Optimizing Electric Vehicle Charging and Energy Consumption: Routing, Booking, and Real-Time Traffic Integration

Modern transportation has been revolutionized by smart technology and vehicle automation, and electric vehicles (EVs) are essential to developing connected, eco-friendly, and effective mobility solutions. Green transportation promotion is more crucial than ever as smart cities develop. With their zero emissions and ability to integrate with smart infrastructure, EVs are central to reducing pollution and supporting sustainable travel. However, challenges like efficient route planning, charging management, and travel cost still hinder widespread EV adoption. Overcoming these issues is essential to fully realizing the benefits of EVs in future smart cities. Continued innovation in intelligent routing systems, real-time traffic analysis, and adaptive charging strategies will be crucial. These advancements will ensure that EVs not only meet current transportation demands but also align with long-term sustainability goals. In this context, this thesis addresses the problem of EV routing and charging by calculating energy consumption, managing charging station waiting queues, and considering real-time traffic conditions, all to minimize total travel time. Additionally, it focuses on selecting the optimal charging station based on real-time charging prices and travel time, aiming to reduce the overall travel cost. This thesis advances beyond existing methodologies by simultaneously addressing the minimization of travel time and total travel cost, as well as the maximization of battery efficiency for electric vehicles, thereby promoting optimal vehicle utilization. To solve these challenges, we first develop a Mixed Integer Linear Programming (MILP) model that provides exact and rigorous solutions for small-scale instances. Both the travel time minimization and cost minimization problems are mathematically formulated within this MILP framework, enabling optimal decision-making under several constraints. Furthermore, due to the computational complexity of the MILP model for large-scale instances, we develop two heuristic approaches to ensure scalability. The first, MT2T (Minimizing Total Travel Time), focuses on reducing overall travel time and is capable of handling large-scale scenarios efficiently. The second, MTC (Minimizing Travel Cost), is designed to minimize total travel costs by incorporating real-time charging prices while maintaining computational feasibility for larger networks. This thesis represents a significant step toward unlocking the full potential of intelligent electric vehicles (IEVs) by optimizing both travel time and total travel cost. Through the proposed approaches, the research offers valuable insights into the efficient utilization of EVs, thereby contributing to the development of sustainable and intelligent transportation systems. By addressing key challenges such as real-time traffic conditions, energy consumption, and dynamic charging costs, this work provides a holistic approach to EV routing and charging. The findings of this thesis can serve as a foundation for future research and practical implementations in smart city transportation planning.