## 6 Discussion
### Prospective Research Directions  
Building upon the findings in the Section 5, this study emphasizes the importance of expanding research directions to address the identified constraints while leveraging advanced techniques and methods. The prospective research directions are as follows:  
1. **Advanced Control Techniques:** While the Section 5 demonstrates the feasibility of balancing energy demand using solar panels and battery storage systems, integrating advanced control frameworks could enhance system adaptability. Observer-based sliding-mode control frameworks [42] and discrete-time interval type-2 fuzzy Markov jump systems [43] can improve the robustness of the EV infrastructure by dynamically adjusting to fluctuations in energy demand and renewable energy output. These techniques can complement the PROMETHEE-based decision framework by providing real-time adjustments to charging schedules and resource allocations. 2. **Integration of Renewable Energy:** The integration of solar energy, as modeled in the previous section, is a critical step toward sustainable EV infrastructure. However, renewable energy sources like wind and solar are intermittent, requiring advanced IoT-enabled monitoring and control systems [44] for seamless integration. Future research could explore strategies to incorporate real-time data from IoT sensors to optimize energy flow between solar panels, batteries, and the grid. These systems can ensure a consistent power supply while maintaining grid stability during peak usage periods. 3. **Hybrid Decision-Making Systems:** The PROMETHEE method provides a robust framework for multi-criteria decision-making, but its integration with predictive models like fuzzy neural networks and machine learning [41] could further enhance decision accuracy. For instance, the insights from the solar panel and battery requirements calculated earlier could be extended by incorporating predictive analytics for seasonal variations in energy production and demand. This hybrid approach would enable proactive decision-making and optimal resource allocation, ensuring long-term sustainability. 4. **Real-Time Monitoring and IoT Integration:** The detailed load scheduling and sizing calculations in the Section 5 highlight the need for real-time monitoring to balance energy consumption and generation. IoT technologies [44] could be employed togather real-time data on energy usage, equipment performance, and environmental conditions. These data streams can feed into adaptive control models to dynamically optimize charging station operations, detect faults, and predict maintenance needs, significantly improving system efficiency. 5. **H\({}^{\infty}\) Control for System Stability:** The modeling section's focus on battery storage highlights the importance of system stability under fluctuating renewable energy inputs. H\({}^{\infty}\) control models [45] offer a robust approach to maintaining operational stability in hybrid energy systems with finite frequency specifications. Integrating these control models with the energy management strategies outlined earlier can ensure reliability and efficiency in high-demand scenarios, especially in large-scale deployments. 6. **Optimization of Charging Infrastructure:** While the current analysis favors larger-capacity stations for urban areas with higher demand, future research should examine cost-effective strategies for deploying smaller stations in rural and remote regions. Decision-making frameworks could be enhanced to factor in localized variables such as solar irradiance, population density, and travel patterns. PROMETHEE, combined with location-specific data, could optimize infrastructure placement to maximize accessibility and energy efficiency. 7. **Economic and Environmental Trade-Offs:** The environmental and cost analyses in the modeling section reveal the importance of balancing financial feasibility with sustainability goals.