Fleet-Level Modeling of Real World Factors Influencing Greenhouse Gas Emission Simulation in ALPHA

Dekraker, Paul; Kargul, John; Moskalik, Andrew; Newman, Kevin; Doorlag, Mark; Barba, Daniel

doi:10.4271/2017-01-0899

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…Details of the function are provided in Ref. [27], where it takes into account the changes in heat transfer, friction and knock sensitivity as an engine is downsized. The 1.6 L Escape engine was downsized to 75% of the original maximum power and the drive traces simulated through ALPHA.…”

Section: Case Study Ii: Further Downsized Enginementioning

confidence: 99%

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Prakash

Kim

Stefaopoulou

2019

Journal of Dynamic Systems, Measurement, and Control

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With the advent of self-driving autonomous vehicles, vehicle controllers are free to drive their own velocities. This feature can be exploited to drive an optimal velocity trajectory that minimizes fuel consumption. Two typical approaches to drive cycle optimization are velocity smoothing and tractive energy minimization. The former reduces accelerations and decelerations, and hence, it does not require information of vehicle parameters and resistance forces. On the other hand, the latter reduces tractive energy demand at the wheels of a vehicle. In this work, utilizing an experimentally validated full vehicle simulation software, we show that for conventional gasoline vehicles the lower energy velocity trajectory can consume as much fuel as the velocity smoothing case. This implies that the easily implementable, vehicle agnostic velocity smoothing optimization can be used for velocity optimization rather than the nonlinear tractive energy minimization, which results in a pulse-and-glide trajectory.

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Section: Case Study Ii: Further Downsized Enginementioning

confidence: 99%

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Prakash

Kim

Stefaopoulou

2019

Journal of Dynamic Systems, Measurement, and Control

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“…A 2.5 L GDI engine map was previously developed for ALPHA modeling based upon engine dynamometer testing of a 2013 GM Chevrolet Malibu 1LS [2]. For MHEV modeling, the 2.5 L GDI map was used in order to create a surrogate fuel map [9] for the engine used by 2013 GM Chevrolet Malibu Eco modeled within this study. Te surrogate BSFC map shown in Figure 2 was then used to develop models of both P0 and P2 48 V MHEVs.…”

Section: System Modelsmentioning

confidence: 99%

“…Te power transmission efciency of a seven-groove belt-pulley drive is less than typical gear and clutch efciencies for P2 systems. Typical published efciencies for a multi-groove beltpulley [9] were used to model 48 V P0 MHEVs. Te power available during P0 regenerative braking was calculated from the motor/generator efciency and the regenerative charging power as shown in the Eq.…”

Section: Power-coupling and Transmission Te P0 And P2mentioning

confidence: 99%

Modeling and Controls Development of 48 V Mild Hybrid Electric Vehicles

Lee¹,

Cherry²,

Safoutin³

et al. 2018

SAE Technical Paper Series

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T he Advanced Light-Duty Powertrain and Hybrid Analysis tool (ALPHA) was created by EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles. ALPHA is a physics-based, forward-looking, full vehicle computer simulator capable of analyzing various vehicle types combined with diferent powertrain technologies. Te ALPHA desktop application was developed using MATLAB/Simulink. Te ALPHA tool was used to evaluate technology efectiveness and of-cycle technologies such as air-conditioning, electrical load reduction technology and road load reduction technologies of conventional, non-hybrid vehicles for the Midterm Evaluation of the 2017-2025 LD GHG rule by the U.S. Environmental Protection Agency (EPA) Ofce of Transportation and Air Quality (OTAQ). Tis paper presents controls development, modeling results, and model validation for simulations of a vehicle with a 48 V Belt Integrated Starter Generator (BISG) mild hybrid electric vehicle and an initial model design for a 48 V inline on-axis P2-confguration mild hybrid electric vehicle. Both confgurations were modeled with a MATLAB/Simulink/Statefow tool, which has been integrated into EPA's ALPHA vehicle model and was also used to model components within Gamma Technology GT-DRIVE simulations. Te mild hybrid electric vehicle model was validated using vehicle data obtained from Argonne National Laboratory (ANL) chassis dynamometer tests of a 2013 Chevrolet Malibu Eco 115 V 15 kW BISG mild hybrid electric vehicle. Te simulated fuel economy, engine torque/speed, motor torque/speed, engine on-of controls, battery voltage, current, and State of Charge (SOC) were all in good agreement with the vehicle test data on a number of drive schedules. Te developed 48 V mild hybrid electric vehicle model can be used to estimate the GHG emissions and fuel economy of 48 V mild hybrid electric vehicles over the EPA regulatory drive cycles and to estimate of-cycle GHG emissions. Te 48 V mild hybrid electric vehicle model will be further validated with additional 48 V mild hybrid electric vehicle test data in the future as more vehicle models become available. EPA has included 48 V BISG mild hybrid electric vehicle technology in its assessment of CO2-reducing technologies available for compliance with U.S. GHG standards.

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“…Transmission and torque converter losses were also modified to account for scaling. [21] For the remainder of ALPHA simulation runs, the acceleration performance of each vehicle package was matched to that of the nominal powertrain package by appropriately resizing the engine. EPA has chosen to maintain consistent acceleration performance for ALPHA fleet modeling so that the relative cost-effectiveness of the technologies can be fairly compared, and the effects of CO 2 reduction technologies on vehicle performance are not unduly ignored.…”

Section: Performance and Engine Sizingmentioning

confidence: 99%

“…The losses in the remaining components of the powertrain, including the transmission, torque converter, and rear end ratio, were sized according to the process laid out in earlier work. [21] The entire vehicle technology package was then run through an ALPHA simulation which calculated the summed acceleration time metric. The engine size steps of approximately 2% lead to vehicle performance times in steps of approximately 1% -1.5%.…”

Section: Performance and Engine Sizingmentioning

confidence: 99%

Representing GHG Reduction Technologies in the Future Fleet with Full Vehicle Simulation

Moskalik

Bolon

Newman

et al. 2018

SAE Int. J. Fuels Lubr.

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As part of an ongoing assessment of the potential for reducing greenhouse gas emissions of lightduty vehicles, the U.S. Environmental Protection Agency (EPA) has implemented an updated methodology for applying the results of full vehicle simulations to the range of vehicles across the entire fleet. The key elements of the updated methodology explored for this paper, responsive to stakeholder input on the EPA's fleet compliance modeling, include 1) greater transparency in the process used to determine technology effectiveness, and 2) a more direct incorporation of full vehicle simulation results.

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Fleet-Level Modeling of Real World Factors Influencing Greenhouse Gas Emission Simulation in ALPHA

Cited by 14 publications

References 24 publications

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Modeling and Controls Development of 48 V Mild Hybrid Electric Vehicles

Representing GHG Reduction Technologies in the Future Fleet with Full Vehicle Simulation

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