Chong Guo

TL;DR: An improved adaptive equivalent consumption minimization strategy (IA-ECMS) is formulated based on identified driving behavior and predicted real time traffic information, which indicates the proposed energy management strategy holds potential in fuel economy improvement than A- ECMS.

TL;DR: An improved adaptive equivalent consumption minimization strategy (IA-ECMS), in which the equivalence factor (EF) can be tuned in real time due to integration with the results of the vehicle-environment cooperative control, is proposed.

TL;DR: A novel velocity-profile prediction method based on the specific CPS architecture which can improve the fuel economy of PHEV significantly and a case study in a plug-in hybrid electric vehicle (PHEV) is performed to evaluate the effect of CPS-based service in the real application.

TL;DR: In this paper, a drive control method for an all-electric car is presented, which consists of utilizing a finished automobile controller to automatically identify a working mode according to current finished automobile acceleration mean values and an acceleration mean square errors, and enabling the finished automobile working mode to be one of a common mode, a power mode and an economic mode.

TL;DR: In this paper, a prescient energy management strategy based on the model predictive control (MPC) for the parallel plug-in hybrid electric vehicles (PHEVs) is presented. And the authors demonstrate the capability of the proposed scheme in terms of fuel economy improvement by comparing the value of this metric with those of other strategies through simulation.