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The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods. • An eco-driving strategy at continuous speed-limit signalized intersections. • The varying speed limits, traffic signal period, and queue effect are considered. • The powertrain efficiency and constraints are incorporated into motion planning. • A long-term co-optimization model of vehicle motion and powertrain operation. • A short-term optimal velocity tracking control for smooth powertrain operation. [ABSTRACT FROM AUTHOR]

Electric vehicles have been considered into effective solutions to address energy problems in urban traffic systems for their remarkable performance in energy costs and carbon emissions reduction. However, the energy-saving effect of electric vehicles in urban traffic systems is restricted due to the complexities arising from mixed traffic conditions. To improve energy efficiency, this paper proposes an eco-driving-based mixed vehicular platoon control model for successive signalized intersections with connected and autonomous vehicles (CAVs), connected vehicles (CVs) and manned vehicles (MVs). Firstly, the dynamics model and spacing policy are analyzed, then the optimal platoon size and speed are calculated according to different traffic scenarios. Secondly, the mixed vehicular platoon control algorithm is established by considering traffic timing, energy consumption and string stability. Thirdly, an actual eco-driving-based platoon control system for tested electric vehicles is designed. The field test results demonstrate that all platoons can pass through intersections during green phase with minimum energy consumption, meanwhile the string stability can be guaranteed. It is noteworthy that the proposed model can greatly improve urban traffic capacity and energy efficiency. • A novel dynamical model of mixed vehicular platoon is explored by integrating different characteristics of CAV, CV, and MV. • An optimal speed algorithm of the leading vehicle to pass through successive signalized intersections is proposed according to signal timing, vehicular speed, and entry time. • The eco-driving-based mixed vehicular platoon control model is established, and the multi-objective function considering traffic environmental constraints are formulated to minimize the travel time and energy consumption. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]