## 1 Introduction
Additionally, interval type-2 fuzzy logic-controlled systems have enhanced charging quality and reduced harmonic distortions during bidirectional operations [13]. The adoption of plug-in electric vehicles has also driven advancements in nonlinear control solutions and genetic optimization algorithms, improving system stability and the longevity of critical components like batteries and electrolyzers [14]. In the realm of mobility, adaptive control algorithms have optimized energy efficiency and regenerative braking systems while integrating EVs with smart grids and renewable energy sources [15]. In microgrids with high renewable penetration, hybrid energy management systems have been instrumental in frequency regulation and stability, employing fuzzy logic and deep reinforcement learning to adapt to random disturbances [16; 17]. Fuzzy logic has also proven to be a powerful tool for managing battery charge/discharge in intelligent energy systems, optimizing energy costs and charging strategies [18]. The planning of grid-connected low-voltage direct current systems has benefited from multi-objective approaches that consider EV demand response, achieving a balance between cost and carbon emissions through fuzzy optimization methods [19]. In hybrid energy storage systems, adaptive low-pass filter-based strategies have extended battery life and improved energy distribution in EV-equipped systems [3; 20]. Advanced controllers, such as adaptive integral sliding mode controllers, have demonstrated their ability to reduce power fluctuations in hybrid systems with multiple energy storage sources [21]. Predictive strategies based on Markov chain models have effectively managed hybrid systems of batteries and supercapacitors [22]. For wind power plants, enhanced kinetic energy control and power distribution methods have mitigated energy fluctuations and optimized the use of batteries and capacitors [23; 24]. Moreover, combined primary frequency regulation and torsional vibration control methods have reduced low-speed shaft fatigue in wind turbines while maintaining frequency stability [25]. Additionally, advanced DC-link voltage control strategies and standalone systems have improved power quality and robustness in wind energy systems [26; 27]. In microgrids with large-scale renewable generation, state-of-charge-based adaptive controls have facilitated smooth transitions between operating modes, mitigating transient impacts [28]. The combination of renewable energy sources and hybrid systems has driven technologies such as solid oxide fuel cells, which store excess energy as hydrogen and ensure continuity in systems with low renewable generation [29]. Furthermore, fault detection in PV systems using deep learning techniques has significantly enhanced their reliability and operational efficiency [30]. Despite significant advancements in energy management for V2G systems, several critical gaps remain unaddressed in the literature. Current control strategies leveraging fuzzy logic and evolutionary optimization [1; 4; 6] have successfully enhanced grid stability in networks with high renewable energy penetration. However, these studies often fail to address the unique operational challenges of weak low-voltage grids characterized by limited capacity, high sensitivity to power fluctuations, and the intermittent nature of PV generation. These grids require tailored solutions capable of dynamically managing variability and ensuring stability under real-world conditions. Existing studies have proposed advanced power smoothing methods [2; 11], yet few provide a comprehensive evaluation of multiple filtering techniques, such as low-pass, moving average, and ramp rate filters, particularly when optimized with fuzzy logic. This lack of systematic comparison under realistic conditions limits their practical applicability, especially in weak low-voltage networks where environmental and operational constraints amplify power variability.