利用通行能力余量的智能网联车队生态驾驶模型

针对非饱和城乡交通干道上存在通行能力余量和高燃油消耗的问题, 提出一种利用通行能力余量的智能网联车队生态驾驶模型, 该模型兼顾燃油经济性和通行能力两个目标, 通过优化求解获取最优速度曲线, 引导一系列小型车队平滑地通过非饱和城乡交通干道。提出近似的速度优化模型并采用遗传算法对其求解。定义3种控制方案对模型进行测试, 仿真结果表明: 与方案1相比, 方案2燃油消耗量减少49.4%, 通行能力增加200%, 绿灯剩余时间减少14.7%;方案3燃油消耗量减少59.5%, 通行能力增加200%, 绿灯剩余时间减少23.5%。与方案2相比, 方案3可以在不影响通行能力的前提下, 通过绿灯剩余时间缩短10.3%和平均速度降低5.2%, 燃油消耗量可以减少20%。结果表明, 当信号交叉口存在通行能力余量时, 可以通过调整车辆的行驶速度曲线以充分利用通行能力余量, 明显改善燃油经济性。

Eco-driving model for connected and automated vehicle platoons using the traffic capacity remainder

Aiming at the problems of capacity remainder and high fuel consumption on unsaturated urban and rural traffic roads, this paper proposed an eco-driving model for connected and automated platoons using the capacity remainder, which considered the two goals of fuel economy and traffic capacity, and obtained the optimal speed profiles by solving optimization, to guide a series of small-sized platoons smoothly through the unsaturated urban and rural traffic corridors. In order to improve the computational efficiency of the proposed eco-driving model, this paper proposed an approximate speed optimization model and used genetic algorithm. In order to verify the performance of the proposed model, three control strategies were defined to test the model. The simulation results showed that compared with Strategy 1, Strategy 2 could reduce fuel consumption by 49.4%, increased traffic capacity by 200%, and reduced remaining green time by 14.7%; Strategy 3 could reduce 59.5% of fuel consumption, increased traffic capacity by 200%, and reduced remaining green time by 23.5%. Compared with Strategy 2, Strategy 3 could reduce fuel consumption by 20% without affecting traffic capacity by reducing remaining green time by 10.3% and the average speed by 5.2%. The results showed that when there was traffic capacity remainder at the signalized intersection, the fuel economy could be significantly improved by adjusting the vehicle speed curve to make full use of the traffic capacity remainder.