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Automatically Fine-Tuned Speed Control System for Fuel and Travel-Time Efficiency: A microscopic simulation case study

机译:自动微调速度控制系统,用于燃料和旅行时间效率:显微仿真案例研究

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Within the current document a model independent, cognitive and adaptive optimization mechanism, namely CAO, is adopted for providing efficient speed/torque control actions. However since real-life tests were not feasible, a simulation model of a 1.41t displacement gasoline car, playing the role of the actual car, was adopted while the potential maximum cruising speed levels are chosen so as to emulate a usual suburban route and the vehicle speed control is replicated by a common PID scheme. The control decisions are applied directly to the car throttle/torque pedal itself. The goal of the optimization application was to minimize the vehicle velocity error, while minimizing the total fuel consumption for a certain 10km trip with varying road angles/slopes. It should be noted that CAO module could be applied directly to a car system in a straightforward manner without any preparatory investigations. Initially PID gain values were selected arbitrary while a PID gain-tuning module from Matlab/Simulink was used in order to fine-tune them under certain road conditions. Finally the tuned values were used as initial ones for all CAO's application cases (A, B, C and D). CAO presented substantial improvements in the specified performance index, with respect to the base case speed strategy, as well as to the PID tuning in all simulation scenarios considered.
机译:内的当前文档的模型独立的,认知和自适应优化机制,即CAO,采用用于提供有效的速度/扭矩控制动作。然而,由于现实生活中的测试是不可行的,一个1.41吨排量的汽油车的仿真模型,打汽车的实际作用,而被潜在的最大巡航速度水平进行选择,以便模拟通常郊区路线和采用车速控制是通过一个共同的PID方案复制。控制决策直接应用到汽车油门/扭矩踏板本身。优化应用程序的目标是最小化车辆速度误差,同时最小化总燃料消耗具有不同道路的角度/斜率一定10公里跳闸。应当指出的是,曹模块可以直接用简单的方式,没有任何准备的研究应用到车载系统。最初PID增益值被选择的任意的同时从Matlab / Simulink仿真一个PID增益调谐模块是为了用于微调它们在一定的路面条件。最后,调谐值用作初始那些对所有CAO的应用例(A,B,C和d)。曹呈现实质性改进在指定的性能指标,相对于基本情况速度的策略,以及在考虑的所有模拟场景的PID调节。

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