Optimizing Control in Smart Electric Heavy Vehicles using Adaptive Q-Learning with Computational Complexity Analysis

Analysis about the advanced motion control strategies for Electric Heavy Vehicles (EHVs), focusing on adaptability, efficiency, and computational feasibility is carried out in this proposed work. It compares traditional and modern methods, including PID, MPC, Fuzzy Logic, SMC, Adaptive Control, and AI-based techniques. Emphasis is placed on integrating AI and IoT for real-time monitoring, predictive maintenance, and smart decision-making. The hike in the cost for computing skills makes it not feasible for embedded applications. The proposed work meets the complexity and performance requirements with the implication of Bellman based Adaptive Q Learning algorithm which will balance the complexity while computing and increased energy efficiency in a long term basis. This work will necessitate the selection of best control strategies for Heavy Electric Vehicles (HEV) with all the significance measures like safety, energy optimization and scalability. The challenges that could occur while implementing, such as limitations in component requirement is specified. The future scope of the work is in to edge computing and communication protocols between Vehicles to Everything.