Energy-Aware Optimization of Connected and Automated Electric Vehicles Considering Vehicle-Traffic Nexus

Ying Zhang, Jinchao Chen, Tao You, Yingjie Zhang, Zhaohua Liu, Chenglie Du

Abstract: The driving economy of the connected and automated electric vehicles (CAEVs) is seriously affected by the vehicle-traffic nexus. In this article, an energy-aware optimization (EAO) strategy for improving the energy efficiency of CAEVs is proposed by considering the vehicle-traffic nexus between the traffic environment's dynamic constraint and the vehicle powertrain's constraint. In order to design the EAO strategy, the parameters of the vehicle dynamics model are identified by a proposed bias deep compensative estimator. Based on the identified parameters, the traffic environment's constraint is converted to the powertrain's constraint of CAEV. To obtain the optimal energy efficiency under the powertrain's constraint, a new velocity-torque coordinate system is constructed to standardize the constraint, and a neighborhood iterative searching algorithm is proposed to search the optimal efficiency in the coordinate system. With the searched optimal efficiency, a torque tracking control strategy is designed to regulate the electric powertrain to make it operate in the high efficiency region. The experiment is conducted in the real-world scenario to compare the proposed method with two state-of-art methods. Compared with the state-of-art methods, the relative energy-saving percentage of the proposed method can reach more than 7.5%.