Multi-algorithm intelligent control using ANN-TSMC and fuzzy logic for enhanced performance of PV and V2G Systems

Abstract

Abstract The growing global energy demand has placed an unprecedented strain on conventional power grids, underscoring the urgent need for sustainable and resilient energy solutions. Renewable energy sources, particularly solar power offers a promising alternative. However, their inherent intermittency and variability pose significant challenges to grid stability, leading to potential outages and inefficiencies. Vehicle-to-Grid (V2G) technology has emerged as an effective solution by enabling bidirectional power flow between electric vehicles (EVs) and the grid, thereby providing ancillary services such as peak shaving and load balancing. This study presented a smart charging framework that integrates renewable energy-powered Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) systems with advanced control algorithms to foster a sustainable and efficient EV charging infrastructure. At the core of this framework is a hierarchical control system where a low-level Artificial Neural Network–Terminal Sliding Mode Control (ANN-TSMC) controller maximizes solar energy capture and a high-level Fuzzy Logic Controller (FLC) provides strategic energy management. The efficacy of this integrated approach was confirmed through comprehensive simulations. The ANN-TSMC controller demonstrated superior performance, delivering a 3.6% higher energy capture from the PV system compared to the conventional Perturb and Observe (P&O) method. Simultaneously, the FLC’s intelligent management of charging and discharging cycles proved highly effective, reducing peak reliance on conventional power by 20%. The system operated at a high charging efficiency of 94% with a rapid response time of 0.12 s. Furthermore, by optimizing charging protocols and mitigating battery stress, the framework offers an estimated 18.5% extension in battery lifespan, a critical factor for both economic viability and sustainability. This research confirms that the strategic integration of hierarchical intelligent controls with renewable energy sources is a highly effective method for advancing EV charging technology. The proposed framework provides a robust path toward developing resilient, grid-friendly charging infrastructures that enhance renewable energy utilization and promote intelligent battery management.