Model Predictive Control with AI Based Predictors for Energy Management in Hybrid Vehicles

Linear Parameter-Varying Model Predictive Control has been shown to provide an effective design approach for developing an Energy Management System for Hybrid Electric Vehicles. However, despite the good performance achieved, modern data-driven Artificial Intelligence methods can improve the performance due to the approximations involved in generating the models. An approach is described for reducing the sub-optimality due to the modelling problem in predictive control using an AI algorithm belonging to the class of data-driven Machine Learning techniques. This provides more effective vehicle speed and driver torque demand predictions that are used within the predictive controller. The proposed combined policy is compared with a baseline control design developed using the well-known Equivalent Consumption Minimization Strategy and an MPC neglecting the use of AI predictors.