Energy management optimization control with the consideration of the driving style for connected hybrid electric vehicles in mixed driving scenarios

Considering the complexity and variability of the actual traffic environment, aiming at the influence of driving styles on the optimal control of hybrid electric vehicles (HEVs) energy management, this paper proposes a speed planning and energy management strategy (EMS) considering driving style recognition in mixed driving scenarios under the framework of model predictive control (MPC). The proposed strategy uses principal component analysis (PCA) to reduce the dimension of the characteristic parameters and fuzzy C-means (FCM) methods to cluster driving styles. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) are used to obtain information on the preceding vehicle and traffic lights to classify the hybrid driving scenario. In the different scenarios, the objective function considering traffic safety is constructed to plan the host vehicle’s speed based on the speed prediction of the preceding vehicle. A sequential quadratic programming optimization algorithm (SQP) distributes engine and motor power. Simulation results show that the proposed EMS can achieve good fuel economy under the premise of driving safety.