MOVESTAR: An Open-Source Vehicle Fuel and Emission Model based on USEPA MOVES
In this paper, we introduce an open-source model "MOVESTAR" to calculate the fuel consumption and pollutant emissions of motor vehicles. This model is developed based on U.S. Environmental Protection Agency's (EPA) Motor Vehicle Emission Simulator (MOVES), which provides an accurate estimate of vehicle emissions under a wide range of user-defined conditions. Originally, MOVES requires users to specify many parameters through its software, including vehicle types, time periods, geographical areas, pollutants, vehicle operating characteristics, and road types. In this paper, MOVESTAR is developed as a simplified version, which only takes the second-by-second vehicle speed data and vehicle type as inputs. To enable easy integration of this model, its source code is provided in various languages, including Python, MATLAB and C++. A case study is introduced in this paper to illustrate the effectiveness of the model in the development of advanced vehicle technology.
Code (1)
Similar Papers 제목 키워드 기반
Low emission zones: Effects on alternative-fuel vehicle uptake and fleet CO2 emissions
This study analyses the actual effect of a representative low-emission zone (LEZ) in terms of shifting vehicle registrations towards alternative fuel technologies and its effectiveness for reducing vehicle fleet CO2 emis…
Systemic Decarbonization of Road Freight Transport: A Comprehensive Total Cost of Ownership Model
The decarbonization of road freight transport is crucial for reducing greenhouse gas emissions (GHG) and achieving climate neutrality goals. This study develops a comprehensive Total Cost of Ownership (TCO) model to eval…
Learning Eco-Driving Strategies at Signalized Intersections
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control…
Autonomous VehiclesReinforcement Learning (RL)NPC: Neural Predictive Control for Fuel-Efficient Autonomous Trucks
Fuel efficiency is a crucial aspect of long-distance cargo transportation by oil-powered trucks that economize on costs and decrease carbon emissions. Current predictive control methods depend on an accurate model of veh…
Analysis of the impact of heterogeneous platoon for mixed traffic flow: control strategy, fuel consumption and emissions
Compared with traditional vehicle longitudinal spacing control strategies, the combination spacing strategy can integrate the advantages of different spacing control strategies. However, the impact mechanism of different…