Online Optimizing Plug-In Hybrid Energy Management Strategy for Autonomous Guidance and Drive-aware Scenarios

Recent advances in autonomous driving and vehicle connectivity help to ensure safety and comfort in various driving conditions. These trends have widenened the system boundary conditions for hybrid powertrain operation with driving trajectory planning hence offering potential to improve powertrain operational efficiency. This paper presents an energy management (EM) controller for a plug-in hybrid vehicle exploiting predicted velocity trajectory together with its integration in both autonomous longitudinal guidance and driver-aware scenarios. The driver-aware scenario uses Markov chain based stochastic modelling of driving characteristics. The proposed EM controller solves online, a discretized version of the fuel consumption minimization problem using direct methods transcribing the problem into a finite dimensional mixed boolean quadratic problem with polytopic constraints. The convex part of the resulting problem is solved using an active set method. Simulation results from different driving situations based on standard driving cycle and real world driving scenarios demonstrate the functionality of the controller and its flexbility to handle varying control objectives.