Shifting primary energy source and NO <sub> <i>x</i> </sub> emission location with plug-in hybrid vehicles
Bibliographic record
Abstract
Plug-in hybrid vehicles (PHEVs) present an interesting technological opportunity for using non-fossil primary energy in light duty passenger vehicles, with the associated potential for reducing air pollutant and greenhouse gas emissions, to the extent that the electric power grid is fed by non-fossil sources. This perspective, accompanying the article by Thompson et al (2011) in this issue, will touch on two other studies that are directly related: the Argonne study (Elgowainy et al 2010) and a PhD thesis from Utrecht (van Vliet 2010). Thompson et al (2011) have examined air quality effects in a case where the grid is predominantly fossil fed. They estimate a reduction of 7.42 tons/day of NO x from motor vehicles as a result of substituting electric VMTs for 20% of the light duty gasoline vehicle miles traveled. To estimate the impact of this reduction on air quality they also consider the increases in NO x emissions due to the increased load on electricity generating units. The NO x emission increases are estimated as 4.0, 5.5 and 6.3 tons for the Convenience , Battery and Night charging scenarios respectively. The net reductions are thus in the 1.1–3.4 tons/day range. The air quality modelling results presented show that the air quality impact from a ground-level ozone perspective is favorable overall, and while the effect is stronger in some localities, the difference between the three scenarios is small. This is quite significant and suggests that localization of the NO x emissions to point sources has a more pronounced effect than the absolute reductions achieved. Furthermore it demonstrates that localization of NO x emissions to electricity generating units by using PHEVs in vehicle traffic has beneficial effects for air quality not only by minimizing direct human exposure to motor vehicle emissions, but also due to reduced exposure to secondary pollutants (i.e. ozone). In an electric power grid with a smaller share of fossil fired generating units, the beneficial effects would be more pronounced. In such a case, it would also be possible to realize reductions in greenhouse gas emissions. The significance of the electric power generation mix for plug-in hybrid vehicles and battery electric vehicles is a key aspect of Argonne National Laboratories' well-to-wheel study which focuses on petroleum use and greenhouse gas emissions (Elgowainy et al 2010). The study evaluates possible reductions in petroleum use and GHG emissions in the electric power systems in four major regions of the United States as well as the US average generation mix, using Argonne's GREET life-cycle analysis model. Two PHEV designs are investigated through a Powertrain System Analysis Toolkit (PSAT) model: the power-split configuration (e.g. the current Toyota Prius model with Hymotion conversion), and a future series configuration where the engine powers a generator, which charges a battery that is used by the electric motor to propel the vehicle. Since the petroleum share is small in the electricity generation mix for most regions in the United States, it is possible to achieve significant reductions in petroleum use by PHEVs. However, GHG reduction is another story. In one of the cases in the study, PHEVs in the charge depleting mode and recharging from a mix with a large share of coal generation (e.g., Illinois marginal mix) produce GHG emissions comparable to those of baseline gasoline internal combustion engine vehicles (with a range from −15% to +10%) but significantly higher than those of gasoline hybrid electric vehicles (with a range from +20% to +60%). In what is called the unconstrained charging scenario where investments in new generation capacity with high efficiency and low carbon intensity are envisaged, it becomes possible to achieve significant reductions in both petroleum use and GHG emissions. In a PhD dissertation at Utrecht University, van Vliet (2010) presents a comprehensive analysis of alternatives to gasoline and diesel by looking at various fuel and vehicle technologies. Three chapters are of particular interest from the perspective of PHEVs: (2) Techno-economic comparison of series hybrid, fuel cell and regular cars ; (3) Energy use, cost and CO 2 emissions of electric cars ; and (4) Combining hybrid cars and synthetic fuels with electricity generation and carbon capture and storage . The study is noteworthy not only for the technical analyses and quantitative cost comparisons, but also for addressing questions relating to the transition from the current state of affairs to future 'optimal' scenarios. Multiple transportation fuel technologies/options (9 different fuels produced with 23 different technologies), vehicle technologies (36 types of cars, buses, trucks, and vans), and electric power generation technologies are considered under nine policy based scenarios. It is not possible to do justice to the thoroughness of the thesis within the context of this brief perspective, but one quote from the thesis may be appropriate: 'Across scenarios, time periods and reduction targets, our least-cost optimal configurations show a preference for biofuels and hybrid cars over electric or fuel cell cars. In addition to having lower costs, this allows for an easier transition as less infrastructure change is required to support hybrid cars than to facilitate large scale use of electric or hydrogen fuel cell cars.' Without forgetting that the analysis is specific to its setting in the Netherlands, it is nevertheless a challenging starting point for similar analyses elsewhere. The accompanying article to this perspective and the studies mentioned above point to the interest in, and the challenges associated with PHEV technology, its adoption and implementation over a realistic time frame, in different geographic regions. Elgowainy et al (2010) estimate the penetration of PHEV technology as 10% share of PHEVs in the 2020 US vehicle population. In one of van Vliet's (2010) scenarios ( Forced Electric Car ) a target of 90% share in 2050 for electric/fuel cell cars in the Netherlands is used. It is not possible to scrutinize here whether these are realistic estimates/scenarios, but it is clear that we can expect a significantly expanded role for electricity as an energy carrier in transportation. PHEVs are likely to play an important role in this transition. References Elgowainy A, Han J, Poch L, Wang M, Vyas A, Mahalik M and Rousseau A 2010 Well-to-Wheels Analysis of Energy Use and Greenhouse Gas Emissions of Plug-In Hybrid Electric Vehicles ANL/ESD/10-1 (Argonne, IL: Energy Systems Division, Argonne National Laboratory) (available at: http://greet.es.anl.gov/publication-xkdaqgyk ) Thompson T M, King C W, Allen D T and Webber M E 2011 Air quality impacts of plug-in hybrid electric vehicles in Texas: evaluating three battery charging scenarios Environ. Res. Lett. 6 024004 van Vliet O P R 2010 Feasibility of alternatives to driving on diesel and petrol PhD Thesis Utrecht University, The Netherlands (available at: http://igitur-archive.library.uu.nl/dissertations/2010-0819-200206/UUindex.html )
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".