Powertrain and Vehicle Longitudinal Motion Control for Personalized Eco-driving of P0+P4 Mild Hybrid Electric Vehicles

Due to the increasing trend of greenhouse gas emissions, the United States Environmental Protection Agency (EPA) has started to publish strict regulations regarding emissions for different types of vehicles. Battery electric vehicles (BEVs) have drawn much attention in recent years because they potentially eliminate all tailpipe emissions. However, due to charging speed and capacity limitations on battery, currently, EV users are facing the problem of range anxiety and the lack of charging stations. Hybrid electric vehicles (HEVs), which possess the advantages of both conventional vehicles and BEVs, appear to be a viable solution to cope with such strict emission regulations while mitigating range anxiety. Among all types of hybrid electric powertrain systems, a P0+P4 system possesses distinct advantages: two electric motors located on the front and rear axles allow brake energy to be recovered from both axles. Moreover, the dual motor configuration enables the driver to switch among front-drive, rear-drive and all-wheel-drive modes. Particularly, a 48V P0+P4 HEV requires less expensive wiring and electric shock protection and hence it is considered to be the most cost-effective HEV for reducing GHG emissions. This dissertation focusses on improving the energy efficiency, ride comfort, and safety of a 48V P0+P4 MHEV. To achieve these goals, this dissertation proposes a hierarchical control design among domains of power-split and vehicle longitudinal motion of 48V P0+P4 MHEV. In the domain of power-split, two real-time implementable controllers are proposed: (1) the optimization-based controller and (2) the learning-based controller. In the optimization-based control design, the approximated adaptive equivalent consumption minimization strategy (AA-ECMS) with a suboptimal braking distribution derived from dynamic programming (DP) analysis is proposed to capture the global optimal operation trends of the P0 motor operation, front/rear tire force distribution. In the learning-based control design, twin delayed deep deterministic policy gradient with prioritized exploration and experience replay (TD3+PEER), a novel prioritized exploration approach, is proposed to encourage the deep reinforcement learning (DRL) agent to explore states with complex dynamics. Both proposed power-split controllers achieve better fuel economy during the test trips compared to state-of-art rule-based and learning-based controllers. In vehicle longitudinal motion control design, two controllers have been developed using model predictive control (MPC): (1) the defensive ecological adaptive cruise control (DEco-ACC) and (2) the personalized one-pedal-driving (POPD). The DEco-ACC is a novel car-following algorithm that balances fuel economy, ride comfort, and avoidance of blind spots from neighboring vehicles. In DEco-ACC, a novel continuous and differentiable penalty function is proposed to describe the projection of several neighboring vehicles’ blind spots to the ego vehicle’s traffic lane. The proposed MPC-based controller considers this blind spot penalty function as a soft constraint within its prediction horizon; and is able to make its own decision to either yield, pass, or stay within the blind spots based on the MPC’s cost function and the traffic scenario. The POPD is a novel personalized one-pedal driving method that can learn the individual driver’s preference during everyday driving. In POPD, two types of MPC constraints that represent distinct driver’s behavior are identified by analyzing 450 real-world drivers’ data. And then, the POPD algorithm is validated in both the simulation environment and the human-in-the-loop (HIL) traffic simulator. These algorithms ensure car following safety and enhance the driver’s comfort. The energy performance of DEco-ACC and POPD with proposed power-split algorithms are evaluated within corresponding chapters.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/175045/1/Yu He Final Dissertation.pdfDescription of Yu He Final Dissertation.pdf : Dissertatio