  In this paper, we develop a model to plan energy-efficient speed trajectories
of electric trucks in real-time by taking into account 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. We then formulate an energy minimization
problem and solve it by an alternating direction method of multipliers (ADMM)
method that exploits the structure of the problem. A model predictive control
framework is then employed to deal with topographic and traffic uncertainties
in real-time. An empirical study is conducted on the performance of the
proposed eco-driving algorithm and its impact on battery degradation. The
experimental results show that the energy consumption by using the developed
method is reduced by up to 5.05%, and the battery life extended by as high as
35.35% compared to benchmarking solutions.
