Paul Dekraker

Benchmarking and Hardware-in-the-Loop Operation of a 2014 MAZDA SkyActiv 2.0L 13:1 Compression Ratio Engine

As part of its technology assessment for the upcoming midterm evaluation (MTE) of the 2022-2025 Light-Duty Vehicle Greenhouse Gas (LD GHG) emissions standards, EPA has been benchmarking engines and transmissions to generate inputs for use in its Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) model, a physics-based, forward-looking, full vehicle computer simulation tool. One of the most efficient engines today, a 2.0L Mazda SkyActiv engine, is of particular interest due to its high geometric compression ratio and use of an Atkinson cycle. EPA benchmarked the 2.0L SkyActiv at its National Vehicle and Fuel Emissions laboratory. EPA then incorporated ALPHA into an engine dynamometer control system so that vehicle chassis testing could be simulated with a hardware-in-the-loop (HIL) approach. In order to model the behavior of current and future vehicles, an algorithm was developed to dynamically generate transmission shift logic from a set of user-defined parameters, a cost function (e.g., engine fuel consumption) and vehicle performance during simulation. This paper first presents the results of EPA’s benchmarking of a Mazda 2.0L 13:1 CR SkyActiv engine. It then details the implementation of the SkyActiv 2.0L engine in an HIL test bed to represent chassis testing of an advanced vehicle configuration, which includes assumptions for a future high-efficiency transmission and reduced vehicle road loads. The engine was operated over simulated EPA city and highway test cycles to assess the greenhouse gas (GHG) emissions performance in the context of EPA’s LD GHG standards through year 2025.

Vehicle Component Benchmarking Using a Chassis Dynamometer

The benchmarking study described in this paper uses data from chassis dynamometer testing to determine the efficiency and operation of vehicle driveline components. A robust test procedure was created that can be followed with no a priori knowledge of component performance, nor additional instrumentation installed in the vehicle. To develop the procedure, a 2013 Chevrolet Malibu was tested on a chassis dynamometer. Dynamometer data, emissions data, and data from the vehicle controller area network (CAN) bus were used to construct efficiency maps for the engine and transmission. These maps were compared to maps of the same components produced from standalone component benchmarking, resulting in a good match between results from in-vehicle and standalone testing. The benchmarking methodology was extended to a 2013 Mercedes E350 diesel vehicle. Dynamometer, emissions, and CAN data were used to construct efficiency maps and operation strategies for the engine and transmission. These maps were used in EP As Advanced Light-duty Powertrain and Hybrid Analysis Tool (ALPHA) vehicle model, which showed a good agreement between the modeled fuel economy and dynamometer test results.

Estimating GHG Reduction from Combinations of Current Best-Available and Future Powertrain and Vehicle Technologies for a Midsized Car Using EPA's ALPHA Model

The Environmental Protection Agency’s (EPA’s) Advanced LightDuty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate greenhouse gas (GHG) emissions from light-duty vehicles[1]. ALPHA is a physics-based, forward-looking, full vehicle computer simulation capable of analyzing various vehicle types with different powertrain technologies, showing realistic vehicle behavior, and auditing of all internal energy flows in the model. The software tool is a MATLAB/Simulink based desktop application. In preparation for the midterm evaluation of the light-duty GHG emission standards for model years 2022-2025, EPA is refining and revalidating ALPHA using newly acquired data from model year 2013-2015 engines and vehicles. From its database of engine and vehicle benchmarking data EPA identified the most efficient, engines, transmissions and vehicle technologies, and then used ALPHA to model a midsized car incorporating combinations of these existing technologies which minimize GHG emissions. In a similar analysis, ALPHA was used to estimate the GHG emissions from future low-GHG technology packages potentially available in model year 2025. This paper presents the ALPHA model inputs, results and the lessons learned during this modeling and assessment activity.

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

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. Introduction

Characterizing Factors Influencing SI Engine Transient Fuel Consumption for Vehicle Simulation in ALPHA

Vehicle simulation is an established and effective method to predict a vehicle’s fuel economy (FE) and greenhouse gas (GHG) emissions from a specific set of vehicle technologies. Accurate testing and analysis of fuel consumption from currently available vehicle technologies is key to validating the simulation model. Once validated the model can then be used to estimate the potential of various combinations of vehicle technologies to meet future GHG standards [1].

Constructing Engine Maps for Full Vehicle Simulation Modeling

The Environmental Protection Agency (EPA) has collected a variety of engine and vehicle test data to assess the efectiveness of new automotive technologies in meeting greenhouse gas (GHG) and criteria emission standards and to monitor their behavior in real world operation. EPA’s Advanced Light-Duty Powertrain and Hybrid Analysis (ALPHA) tool was created to estimate GHG emissions from vehicles using various combinations of advanced technologies and has been refned using data from testing conducted at EPA’s National Vehicle and Fuel Emissions Laboratory.

Benchmarking a 2016 Honda Civic 1.5-liter L15B7 Turbocharged Engine and Evaluating the Future Efficiency Potential of Turbocharged Engines

As part of the U.S. Environmental Protection Agencys (EPAs) continuing assessment of advanced light-duty automotive technologies to support the setting of appropriate national greenhouse gas standards and to evaluate the impact of new technologies on in- use emissions, a 2016 Honda Civic with a 4-cylinder 1.5-liter L15B7 turbocharged engine and continuously variable transmission (CVT) was benchmarked. The test method involved installing the engine and its CVT in an engine dynamometer test cell with the engine wiring harness tethered to its vehicle parked outside the test cell. Engine and transmission torque, fuel flow, key engine temperatures and pressures, and onboard diagnostics (OBD)/CAN bus data were recorded. This paper documents the test results for idle, low, medium and high load engine operation, as well as motoring torque, wide-open throttle torque and fuel consumption during transient operation using both EPA Tier 2 and Tier 3 test fuels. Particular attention is given to characterizing enrichment control during high load engine operation. Results are used to create complete engine fuel consumption and efficiency maps and estimate CO2 emissions using EPAs ALPHA full vehicle simulation model, over regulatory drive cycles. The design and performance of the 1.5-liter Honda engine are compared to several other past, present, and future downsized-boosted engines and potential advancements are evaluated.