construct and articulate the coding layers and moving parts that might otherwise seem very abstract. Linking to and being inspired by biology is a common practice in many complex optimization problems.

If you’re interested in seeing how the above steps are represented by equations, they’re included below.

- For Advanced Users -

Where lj is the lower bound and uj is the upper bound, and rand(0,1) is a random decimal value between 0 and 1.

The employed bees are then moved out from the initial food sources to try and find more fruitful nectar, by moving via the following equation:

The following example is taken from Jiang, L. L., Maskell, D. L., & Patra, J. C.’s 2013 paper entitled “Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm,” in Applied Energy.

Our hybrid algorithm example, as we’ve mentioned above, combines analytical solutions with iterative numerical approaches. Genetic algorithms are a broad class of methods that have been used to solve both constrained and unconstrained optimization problems.

The general approach is that there is a population of individuals who are repeatedly modified, and at each step, a group is randomly selected to be parents of the next generation.

There are three main rules used to form the next generation:

**Selection rules: those that select the parents****Crossover rules: those that combine two parents to make a baby****Mutation rules: those that randomly apply changes to individuals.**

If the genetic algorithm were used by itself to generate solutions for the IV curve parameters, then it would be an entirely numerical, metaheuristic approach. However, what the researchers in this example did was to evaluate a variety of scenarios where some parameters were determined by solving the equations analytically (similar to the methods discussed in the one and two-diode model sections), while the remainder were then found using a genetic algorithm. In one case, they determined most of the parameters of the decision vector through analytic means, then used the genetic algorithm to calculate the ideality factor, the series resistance, and the parallel resistance.

Such combined approaches allow the minimization of computational resources while maximizing the accuracy of the obtained parameters.

There are many other hybrid strategies employed by researchers. Some of the ones that use the single-diode model have been assembled in the table below for reference.

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