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首页> 外文会议>6th Hybrid and Electric Vehicles Conference >Derivation of real-world driving cycles corresponding to traffic situation and driving style on the basis of Markov models and cluster analyses
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Derivation of real-world driving cycles corresponding to traffic situation and driving style on the basis of Markov models and cluster analyses

机译：在马尔可夫模型和聚类分析的基础上推导与交通状况和驾驶方式相对应的实际驾驶循环

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摘要

Reducing fuel consumption while coping with continually increasing customer demands with regard to driving dynamics, is a conflict of objectives in vehicle development. At the same time, hybrid vehicles offer a chance to meet this challenge. For calibrating the hybrid car's operational strategy, realistic driving cycles are of great importance. Studies have shown that the driver's driving style as well as the traffic situation have a considerable influence on fuel consumption and pollutant emission [1]. Deriving replacement cycles from extensive customer data to support the vehicle calibration in consideration of driving styles and traffic situations seems to be an approach. This contribution describes the generation of these replacement cycles by making use of stochastic models and cluster analysis. Therefore, extensive customer records can be summarised and complex signal sequences can be obtained. Cluster analysis allow for arranging extensive data sets in similar groups according to driving style and traffic situation. Following this, an optimisation algorithm assembles several generated velocity progressions to one replacement cycle. The aim is to keep important characteristics in relation to the consumption and the customer behaviour. The resulting replacement cycles offer the possibility to calibrate a hybrid car's operational strategy to the market in a better way and to conduct sensitivity analysis as well as consumption forecasts [2].
机译：减少油耗，同时满足不断增长的客户对行驶动力的需求，这是车辆开发目标的冲突。同时，混合动力汽车为应对这一挑战提供了机会。为了校准混合动力汽车的运行策略，切合实际的驾驶循环至关重要。研究表明，驾驶员的驾驶方式以及交通状况对油耗和污染物排放都有相当大的影响[1]。考虑到驾驶风格和交通状况，从大量的客户数据中得出更换周期以支持车辆校准似乎是一种方法。该贡献描述了通过使用随机模型和聚类分析来生成这些替换周期。因此，可以汇总大量的客户记录并获得复杂的信号序列。聚类分析允许根据驾驶方式和交通状况将大量数据集布置在相似的组中。此后，一种优化算法将几个生成的速度进程组合到一个替换循环中。目的是保持与消费和客户行为有关的重要特征。由此产生的更换周期为以更好的方式向市场校准混合动力汽车的运营策略，进行敏感性分析以及消耗预测提供了可能性[2]。

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