The present paper extends the concept of a stochastic operating cycle (sOC) by introducing additional models for weather and traffic. In regard to the weather parameters, dynamic models for air temperature, atmospheric pressure, relative humidity, precipitation, wind speed and direction are included. The traffic models is instead based on a macroscopic approach which describes the density dynamically by means of a simple autoregressive process. The enhanced format is structured in a hierarchical fashion, allowing for ease of implementation and modularity. The novel models are parametrised starting from data available from external databases. The possibility of generating synthetic data using the statistical descriptors introduced in the paper is also discussed.

To investigate the impact of the novel parameters over energy efficiency, a sensitivity analysis is conducted with a combinatorial test design. Simulation results show that both seasonality and traffic conditions are responsible for introducing major variations in the ${\mathrm{CO}}_2$ emissions.

本文通过引入额外的天气和交通模型扩展了随机运行周期 (sOC) 的概念。在天气参数方面，包括气温、气压、相对湿度、降水、风速和风向的动态模型。相反，交通模型基于宏观方法，该方法通过简单的自回归过程动态描述密度。增强的格式以分层方式构建，便于实现和模块化。新模型从外部数据库中可用的数据开始参数化。还讨论了使用本文中介绍的统计描述符生成合成数据的可能性。

为了研究新参数对能源效率的影响，使用组合测试设计进行了敏感性分析。模拟结果表明，季节性和交通状况都是造成交通流量发生重大变化的原因。${\mathrm{二氧化碳}}_2$ 排放。