Enhancing Energy Efficiency in Hybrid Vehicles Using Model Predictive Control with Reinforcement Learning

In order to improve hybrid car energy efficiency, this research looks into integrating Reinforcement Learning (RL) and Model Predictive Control (MPC). Real-world settings present challenges for traditional control systems because of uncertainties and variability. Through the integration of RL's flexibility and MPC's predictive power, this study seeks to create novel control algorithms that enhance hybrid electric vehicles' energy management. Through the use of real-time environmental data, the suggested approach enables cars to dynamically modify their control strategies, enabling effective power distribution between electric propulsion systems and combustion engines. To show how the combined MPC-RL framework can increase energy efficiency while still meeting performance standards, case studies and simulation data are provided. By enhancing energy-efficient hybrid vehicle control systems, this research helps to promote environmentally friendly transportation methods and lessen the influence on the environment.