Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control methods depend on an accurate model of vehicle dynamics and engine, including weight, drag coefficient, and the Brake-specific Fuel Consumption (BSFC) map of the engine. We propose a pure data-driven method, Neural Predictive Control (NPC), which does not use any physical model for the vehicle. After training with over 20,000 km of historical data, the novel proposed NVFormer implicitly models the relationship between vehicle dynamics, road slope, fuel consumption, and control commands using the attention mechanism. Based on the online sampled primitives from the past of the current freight trip and anchor-based future data synthesis, the NVFormer can infer optimal control command for reasonable fuel consumption. The physical model-free NPC outperforms the base PCC method with 2.41% and 3.45% more significant fuel saving in simulation and open-road highway testing, respectively.

翻译：燃油效率是燃油动力卡车长途货运中节约成本与降低碳排放的关键因素。现有预测控制方法依赖于精确的车辆动力学与发动机模型，包括车辆重量、风阻系数及发动机的制动燃油消耗率图谱。本文提出一种纯数据驱动的神经预测控制方法，该方法无需使用任何车辆物理模型。经过超过20,000公里历史数据训练，新颖提出的NVFormer通过注意力机制隐式建模了车辆动力学、道路坡度、燃油消耗与控制指令间的关联关系。基于当前货运行程历史数据的在线采样基元及锚点式未来数据合成，NVFormer能够推断出实现合理燃油消耗的最优控制指令。在仿真与开放道路高速公路测试中，无需物理模型的NPC方法分别实现了比基准PCC方法显著提升2.41%与3.45%的节油效果。