Development of Advanced Light-Duty Powertrain and Hybrid Analysis Tool

Lee, Byung-Ho; Lee, SoDuk; Cherry, Jeff; Neam, Anthony; Sanchez, James; Nam, Ed

doi:10.4271/2013-01-0808

Cited by 35 publications

(16 citation statements)

References 5 publications

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“…Engine Most of the previously published engine system models in [1,2] can be used for hybrid electric vehicle applications by simply updating to include the engine operational maps, such as from a gasoline direct injection (GDI) or an Atkinson cycle port fuel injection (PFI) engine, and by eliminating some portion of the engine controls and operating modes, e.g. idle speed control and idle operation.…”

Section: System Modelsmentioning

confidence: 99%

“…Te ALPHA model can be used as a support tool for the development of future GHG emissions regulations and as a research tool to evaluate the efciency of new advanced technologies. Te ALPHA [1,2] hybrid model is related to the heavy-duty vehicle Greenhouse Gas Emissions Model (GEM) currently used for determining GHG emissions compliance for heavy-duty vehicle applications in the U.S. [3]. Te basic model strategies and controls within GEM (with the exceptions of specifc traction motor, generator, battery, regenerative braking control, hybrid vehicle supervisory control, etc.)…”

Section: Introductionmentioning

confidence: 99%

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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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Section: System Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Controls Development of 48 V Mild Hybrid Electric Vehicles

Lee¹,

Cherry²,

Safoutin³

et al. 2018

SAE Technical Paper Series

Self Cite

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“…A set of possible progressions is described by equation (1) [11] and has been used directly or with some modification in previous work of a similar nature [6,7,8]. (1) Where: Spread = transmission spread (the ratio of maximum to minimum gear ratios) z = total number of gears (> 1) n = gear number of ratio being calculated (varies from 1..z) φ 2 = progression factor (typically ∼1 to 1.2) i n = the nth gear ratio…”

Section: Step Gear Transmission Ratio Progressionsmentioning

confidence: 99%

“…As a concrete example of equation (1) in use, see the results shown in Table 2 which shows ratio progressions for the six-and eight-speed transmissions using the average Step Ratios from Table 1 as the values for φ 2 . For each transmission the spread and minimum and maximum ratios are held constant and the intermediate ratios are determined by equation (1). For the six-speed, the ratios are quite a good match with 4 th gear showing the largest difference.…”

Section: Step Gear Transmission Ratio Progressionsmentioning

confidence: 99%

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Modeling the Effects of Transmission Gear Count, Ratio Progression, and Final Drive Ratio on Fuel Economy and Performance Using ALPHA

Newman¹,

Dekraker²

2016

SAE Technical Paper Series

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The Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created by EPA to evaluate the Greenhouse Gas (GHG) emissions of Light-Duty (LD) vehicles [1]. ALPHA is a physicsbased, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types combined with different powertrain technologies. The software tool is a MATLAB/Simulink based desktop application. The ALPHA model has been updated from the previous version to include more realistic vehicle behavior and now includes internal auditing of all energy flows in the model [2]. As a result of the model refinements and in preparation for the mid-term evaluation (MTE) of the 2022-2025 LD GHG emissions standards, the model is being revalidated with newly acquired vehicle data. This paper presents an analysis of the effects of varying the absolute and relative gear ratios of a given transmission on carbon emissions and performance. Energy-based methods of selecting absolute gear ratios are considered and the effects of alternative engine selections are also examined. An algorithm is presented for automatically determining ALPHAshift parameter sets based on the selected engine and transmission combination. It is observed that no single ratio progression optimizes fuel consumption for all applications, however, fuel consumption is also relatively insensitive to progression which implies a fixed set of ratios can still be used for a range of applications without necessarily compromising consumption. The energy-based ratio analysis may prove useful in determining the optimal overall top gear ratio for a given engine-vehicle combination and also helps to explain the relative insensitivity to ratio progression. Individual performance metrics can show high sensitivity to ratio progression, final drive ratio and shift calibration, in particular 30-50 and 50-70 MPH passing times.

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