I am working on developing different EMSs for optimal load sharing among energy resources. I have developed a fuzzy-based EMS but I want to improve my results by utilizing GA to optimize and modify the fuzzy rules in MATLAB. Any help or material related to this query will be highly appreciated. Thanks
Hi Mubashir Rasool
There are many articles related to the GA-optimized fuzzy rules found here:
https://www.sciencedirect.com/search?qs=Genetic%20algorithm%20fuzzy%20rules
First, it is advisable to have a working FIS that is designed according to the standard human expert knowledge on the energy management of EVs.
Then, you can dictate the GA to fine-tune from the existing FIS such that your defined/desired energy cost performance index is minimized.
To tune the fuzzy rules for energy management of EVs using Genetic Algorithm in MATLAB, you can follow these steps:
1/Define the fuzzy rules: First, you need to define the fuzzy rules for your energy management system. These rules should be based on your knowledge and experience of the system. You can use the Fuzzy Logic Toolbox in MATLAB to define the fuzzy rules.
2/Define the fitness function: The fitness function is a measure of how well the fuzzy rules are performing. In your case, the fitness function should be based on the performance of the energy management system, such as the battery state of charge, fuel economy, or any other relevant metric.
3/Initialize the population: The population consists of a set of fuzzy rules, where each rule is represented by a set of parameters. You can initialize the population randomly or based on your knowledge of the system.
4/Evaluate the fitness of each individual: Evaluate the fitness of each individual in the population using the fitness function defined in step 2.
5/Select individuals for reproduction: Select the best individuals from the population for reproduction. The selection process can be based on different criteria, such as fitness proportionate selection or tournament selection.
6/Apply genetic operators: Apply genetic operators, such as crossover and mutation, to create new individuals from the selected individuals.
7/Evaluate the fitness of the new individuals: Evaluate the fitness of the new individuals created in step 6 using the fitness function.
Repeat steps 5 to 7 until convergence: Repeat steps 5 to 7 until the population converges to a set of optimal fuzzy rules.
thankyou for your valuable answers, plz do share any supporting file if available
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