Abstract
• The new stochastic operating cycle includes models for the weather and traffic. • A primary model for the weather category is introduced to account for seasonality. • A regression analysis is made to investigate spread in CO 2 emissions. • It is found that both seasonal and traffic settings influence CO 2 emissions. 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 CO 2 emissions.
Highlights
Several sources highlight the incontestable nature of an anthropogenic cause of climate change
Liu et al (2018), Eymen and Koylii (2019) and relatively easy to model intuitively. For both physical quantities, whose sequences are denoted by {Tair,k} and {ΨRH,k} respectively, a distinction is made between a deterministic component and a stochastic one
This paper has extended the stochastic operating cycle description to include models for both the weather and traffic cat egories
Summary
Several sources highlight the incontestable nature of an anthropogenic cause of climate change. Brake pedal position – Constant term for wind autoregressive model km h− 1, rad Daily ceiling operator – Annual ceiling operator – Error term for air temperature ◦C Error term for atmospheric pressure hPa Error term for wind speed and direction km h− 1, rad Error term for traffic density km− 1. Transportation Research Part D 97 (2021) 102878 the construction and testing of physical prototypes In this context, the CO2 emissions and energy consumption of road vehicles are often estimated and compared using reference speed and elevation profiles, known as driving cycles, in conjunction with simplified models of longitudinal dynamics Guzzella and Sciarretta (2013). CMEM uses a power-demand approach based on a parametric description of fuel consumption and emissions Individual contributions to these are allocated to components corresponding to physical phenomena associated with specific operational conditions. These include road and mission properties, and weather and traffic conditions Wyatt et al (2014), Sentoff et al (2015), Llopis-Castello et al (2018), Sciarretta (2020)
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