Multiobjective Eco-Driving Strategy for Connected and Automated Electric Vehicles Considering Complex Urban Traffic Influence Factors

To improve the driving experience of connected and automated electric vehicles (CAEVs) in urban scenarios, this article proposes a novel hierarchical multiobjective eco-driving strategy. This strategy aims to co-optimize energy economy, ride comfort, and travel efficiency while prioritizing driving safety. In the upper level controller, we present a driving safety model and a driving speed advisor model that transform complex, multiscale, and multidimensional traffic influence factors into speed constraints. This transformation facilitates the formulation of the multiobjective eco-driving problem as an optimal control problem, characterized by stringent safety constraints and a multiobjective cost function. Subsequently, we design a model predictive control (MPC)-based controller to solve this eco-driving problem in real time. The upper level controller generates an optimal reference target speed, which is transmitted to the lower level vehicle controller. In the lower level controller, we derive an analytical optimal motor torque control law based on linearized system state equations, enabling real-time tracking of the reference speed. Finally, to validate our proposed strategy, we conducted simulations within a dynamic virtual traffic simulation scenario. This scenario is modeled using real road and traffic data from Shanghai, China, effectively simulating a real-world traffic environment. The simulation results affirm the effectiveness of the proposed strategy, demonstrating its capacity to safely and robustly control ego vehicles in complex traffic scenarios. In addition, our strategy optimizes energy efficiency and ride comfort while maintaining travel times comparable to the contrast eco-driving strategies.