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This article develops a model to plan energy-efficient speed trajectories of electric trucks in real time by considering the information of topography and traffic ahead of the vehicle. In this real-time control model, a novel state-space model is first developed to capture vehicle speed, acceleration, and state of charge. An energy minimization problem is then formulated and solved by an alternating direction method of multipliers (ADMM) that exploits the structure of the problem. A model predictive control (MPC) framework is further employed to deal with topographic and traffic uncertainties in real time. An empirical study is finally conducted on the performance of the proposed eco-driving algorithm and its impact on battery degradation. The simulation results show that the energy consumption by using the developed method is reduced by up to 5.05&#x0025;, and the battery life is extended by more than 100&#x0025; compared to benchmarking solutions.

Optimal Eco-Driving Control of Autonomous and Electric Trucks in Adaptation to Highway Topography: Energy Minimization and Battery Life Extension