Jason Kwon

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.

“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.

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.

Maximizing Net Present Value of a Series PHEV by Optimizing Battery Size and Vehicle Control Parameters

For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle. The initial investment on the battery and the future savings that result from reduced gasoline consumption are compared. The investment and savings results depend on the battery size and the vehicle usage. For each battery size, the control parameters were optimized to ensure the best performance possible with the battery design under consideration. Real-world driving patterns and survey results from the National Highway Traffic Safety Administration were used to simulate the usage of vehicles over their lifetime.

Instantaneously Optimized Controller for a Multimode Hybrid Electric Vehicle

A multimode transmission combines several power-split modes and possibly several fixed gear modes, thanks to complex arrangements of planetary gearsets, clutches and electric motors. Coupled to a battery, it can be used in a highly flexible hybrid configuration, which is especially practical for larger cars. The Chevrolet Tahoe Hybrid is the first light-duty vehicle featuring such a system. This paper introduces the use of a high-level vehicle controller based on instantaneous optimization to select the most appropriate mode for minimizing fuel consumption under a broad range of vehicle operating conditions. The control uses partial optimization: the engine ON/OFF and the battery power demand regulating the battery state-of-charge are decided by a rulebased logic; the transmission mode as well as the operating points are chosen by an instantaneous optimization module that aims at minimizing the fuel consumption at each time step. The controller is then implemented in a Simulink/Stateflow controller that can be used in Argonnes PSAT (Powertrain System Analysis Toolkit), a forward-looking powertrain simulation toolkit with dynamic plant models. As a result, the controller described in this paper is realistically implementable on an actual vehicle. Simulation results show the mode use and describe the practical operations of the system.

Trade-off between PHEV fuel efficiency and estimated battery cycle life with cost analysis

The battery life and cost of plug-in hybrid electric vehicles (PHEVs) are two key factors that impede the introduction of PHEVs in the current market. For a given drive pattern, battery cycle life has an inverse relationship with battery utilization, and gasoline savings (petroleum displacement) has a direct correlation with battery utilization. This paper attempts to determine the trade-off between battery cycle life and gasoline fuel savings by varying the battery utilization for a fixed distance and drive pattern. By varying the vehicle energy management, different battery utilization scenarios are created. Battery hardware-in-the-loop (a real battery tested in a virtual vehicle) is used to evaluate battery utilization under different vehicle energy management scenarios. The virtual vehicle is a real-time simulation of a power-split midsize vehicle, developed by using Argonnes Powertrain Simulation Analysis Toolkit (PSAT). The real battery is the JCS VL41M 10-kWH lithium-ion PHEV battery. Cost analysis provides insights into the economic impact of the above trade-off.

Comparison of Powertrain Configuration Options 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. INTRODUCTION Plug-in hybrid vehicles (PHEVs) combine an internal combustion engine (ICE) and an electrical energy/power source that is composed of a battery and one or two electric machines. Compared with the common hybrid electric vehicle (HEV), a PHEV has greater potential to improve fuel efficiency and reduce emissions, since it allows full electric driving and can obtain electric power easily from the home electricity grid [1]. The PHEV is also competent for long-distance driving with its HEV function. The Volt is manufactured by the Chevrolet division of General Motors (GM). This sedan-type PHEV was rated in model-year 2011 by the U.S. Environmental Protection Agency. According to GM, the Volt can travel 20 to 50 miles on its lithium-ion battery of 16 kWh. The GM Voltec powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt’s efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an ICE. The Voltec powertrain introduces a unique, two-motor electric vehicle (EV) driving mode that allows both the driving motor and the generator to provide tractive effort while simultaneously reducing electric motor speeds and the total associated electric motor losses. For HEV operation, the Voltec transaxle uses the same hardware that enables one-motor and two-motor operation to provide the completely decoupled action of a pure series hybrid, as well as a more efficient flow of power with decoupled action for driving at light load and high vehicle speed [2, 3]. When designing a vehicle for a specific application, the goal is to select the powertrain configuration that maximizes the fuel displaced and yet minimizes the sizes of components. The power-split system is the most commonly used system in currently available hybrid vehicles. However, the design of the power-split system for the PHEV is based on the blended strategy, and it has a relatively short electric driving range [4]. The series configuration for the PHEV, on the other hand, is often considered to be closer to a pure electric vehicle when compared with a split configuration. In this study, a comparative analysis is conducted on the Voltec and a pure series for the PHEV. Two vehicle powertrain configurations are sized to achieve similar performance for all-electric range (AER) approaches based on midsize vehicle applications. The component sizes and the fuel economy of each option are examined.

Evaluation of the efficiency and the drive cycle emissions for a hydrogen direct-injection engine

Hydrogen is seen as a sustainable energy carrier for transportation because it can be generated using renewable energy sources and it is a favorable fuel for clean vehicle powertrains. Hydrogen internal-combustion engines have been identified as a cost-effective consumer of hydrogen in the near term to aid in the development of a large-scale hydrogen infrastructure. Current research on hydrogen internal-combustion engines is directed by a series of efficiency and emissions targets defined by the US Department of Energy including a peak brake thermal efficiency of 45% and nitrogen oxide emissions of less than 0.07 g/mile. A high-efficiency hydrogen direct-injection engine was developed at Argonne National Laboratory to take advantage of the combustion characteristics of hydrogen. The engine employs a lean control strategy with turbocharging for power density comparable with that of gasoline engines. The injection strategy was optimized through collaborative three-dimensional computational fluid dynamics and experimental efforts to achieve mixture stratification that is beneficial for both a high efficiency and low nitrogen oxide emissions. The efficiency maps of the hydrogen engine demonstrate a peak brake thermal efficiency of 45.5% together with nitrogen oxide maps showing emissions of less than 0.10 g/kW h in much of the operating regime. In order to evaluate the driving-cycle nitrogen oxide emissions, the engine maps were fed into a vehicle simulation assuming a midsize sedan with a conventional (non-hybrid) powertrain. With a 3.0 l hydrogen engine, nitrogen oxide emissions from a Urban Dynamometer Driving Schedule cycle are 0.017 g/mile which fulfills the project goal and are even sufficiently low to meet the Super-Ultra-Low-Emissions Vehicle II emissions specification. The city or highway fuel economy, normalized to gallons of gasoline, is 32.4/51.5 mile/gal(US) for a combined average of 38.9 mile/gal(US), exceeding the 2016 Corporate Average Fuel Economy standard. Further vehicle simulations were performed to show the effect of engine downsizing. With a smaller 2.0 l engine, nitrogen oxide emissions increase to 0.028 g/mile, which still exceeds the US Department of Energy target together with the benefit of a fuel economy improvement to 45.4 mile/gal(US) (combined).

Analyzing the Uncertainty in the Fuel Economy Prediction for the EPA MOVES Binning Methodology

Developed by the U.S. Environmental Protection Agency (EPA), the Multi-scale mOtor Vehicle Emission Simulator (MOVES) is used to estimate inventories and projections through 2050 at the county or national level for energy consumption, nitrous oxide (N2O), and methane (CH4) from highway vehicles. To simulate a large number of vehicles and fleets on numerous driving cycles, EPA developed a binning technique characterizing the energy rate for varying Vehicle Specific Power (VSP) under predefined vehicle speed ranges. The methodology is based upon the assumption that the vehicle behaves the same way for a predefined vehicle speed and power demand. When this has been validated for conventional vehicles, it has not been for advanced vehicle powertrains, including hybrid electric vehicles (HEVs) where the engine can be ON or OFF depending upon the battery State-of-Charge (SOC). The Powertrain System Analysis Toolkit (PSAT), a vehicle simulation software developed by Argonne National Laboratory, will be used to generate the MOVES bins as well as evaluate the errors. This paper will quantify and explain the fuel economy uncertainties introduced by the “average” vehicle in the representative source bins for several powertrain configurations, control strategies, and drive cycles defined in MOVES.

Impact of fuel cell system design used in series fuel cell HEV on net present value (NPV)

For a series fuel cell hybrid electric vehicle (FCHEV), it is critical that the degree of hybridization between the fuel cell power and battery power be determined so as to maximize the vehicles performance variables, such as fuel efficiency and fuel savings. Because of the cost of and wide range of design choices for prototype vehicles, a development process that can quickly and systematically determine the design characteristics of hybrid systems (including battery size and vehicle-level control parameters that maximize the vehicles net present value [NPV] during the planning stage) is needed. Argonne National Laboratory developed AUTONOMIE, a modeling and simulation framework, and, with support from MathWorks, the laboratory has integrated that software with an optimization algorithm and parallel computing tools to enable that development process. This paper presents the results of a study that used the development process, in which the NPV was the present value of all the future expenses and savings associated with a vehicle. The initial investment in the battery and the future savings that will result from reduced gasoline consumption are compared. The investment and savings results depend on the battery size and vehicle usage. For each battery size at the given fuel cell power and efficiency, the control parameters were optimized to ensure the best performance possible from using the battery design under consideration. Real-world driving patterns and survey results from the National Highway Traffic Safety Administration (NHTSA) were used to simulate the usage of vehicles over their lifetimes.