Zero-Emission Medium- and Heavy-duty Truck Technology, Markets, and Policy Assessments for California

This report assesses zero emissions medium- and heavy-duty vehicle technologies, their associated costs, projected market share, and possible policy mandates and incentives to support their adoption. Cost comparisons indicate that battery-electric transit buses and city delivery trucks are the most economically attractive of the zero-emission vehicles (ZEVs) based on their break-even mileage being a small fraction of the expected total mileage. These ZEVs using fuel cells are also attractive for a hydrogen cost of $5/kg. The most economically unattractive vehicle types for ZEV adoption are long-haul trucks and inter-city buses. Developing mandates for buses and trucks will be more difficult than for passenger cars for several reasons, including the large differences in the size and cost of the vehicles and the ways they are used in commercial, profit-oriented fleets. The best approach will be to develop separate mandates for classes of vehicles that have similar sizes, cost characteristics, use patterns, and ownership/business models. These mandates should be coupled to incentives that vary by vehicle type/class and by year or accumulated sales volume, to account for the effects of expected price reductions with time

Carbon Free Boston: Transportation Technical Report

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical ReportOVERVIEW: Transportation connects Boston’s workers, residents and tourists to their livelihoods, health care, education, recreation, culture, and other aspects of life quality. In cities, transit access is a critical factor determining upward mobility. Yet many urban transportation systems, including Boston’s, underserve some populations along one or more of those dimensions. Boston has the opportunity and means to expand mobility access to all residents, and at the same time reduce GHG emissions from transportation. This requires the transformation of the automobile-centric system that is fueled predominantly by gasoline and diesel fuel. The near elimination of fossil fuels—combined with more transit, walking, and biking—will curtail air pollution and crashes, and dramatically reduce the public health impact of transportation. The City embarks on this transition from a position of strength. Boston is consistently ranked as one of the most walkable and bikeable cities in the nation, and one in three commuters already take public transportation. There are three general strategies to reaching a carbon-neutral transportation system: • Shift trips out of automobiles to transit, biking, and walking;1 • Reduce automobile trips via land use planning that encourages denser development and affordable housing in transit-rich neighborhoods; • Shift most automobiles, trucks, buses, and trains to zero-GHG electricity. Even with Boston’s strong transit foundation, a carbon-neutral transportation system requires a wholesale change in Boston’s transportation culture. Success depends on the intelligent adoption of new technologies, influencing behavior with strong, equitable, and clearly articulated planning and investment, and effective collaboration with state and regional partners.Published versio

Transportation Futures: Policy Scenarios for Achieving Greenhouse Gas Reduction Targets, MNTRC Report 12-11

It is well established that GHG emissions must be reduced by 50% to 80% by 2050 in order to limit global temperature increase to 2°C. Achieving reductions of this magnitude in the transportation sector is a challenge and requires a multitude of policies and technology options. The research presented here analyzes three scenarios: changes in the perceived price of travel, land-use intensification, and increases in transit. Elasticity estimates are derived using an activity-based travel model for the state of California and broadly representative of the U.S. The VISION model is used to forecast changes in technology and fuel options that are currently forecast to occur in the U.S., providing a life cycle GHG forecast for the road transportation sector. Results suggest that aggressive policy action is needed, especially pricing policies, but also more on the technology side. Medium- and heavy-duty vehicles are in particular need of additional fuel or technology-based GHG reductions

Heavy Duty Vehicle Fuel Consumption Modelling Using Artificial Neural Networks.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly, a transient state test was performed, in order to evaluate the polynomial regression and 25 ANN models with different parameters. Based on the results, the best ANN model was chosen. Then, validation test was conducted using real duty cycle loads for model comparison. The neural network model outperformed the conventional method and represents fuel consumption of the engine operating in transient states significantly better. The presented method can be applied in order to reduce fuel consumption in utility vehicles delivering accurate fuel economy model of truck engines, in particular in low engine speed and torque range

Future heavy duty trucking engine requirements

Developers of advanced heavy duty diesel engines are engaged in probing the opportunities presented by new materials and techniques. This process is technology driven, but there is neither assurance that the eventual users of the engines so developed will be comfortable with them nor, indeed, that those consumers will continue to exist in either the same form, or numbers as they do today. To ensure maximum payoff of research dollars, the equipment development process must consider user needs. This study defines motor carrier concerns, cost tolerances, and the engine parameters which match the future projected industry needs. The approach taken to do that is to be explained and the results presented. The material to be given comes basically from a survey of motor carrier fleets. It provides indications of the role of heavy duty vehicles in the 1998 period and their desired maintenance and engine performance parameters

A review of regulatory instruments to control environmental externalities from the transport sector

This study reviews regulatory instruments designed to reduce environmental externalities from the transport sector. The study finds that the main regulatory instruments used in practice are fuel economy standards, vehicle emission standards, and fuel quality standards. Although industrialized countries have introduced all three standards with strong enforcement mechanisms, most developing countries have yet to introduce fuel economy standards. The emission standards introduced by many developing countries to control local air pollutants follow either the European Union or United States standards. Fuel quality standards, particularly for gasoline and diesel, have been introduced in many countries mandating 2 to 10 percent blending of biofuels, 10 to 50 times reduction of sulfur from 1996 levels, and banning lead contents. Although inspection and maintenance programs are in place in both industrialized and developing countries to enforce regulatory standards, these programs have faced several challenges in developing countries due to a lack of resources. The study also highlights several factors affecting the selection of regulatory instruments, such as countries'environmental priorities and institutional capacities.Transport Economics Policy&Planning,Transport and Environment,Energy Production and Transportation,Environmental Economics&Policies,Environment and Energy Efficiency

Development of a heavy duty diesel vehicle emissions inventory prediction methodology

Emissions from heavy-duty diesel vehicles are known to contribute a substantial fraction of the oxides of nitrogen (NOx), and particulate matter (PM) to the atmospheric inventory. Prediction of heavy-duty diesel vehicle emissions inventory is substantially less mature than the prediction of gasoline car emissions.;Heavy-duty truck emissions are affected by various parameters like vehicle weight/load, driving schedule used, and injection timing control strategies employed to operate the engine at more fuel-efficient (but higher NO x) mode.;Research has revealed a variety of options for inventory prediction, including the use of emissions factors based upon instantaneous engine power and instantaneous vehicle behavior. Effects of various parameters on the heavy-duty diesel emissions were studied in great detail and a speed-acceleration based emissions prediction approach was developed for heavy-duty diesel vehicle emissions prediction. A suite of emissions factor tables was generated for emissions inventory prediction. Driving schedules, vehicle weight, and off-cycle injection strategy were found to affect emissions to varying extents. Detailed analyses of a large body of data enabled to quantitatively as well as qualitatively characterize effect of various parameters on heavy duty diesel vehicle emissions. A doubling of vehicle weight was found to result in roughly a 50% increase in NOx emissions. The accuracy was found to improve with the inclusion of a large number of data covering wide range of model year groups and driving schedules.;Off-cycle operation was found to increase the NOx emissions by more than double. The speed-acceleration model predicted the emissions with reasonable accuracy

DEVELOPMENT OF MACHINE LEARNING ALGORITHM TO IDENTIFY HIGH-EMITTERS FROM ON-ROAD DATA FOR HEAVY-DUTY (HD) VEHICLES

The process of on-road, heavy-duty engine family certification is regulated by the United States Environmental Protection Agency (US EPA). Currently, the US EPA 2010 emissions standards require the threshold from the Federal Testing Procedure (FTP) engine dynamometer cycle to be at or below a brake-specific NOx (bs-NOx) value of 0.20 g/bhp-hr for heavy-duty (HD) engines. The engine manufacturers are also required to conduct in-use portable emission measurement system (PEMS) testing to prove their products\u27 compliance. The selected vehicles are required to satisfy not-to-exceed (NTE) analysis under normal driving conditions in the heavy-duty in-use testing (HDIUT) program. California Air Resources Board (CARB) also independently performs PEMS testing in the heavy-duty in-use compliance (HDIUC) program. The regulatory standards are becoming more stringent on certification level and on-road requirements. Heavy-duty engine manufacturers are also required to satisfy regulatory agencies\u27 current compliance standards for engine certification. However, real-world driving conditions differ from controlled testing environments; hence, the NTE evaluation protocol has been developed to verify emissions compliance under real-world driving conditions. However, while regulatory effort has been made, studies are implying that regulatory measurements are not achieving the desired low emissions levels. The studies show that on-road measurements are higher than the NOx certification and in-use standards. In-use emissions depend highly on the duty-cycle, which dominates the results, especially if the vehicle has a higher idle and low-load operation fraction. The aim of this study was to develop a model structure and to train a model based on chassis dynamometer datasets and subsequently use the trained model in conjunction with PEMS datasets in order to identify vehicles as possible high-NOx emitting vehicles. The long-short term memory (LSTM) model developed based on a single reference vehicle (i.e., Vehicle A) dataset was applied to the entire 12 diesel vehicle PEMS datasets in order to identify high-NOx emitters. The results showed that the vehicles that were manually identified as high emitting vehicles (i.e., control subjects) were also identified by the LSTM model to exceed real-world NOx emissions. The prediction results show that high NOx emitting vehicles exhibited a ratio of predicted-to-measured NOx emissions that were lower than one (1). Similarly, a random forest (RF) model was developed for a reference CNG vehicle (i.e., Vehicle N) and subsequently applied to 11 CNG vehicles with a 0.2 g/bhp-hr NOx regulation limit using PEMS data in order to identify any possible high NOx emitting vehicles. The results showed that the vehicles that were manually labeled as high emitters were also identified by the RF model to exhibit high real-world NOx emissions beyond any properly working vehicle. The prediction results show that high NOx emitting vehicles had a ratio of predicted versus measured NOx emissions that were lower than unity. In addition, the model\u27s high accuracy during the evaluation of the test datasets indicated that the models could potentially be used for predicting the NOx emissions for any random selected vehicle during chassis dynamometer testing for both diesel and 0.2 g/bhp-hr NOx emissions limit CNG vehicles

Environmental Impacts of Heavy-Duty Natural Gas Vehicle Incentives in California

Society has an interest in reducing pollutants emitted from the vehicles used for transporting people and goods. The main goal of heavy-duty natural gas vehicle (NGV) incentive projects is to offer upfront monetary incentives to reduce greenhouse gas emissions and the production of regulated pollutants in the state. However, these incentives are often based on vehicle weight and do not account for environmental impacts. In addition, although heavy-duty NGVs are being used in a variety of vocation types, conventional emission models only support a limited number of these vocation types. Because of this, it is challenging to assess the precise impacts of the heavy-duty NGV (HD NGV) adoption and predict the specific environmental benefits per given operational conditions and vocation type. If government agencies realize the environmental benefits of alternative fuel vehicles (AFVs), like NGVs, with respect to vocation type and operating characteristics, it would be beneficial to design cost-effective incentive structures and implementation plans. This study primarily focused on the operational characteristics and environmental impacts of the HD NGVs incentivized in California. This study conducted pattern clustering and classification analyses to obtain drive mode compositions (DMC) over duty cycles and showed the heterogeneity of operational and emission characteristics of the vocational HD NGVs. The vocational impact analysis computed the adoption impact of 40 NGVs operating in California across ten different vocation types. The proposed evaluation framework included life-cycle nitrogen oxides (NOx) and carbon dioxide (CO2) emissions of natural gas, renewable natural gas and diesel fuel pathways and compared the lifetime NOx emission reduction potential of the considered vocation type vehicles. The resulting emission benefits of the fuel pathways were used to determine the most incentive-effective vocation types among the considered NGV applications. The multi-criteria decision-making analysis prioritized the fuel pathways based on multiple criteria which are related to an incentive effectiveness index as well as life cycle emissions. Refuse truck and transit bus pathways are likely to achieve the highest return for the total incentive granted when the vehicles are renewable natural gas (RNG)-powered. For compressed natural gas (CNG) fuel pathways, school and transit buses take the highest ranks over the various analysis scenarios. Each vocation type showed different incentive effects and emission reduction potential, which means that some vocational vehicles can play a critical role in the state’s funding and emission reduction plans. The suggested decision-making tool and assessment framework can provide useful reference data to improve the performance of future alternative fuel vehicle incentive programs

Does California’s EMFAC2017 Vehicle Emissions Model Under-predict California Light-duty Gasoline Vehicle NOx Emissions?

On-road remote sensing measurements of light and medium-duty gasoline vehicles collected within California’s South Coast Air Basin since 1999 generally fall within the range of observed summer ambient molar NOx/CO measurements collected during morning rush hours. Compared with ambient and on-road emissions, the California Air Resources Board EMFAC model under predicts 2018 gasoline vehicle NOx emission factors by more than a factor of 2.6. Contributing to these differences is that vehicles older than model year 2006 have NOx emission deterioration rates that are up to 4 time’s higher on-road than predicted by the EMFAC model. A fuel-based inventory using the 2018 on-road gasoline emission factors for CO and NOx results in total CO emissions similar to the basin inventory but NOx emissions that are 74% higher than the inventory. The higher NOx emission estimates from on-road gasoline vehicle measurements makes their contribution to the inventory slightly larger than heavy-duty diesel vehicles. We have found LEV I (1994 - 2003) gasoline vehicles are a major source of these on-road emissions and that significant NOx reductions in the South Coast Air Basin are being overlooked by not targeting the high emitters for removal