为了提高汽车在道路上的通行效率，解决城市道路上由于不合理的速度控制造成汽车额外的能耗，甚至在信号灯路口被迫停车的问题，针对现阶段研究中主要采用基于简单规则的速度模型来优化求解速度曲线，且研究对象多局限于传统内燃机汽车而忽略了未来混合动力汽车能量管理优势带来的潜力等问题，提出基于遗传算法的混合动力汽车连续信号灯路口通行优化求解方法，建立变加速模型下的多约束非线性优化方程组和连续多个信号灯路口通行的场景模型。通过汽车行驶加速度和动力源的转矩分配关系，同时将速度曲线和混合动力汽车能量管理进行优化求解。研究结果表明：通用的优化算法在低车速时发动机效率低，额外的行驶时间带来了额外的油耗；在保证绿灯区间通行的条件下，所提方法在降低总油耗的同时实现了混合动力汽车能耗模型的优化管理，能够减少总油耗5%~29%，且该方法能有效降低通行时间，最高能够减少20.4%。
Abstract
The purpose of this paper is to improve the traffic efficiency of vehicles, and solve the problem of extra energy consumption and forced stop at signalized intersections in the urban area caused by the unreasonable speed control. In allusion to the current situation where the rule-based methods and simple velocity model were mainly applied in related researches to optimize and solve the speed curves, and the speed optimization of vehicles was limited to the traditional internal combustion engine vehicles, neglecting the potential of the energy management of hybrid vehicles in the future, an approach aiming to optimize green light optimal speed advisory (GLOSA) and energy management for hybrid vehicles based on genetic algorithm was proposed. Meanwhile, the scene model of GLOSA and the multi-constrained nonlinear optimization equations in view of varying accelerated model were established. The speed curve and the energy management of the hybrid vehicles were optimized simultaneously by dint of the optimization of acceleration of the vehicle and torque distribution relationship of energy sources. The results show that, at low vehicle speed, the efficiency of the engine calculated by general methods of GLOSA is low. The extra energy consumption is caused by extra travel time. Under the condition that the green light interval is ensured, the total fuel consumption decreases by 5%-29% the optimal energy management of hybrid vehicles is achieved by the proposed method. Meanwhile, the travel time decreases by 20.4% in simulation.
关键词
交通工程 /
信号灯路口通行 /
遗传算法 /
混合动力汽车 /
多约束优化
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Key words
traffic engineering /
GLOSA /
genetic algorithm /
hybrid vehicle /
multi-constrained optimization
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中图分类号：
U491.51
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参考文献
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脚注
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基金
国家自然科学基金项目（51575295）。
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