  To improve the driving mobility and energy efficiency of connected autonomous
electrified vehicles, this paper presents an integrated longitudinal speed
decision-making and energy efficiency control strategy. The proposed approach
is a hierarchical control architecture, which is assumed to consist of
higher-level and lower-level controls. As the core of this study, model
predictive control and reinforcement learning are combined to improve the
powertrain mobility and fuel economy for a group of automated vehicles. The
higher-level exploits the signal phase and timing and state information of
connected autonomous vehicles via vehicle to infrastructure and vehicle to
vehicle communication to reduce stopping at red lights. The higher-level
outputs the optimal vehicle velocity using model predictive control technique
and receives the power split control from the lower-level con-troller. These
two levels communicate with each other via a controller area network in the
real vehicle. The lower-level utilizes a model-free reinforcement learning
method to improve the fuel economy for each connected autonomous vehicle.
Numerical tests illustrate that vehicle mobility can be noticeably improved
(traveling time reduced by 30%) by reducing red-light idling. The effectiveness
and performance of the proposed method are validated via comparison analysis
among different energy efficiency controls (fuel economy promoted by 13%).
