온실가스 감축을 위한 대형 화물차 좌회전 우선신호 알고리즘 개발

This paper aims to study the characteristics of greenhouse gas (GHG) emissions from heavy-duty trucks in the Beijing-Tianjin-Hebei (BTH) region, which is located in Northern China. The multiyear emissions of GHG (CO2, CH4 and N2O) from heavy-duty trucks fueled by diesel and natural gas during the period of 2006–2015 were compared and analyzed. The results show that the GHG emissions from heavy-duty trucks increase with time, which is consistent with the trend of the population growth. The total amount of carbon dioxide equivalence (CO2e) emissions in the BTH region was about 5.12 × 106 t in 2015. Among the three sub-regions, Hebei possesses the largest number of heavy-duty trucks due to the size of its heavy-duty industries. As a consequence, the GHG emissions are about 10 times compared to Beijing and Tianjin. Tractor trailers account for the major proportion of heavy-duty trucks and hence contribute to about 74% of GHG emissions. Diesel- and liquefied natural gas (LNG)-powered heavy-duty trucks can reduce GHG emissions more effectively under current national standard IV than can the previous standard. The widespread utilization of the alternative fuel of LNG to mitigate emissions must be accompanied with engine technology development in China. This study has provided new insight on management methods and the policy-making as regards trucks in terms of environmental demand.

Mobile source emissions estimation techniques play a critical role for regional planning and development of emission control strategies. The primary models for mobile source emissions estimation have been the U.S. Environmental Protection Agency’s MOBILE model and the California Air Resources Board’s EMFAC model. These models work well for large regional areas but are not as well suited for “microscale” evaluation. Over the last several years, the College of Engineering–Center for Environmental Research and Technology (CE-CERT) has been evaluating in-use, light-duty vehicles as part of NCHRP Project 25-11, resulting in the development of a Comprehensive Modal Emissions Model (CMEM). An essential part of any model development process is validating the model. Various validation techniques have been applied to CMEM. This paper describes some of the latest validation work carried out in comparing CMEM results to independent emission testing results (independent in both vehicles and driving cycles). Further, CMEM has been compared with the latest versions of EMFAC and MOBILE. In general, compared with the independent emission measurements, CMEM predicts well. It has been found that CMEM is consistent with MOBILE and EMFAC at low to medium speeds. Greater deviations were found at very low speeds and very high speeds. At high speeds, CMEM tends to predict higher hydrocarbon (HC) emissions and lower oxides of nitrogen (NOx) emissions. At the very low speeds, CMEM tends to predict lower than EMFAC and MOBILE for all emissions. These comparisons are part of an ongoing validation process for development of CMEM.

Fuel consumption model for heavy duty diesel trucks: Model development and testing

Heavy-duty vehicles are the second-largest source of greenhouse gas emissions and energy use within the transportation sector even though they represent only a small portion of on-road vehicles. Heavy-duty diesel vehicles (HDDVs) emit about half of all on-road emissions of nitrogen oxide (NOx). However, because of the limited amount of HDDV emissions data, research has focused on light-duty vehicle emissions. The majority of these microscopic models suffer from two major limitations: the models result in a bang-bang control system and calibration of the model parameters is not possible with publicly available data. This paper proposes to extend the Virginia Tech Comprehensive Power-Based Fuel Consumption Model (VT-CPFM) to overcome the two shortcomings in state-of-the-practice HDDV emissions models of carbon monoxide (CO), hydrocarbons (HCs), and NOx. Heavy-duty diesel truck (HDDT) data from the University of California, Riverside, were used for the calibration and validation processes. The study’s results were satisfying, especially for NOx, which was the main concern in HDDV emissions. Model validity and performance were evaluated by comparing the correlation of measured field data and estimated emissions between the VT-CPFM model and the comprehensive modal emissions model (CMEM). The results demonstrate the efficacy of the VT-CPFM model in replicating empirical observations producing better accuracy compared with other state-of-the-practice models (e.g., CMEM). Moreover, unlike the CMEM model, which requires extensive data collection for calibration purposes, the VT-CPFM model needs only GPS and publicly accessible data for calibration.

The feasibility of heavy battery electric trucks

Reducing atmospheric pollutant and greenhouse gas emissions of heavy duty trucks by substituting diesel with hydrogen in Beijing-Tianjin-Hebei-Shandong region, China

The evolution of medium and heavy duty trucks and buses has been driven by market and regulatory demands. Over the years, criteria pollutant reductions have been realized with improvements in engine design and advanced after treatment systems. More recently the interest in reducing greenhouse gas emissions has driven the development of hybrid powertrains for these applications. Hybridization provides the opportunity to satisfy customer demands of increased fuel economy, decreased emissions and reducing incremental cost of adoption, while still providing value and performance. One of the major hurdles for adoption of hybrid technologies lies in the current limitations of engine and full vehicle certification. The regulations fail to account for the size and vocational diversity of the medium and heavy duty truck market. This is further complicated by range of truck modifiers that adapt the basic vehicle platform to accommodate the individual customer requirements. As a result, medium and heavy duty truck manufacturers have been prevented from certifying a full vehicle level platform (unlike passenger vehicle manufacturers) due to the current engine only certification requirements. The problem with most hybrid technologies is they affect more than just the engine and after-treatment. In order to bridge the increasing gap between certification and real world fuel economy and emissions, Navistar initiated a study in mid-2010 to look at Hardware-in-the-Loop Simulation (HiLS) with the possibility for Model-in-the-Loop Simulation (MiLS) interfaces as a tool for certification. This effort focused primarily for hybrids, but with the intent applying the same principles to a conventional truck. This study included the evaluation of current certification and regulatory processes, as well as customer in-use data. The goal was to develop a methodology that would minimize the discrepancy between certification, real world fuel economy and emissions, while using existing engine, powertrain or chassis test methodologies where possible. This paper outlines the certification and control considerations required for a pathway to HiLS.

Importance of reducing GHG emissions in power transmission and distribution systems

To achieve net zero emissions, the global transportation sector needs to reduce emissions by 90% from 2020 to 2050, and road freight has a significant potential to reduce emissions. In this context, emission reduction paths should be explored for road freight over the fuel life cycle. Based on panel data from 2015 to 2020 in China, China's version of the GREET model was established to evaluate the impact of crude oil mix, electricity mix, and vehicle technology on China's reduction in road freight emissions. The results show that the import share of China's crude oil has increased from 2015 to 2020, resulting in an increase in the greenhouse gas (GHG) emission intensity of ICETs in the well-to-tank (WTT) stage by 7.3% in 2020 compared with 2015. Second, the share of China's coal-fired electricity in the electricity mix decreased from 2015 to 2020, reducing the GHG emission intensity of battery electric trucks (BETs), by approximately 6.5% in 2020 compared to 2015. Third, different vehicle classes and types of BETs and fuel cell electric trucks (FCETs) have different emission reduction effects, and their potentials for energy-saving and emission reduction at various stages of the fuel life cycle are different. In addition, in a comparative study of vehicle technology, the results show that (1) for medium-duty trucks (MDTs) and heavy-duty trucks (HDTs), FCETs have lower GHG emission intensity than BETs, and replacing diesel-ICETs can significantly reduce GHG emissions from road freight; (2) for light-duty trucks (LDTs), BETs and FCETs have the highest GHG emission reduction potential; thus, improving technologies such as electricity generation, hydrogen fuel production, hydrogen fuel storage, and transportation will help to improve the emission reduction capabilities of BETs and FCETs. Therefore, policymakers should develop emission standards for road freight based on vehicle class, type, and technology.

Deployment of fuel cell vehicles in China: Greenhouse gas emission reductions from converting the heavy-duty truck fleet from diesel and natural gas to hydrogen

The greenhouse gas (GHG) emissions reduction potentials of various near- and long-term transportation technologies were estimated. The estimated per-travel-distance GHG emissions results indicate that alternative transportation fuels and advanced vehicle technologies can help to significantly reduce transportation-related GHG emissions. Of the near-term technologies evaluated, electric vehicles, hybrid electric vehicles, compression-ignition, direct-injection vehicles, and E85 (85 percent ethanol and 15 percent gasoline) flexible-fuel vehicles can reduce fuelcycle GHG emissions by more than 25 percent on a fuel-cycle basis. Electric vehicles powered by electricity generated primarily from nuclear and renewable sources can reduce GHG emissions by 80 percent. Other alternative fuels (such as compressed natural gas and liquefied petroleum gas) offer limited, but positive, GHG emissions reduction benefits. Among the long-term technologies evaluated, conventional sparkignition and compression-ignition engines powered by alternative fuels and gasoline- and diesel-powered advanced vehicles can reduce GHG emissions by 10 to 30 percent. Dedicated ethanol vehicles, electric vehicles, hybrid electric vehicles, and fuel-cell vehicles can reduce GHG emissions by more than 40 percent. Spark-ignition engines and fuel-cell vehicles powered by cellulosic ethanol and solar hydrogen (for fuel-cell vehicles only) can reduce GHG emissions by over 80 percent. In conclusion, both near- and long-term alternative fuels and advanced transportation technologies can play a role in reducing GHG emissions from the transportation sector.

The identification of truck-related greenhouse gas emissions and critical impact factors in an urban logistics network

A detailed investigation was carried out to assess the concentration of near-road traffic-related air pollution (TRAP) using a dispersion model in Muscat. Two ambient air quality monitoring (AQM) stations were utilized separately at six locations near major roadways (each location for 2months) to monitor carbon monoxide (CO) and nitrogen oxides (NOx). The study aimed to measure the concentration of near-road TRAP in a city hot spots and develop a validated dispersion model via performance measures. The US Environmental Protection Agency (US EPA) Line Source Model was implemented in which the pollutant emission factors were obtained through Comprehensive Modal Emission Model (CMEM) and COmputer Programme to calculate Emissions from Road Transport (COPERT) model. Traffic data of all vehicle categories under normal driving conditions including average vehicle speed limits and local meteorological conditions were included in the modeling study. The analysis of monitoring data showed that hourly (00:00 to 23:00) concentrations of CO were within the US EPA limits, while NOx concentration was exceeded in most locations. Also, the measured pollutant levels were consistent with hourly peak and off-peak traffic volumes. The overall primary statistical performance measures showed that COPERT model was better than CMEM due to the high sensitivity of CMEM to the local meteorological factors. The best fractional bias (0.47 and 0.39), normalized mean square error (0.44 and 0.50), correlation coefficient (0.64 and 0.70), geometric mean bias (1.07 and 1.57), and geometric variance (2.00 and 2.32) were obtained for CO and NOx, respectively. However, the bootstrap 95% CI estimates over normalized mean square error, fractional bias, and correlation coefficient for COPERT and CMEM were found to be statistically significant from 0 in the case of combined model comparison across all the traffic locations for both CO and NOx. In overall, certain roads showed weak performance mainly due to the terrain features and the lack of reliable background concentrations, which need to be considered in the future study.

The paper compares the MOBILE5a, MOBILE6, Virginia Tech microscopic energy and emission model (VT-Micro), and comprehensive modal emissions model (CMEM) models for estimating hot-stabilized, light-duty vehicle emissions. Specifically, Oak Ridge National Laboratory (ORNL) and Environmental Protection Agency (EPA) laboratory fuel consumption and emission databases are used for model comparisons. The comparisons demonstrate that CMEM exhibits some abnormal behaviors when compared with the ORNL data, EPA data, and the VT-Micro model estimates. Specifically, carbon monoxide (CO) emissions exhibit abrupt changes at low speeds and high acceleration levels and constant emissions at negative acceleration levels. Furthermore, oxides of nitrogen (NOx) emissions exhibit abrupt drops at high engine loads. In addition, the study demonstrates that MOBILE5a emission estimates compare poorly with EPA field data, while MOBILE6 model estimates show consistency with EPA field data and VT-Micro model estimates over various driving cycles. The VT-Micro model appears to be accurate in estimating hot-stabilized, light-duty, normal vehicle tailpipe emissions. Specifically, the emission estimates of the VT-Micro and MOBILE6 models are consistent in trends with laboratory measurements. Furthermore, the VT-Micro and MOBILE6 models accurately capture emission increases for aggressive acceleration drive cycles in comparison with other drive cycles.Key words: transportation energy, transportation environmental impacts, VT-Micro Model, CMEM, MOBILE5, MOBILE6, fuel consumption models, emission models.

The accuracy of carbon emission and fuel consumption computations in green vehicle routing