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%, and the battery life is extended by more than 100% compared to benchmarking solutions.

中文翻译：

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自动驾驶和电动卡车适应公路地形的最佳生态驾驶控制：能量最小化和电池寿命延长


本文开发了一种模型，通过考虑车辆前方的地形和交通信息来实时规划电动卡车的节能速度轨迹。在这个实时控制模型中，首先开发了一种新颖的状态空间模型来捕获车辆速度、加速度和充电状态。然后通过利用问题结构的交替方向乘法器 (ADMM) 来制定和解决能量最小化问题。进一步采用模型预测控制（MPC）框架来实时处理地形和交通不确定性。最后对所提出的生态驾驶算法的性能及其对电池退化的影响进行了实证研究。仿真结果表明，与基准方案相比，采用该方法的能耗降低高达5.05%，电池寿命延长100%以上。