An examination of heavy-duty trucks drivetrain options to reduce GHG emissions in British Columbia
Date
2020-01-03
Authors
Lajevardi, S. Mojtaba
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Abstract
Heavy-duty trucks (HDTs) are vital in delivering products to the consumers around the world and help maintain the quality of life. However, they are heavily depending on fossil diesel use, which causing global climate change as well as local air pollutions. Although they represent a small percentage of vehicle population, they emit more than 30% of GHGs in road transportation or 5% of global greenhouse gas (GHG) emissions. Furthermore, GHG emissions from this sector are expected to steadily grow due to economic growth, globalization, industrialization, online shopping, and fast delivery expectations.
This study was focused on the Canadian province of British Columbia (BC) as a case study where HDTs are responsible for more than 4% of total provincial GHGs. BC, along with many regions around the world, has been committed to reduce its GHG emissions by 80% below 2007 levels by 2050. The goal of this study was to evaluate the potential of meeting this objective for BC HDTs using alternative drivetrain technologies. First, a component-level model was developed in Matlab to compute on-road energy consumption and CO2 emissions of compressed natural gas and diesel HDTs based on their physical parameters (e.g. mass) over several selected drive cycles. Results of the first contribution indicated a compressed natural gas (CNG) drivetrain emits 13-15% fewer GHG than a comparable diesel. Road grades of several main BC routes were included in the drive cycle simulations, which is an important factor that can increase the fuel consumption and CO2 emission by as much as 24% relative to a flat route assumption.
In the second contribution, the physical energy consumption model was extended to compare 16 diverse drivetrain technologies, including a pure battery electric. The comparison metrics were also extended to well-to-wheel GHG emissions, total ownership costs (TOC) (including infrastructure), and abatement costs (based on incremental TOC cost over GHG emissions reduction), and cargo capacity impacts. The 16 considered drivetrains were distinguished by their fuel types, combustion technology, drivetrain architecture, and connection to the electricity grid (e.g. catenary vs fast charging stations). Next, the activity data of 1,616 HDTs operating in BC with sparse recording intervals was used to select 6 representative freight routes with different ranges of 120-950 km split into short and long haul routes. A combination of filtering and interpolation techniques was used to develop 1-Hz drive cycles compatible with the characteristic of HDTs categorized by the U.S. National Renewable Energy Laboratory. Results indicated a battery electric and battery electric catenary using hydroelectricity emits 95–99% lower GHGs than a baseline diesel. Furthermore, the parallel hybrid diesel was found to have both the lowest TOC and abatement costs for both short and long haul routes. Moreover, plug-in parallel hybrid fuel cell and conventional diesel drivetrains were found to have the highest cargo capacity on short and long haul routes respectively. Finally, a Monte Carlo analysis using 5000 simulations was performed for the longest freight routes to observe sensitivities to input parameters. Comparing median magnitudes, the uncertainty analysis indicated that the battery electric drivetrain has the lowest WTW GHG emissions, while the parallel hybrid diesel drivetrain has the lowest TOC.
In the third contribution, the energy consumption models that developed in chapter 2 and 3 were used to represent drivetrains (with a high technical resolution) in a dynamic vehicle adoption model to provide a realistic picture of emerging drivetrains under different scenarios up to 2050. Using the dynamic vehicle adoption model the diffusion rate of alternative drivetrains HDT was projected up to 2050 considering two zero emission vehicle (ZEV) mandates and various infrastructure roll-out scenarios. The HDT market was split into short and long haul segments. The vehicle adoption model was combined with a Monte Carlo analysis to evaluate the collective impact of input parameter variations on GHG emissions and market share projections. Both considered ZEV mandates included a linear adoption rate for ZEV drivetrains starting from 25% in 2025 and reaching 100% by 2040. They were also distinguished based on a constraint on the level of plug-in hybrid adoption. It was found infrastructure density increases the probability of meeting the 2050 target on both short and long haul HDTs. However, the increase in the probability is much higher for the short haul segment. Among various infrastructure roll-out scenarios, rapid deployment of hydrogen fueling stations was found to have the highest positive impact on GHG emissions reduction for both short and long haul markets. Both battery electric and hydrogen fuel cell drivetrains can succeed in the short haul market, depending on whether the infrastructure development is toward charging or H2 station deployments. A similar result was found for the long haul market, except in all scenarios plug-in hybrid diesel captures market domination. Fuel cell was found as the second drivetrain option for long haul market that gains domination in most scenarios.
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Keywords
Alternative drivetrains, CO2 emissions, Compressed natural gas (CNG), Diesel, Energy consumption, Physical energy consumption model, Drive cycle, Freight, Battery electric, Fuel cell, Greenhouse gas (GHG) emissions, Heavy-duty truck, Hybrid, Parallel, Series, Hydrogen, Long haul, Monte Carlo, Market share, ZEV mandate, Winner, Well to wheel, Short haul, Tractor-trailer, Infrastructure, Total ownership cost, Vehicle adoption model, Consumer behavior, Intangible cost, Plug-in hybrid