The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Nasser L. Azad - One of the best experts on this subject based on the ideXlab platform.
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Component sizing of a power-split plug-in hybrid electric Vehicle for optimal fuel economy
International Journal of Powertrains, 2016Co-Authors: Maryyeh Chehresaz, Ahmad Mozaffari, Mahyar Vajedi, Nasser L. AzadAbstract:Plug-in hybrid electric Vehicles (PHEVs) were introduced in response to rising environmental challenges facing the automotive sector. Of the three primary PHEV architectures, power-split architectures tend to provide greater efficiencies than the other ones; however, they also demonstrate more complicated dynamics. In this study, the problem of optimising the component sizes of a power-split PHEV was addressed in an effort to exploit the flexibility of this powertrain system and further improve the Vehicle's fuel economy, using a Toyota plug-in Prius as the Baseline Vehicle. Autonomie software was used to develop the Vehicle model. The engine's maximum power and the electric motor's maximum power were considered as the design variables. The genetic algorithm approach was employed to solve the optimisation problem. Comparing to the Baseline Vehicle, a significant reduction in fuel consumption was achieved thorough the sizing process for various drive cycles of FTP, HWFET and EPA. The model was validated against a MapleSim multi-domain model.
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real time immune inspired optimum state of charge trajectory estimation using upcoming route information preview and neural networks for plug in hybrid electric Vehicles fuel economy
Frontiers in Mechanical Engineering, 2015Co-Authors: Ahmad Mozaffari, Mahyar Vajedi, Nasser L. AzadAbstract:The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric Vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the Vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the Vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the Baseline Vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.
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Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric Vehicles fuel economy
Frontiers of Mechanical Engineering, 2015Co-Authors: Ahmad Mozaffari, Mahyar Vajedi, Nasser L. AzadAbstract:The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric Vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the Vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the Vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the Baseline Vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.
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Multi-objective component sizing of a power-split plug-in hybrid electric Vehicle powertrain using Pareto-based natural optimization machines
Engineering Optimization, 2015Co-Authors: Ahmad Mozaffari, Maryyeh Chehresaz, Mahyar Vajedi, Nasser L. AzadAbstract:The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric Vehicle and plug-in hybrid electric Vehicle (PHEV) designs. In particular, research has focused on Vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the Vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the Baseline Vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-bas...
Constantinos Sioutas - One of the best experts on this subject based on the ideXlab platform.
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Contribution of transition metals in the reactive oxygen species activity of PM emissions from retrofitted heavy-duty Vehicles
Atmospheric Environment, 2010Co-Authors: Vishal Verma, Martin M. Shafer, James J. Schauer, Constantinos SioutasAbstract:Abstract We assessed the contribution of water-soluble transition metals to the reactive oxygen species (ROS) activity of diesel exhaust particles (DEPs) from four heavy-duty Vehicles in five retrofitted configurations (V-SCRT, Z-SCRT, DPX, hybrid, and school bus). A heavy-duty truck without any control device served as the Baseline Vehicle. Particles were collected from all Vehicle-configurations on a chassis dynamometer under three driving conditions: cruise (80 km h −1 ), transient UDDS, and idle. A sensitive macrophage-based in vitro assay was used to determine the ROS activity of collected particles. The contribution of water-soluble transition metals in the measured activity was quantified by their removal using a Chelex ® complexation method. The study demonstrates that despite an increase in the intrinsic ROS activity (per mass basis) of exhaust PM with use of most control technologies, the overall ROS activity (expressed per km or per h) was substantially reduced for retrofitted configurations compared to the Baseline Vehicle. Chelex treatment of DEPs water extracts removed a substantial (≥70%) and fairly consistent fraction of the ROS activity, which ascertains the dominant role of water-soluble metals in PM-induced cellular oxidative stress. However, relatively lower removal of the activity in few Vehicle-configurations (V-SCRT, DPX and school bus idle), despite a large aggregate metals removal, indicated that not all species were associated with the measured activity. A univariate regression analysis identified several transition metals (Fe, Cr, Co and Mn) as significantly correlated ( R > 0.60; p
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oxidative potential of semi volatile and non volatile particulate matter pm from heavy duty Vehicles retrofitted with emission control technologies
Environmental Science & Technology, 2009Co-Authors: Subhasis Biswas, Vishal Verma, James J. Schauer, Flemming R Cassee, Arthur K Cho, Constantinos SioutasAbstract:Advanced exhaust after-treatment devices for diesel Vehicles are less effective in controlling semivolatile species than the refractory PM fractions. This study investigates the oxidative potential (OP) of PM from Vehicles with six retrofitted technologies (vanadium and zeolite based selective catalytic reduction (V-SCRT, Z-SCRT), Continuously regenerating technology (CRT), catalyzed DPX filter, catalyzed continuously regenerating trap (CCRT), and uncatalyzed Horizon filter) in comparison to a “Baseline” Vehicle (without any control device). Vehicles were tested on a chassis dynamometer at three driving conditions, i.e., cruise, transient urban dynamometer driving schedule (UDDS), and idle. The consumption rate of dithiothreitol (DTT), one of the surrogate measures of OP, was determined for PM samples collected at ambient and elevated temperatures (thermally denuded of semivolatile species). Control devices reduced the OP expressed per Vehicle distance traveled by 60−98%. The oxidative potential per unit ...
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chemical speciation of pm emissions from heavy duty diesel Vehicles equipped with diesel particulate filter dpf and selective catalytic reduction scr retrofits
Atmospheric Environment, 2009Co-Authors: Subhasis Biswas, Vishal Verma, James J. Schauer, Constantinos SioutasAbstract:Abstract Four heavy-duty diesel Vehicles (HDDVs) in six retrofitted configurations (CRT ® , V-SCRT ® , Z-SCRT ® , Horizon, DPX and CCRT ® ) and a Baseline Vehicle operating without after--treatment were tested under cruise (50 mph), transient UDDS and idle driving modes. As a continuation of the work by Biswas et al. [Biswas, S., Hu, S., Verma, V., Herner, J., Robertson, W.J., Ayala, A., Sioutas, C., 2008. Physical properties of particulate matter (PM) from late model heavy-duty diesel Vehicles operating with advanced emission control technologies. Atmospheric Environment 42, 5622–5634.] on particle physical parameters, this paper focuses on PM chemical characteristics (Total carbon [TC], Elemental carbon [EC], Organic Carbon [OC], ions and water-soluble organic carbon [WSOC]) for cruise and UDDS cycles only. Size-resolved PM collected by MOUDI–Nano-MOUDI was analyzed for TC, EC and OC and ions (such as sulfate, nitrate, ammonium, potassium, sodium and phosphate), while Teflon coated glass fiber filters from a high volume sampler were extracted to determine WSOC. The introduction of retrofits reduced PM mass emissions over 90% in cruise and 95% in UDDS. Similarly, significant reductions in the emission of major chemical constituents (TC, OC and EC) were achieved. Sulfate dominated PM composition in Vehicle configurations (V-SCRT ® -UDDS, Z-SCRT ® -Cruise, CRT ® and DPX) with considerable nucleation mode and TC was predominant for configurations with less (Z-SCRT ® -UDDS) or insignificant (CCRT ® , Horizon) nucleation. The transient operation increases EC emissions, consistent with its higher accumulation PM mode content. In general, solubility of organic carbon is higher (average ∼5 times) for retrofitted Vehicles than the Baseline Vehicle. The retrofitted Vehicles with catalyzed filters (DPX, CCRT ® ) had decreased OC solubility (WSOC/OC: 8–25%) unlike those with uncatalyzed filters (SCRT ® s, Horizon; WSOC/OC ∼ 60–100%). Ammonium was present predominantly in the nucleation mode, indicating that ternary nucleation may be the responsible mechanism for formation of these particles.
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physical properties of particulate matter pm from late model heavy duty diesel Vehicles operating with advanced pm and nox emission control technologies
Atmospheric Environment, 2008Co-Authors: Subhasis Biswas, Vishal Verma, Jorn D Herner, William H Robertson, Alberto Ayala, Constantinos SioutasAbstract:Abstract Emission control technologies designed to meet the 2007 and 2010 emission standards for heavy-duty diesel Vehicles (HDDV) remove effectively the non-volatile fraction of particles, but are comparatively less efficient at controlling the semi-volatile components. A collaborative study between the California Air Resources Board (CARB) and the University of Southern California was initiated to investigate the physicochemical and toxicological characteristics of the semi-volatile and non-volatile particulate matter (PM) fractions from HDDV emissions. This paper reports the physical properties, including size distribution, volatility (in terms of number and mass), surface diameter, and agglomeration of particles emitted from HDDV retrofitted with advanced emission control devices. Four Vehicles in combination with six after-treatment devices (V-SCRT®, Z-SCRT®, CRT®, DPX, Hybrid-CCRT®, EPF) were tested under three driving cycles: steady state (cruise), transient (urban dynamometer driving schedule, UDDS), and idle. An HDDV without any control device is served as the Baseline Vehicle. Substantial reduction of PM mass emissions (>90%) was accomplished for the HDDV operating with advanced emission control technologies. This reduction was not observed for particle number concentrations under cruise conditions, with the exceptions of the Hybrid-CCRT® and EPF Vehicles, which were efficient in controlling both—mass and number emissions. In general, significant nucleation mode particles (
Ahmad Mozaffari - One of the best experts on this subject based on the ideXlab platform.
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Component sizing of a power-split plug-in hybrid electric Vehicle for optimal fuel economy
International Journal of Powertrains, 2016Co-Authors: Maryyeh Chehresaz, Ahmad Mozaffari, Mahyar Vajedi, Nasser L. AzadAbstract:Plug-in hybrid electric Vehicles (PHEVs) were introduced in response to rising environmental challenges facing the automotive sector. Of the three primary PHEV architectures, power-split architectures tend to provide greater efficiencies than the other ones; however, they also demonstrate more complicated dynamics. In this study, the problem of optimising the component sizes of a power-split PHEV was addressed in an effort to exploit the flexibility of this powertrain system and further improve the Vehicle's fuel economy, using a Toyota plug-in Prius as the Baseline Vehicle. Autonomie software was used to develop the Vehicle model. The engine's maximum power and the electric motor's maximum power were considered as the design variables. The genetic algorithm approach was employed to solve the optimisation problem. Comparing to the Baseline Vehicle, a significant reduction in fuel consumption was achieved thorough the sizing process for various drive cycles of FTP, HWFET and EPA. The model was validated against a MapleSim multi-domain model.
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real time immune inspired optimum state of charge trajectory estimation using upcoming route information preview and neural networks for plug in hybrid electric Vehicles fuel economy
Frontiers in Mechanical Engineering, 2015Co-Authors: Ahmad Mozaffari, Mahyar Vajedi, Nasser L. AzadAbstract:The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric Vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the Vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the Vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the Baseline Vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.
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Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric Vehicles fuel economy
Frontiers of Mechanical Engineering, 2015Co-Authors: Ahmad Mozaffari, Mahyar Vajedi, Nasser L. AzadAbstract:The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge (SOC) trajectory in plug-in hybrid electric Vehicles (PHEVs). The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the Vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categorizing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm (AIA). The objective function of the optimizer is derived from a computationally efficient artificial neural network (ANN) which is trained by a database obtained from a high-fidelity model of the Vehicle built in the Autonomie software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the Baseline Vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.
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Multi-objective component sizing of a power-split plug-in hybrid electric Vehicle powertrain using Pareto-based natural optimization machines
Engineering Optimization, 2015Co-Authors: Ahmad Mozaffari, Maryyeh Chehresaz, Mahyar Vajedi, Nasser L. AzadAbstract:The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric Vehicle and plug-in hybrid electric Vehicle (PHEV) designs. In particular, research has focused on Vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the Vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the Baseline Vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-bas...
Joshué Pérez - One of the best experts on this subject based on the ideXlab platform.
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ICARCV - An Intelligent Torque Vectoring performance evaluation comparison for electric Vehicles
2018 15th International Conference on Control Automation Robotics and Vision (ICARCV), 2018Co-Authors: Alberto Parra, Asier Zubizarreta, Joshué PérezAbstract:Nowadays, intelligent transportation systems (ITS) have become one of the main research areas, being electric Vehicles (EVs) and automated Vehicles key topics. To guarantee safety and comfort and maximize their efficiency, proper Vehicle dynamics control systems such as Torque Vectoring (TV) are mandatory. This work proposes an intelligent TV approach for EVs which considers the vertical force distribution among the tractive wheels. This approach allows to maximize Vehicle cornering capacity and also its efficiency. In order to demonstrate its effectiveness, its performance is compared using Dynacar High Fidelity Vehicle simulator with three traditional approaches found in the literature: PID, Second Order Sliding Mode Control (SOSMC) and Fuzzy Control. Results show that all evaluated controllers improve the handling of the Vehicle and the efficiency with respect to the Baseline Vehicle. However, the proposed intelligent TV system provides better overall results.
Vishal Verma - One of the best experts on this subject based on the ideXlab platform.
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Contribution of transition metals in the reactive oxygen species activity of PM emissions from retrofitted heavy-duty Vehicles
Atmospheric Environment, 2010Co-Authors: Vishal Verma, Martin M. Shafer, James J. Schauer, Constantinos SioutasAbstract:Abstract We assessed the contribution of water-soluble transition metals to the reactive oxygen species (ROS) activity of diesel exhaust particles (DEPs) from four heavy-duty Vehicles in five retrofitted configurations (V-SCRT, Z-SCRT, DPX, hybrid, and school bus). A heavy-duty truck without any control device served as the Baseline Vehicle. Particles were collected from all Vehicle-configurations on a chassis dynamometer under three driving conditions: cruise (80 km h −1 ), transient UDDS, and idle. A sensitive macrophage-based in vitro assay was used to determine the ROS activity of collected particles. The contribution of water-soluble transition metals in the measured activity was quantified by their removal using a Chelex ® complexation method. The study demonstrates that despite an increase in the intrinsic ROS activity (per mass basis) of exhaust PM with use of most control technologies, the overall ROS activity (expressed per km or per h) was substantially reduced for retrofitted configurations compared to the Baseline Vehicle. Chelex treatment of DEPs water extracts removed a substantial (≥70%) and fairly consistent fraction of the ROS activity, which ascertains the dominant role of water-soluble metals in PM-induced cellular oxidative stress. However, relatively lower removal of the activity in few Vehicle-configurations (V-SCRT, DPX and school bus idle), despite a large aggregate metals removal, indicated that not all species were associated with the measured activity. A univariate regression analysis identified several transition metals (Fe, Cr, Co and Mn) as significantly correlated ( R > 0.60; p
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oxidative potential of semi volatile and non volatile particulate matter pm from heavy duty Vehicles retrofitted with emission control technologies
Environmental Science & Technology, 2009Co-Authors: Subhasis Biswas, Vishal Verma, James J. Schauer, Flemming R Cassee, Arthur K Cho, Constantinos SioutasAbstract:Advanced exhaust after-treatment devices for diesel Vehicles are less effective in controlling semivolatile species than the refractory PM fractions. This study investigates the oxidative potential (OP) of PM from Vehicles with six retrofitted technologies (vanadium and zeolite based selective catalytic reduction (V-SCRT, Z-SCRT), Continuously regenerating technology (CRT), catalyzed DPX filter, catalyzed continuously regenerating trap (CCRT), and uncatalyzed Horizon filter) in comparison to a “Baseline” Vehicle (without any control device). Vehicles were tested on a chassis dynamometer at three driving conditions, i.e., cruise, transient urban dynamometer driving schedule (UDDS), and idle. The consumption rate of dithiothreitol (DTT), one of the surrogate measures of OP, was determined for PM samples collected at ambient and elevated temperatures (thermally denuded of semivolatile species). Control devices reduced the OP expressed per Vehicle distance traveled by 60−98%. The oxidative potential per unit ...
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chemical speciation of pm emissions from heavy duty diesel Vehicles equipped with diesel particulate filter dpf and selective catalytic reduction scr retrofits
Atmospheric Environment, 2009Co-Authors: Subhasis Biswas, Vishal Verma, James J. Schauer, Constantinos SioutasAbstract:Abstract Four heavy-duty diesel Vehicles (HDDVs) in six retrofitted configurations (CRT ® , V-SCRT ® , Z-SCRT ® , Horizon, DPX and CCRT ® ) and a Baseline Vehicle operating without after--treatment were tested under cruise (50 mph), transient UDDS and idle driving modes. As a continuation of the work by Biswas et al. [Biswas, S., Hu, S., Verma, V., Herner, J., Robertson, W.J., Ayala, A., Sioutas, C., 2008. Physical properties of particulate matter (PM) from late model heavy-duty diesel Vehicles operating with advanced emission control technologies. Atmospheric Environment 42, 5622–5634.] on particle physical parameters, this paper focuses on PM chemical characteristics (Total carbon [TC], Elemental carbon [EC], Organic Carbon [OC], ions and water-soluble organic carbon [WSOC]) for cruise and UDDS cycles only. Size-resolved PM collected by MOUDI–Nano-MOUDI was analyzed for TC, EC and OC and ions (such as sulfate, nitrate, ammonium, potassium, sodium and phosphate), while Teflon coated glass fiber filters from a high volume sampler were extracted to determine WSOC. The introduction of retrofits reduced PM mass emissions over 90% in cruise and 95% in UDDS. Similarly, significant reductions in the emission of major chemical constituents (TC, OC and EC) were achieved. Sulfate dominated PM composition in Vehicle configurations (V-SCRT ® -UDDS, Z-SCRT ® -Cruise, CRT ® and DPX) with considerable nucleation mode and TC was predominant for configurations with less (Z-SCRT ® -UDDS) or insignificant (CCRT ® , Horizon) nucleation. The transient operation increases EC emissions, consistent with its higher accumulation PM mode content. In general, solubility of organic carbon is higher (average ∼5 times) for retrofitted Vehicles than the Baseline Vehicle. The retrofitted Vehicles with catalyzed filters (DPX, CCRT ® ) had decreased OC solubility (WSOC/OC: 8–25%) unlike those with uncatalyzed filters (SCRT ® s, Horizon; WSOC/OC ∼ 60–100%). Ammonium was present predominantly in the nucleation mode, indicating that ternary nucleation may be the responsible mechanism for formation of these particles.
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physical properties of particulate matter pm from late model heavy duty diesel Vehicles operating with advanced pm and nox emission control technologies
Atmospheric Environment, 2008Co-Authors: Subhasis Biswas, Vishal Verma, Jorn D Herner, William H Robertson, Alberto Ayala, Constantinos SioutasAbstract:Abstract Emission control technologies designed to meet the 2007 and 2010 emission standards for heavy-duty diesel Vehicles (HDDV) remove effectively the non-volatile fraction of particles, but are comparatively less efficient at controlling the semi-volatile components. A collaborative study between the California Air Resources Board (CARB) and the University of Southern California was initiated to investigate the physicochemical and toxicological characteristics of the semi-volatile and non-volatile particulate matter (PM) fractions from HDDV emissions. This paper reports the physical properties, including size distribution, volatility (in terms of number and mass), surface diameter, and agglomeration of particles emitted from HDDV retrofitted with advanced emission control devices. Four Vehicles in combination with six after-treatment devices (V-SCRT®, Z-SCRT®, CRT®, DPX, Hybrid-CCRT®, EPF) were tested under three driving cycles: steady state (cruise), transient (urban dynamometer driving schedule, UDDS), and idle. An HDDV without any control device is served as the Baseline Vehicle. Substantial reduction of PM mass emissions (>90%) was accomplished for the HDDV operating with advanced emission control technologies. This reduction was not observed for particle number concentrations under cruise conditions, with the exceptions of the Hybrid-CCRT® and EPF Vehicles, which were efficient in controlling both—mass and number emissions. In general, significant nucleation mode particles (