Aymeric Rousseau

Well-To-Wheels Energy Use and Greenhouse Gas Emissions of Plug-in Hybrid Electric Vehicles

Researchers at Argonne National Laboratory expanded the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model and incorporated the fuel economy and electricity use of alternative fuel/vehicle systems simulated by the Powertrain System Analysis Toolkit (PSAT) to conduct a well-to-wheels (WTW) analysis of energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). The WTW results were separately calculated for the blended charge-depleting (CD) and charge-sustaining (CS) modes of PHEV operation and then combined by using a weighting factor that represented the CD vehicle-miles-traveled (VMT) share. As indicated by PSAT simulations of the CD operation, grid electricity accounted for a share of the vehicles total energy use, ranging from 6% for a PHEV 10 to 24% for a PHEV 40, based on CD VMT shares of 23% and 63%, respectively. In addition to the PHEVs fuel economy and type of on-board fuel, the marginal electricity generation mix used to charge the vehicle impacted the WTW results, especially GHG emissions. Three North American Electric Reliability Corporation regions (4, 6, and 13) were selected for this analysis, because they encompassed large metropolitan areas (Illinois, New York, and California, respectively) and provided a significant variation of marginal generation mixes. The WTW results were also reported for the U.S. generation mix and renewable electricity to examine cases of average and clean mixes, respectively. For an all-electric range (AER) between 10 mi and 40 mi, PHEVs that employed petroleum fuels (gasoline and diesel), a blend of 85% ethanol and 15% gasoline (E85), and hydrogen were shown to offer a 40-60%, 70-90%, and more than 90% reduction in petroleum energy use and a 30-60%, 40-80%, and 10-100% reduction in GHG emissions, respectively, relative to an internal combustion engine vehicle that used gasoline. The spread of WTW GHG emissions among the different fuel production technologies and grid generation mixes was wider than the spread of petroleum energy use, mainly due to the diverse fuel production technologies and feedstock sources for the fuels considered in this analysis. The PHEVs offered reductions in petroleum energy use as compared with regular hybrid electric vehicles (HEVs). More petroleum energy savings were realized as the AER increased, except when the marginal grid mix was dominated by oil-fired power generation. Similarly, more GHG emissions reductions were realized at higher AERs, except when the marginal grid generation mix was dominated by oil or coal. Electricity from renewable sources realized the largest reductions in petroleum energy use and GHG emissions for all PHEVs as the AER increased. The PHEVs that employ biomass-based fuels (e.g., biomass-E85 and -hydrogen) may not realize GHG emissions benefits over regular HEVs if the marginal generation mix is dominated by fossil sources. Uncertainties are associated with the adopted PHEV fuel consumption and marginal generation mix simulation results, which impact the WTW results and require further research. More disaggregate marginal generation data within control areas (where the actual dispatching occurs) and an improved dispatch modeling are needed to accurately assess the impact of PHEV electrification. The market penetration of the PHEVs, their total electric load, and their role as complements rather than replacements of regular HEVs are also uncertain. The effects of the number of daily charges, the time of charging, and the charging capacity have not been evaluated in this study. A more robust analysis of the VMT share of the CD operation is also needed.

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.

Comparison of Powertrain Configuration for Plug-in HEVs from a Fuel Economy Perspective

The first commercially available plug-in hybrid electric vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in mid-December 2010. The Volt uses a series-split powertrain architecture, which provides benefits over the series architecture that typically has been considered for use in electric-range extended vehicles (EREVs). A specialized EREV powertrain, called the Voltec, drives the Volt through its entire range of speed and acceleration with battery power alone and within the limit of battery energy, thereby displacing more fuel with electricity than a PHEV, which characteristically blends electric and engine power together during driving. This paper assesses the benefits and drawbacks of these two different plug-in hybrid electric architectures (series versus series-split) by comparing component sizes, system efficiency, and fuel consumption over urban and highway drive cycles. Based on dynamic models, a detailed component control algorithm was developed for each PHEV. In particular, for the GM Voltec, a control algorithm was proposed for both electric machines to achieve optimal engine operation. The powertrain components were sized to meet all-electric-range, performance, and grade capacity requirements. This paper presents and compares the impact of these two different powertrain configurations on component size and fuel consumption.

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.

Sufficient conditions of optimal control based on Pontryagin's minimum principle for use in hybrid electric vehicles

Over 10 years ago, the equivalent consumption minimization strategy was introduced as an effective approach, using the concept of equivalent fuel consumption for electricity use, to solve a control problem for hybrid electric vehicles. Although numerous studies have documented outstanding results as a consequence of applying the concept and have shown that the equivalent consumption minimization strategy could be explained on the basis of an optimal control concept such as Pontryagin’s minimum principle, few studies have proven, mathematically, its optimal performance when solving the control problem of hybrid electric vehicles. The present research builds upon previous research studies that proved that the control based on Pontryagin’s minimum principle can be a global optimal solution for hybrid electric vehicles under the assumption that the battery efficiency is not a function of the state of charge. In this paper, we expand upon the original concept, deriving the optimality within more generalized cases than previously reported. In conclusion, if the battery efficiency is a concave function of the state of charge, which is possibly a natural characteristic of the battery, the optimal control based on Pontryagin’s minimum principle enables optimal performance to be achieved. We can therefore apply this control concept to hybrid electric vehicles which use a wide range of states of charge, such as plug-in hybrid electric vehicles.

Autonomie model validation with test data for 2010 Toyota Prius

The Prius — a power-split hybrid electric vehicle from Toyota — has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration ― an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory’s Advanced Powertrain Research Facility (ARRF). Whereas changes in the configuration, such as the reduction gear, are possibly noticeable, it is not easy to determine the effect of the energy management strategy because the supervisory control algorithm is, generally, not published. Further, it is almost impossible to analyze the algorithm without testing results obtained from a well-designed testing process. On the basis of extensive experience in designing the controllers of power-split hybrid electric vehicles in Autonomie, we could identify the supervisory control algorithm by analyzing the testing results obtained from the APRF. A vehicle model and a control model for the Prius MY10 have been developed to reproduce the real-world behaviors, and the simulation results are compared with the testing results. In the simulation, the developed vehicle model achieves fuel consumption that is close to the testing value, within 5%, and the operation of the engine model was similar to that of the real-world engine.

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.

Comparison between Rule-Based and Instantaneous Optimization for a Single-Mode, Power-Split HEV

Over the past couple of years, numerous Hybrid Electric Vehicle (HEV) powertrain configurations have been introduced into the marketplace. Currently, the dominant architecture is the power-split configuration, notably the input splits from Toyota Motor Sales and Ford Motor Company. This paper compares two vehicle-level control strategies that have been developed to minimize fuel consumption while maintaining acceptable performance and drive quality. The first control is rules based and was developed on the basis of test data from the Toyota Prius as provided by Argonne National Laboratory’s (Argonne’s) Advanced Powertrain Research Facility. The second control is based on an instantaneous optimization developed to minimize the system losses at every sample time. This paper describes the algorithms of each control and compares vehicle fuel economy (FE) on several drive cycles. Results demonstrate that both algorithms achieve similar FE values, which serve to demonstrate the benefits of the instantaneous optimal control: because it does not require tuning by the engineers, control development time is accelerated.

Automated Model Based Design Process to Evaluate Advanced Component Technologies

To reduce development time and introduce technologies faster to the market, many companies have been turning more and more to Model Based Design. In Model Based Design, the development process centers around a system model, from requirements capture and design to implementation and test. Engineers can skip over a generation of system design processes on the basis of hand coding and use graphical models to design, analyze, and implement the software that determines machine performance and behavior. This paper describes the process implemented in Autonomie, a Plug-and-Play Software Environment, to design and evaluate component hardware in an emulated environment. We will discuss best practices and provide an example through evaluation of advanced high-energy battery pack within an emulated Plug-in Hybrid Electric Vehicle.