Tarik Hawsawi

This work introduces the utilization of Edge Impulse in the context of EV battery RUL prediction. The proposed method enables efficient deployment of machine learning models directly onto EVs, facilitating real-time monitoring and adaptive maintenance strategies. TensorFlow, a robust machine learning framework known for its scalability and efficiency, is incorporated, enhancing the predictive capabilities of the proposed method.

1. Introduction The global energy landscape is currently experiencing a transformative shift, driven by the urgent need to combat climate change and ensure the availability of sustainable energy sources for future generations [1]. Within this context, solar energy has emerged as a central player, offering a vast and eco-friendly alternative to conventional fossil fuels.

5. Review of Case Studies: Flying Vehicle Routing Optimization Several case studies have been conducted to explore the application of GAs for flying vehicle routing optimization. These studies aim to address the unique challenges associated with routing UAVs or drones, considering factors such as airspace regulations, battery limitations, and varying mission objectives. The distribution of these studies considered in this work over the years is depicted in Figure 5.