Real-time Eco-Driving Control in Electrified Connected and Autonomous Vehicles Using Approximate Dynamic Programming

Connected and Automated Vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the Eco-Driving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multi-layer Model Predictive Control (MPC) approach for improving the fuel economy of a 48V mild-hybrid powertrain in a connected vehicle environment. Approximate Dynamic Programming (ADP) is used to solve the Receding Horizon Optimal Control Problem (RHOCP), where the terminal cost for the RHOCP is approximated as the base-policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.

Analysis of the influence of power coupling type and transmission type of the powertrain on the performance of single-motor hybrid electric vehicles

Existing research on parallel hybrid electric vehicles (HEV) mainly focuses on optimizing the component sizes and control strategies of the single-motor parallel hybrid electric powertrain (SMPHP), and less analyzes the influence of powertrain configuration on the performance of the vehicle. Therefore, the influence of the power coupling type and transmission type of the powertrain configuration on the fuel economy and drivability performance of parallel HEVs is studied in this paper. Considering three types of powertrain topologies (P2 torque-coupled, P2 dual-mode coupled and P3 torque-coupled) and two types of automatic transmissions (DCT and CVT), six typical types of SMPHP configurations to be discussed are determined. To obtain their optimal fuel economy and drivability performance, a multi-objective optimization and analysis method based on dynamic programming and multi-objective particle swarm optimization algorithm is proposed to optimize the component sizes and control variables of powertrain configurations. Finally, the optimal performance and component size optimization results of six typical SMPHP configurations are analyzed and compared, and the influence of powertrain configuration on the performance and components sizing of the SMPHP is obtained, which contributes to the configuration design of the parallel hybrid electric powertrain.