Tailpipe CO2 Emissions Research Articles

A comparative analysis of car fleet efficiency evolution in Europe and Australia insights on policy influence

Regulators worldwide introduce measures to improve energy consumption and reduce greenhouse emissions of road vehicles. The present study focuses on the impacts of passenger car carbon dioxide (CO2) and fuel efficiency measures, attempting to evaluate their effectiveness using vehicle and fleet modelling. To obtain a robust counterfactual result, two regions are compared as a case study: the European Union (EU), where CO2 emissions have been regulated for over fifteen years, and Australia, a region that recently introduced such a policy element. The average difference in the certified CO2 emissions of new cars registered in the two regions increased from 50 g/km in 2018 to 60 g/km in 2021 due to accelerated electrification in the EU. To demonstrate the importance of mandatory targets, a sensitivity analysis of the 2020 EU registrations showed that lower by one-third sales of zero and low-emission vehicles (Z-L-EVs) would have resulted in seven out of ten manufacturer pools failing to meet the 2020 target of 95 g/km. The simulation framework was validated against certified values of 2021 registrations and was subsequently used to calculate real-world fleet performance to quantify the actual emissions savings. The real-world CO2 emissions in the registered fleets for 2021 were estimated to be 143 g/km and 204 g/km, for the EU and Australia, respectively. With these results as a reference, the share of ZEVs required to meet the targets set in both regions is calculated: 51% of the new registrations in 2030 in the EU for achieving 49.5 g/km, and 60% in Australia for recently set target of 58 g/km for 2029. The respective calculated average fleet-wide real-world tailpipe CO2 emissions were estimated to be 66 g/km (EU) and 77 g/km (Australia). As a last step, the study discusses the application of similar tools and analysis in the design of future policies.

Development and validation of a predictive combustion model for hydrogen-fuelled internal combustion engines

Internal combustion engines (ICEs) fuelled with hydrogen can play a major role in the short-term future transportation sector since they abate all criteria pollutants at engine-out reducing tailpipe CO2 emissions to near-zero levels. However, optimizing hydrogen ICEs is a challenging task that can be addressed through the development of a robust simulation tool capable to predict the H2 combustion process. In this study, a previously developed two-zone combustion model has been updated considering different laminar flame speed computations, both based on a detailed chemistry scheme: a polynomial correlation function and a tabulated approach. The predictive capabilities of the combustion model have been validated against experimental data coming from a 0.5L PFI single-cylinder engine under several operating conditions. The tabulated approach for laminar flame speed definition proved to be the best solution, leading to a combustion duration average error lower than 3 deg over a dataset containing more than 45 different operating conditions.

A simulation model of the real-world fuel and energy consumption of light-duty vehicles

The European Union has intensified efforts to reduce CO2 emissions from the transport sector, with the target of reducing tailpipe CO2 emissions from light-duty vehicle new registrations by 55% by 2030 and achieving zero emissions by 2035 according to the “Fit for 55” package. To promote fuel and energy consumption awareness among users under real-world conditions the MILE21—LIFE project provided tools such as a self-reporting tool and a find-a-car tool that included the official and representative on-road fuel/energy consumption values. In order to produce representative values, an in-house vehicle longitudinal dynamics simulation model was developed for use in the background of the on-line platform utilizing only a limited amount of inputs. To achieve this, the applied methodology is based on precalculated efficiency values. These values have been produced using vehicle micro-model simulations covering a wide range of operating conditions. The model was validated using measurements from a dedicated testing campaign and performed well for petrol vehicles with an average divergence of −1.1%. However, the model showed a divergence of 9.7% for diesel vehicles, 10.6% for hybrids and 8.7% for plug-in hybrids. The model was also applied to US vehicles and showed a divergence of 1.2% and 10% for city and highway driving, respectively. The application of the developed model presented in this work showed that it is possible to predict real-world fuel and energy consumption with the desired accuracy using a simplified approach with limited input data.

Prediction of heavy-duty engine performance for renewable fuels based on fuel property characteristics

Renewable drop-in fuels can bring timely and efficient defossilization of the current fleet of heavy-duty vehicles. In the present study, different blends of renewable components with standard diesel were analyzed in the context of end-use performance. Based on experimental data from driving cycles, a novel modeling approach was applied to develop a state-of-the-art mathematical model that enables an accurate estimation of fuel consumption and tailpipe CO2 emissions from heavy-duty vehicles relying solely on fuel properties. The predictions revealed strong agreements with experimental data confirmed by the high coefficient of determination (0.975). The final model represents fuel properties’ collective impact on heavy-duty vehicle’s fuel economy over the Braunschweig cycle where heating value, density, and cetane number showed the strongest impact (p-values <0.01). The developed model was applied to simulate the effect of alternative diesel fuels on end-use performance. The increase in mass-based fuel consumption was observed for FAME (14%), oxymethylene ether blends (up to 65%), moderate contents of butanol and pentanol blends (up to 11%), while neat HVO improved fuel economy (6%). The introduced model can be applied to the assessment of renewable liquid fuel blends in heavy-duty transport and serves as a support for industry and decision-makers.

The potential of dimethyl ether (DME) to meet current and future emissions standards in heavy-duty compression-ignition engines

Dimethyl Ether (DME, H3C-O-CH3) is regarded as a meaningful alternative fuel as it has a comparably high energy density, can easily be stored on vehicles, can be produced via different renewable pathways, has a high Cetane number and is therefore suitable for use in efficient and durable compression ignition engines. Moreover, the DME molecule lacks carbon bonds and due to the inbuilt oxygen atom enables low-sooting diffusion combustion. This study reports on the performance and emission characteristics of a state-of-the-art heavy-duty compression ignition engine with 11 L displacement which was optimized specifically for DME combustion. This includes the adaptation of the fuel supply and injection system as well as the combustion chamber geometry. In addition, the engine is equipped with an electrically driven volumetric pump in the exhaust gas recirculation (EGR) path which gives the freedom to set any desired EGR rate, independently from the pressure ratios across the turbocharger. The experimental results show that DME operation retains the performance on the base Diesel engine (338 kW peak power) while significantly reducing the engine out emissions. The NOx-soot tradeoff, typical of Diesel applications, basically does not exist anymore so that a desired NOx level can basically be set without the restrictions of sooting combustion. In addition, the combustion is very complete and very low levels of regulated and non-regulated pollutant emissions can be detected (e.g. for carbon monoxide, DME, methane, benzene, toluene, acetaldehyde, formaldehyde, formic acid). Transient operation is unproblematic and very low emissions can be achieved using the same exhaust gas treatment system as for diesel. Actually, total hydrocarbon, carbon monoxide and particle emission levels of the actual EuroVI and possibly even for the proposed EuroVII on-road legislation could be met without any exhaust gas purification. In terms of tailpipe CO2 emissions, a reduction of around 11% is possible compared with a diesel-fueled engine.

Advancements in hydrogen production, storage, distribution and refuelling for a sustainable transport sector: Hydrogen fuel cell vehicles

Hydrogen is considered as a promising fuel in the 21st century due to zero tailpipe CO2 emissions from hydrogen-powered vehicles. The use of hydrogen as fuel in vehicles can play an important role in decarbonising the transport sector and achieving net-zero emissions targets. However, there exist several issues related to hydrogen production, efficient hydrogen storage system and transport and refuelling infrastructure, where the current research is focussing on. This study critically reviews and analyses the recent technological advancements of hydrogen production, storage and distribution technologies along with their cost and associated greenhouse gas emissions. This paper also comprehensively discusses the hydrogen refuelling methods, identifies issues associated with fast refuelling and explores the control strategies. Additionally, it explains various standard protocols in relation to safe and efficient refuelling, analyses economic aspects and presents the recent technological advancements related to refuelling infrastructure. This study suggests that the production cost of hydrogen significantly varies from one technology to others. The current hydrogen production cost from fossil sources using the most established technologies were estimated at about $0.8–$3.5/kg H2, depending on the country of production. The underground storage technology exhibited the lowest storage cost, followed by compressed hydrogen and liquid hydrogen storage. The levelised cost of the refuelling station was reported to be about $1.5–$8/kg H2, depending on the station's capacity and country. Using portable refuelling stations were identified as a promising option in many countries for small fleet size low-to-medium duty vehicles. Following the current research progresses, this paper in the end identifies knowledge gaps and thereby presents future research directions.

An Experimental and Numerical Investigation to Improve the Efficiency of Combustion Systems for Heavy-Duty Applications

Abstract The European Union made new CO2 limits for heavy-duty vehicles mandatory in 2019, aiming to reduce the average tailpipe CO2 emissions by 15% in 2025 and by 30% in 2030, relative to the 2019 baseline. To meet these challenges, new technologies are needed to further reduce the fuel consumption of new heavy-duty trucks (“tank to wheel”) by using more efficient engines. The present study shows the pathways to improve thermal efficiency through better mixture formation and air utilization. This is achieved by designing a new piston bowl shape that has been numerically optimized iteratively to improve the air/fuel mixing by carefully considering the interaction between the fuel spray plume and the piston bowl. The computational fluid dynamics (CFD) model was calibrated using data from experimental studies with a heavy-duty single-cylinder diesel engine in the best efficiency and rated power operation. Heat transfer and turbulence models are studied to determine their influence on the combustion process and NOX emissions. Indicated thermal efficiency and air utilization were used to evaluate the performance of the piston bowl shape. For three different bowl shapes, the fuel spray interaction with the piston bowl was investigated and compared with the base bowl shape with a compression ratio CR = 18.3. Moreover, the effect of a higher CR of 21 on performance and mixture formation was analyzed for the optimized bowl shape. The higher CR of 21 was attained by a geometrically similar reduction of the optimized piston bowl. The results of the numerical and experimental investigations show that a CR of 21 leads to an increase in the indicated thermal efficiency of ∼3% in absolute values.

Assessment of a Second Life City Vehicle Refurbished to Include Hybrid Powertrain Technology

Due to increased powertrain efficiency, electrified propulsion has seen significant diffusion in the automotive sector in recent years. Despite the possible reduction in tailpipe CO2 emissions, the advancements in the technology are not sufficient to tackle the challenge of global greenhouse emissions. An additional action could be the use of second life vehicles to drastically reduce the emissions associated with vehicle manufacturing and recycling/disposal. Urban vehicles are the most suitable to be electrified due to the large start-and-stop cycling and the possibility of using regenerative braking. Therefore, this work considered the hypothesis of hybridizing a small size passenger car with parallel and Series technology. The powertrain is designed for an old vehicle suitable for second life use after refurbishment. A numerical model of the propulsion components was built and applied after previous validation in homologation conditions. Several urban cycles representative of European cities were considered. The final hybrid model is compared with two baselines: non-hybrid and pure electric version already lunched in the market by the manufacturer. The findings indicate that used HEV cars could be a viable option for cutting CO2 emissions from city vehicles without reducing their range. In comparison to non-hybrid vehicles, the series can typically reduce CO2 emissions by 41%, compared to the P2’s 32%.

Improving the efficiency of patient diagnostic specimen collection with the aid of a multi-modal routing algorithm

The Sustainable Specimen Collection Problem (SSCP), in which diagnostic specimens are collected from GP surgeries (doctor’s office/clinics) and subsequently transported to a hospital laboratory for analysis using more sustainable transport modes, is introduced in this paper. Using a weighted objective function, we solve a new multi-objective problem using cycle consolidation to limit driving time and the numbers of vans used whilst improving overall service quality, reducing costs and emissions. This particular heterogeneous vehicle routing problem is explored and applied to two real-world case studies in the UK, where 97 and 22 sites (respectively) are currently served, using a column generation based heuristic algorithm with some additional improvement heuristics. The results demonstrated a potential improvement in the system’s maximum delivery time between 41% and 74% compared to business-as-usual activity using solely road vehicles. Road vehicle (van) fleets could be reduced by up to 40%, and the total driving time across the fleet by between 41% and 65%. Operational costs were estimated to increase by up to 38%, though additional workloads for gig-economy cycle couriers and improvement in specimen quality and service reliability may make this trade-off worthwhile. Tailpipe CO2 emissions were also reduced by up to 43%. The proposed algorithm was effective, reducing computational time by up to 99% whilst achieving an average of 5% deviation from optimality.

Additive manufacturing new piston design and injection strategies for highly efficient and ultra-low emissions combustion in view of 2030 targets

Internal combustion engines have been the most effective solution as the main mover for mobility. Despite the recent European regulations targeting zero tailpipe CO2 emissions for new passenger cars and light vans from 2035 onwards, the upcoming emission regulations still require further technology and fuel developments in the transport sectors. In recent years, studies on internal combustion engines have been increasingly shifting from performance and refinement through advanced fuel injection and air charging technologies to ultra-low emissions and efficiency. Particular attention has also been paid to carbon–neutral fuels. The problem merits further investigation since all are key factors for the powertrain competitiveness in the electrified automotive future, as the pollutants and the CO2 emissions will soon approach the Euro 7 regulation and the Fit to 55 standards, respectively. To this aim, the combustion system design is a crucial part of the internal combustion engine development in the view of exploiting renewable fuel characteristics. This study seeks to assess the effectiveness of a steel piston, realized through a specific design for additive manufacturing technologies, enabling an innovative bowl geometry. It features a highly re-entrant sharp-step and radial lips bowl profile enabling spray separation and radial mixing zone concept. A single-cylinder compression ignition engine has been employed to demonstrate the effectiveness of the innovative bowl shape to meet the challenging future ultra-low emission targets. The results confirm the additive manufacturing technology as a possible solution for innovative highly-stressed steel piston designs. The new bowl shape design, combined with optimised spray targeting and injection strategies, allows outstanding soot reductions, up to 80%, and ultra-low NOx emission levels compared to the conventional combustion system. Gains in fuel economy are also observed. Further emissions advantages are expected with renewable fuel applications.

Finding an energy efficient path for plug-in electric vehicles with speed optimization and travel time restrictions

Transportation is one of the main factors when global total energy consumption is considered and is a significant contributor to emissions of harmful gases including carbon dioxide (CO2). Due to their lower tailpipe CO2 emissions compared to the vehicles with internal combustion engines, electric vehicles provide an opportunity to reduce environmental impacts of transportation. In this direction, a problem for plug-in electric vehicles (PEVs) is studied where the aim is to find an energy efficient path. Given an origin–destination pair over a directed network, this problem involves determining a path joining origin and destination, the speed of the PEV on each road segment, i.e., arc, along the path, the charging stations the PEV will stop by, and how much to recharge at each stop so as to minimize the total amount energy consumption. There are speed limits on each road segment, and PEV has to arrive at the destination on or before a given total time limit. For this problem, firstly, a mixed-integer second order cone programming formulation (MISOCP) is proposed. Secondly, to be able to solve larger size instances, a matheuristic is developed. Lastly, an iterated local search (ILS) algorithm is designed for this problem. Solution quality and computation times of the heuristics and the exact algorithm are compared on different instances. Differently from the literature, the speed values of the PEV on the arcs are considered as continuous decision variables in all proposed solution approaches. Moreover, consideration of the speed limits which can be legal limits or limits imposed by congestion makes our problem more realistic. The analysis of the results of the computational experiments gives the user an insight to select the proper solution approach based on the instance settings. MISOCP formulation becomes inadequate for larger instances. On the other hand, the heuristic solution approaches can solve such instances within reasonable computational times and therefore they have the potential to be integrated in some software to dynamically find energy efficient paths.

A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions

Hybrid electric vehicles (HEV) are nowadays proving to be one of the most promising technologies for the improvement of the fuel economy of several transportation segments. As far as the on-road category is concerned, a wise selection of the powertrain design is needed to exploit the best energetic performance achievable by a HEV. Amongst the methodologies developed for comparing different hybrid architectures, global optimizers have demonstrated the capability of leading to optimal design solutions at the expense of a relevant computational burden. In the present paper, an innovative deep neural networks-based model for the prediction of tank-to-wheel carbon dioxide emissions as estimated by a Dynamic Programming (DP) algorithm is presented. The model consists of a pipeline of neural networks aimed at catching the correlations lying between the design parameters of a HEV architecture and the main outcomes of the DP, namely powertrain feasibility and tail pipe CO2 emissions. Moreover, an automatic search tool (AST) has been developed for tuning the main hyper-parameters of the networks. Interesting results have been registered by applying the pipeline to three databases related to three different HEV parallel architectures. The capability of the pipeline has been proved through an extensive testing campaign made up by multiple experiments. Classification performances above 91% as well as average regression errors below 1% have been achieved during an extensive set of simulations. The presented model could hence be considered as an effective tool for supporting HEV design optimization phases.

Sensitivity of pollutants abatement in oxidation catalysts to the use of alternative fuels

The aim to reduce well to wheel CO2 emissions incentives the utilisation of alternative fuels (low to zero carbon content and/or low well to tank CO2 emissions) as well as the enhancement of engine efficiency. In parallel, the reduction of engine tailpipe CO2 emissions brings new challenges such as the decrease of the exhaust gas temperature. This trend penalises the ability of the exhaust aftertreatment system to eliminate pollutant emissions. In addition, the combustion of alternative fuels and new combustion modes induce changes in the nature and concentration of the exhaust species, which is known to affect the pollutants abatement mechanisms. This investigation provides new understanding on the sensitivity of pollutants abatement in oxidation catalysts to the use of alternative fuels. The studied fuels are conventional diesel, alternative fuels (rapeseed methyl ester and gas to liquid) as well as propane using a dual-fuel combustion strategy. The research combines experimental conversion efficiency from genuine exhaust gases with modelling work useful to explain the reasons for the change in light-off temperature as a function of the fuel. In addition to the CO and NO impact, HC surrogates are proposed distinguishing species of different reactivity for each fuel based on the experimental HC speciation. The results highlight the role of the engine-out emissions on the pollutants conversion efficiency. Their fashion with different fuels contributes to evidence the interest for low engine-out emissions along with low light alkanes content in total HC, as promoted by alternative fuels, to reduce the oxidation light-off temperature.

Overview of Clean Automotive Thermal Propulsion Options for India to 2030

This paper presents the evaluation of near-future advanced internal combustion engine technologies to reach near zero-emission in vehicles with in the Indian market. Extensive research was carried out to propose the rationalise the most promising, new ICE technologies which can be implemented in the vehicles to reduce CO2 emissions until the year 2030. A total of six technologies were considered that could be implemented in the Indian market. An initial market survey was carried out on the Indian automotive industry and electric vehicles in India, followed by an in-depth analysis and understanding of each technology through literature review. The main aim of the paper was to construct methods for a successful implementation of clean ICE technologies in the near future and to, also, predict a percentage reduction of CO2 tailpipe emissions from the vehicles. To do this, different objectives were laid out with a view to reducing the tailpipe CO2 emissions. Especially with the recent and legitimate focus on climate change in the world, this study aims to provide practical solutions pathway for India. Widespread research was carried out on all six technologies proposed within the automotive market in India and a set of main graphs represent CO2 emission reduction starting from 2020 until 2030. A significant reduction of CO2 was observed in the graph plot at the end of the paper and the technologies were successfully implemented for the Indian market to curb tailpipe CO2 emissions. A methodology based on calculating the vehicle fuel consumption was implemented and a graph was plotted showing the reduction of CO2 emissions until 2030. The starting point of the graph is 2020, when BS-VI comes into effect in India (April 2020). The CO2 limit taken into consideration here has been defined by the Government at 113 CO2 g/km. The paper fulfilled the aim of predicting the effects of implementing the technologies and the subsequent reductions of CO2 emissions for India.

Modelling the impacts of EU countries\u2019 electric car deployment plans on atmospheric emissions and concentrations

The purpose of this work is to quantify key environmental impacts of electric vehicles deployment in the European Union. This is achieved by soft-linking three models (PRIMES-TREMOVE, DIONE and SHERPA) to explore a base and an alternative scenario. The alternative scenario draws on the assessment of the national policy frameworks for alternative fuels infrastructure requested by the Directive (2014/94/EU). Five environmental indicators are examined: tailpipe CO2, NOx and PM2.5 emissions as well as NO2 and PM2.5 urban background concentrations. By 2030, car travel activity is simulated to generate ca. 425 MtCO2/year in the EU28 under the alternative scenario. Compared to the base scenario, electric vehicles contribute to a 3% reduction in tailpipe CO2 emissions. Only two countries attain CO2 emission reductions greater than 10% in the model. The need for a higher level of policy ambition towards the deployment of less polluting vehicles in Europe is highlighted as a conclusion.

Reducing CO2 emissions of conventional fuel cars by vehicle photovoltaic roofs

The European Union has adopted a range of policies aiming at reducing greenhouse gas emissions from road transport, including setting binding targets for tailpipe CO2 emissions for new light-duty fleets. The legislative framework for implementing such targets allows taking into account the CO2 savings from innovative technologies that cannot be adequately quantified by the standard test cycle CO2 measurement. This paper presents a methodology to define the average productivity of vehicle-mounted photovoltaic roofs and to quantify the resulting CO2 benefits for conventional combustion engine-powered passenger cars in the European Union. The method relies on the analysis of a large dataset of vehicles activity data, i.e. urban driving patterns acquired with GPS systems, combined with an assessment of the shading effect from physical obstacles and indoor parking. The results show that on average the vehicle photovoltaic roof receives 58% of the available solar radiation in real-world conditions, making it possible to reduce CO2 emissions from passenger cars in a range from 1% to 3%, assuming a storage capacity of 20% of the 12 V battery dedicated to solar energy. This methodology can be applied to other vehicles types, such as light and heavy-duty, as well as to different powertrain configurations, such as hybrid and full electric.

The Effect of Compression Ratio, Fuel Octane Rating, and Ethanol Content on Spark-Ignition Engine Efficiency.

Light-duty vehicles (LDVs) in the United States and elsewhere are required to meet increasingly challenging regulations on fuel economy and greenhouse gas (GHG) emissions as well as criteria pollutant emissions. New vehicle trends to improve efficiency include higher compression ratio, downsizing, turbocharging, downspeeding, and hybridization, each involving greater operation of spark-ignited (SI) engines under higher-load, knock-limited conditions. Higher octane ratings for regular-grade gasoline (with greater knock resistance) are an enabler for these technologies. This literature review discusses both fuel and engine factors affecting knock resistance and their contribution to higher engine efficiency and lower tailpipe CO2 emissions. Increasing compression ratios for future SI engines would be the primary response to a significant increase in fuel octane ratings. Existing LDVs would see more advanced spark timing and more efficient combustion phasing. Higher ethanol content is one available option for increasing the octane ratings of gasoline and would provide additional engine efficiency benefits for part and full load operation. An empirical calculation method is provided that allows estimation of expected vehicle efficiency, volumetric fuel economy, and CO2 emission benefits for future LDVs through higher compression ratios for different assumptions on fuel properties and engine types. Accurate "tank-to-wheel" estimates of this type are necessary for "well-to-wheel" analyses of increased gasoline octane ratings in the context of light duty vehicle transportation.

Hybrid-Electric Passenger Car Carbon Dioxide and Fuel Consumption Benefits Based on Real-World Driving.

Hybrid-electric vehicles (HEVs) have lower fuel consumption and carbon dioxide (CO2) emissions than conventional vehicles (CVs), on average, based on laboratory tests, but there is a paucity of real-world, on-road HEV emissions and performance data needed to assess energy use and emissions associated with real-world driving, including the effects of road grade. This need is especially great as the electrification of the passenger vehicle fleet (from HEVs to PHEVs to BEVs) increases in response to climate and energy concerns. We compared tailpipe CO2 emissions and fuel consumption of an HEV passenger car to a CV of the same make and model during real-world, on-the-road network driving to quantify the in-use benefit of one popular full HEV technology. Using vehicle specific power (VSP) assignments that account for measured road grade, the mean CV/HEV ratios of CO2 tailpipe emissions or fuel consumption defined the corresponding HEV "benefit" factor for each VSP class (1 kW/ton resolution). Averaging over all VSP classes for driving in all seasons, including temperatures from -13 to +35 °C in relatively steep (-13.2 to +11.5% grade), hilly terrain, mean (±SD) CO2 emission benefit factors were 4.5 ± 3.6, 2.5 ± 1.7, and 1.4 ± 0.5 for city, exurban/suburban arterial and highway driving, respectively. Benefit factor magnitude corresponded to the frequency of electric-drive-only (EDO) operation, which was modeled as a logarithmic function of VSP. A combined model explained 95% of the variance in HEV benefit for city, 75% for arterial and 57% for highway driving. Benefit factors consistently exceeded 2 for VSP classes with greater than 50% EDO (i.e., only city and arterial driving). The reported HEV benefits account for real-world road grade that is often neglected in regulatory emissions and fuel economy tests. Fuel use HEV benefit factors were 1.3 and 2 for the regulatory highway (HWFET) and city (FTP) cycles, respectively, 18% and 31% higher than the EPA adjusted fuel economy values. This study establishes the significant need for high-resolution vehicle activity and road grade data in transportation data sets to accurately forecast future petroleum and GHG emissions savings from hybridization of the passenger vehicle fleet.

SiC—Emerging Power Device Technology for Next-Generation Electrically Powered Environmentally Friendly Vehicles

The automotive industry is developing a range of electrically powered environmentally friendly vehicles such as hybrid vehicles (HVs), plug-in hybrid vehicles, full electric vehicles, and fuel cell vehicles to help reduce tailpipe CO2 emissions and achieve energy diversification. HVs are regarded as one of the most practical types of environmentally friendly vehicle and have already been widely accepted in the market. Toyota Motor Corporation has positioned HV systems as a core technology that can be applied to all next-generation electrically powered environmentally friendly vehicles and is currently working to enhance the performance of HV system components. Because of its low loss and high-temperature operation properties, silicon carbide (SiC) is regarded as a highly promising material for power semiconductor devices to help reduce the size and weight of the power control unit, one of the key components of a HV system. Wide-ranging activities are under way to meet the challenges of adopting SiC in an automotive environment, such as the development of crystal growth technologies, device structures, process technologies, defect analysis, and application to on-board systems. This paper describes the current situation and future prospects for on-board SiC power devices and the development of SiC-based technologies.

Intermediate Alcohol-Gasoline Blends, Fuels for Enabling Increased Engine Efficiency and Powertrain Possibilities

The present study experimentally investigates spark-ignited combustion with 87 AKI E0 gasoline in its neat form and in mid-level alcohol-gasoline blends with 24% vol./vol. iso-butanol-gasoline (IB24) and 30% vol./vol. ethanol-gasoline (E30). A single-cylinder research engine is used with a low and high compression ratio of 9.2:1 and 11.85:1 respectively. The engine is equipped with hydraulically actuated valves, laboratory intake air, and is capable of external exhaust gas recirculation (EGR). All fuels are operated to full-load conditions with =1, using both 0% and 15% external cooled EGR. The results demonstrate that higher octane number bio-fuels better utilize higher compression ratios with high stoichiometric torque capability. Specifically, the unique properties of ethanol enabled a doubling of the stoichiometric torque capability with the 11.85:1 compression ratio using E30 as compared to 87 AKI, up to 20 bar IMEPg at =1 (with 15% EGR, 18.5 bar with 0% EGR). EGR was shown to provide thermodynamic advantages with all fuels. The results demonstrate that E30 may further the downsizing and downspeeding of engines by achieving increased low speed torque, even with high compression ratios. The results suggest that at mid-level alcohol-gasoline blends, engine and vehicle optimization can offset the reduced fuel energy content of alcohol-gasoline more » blends, and likely reduce vehicle fuel consumption and tailpipe CO2 emissions. « less