Comparison of adaptive and genetic fuzzy control methods in energy management of battery–supercapacitor hybrid energy systems

The performance of energy storage systems used in electric vehicles affects vehicle range, energy efficiency, and battery lifespan. The success of energy storage systems depends not only on factors such as the components used and system design, but also on the energy management strategy. In this thesis study, appropriate energy management strategies have been evaluated for hybrid energy storage systems in which lithium-ion batteries and supercapacitors are used together. The hybrid structure, combining the high energy density of batteries with the high-power density of supercapacitors, enables faster responses to sudden energy demands and reduces the load on the battery. Within the scope of the study, two different control algorithms were examined through the MATLAB/Simulink-based model developed. These algorithms are the Adaptive Fuzzy Control method and the Genetic Algorithm-based Fuzzy Control method. Both control strategies aim to optimize energy flow by managing energy sharing between the battery and the supercapacitor via the duty cycle. The performance of the applied control structures was evaluated based on criteria such as energy efficiency, fluctuations in battery current, and total cost value. The developed models and the analyses carried out serve as a guide for both academic studies and industrial applications. Both control methods provided an increase in efficiency compared to the uncontrolled method in passive topology. When the Adaptive and Genetic fuzzy algorithms were compared, it was observed that the Genetic Fuzzy Control algorithm gave better results in all performance criteria. In terms of total cost, which considers both the battery degradation cost and the energy efficiency cost together, the genetic algorithm showed 29.13% better performance compared to the passive connection, and 24.45% better performance compared to the adaptive control.