The growing demand for efficient and reliable energy storage systems has led to increased research and development in the field of advanced control strategies. This research evaluates and compares the effectiveness of advanced control strategies such as Proportional and Integral controller (PI), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for energy storage systems employing a DC-DC bi-directional converter. ANFIS control combines the strengths of fuzzy logic and neural networks to provide a hybrid approach, particularly appealing for its adaptability and capacity to handle complex and uncertain operational environments. Energy storage systems have emerged as vital components in modern energy management, and they play a pivotal role in addressing renewable energy intermittency, enhancing grid stability, and efficiently managing energy demands. At the heart of these systems lies the DC-DC bi-directional buck/boost converter, which plays a critical component in enabling bidirectional energy transfer between the storage system (lead acid battery) and the DC source. This research employs a simulation-based methodology for a comprehensive evaluation and comparison of these control strategies. The aim is to provide valuable insights into the accuracy, stability, control complexity and suitability of various control approaches in optimizing the operation of such systems.
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Kalaiyarasan et al. (2024) studied this question.
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