This work proposes a real-time implementable control strategy to optimize vehicle speed and transmission gear position simultaneously for connected and autonomous vehicles (CAVs). Co-optimization of vehicle speed and transmission gear position has the advantage to maximize the fuel benefits. Drivability is considered during the optimization to satisfy the acceleration requirement and avoid shift busyness. The target vehicle’s speed and gear position are controlled intelligently using predicted future traffic conditions based on information enabled by connectivity. The optimal control problem is a hybrid one with both continuous (vehicle speed) and discrete (gear position) control inputs. The problem is formulated and simplified to a mixed-integer programming problem with a convex quadratic objective function and mixed-integer linear constraints. The optimal control solutions are obtained in real time using an efficient numerical solver in the model predictive control (MPC) fashion. Future traffic conditions are anticipated using a traffic prediction method based on a traffic flow model. The traffic prediction method can be applied to scenarios where both connected vehicles and nonconnected vehicles share the road. As a case study, a vehicle platooning scenario on an urban road is evaluated in both simulation and experiment. The target vehicle is at the end of the vehicle platoon and follows the preceding vehicle. The average computational time of the optimization is 0.44 s. By co-optimizing vehicle speed and gear position, the target vehicle can achieve 10.6% fuel benefits compared with the immediate preceding vehicle and 8.9% energy benefits compared with a human-driven vehicle (driven by VISSIM’s car-following model). The proposed control strategy can be potentially extended to various CAV applications and traffic scenarios as well.

中文翻译：

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节能互联和自动驾驶车辆的车速和档位协同优化


这项工作提出了一种实时可实施的控制策略，以同时优化联网和自动驾驶车辆（CAV）的车速和变速箱档位。车速和变速箱档位的共同优化有利于最大限度地提高燃油效益。优化时考虑驾驶性能，满足加速要求，避免换班繁忙。根据连接所提供的信息，使用预测的未来交通状况来智能控制目标车辆的速度和档位。最优控制问题是一个混合问题，具有连续（车速）和离散（档位）控制输入。该问题被公式化并简化为具有凸二次目标函数和混合整数线性约束的混合整数规划问题。使用模型预测控制 (MPC) 方式的高效数值求解器实时获得最优控制解决方案。使用基于交通流模型的交通预测方法来预测未来的交通状况。该交通预测方法可以应用于联网车辆和非联网车辆共享道路的场景。作为案例研究，通过模拟和实验评估了城市道路上的车辆队列场景。目标车辆位于车辆排的末尾并跟随前面的车辆。优化的平均计算时间为0.44 s。通过车速和档位的协同优化，目标车辆与前车相比可实现 10.6% 的燃油效益，与人工驾驶车辆（由 VISSIM 跟车模型驱动）相比可实现 8.9% 的能源效益。 所提出的控制策略也可以扩展到各种 CAV 应用和交通场景。