Optimal smart contracts for controlling the environment in electric vehicles based on an Internet of Things network

The scientific community has recently focused on intelligent models for predicting and optimizing EV energy management. Despite numerous studies in energy management optimization, there is a critical need to address the trade-off between energy consumption and occupant comfort. Existing IoT systems face challenges in data analytics security and authenticity, highlighting the need for contemporary models to overcome data privacy and cost-related issues. This study introduces a smart contract model based on optimization and control modules, aiming to manage energy consumption while satisfying user comfort requirements intelligently. Introducing a smart contract model with hierarchical layers—prediction, optimization, control, and Blockchain—the proposed approach intelligently manages energy consumption while meeting user comfort requirements. Utilizing a Kalman filter for prediction and the BAT algorithm for optimization, the model integrates modules to tailor user preferences and enhance comfort. The synergy between the optimization module and a convolutional FLC enhances system performance, ensuring minimized energy usage and elevated user comfort levels. The study also evaluates the model's implementation of the Hyperledger Fabric network, assessing outcomes regarding caliper, latency, throughput, and resource utilization.