Representative driving cycles are very important for testing energy consumption and pollutant emission, optimizing control strategy, and designing power resource components of vehicles. Multi-parameter representative driving cycles are needed to improve vehicles’ adaptability to the driving environment. Thus, high-dimensional driving cycles need to be efficiently generated. Additionally, the generation method should be flexible enough to facilitate use by automobile engineers. This study introduces a hyper-heuristic framework into Markov chain evolution (MCE). A boundary variable is introduced to refine strategies, and multiple evolution strategies are proposed for self-adaptivity. Then an evaluation function based on the desired driving cycles is designed using allocation and update mechanisms. Finally, an efficient framework is established for generating multi-parameter driving cycles. As an example, the generation efficiency of this framework is increased by 63.25% over the standard MCE method through collecting real-world driving data and considering representative driving cycles with three parameters (velocity, acceleration, and road slope). Analyzing the proportions of hyper-heuristic evolution strategies indicates the self-adaptivity of this method. Compared with an adaptive MCE method with two strategies, the proposed method has greater running efficiency and application flexibility.

中文翻译：

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用于生成车辆多参数驾驶循环的自适应超启发式马尔可夫链进化

具有代表性的工况对于测试车辆能耗和污染物排放、优化控制策略、设计动力资源组件等具有重要意义。需要多参数代表驾驶循环来提高车辆对驾驶环境的适应性。因此，需要有效地生成高维驱动循环。此外，生成方法应该足够灵活，以方便汽车工程师使用。本研究将超启发式框架引入马尔可夫链进化 (MCE)。引入边界变量来细化策略，并提出多种进化策略以实现自适应。然后使用分配和更新机制设计基于所需驱动循环的评估函数。最后，建立了一个有效的框架来生成多参数驱动循环。例如，通过收集真实世界的驾驶数据并考虑具有三个参数（速度、加速度和道路坡度）的代表性驾驶循环，该框架的生成效率比标准 MCE 方法提高了 63.25%。分析超启发式进化策略的比例表明该方法的自适应性。与采用两种策略的自适应MCE方法相比，该方法具有更高的运行效率和应用灵活性。通过收集真实世界的驾驶数据并考虑具有三个参数（速度、加速度和道路坡度）的代表性驾驶循环，比标准 MCE 方法高 25%。分析超启发式进化策略的比例表明该方法的自适应性。与采用两种策略的自适应MCE方法相比，该方法具有更高的运行效率和应用灵活性。通过收集真实世界的驾驶数据并考虑具有三个参数（速度、加速度和道路坡度）的代表性驾驶循环，比标准 MCE 方法高 25%。分析超启发式进化策略的比例表明该方法的自适应性。与采用两种策略的自适应MCE方法相比，该方法具有更高的运行效率和应用灵活性。