Vehicle Speed and Gear Position Co-Optimization for Energy-Efficient Connected and Autonomous Vehicles

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.