Sylvain Pagerit

Integrating Data, Performing Quality Assurance, and Validating the Vehicle Model for the 2004 Prius Using PSAT

Argonne National Laboratory (ANL), working with the FreedomCAR Partnership, maintains the hybrid vehicle simulation software, Powertrain System Analysis Toolkit (PSAT). The importance of component models and the complexity involved in setting up optimized control laws require validation of the models and control strategies. Using its Advanced Powertrain Research Facilities (APRF), ANL thoroughly tested the 2004 Toyota Prius to validate the PSAT drivetrain. In this paper, we will first describe the methodology used to quality check test data. Then, we will explain the validation process leading to the simulated vehicle control strategy tuning, which is based on the analysis of the differences between test and simulation. Finally, we will demonstrate the validation of PSAT Prius component models and control strategy, using APRF vehicle test data.

Fuel Economy Sensitivity to Vehicle Mass for Advanced Vehicle Powertrains

In 2002, the U.S. Department of Energy (DOE) launched FreedomCAR, which is a partnership with automakers to advance high-technology research needed to produce practical, affordable advanced vehicles that have the potential to significantly improve fuel economy in the near-term. Advanced materials (including metals, polymers, composites, and intermetallic compounds) can play an important role in improving the efficiency of transportation vehicles. Weight reduction is one of the most practical ways of increasing vehicle fuel economy while reducing exhaust emissions. In this paper, we evaluate the impact of vehicle mass reduction for several vehicle platforms and advanced powertrain technologies, including Internal Combustion Engine (ICE) Hybrid Electric Vehicles (HEVs) and fuel cell HEVs, in comparison with conventional vehicles. We also explain the main factors influencing the fuel economy sensitivity.

Model Architecture, Methods, and Interfaces for Efficient Math-Based Design and Simulation of Automotive Control Systems

Many of today’s automotive control system simulation tools are suitable for simulation, but they provide rather limited support for model building and management. Setting up a simulation model requires more than writing down state equations and running them on a computer. The role of a model library is to manage the models of physical components of the system and allow users to share and easily reuse them. In this paper, we describe how modern software techniques can be used to support modeling and design activities; the objective is to provide better system models in less time by assembling these system models in a “plug-and-play” architecture. With the introduction of hybrid electric vehicles, the number of components that can populate a model has increased considerably, and more components translate into more possible drivetrain configurations. To address these needs, we explain how users can simulate a large number of drivetrain configurations. The proposed approach could be used to establish standards within the automotive modeling community.

“Fair” Comparison of Powertrain Configurations for Plug-In Hybrid Operation Using Global Optimization

Plug-in Hybrid Electric Vehicles (PHEVs) use electric energy from the grid rather than fuel energy for most short trips, therefore drastically reducing fuel consumption. Different configurations can be used for PHEVs. In this study, the parallel pre-transmission, series, and power-split configurations were compared by using global optimization. The latter allows a fair comparison among different powertrains. Each vehicle was operated optimally to ensure that the results would not be biased by non-optimally tuned or designed controllers. All vehicles were sized to have a similar allelectric range (AER), performance, and towing capacity. Several driving cycles and distances were used. The advantages of each powertrain are discussed.

Midsize and SUV Vehicle Simulation Results for Plug-In HEV Component Requirements

Because Plug-in Hybrid Electric Vehicles (PHEVs) substitute electrical power from the utility grid for fuel, they have the potential to reduce petroleum use significantly. However, adoption of PHEVs has been hindered by expensive, low-energy batteries. Recent improvements in Li-ion batteries and hybrid control have addressed battery-related issues and have brought PHEVs within reach. The FreedomCAR Office of Vehicle Technology has a program that studies the potential benefit of PHEVs. This program also attempts to clarify and refine the requirements for PHEV components. Because the battery appears to be the main technical barrier, both from a performance and cost perspective, the main efforts have been focused on that component. Working with FreedomCAR energy storage and vehicle experts, Argonne National Laboratory (Argonne) researchers have developed a process to define the requirements of energy storage systems for plug-in applications. This paper describes the impact of All Electric Range (AER), drive cycle, and control strategy on battery requirements for both the midsize and SUV classes of vehicles.

Impact of Drive Cycles on PHEV Component Requirements

Plug-in Hybrid Electric Vehicles (PHEVs) offer the ability to significantly reduce petroleum consumption. Argonne National Laboratory, working with the FreedomCAR and Fuels Partnership, participated in the definition of the battery requirements for PHEVs. Previous studies have demonstrated the impact of such vehicle characteristics as vehicle class, mass, or electrical accessories on battery requirements. However, outstanding questions remain regarding the impact of drive cycles on the requirements. In this paper, we will first evaluate the consequences of sizing the electrical machine and battery power to follow the Urban Dynamometer Driving Schedule (UDDS) to satisfy California Air Resources Board (CARB) requirements and determine the number of other driving cycles that can be followed in Electric Vehicle (EV) mode. Then, we will study the impact of sizing the electrical components on other driving cycles.

Impact of Real-World Drive Cycles on PHEV Battery Requirements

Plug-in hybrid electric vehicles (PHEVs) have the ability to significantly reduce petroleum consumption. Argonne National Laboratory (Argonne), working with the FreedomCAR and Fuels Partnership, helped define the battery requirements for PHEVs. Previous studies demonstrated the impact of the vehicle’s characteristics, such as its class, mass, or electrical accessories, on the requirements. However, questions on the impact of drive cycles remain outstanding. In this paper, we evaluate the consequences of sizing the electrical machine and the battery to follow standard drive cycles, such as the urban dynamometer driving schedule (UDDS), as well as real-world drive cycles in electric vehicle (EV) mode. The requirements are defined for several driving conditions (e.g., urban, highway) and types of driving behavior (e.g., smooth, aggressive).

Evolution of Hydrogen Fueled Vehicles Compared to Conventional Vehicles from 2010 to 2045

Fuel cell vehicles are undergoing extensive research and development because of their potential for high efficiency and low emissions. Because fuel cell vehicles remain expensive and there is limited demand for hydrogen at present, very few fueling stations are being built. To try to accelerate the development of a hydrogen economy, some original equipment manufacturers in the automotive industry have been working on a hydrogenfueled internal combustion engine (ICE) as an intermediate step. This paper compares the fuel economy potential of hydrogen powertrains to conventional gasoline vehicles. Several timeframes are considered: 2010, 2015, 2030, and 2045. To address the technology status uncertainty, a triangular distribution approach was implemented for each component technology. The fuel consumption and cost of five powertrain configurations will be discussed and compared with the conventional counterpart.

Impact of FreedomCAR Goals on Well-to-Wheel Analysis

Because of their high efficiency and low emissions, fuel-cell vehicles are undergoing extensive research and development. When considering the introduction of advanced vehicles, engineers must perform a well-to-wheel (WTW) evaluation to determine the potential impact of a technology on carbon dioxide and Greenhouse Gas (GHG) emissions and to establish a basis that can be used to compare other propulsion technology and fuel choices. Several modeling tools developed by Argonne National Laboratory (ANL) were used to evaluate the overall environmental and fuel-saving impacts associated with an advanced powertrain configuration. The Powertrain System Analysis Toolkit (PSAT) transient vehicle simulation software was used for pump-to-wheel (PTW) analysis, and GREET (Greenhouse gases, Regulated Emissions and Energy use in Transportation) was used for well-to-pump (WTP) analysis. This paper assesses the impact of FreedomCAR vehicle goals on a WTW energy basis. We will demonstrate that, on the basis of near-term (2010) advanced propulsion technologies, fuel cell hybrid vehicles achieve higher fuel economy than their Internal Combustion Engine (ICE) counterparts. However, when the North American natural gas hydrogen pathway is used to produce hydrogen (the most likely lowest-cost source of hydrogen in the near term), diesel hybrids perform the best. To gain the full benefits of hydrogen technology, a more efficient pathway to produce hydrogen, such as renewable energy, should be considered.

Rapid Partitioning, Automatic Assembly and Multi-Core Simulation of Distributed Vehicle Systems

The advent of heterogeneous specifications of complex systems utilizing both continuous (viz. physics or maths based models) and discrete domain models, communicating via accurate network models, enables a generalization of the usual model-based design methodology. The use of individual notations appropriate for sensor, actuator, plant, controller and accurate network modeling, enable both the natural specification of various models and the continued usage of legacy libraries containing models described in these various notations. The ability to choose fixed or variable-step solvers individually for the simulation of each continuous domain model in a system, enables designers to choose levels of accuracy of models, appropriate network communication models, and speeds of simulation. In this paper, we describe how engineers can rapidly define the connection of hierarchies of modules contained in Simulink models, and their topography distributed across multi-core computers, for fast and accurate simulation.