A Stochastic Particle Filter Energy Optimization Approach for Power-Split Trajectory Planning for Hybrid Electric Autonomous Vehicles

In recent years, all major car manufacturers have started to introduce predictive functionalities based on an electronic horizon for the autonomous on-highway operation of their vehicles. Using the Advanced Driver Assistance Systems (ADAS) for anticipatory driving is a fundamental approach to significantly reduce the fuel consumption and pollutant emissions of the internal combustion engines. Today's Adaptive Cruise Control (ACC) systems try to maintain a constant speed selected by the driver without regarding the energy consumption of the vehicle. There is, however, a degree of freedom to apply cruise speed limits without direct driver involvement in order to save propulsion energy in an Autonomous Vehicle (AV). This work presents a novel velocity and energy optimization method for AV hybrid electric vehicles (HEVs) by using a stochastic optimization technique. By applying Particle Filters (PFs) in a routine of Stochastic Dynamic Programming (SDP) to solve the power split efficiently. The sweet-point operation of the powertrain is calculated by probability hypothesis densities along the distance-based prediction horizon. The optimization approach shows a cost trade-off between horizon resolution, length, iterative combinatorial optimality, and computational efficiency. Finally, the approach is applied to a PHEV vehicle model in a real-time ECU in the Worldwide harmonized Light Duty Test Cycle (WLTC).