Powertrain System Analysis Toolkit Research Articles

Modeling, Simulation and Performance Comparison of Conventional Vehicle Against Three Configurations of Hybrid Vehicles

In the last two decades, an extensive research work has been conducted in the automotive industry to develop and improve vehicles’ performance. Different vehicular powertrain configurations such as electric vehicles, hybrid ICE/battery vehicles, and recently hybrid FC/battery vehicles have been investigated to find more efficient alternatives for conventional combustion engines vehicles. Because of the many hybrid and electrical vehicle configurations and powertrain technologies, modeling and simulation of such vehicles are very important tools for final design development. Simulation saves time and cost in predicting performance, selecting powertrain components, and tuning control systems. In this paper, three hybrid vehicle models are developed and tested based on forward looking modeling technique and utilizing the Powertrain System Analysis Toolkit (PSAT) software package. Unlike most of the literature, this paper shows more details about sizing of the major components of the proposed powertrains. The main hybrid powertrain components were sized such that acceptable drivability, performance, and fuel economy are achieved. The performance of developed vehicle models is compared with an internal combustion engine (ICE) Nissan Sunny vehicle model using a non-standard driving cycle that was developed to reflect a local driving pattern. The hybrid models under investigation are hybrid fuel cell/battery vehicle, and two hybrid ICE/battery vehicles; one with series configuration, and the other with parallel configuration. The performance of the models is investigated in terms of fuel economy, drivability, emissions, and efficiency. The introduced simulation results demonstrate that the hybrid FC/battery configuration performs the best and is consequently recommended as the powertrain of choice for future vehicles.

Golf Car Application Based Performance Analysis of a Generic Neighborhood Fuel Cell Vehicle (NFCV) Powertrain

In this work, a preliminary lay out of hybrid fuel cell-battery vehicle is developed and a 2012 GEM eL Golf car is evaluated and designed for analogous vehicle requirements. Packaging of electrified powertrain modules have been assessed and proposed. Further, the conceptual design is simulated as a series hybrid PEM fuel cell-Li-ion battery vehicle model in Powertrain System Analysis Toolkit (PSAT®) software. The output performance of the vehicle with the proposed electrified powertrain components is obtained, analyzed and the results are deemed satisfactory and in par with the performance of the conventional vehicle. This design can be implemented in the long run to general vehicles also, thus creating independence from fossil fuels.

Intelligent Hybrid Vehicle Power Control—Part II: Online Intelligent Energy Management

This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). In the first paper, we presented the machine-learning framework ML_EMO_HEV, which was developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine-learning algorithms for predicting driving environments and generating the optimal power split of the HEV system for a given driving environment. In this paper, we present the following three online intelligent energy controllers: 1) IEC_HEV_SISE; 2) IEC_HEV_MISE ; and 3) IEC_HEV_MIME. All three online intelligent energy controllers were trained within the machine-learning framework ML_EMO_HEV to generate the best combination of engine power and battery power in real time such that the total fuel consumption over the whole driving cycle is minimized while still meeting the driver's demand and the system constraints, including engine, motor, battery, and generator operation limits. The three online controllers were integrated into the Ford Escape hybrid vehicle model for online performance evaluation. Based on their performances on ten test drive cycles provided by the Powertrain Systems Analysis Toolkit library, we can conclude that the roadway type and traffic congestion level specific machine learning of optimal energy management is effective for in-vehicle energy control. The best controller, IEC_HEV_MISE, trained with the optimal power split generated by the DP optimization algorithm with multiple initial SOC points and single ending point, can provide fuel savings ranging from 5% to 19%. Together, these two papers cover the innovative technologies for modeling power flow, mathematical background of optimization in energy management, and machine-learning algorithms for generating intelligent energy controllers for quasioptimal energy flow in a power-split HEV.

Using the Pareto Set Pursuing Multiobjective Optimization Approach for Hybridization of a Plug-In Hybrid Electric Vehicle

A plug-in hybrid electric vehicle (PHEV) can improve fuel economy and emission reduction significantly compared to hybrid electric vehicles and conventional internal combustion engine (ICE) vehicles. Currently there lacks an efficient and effective approach to identify the optimal combination of the battery pack size, electric motor, and engine for PHEVs in the presence of multiple design objectives such as fuel economy, operating cost, and emission. This work proposes a design approach for optimal PHEV hybridization. Through integrating the Pareto set pursuing (PSP) multiobjective optimization algorithm and powertrain system analysis toolkit (PSAT) simulator on a Toyota Prius PHEV platform, 4480 possible combinations of design parameters (20 batteries, 14 motors, and 16 engines) were explored for PHEV20 and PHEV40 powertrain configurations. The proposed approach yielded the optimal solution in a small fraction of computational time, as compared to an exhaustive search. This confirms the efficiency and applicability of PSP to problems with discrete variables. In the design context we have found that battery, motor, and engine collectively define the optimal hybridization scheme, which also varies with the drive cycle and all electric range (AER). The proposed method and software platform could be applied to optimize other powertrain designs.

Intelligent Hybrid Vehicle Power Control—Part I: Machine Learning of Optimal Vehicle Power

In this series of two papers, we present our research on intelligent energy management for hybrid electric vehicles (HEVs). These two papers cover the modeling of power flow in HEVs, the mathematical background of optimization in energy management in HEVs, a machine learning framework that combines dynamic programming (DP) with machine learning to learn about roadway-type- and traffic-congestion-level-specific energy optimization, machine learning algorithms, and real-time quasi-optimal control of energy flow in an HEV. This first paper presents our research on machine learning for optimal energy management in HEVs. We will present a machine learning framework ML_EMO_HEV developed for the optimization of energy management in an HEV, machine learning algorithms for predicting driving environments, and the generation of an optimal power split for a given driving environment. Experiments are conducted based on a simulated Ford Escape Hybrid vehicle model provided by Argonne National Laboratory's Powertrain Systems Analysis Toolkit (PSAT). Based on the experimental results on the test data, we can conclude that the neural networks trained under the ML_EMO_HEV framework are effective in predicting roadway type and traffic congestion levels, predicting driving trends, and learning optimal engine speed and optimal battery power from DP.

Cold-start emissions control in hybrid vehicles equipped with a passive adsorber for hydrocarbons and nitrogen oxides

We present results from a computational study of the potential for using low-cost sorbent materials to trap the emissions of hydrocarbons and nitrogen oxides temporally during cold-start periods in hybrid electric vehicles and plug-in hybrid electric vehicles operating over transient driving cycles. The hydrocarbon adsorption behavior of a candidate sorbent composed of Ag-beta-zeolite was characterized in a laboratory flow reactor to estimate the kinetic parameters for a one-dimensional transient adsorber device model. This model was then implemented in the Powertrain Systems Analysis Toolkit to simulate a passive hydrocarbon adsorber device on a hybrid vehicle. The results indicate that such an adsorber can substantially reduce the hydrocarbon emissions by temporarily storing them until the three-way catalyst is sufficiently warm to remove them from the exhaust. A similar adsorber device model was simulated for nitrogen oxide control, using an initial set of conjectured kinetic parameters for transition metal oxides based on limited information in the literature. These latter simulations revealed the need to pursue additional experimental studies to characterize fully this class of sorbents. Such studies are especially relevant in the present context of rapidly evolving vehicle technology, because emission controls of this type do not involve any penalty in fuel consumption or require any change in engine operation.

MPC-Based Energy Management of a Power-Split Hybrid Electric Vehicle

A power-split hybrid electric vehicle (HEV) combines the advantages of both series and parallel hybrid vehicle architectures by utilizing a planetary gear set to split and combine the power produced by electric machines and a combustion engine. Because of the different modes of operation, devising a near optimal energy management strategy is quite challenging and essential for these vehicles. To improve the fuel economy of a power-split HEV, we first formulate the energy management problem as a nonlinear and constrained optimal control problem. Then two different cost functions are defined and model predictive control (MPC) strategies are utilized to obtain the power split between the combustion engine and electrical machines and the system operating points at each sample time. Simulation results on a closed-loop high-fidelity model of a power-split HEV over multiple standard drive cycles and with different controllers are presented. The results of a nonlinear MPC strategy show a noticeable improvement in fuel economy with respect to those of an available controller in the commercial Powertrain System Analysis Toolkit (PSAT) software and the other proposed methodology by the authors based on a linear time-varying MPC.

A comparative study on the power characteristics and control strategies for plug-in hybrid electric vehicles

A comparative study was performed on two types of plug-in hybrid electric vehicles (PHEVs): the GM Volt and the Toyota Prius Plug-in Hybrid. First, the powertrain models of the two vehicles were derived. Based on the dynamic models, a detailed component control algorithm was developed for each PHEV. Specifically, a control algorithm was proposed for motor generator 1 (MG1) and MG2 to achieve optimal engine operation. Additionally, an energy management strategy for selecting the operation mode was developed from the viewpoint of fuel economy, battery state of charge and vehicle velocity. Using the dynamic model of the control algorithm for each PHEV, simulations were performed, and the simulation results were verified by comparing them with those obtained using the Powertrain System Analysis Toolkit simulator for the plug-in Prius. Based on the simulation results, a comparative study was performed, and it was found that the role and capacity of MG1 and MG2 and the mode selection algorithm must be determined depending on the configuration of the PHEV.

Electrification Potential Factor: Energy-Based Value Proposition Analysis of Plug-In Hybrid Electric Vehicles

Automotive electrification in the past decade was accompanied by infrastructural changes in the electric power industry. The dual fuel (gasoline and electricity) and dual operation (vehicle to grid (V2G) and grid to vehicle) modes of plug-in hybrid electric vehicles (PHEVs) make them attractive to the automotive and the electric power industries. The value proposition of PHEVs from consumer and automotive perspectives depends on a number of key indicators, such as fuel economy, emissions, battery lifetime, and incremental premium over a hybrid electric (HEV) or a conventional vehicle (CV). In addition, the value proposition of a PHEV from a utility perspective can diverge or even conflict with the consumer or automotive industry perspective. The traditional distance-based utility factor (UF) is primarily used to estimate the vehicle miles that are electrifiable. In this paper, a novel energy-based electrification potential factor (EPF) is proposed to differentiate between travel miles that are electrified and electrifiable. The EPF also acts as a coupling factor that can be also used to assess the overall benefits and impacts of PHEVs from consumer, automotive industry, and utility perspectives. The EPF is developed using existing transportation survey data and detailed simulations using the Powertrain System Analysis Toolkit (PSAT). For different driving profiles and control strategies (charge sustaining, charge depleting, blended, and all electric), the tradeoffs between driving cost and battery lifetime are analyzed. Specific battery lifetime targets are set, and a baseline HEV is used to compare the driving cost savings of the PHEV before determining the EPF. The viability and usefulness of the proposed EPF is demonstrated through comprehensive vehicular simulations and tradeoff analyses.

Depleting Mode Control Strategies for Plug-in Hybrid Electric Vehicles

Control strategy is the most important issue in the Plug-in Hybrid electric vehicles (PHEV) design, which has two modes: charge depleting mode (CD) and charge sustaining mode (CS). The different control strategies in depleting mode will have a great influence on PHEV dynamic performance and fuel economy. The engine optimal torque control strategy was proposed in the paper. The vehicle simulation model in Powertrain Systems Analysis Toolkit (PSAT) was adopted to evaluate the proposed control strategy. The aggressive highway drive cycle Artemis_hwy and a random drive cycle generated by Markov Process were used. The simulation results indicate the proposed control strategy has great improvement in fuel economy.

Analysis and validation of a Powertrain System Analysis Toolkit model of a fuel cell hybrid rickshaw

SUMMARY Auto rickshaws are three-wheeled vehicles that are common in Asian cities. Because of the large number of rickshaws on the road and their older two-stroke engines, they have a significant negative impact on urban air quality. Recent changeovers to four-stroke engines as well as diesel, compressed natural gas, and liquefied petroleum gas engines have succeeded in reducing the quantity of pollution and greenhouse gas emissions. However, because of the large number of vehicles, the rickshaw “fleet” still represents a significant source of air pollution. There have been several studies that have examined the feasibility of converting an internal combustion engine (ICE) rickshaw to a photovoltaic or fuel cell (FC) hybrid electric configuration. These studies have typically used or modified a standard urban drive cycle. It is argued that such drive cycles do not accurately portray the demands on a rickshaw. Thus, the results may be misleading. In this study, a model of an ICE rickshaw was created and validated against experimental results. Two drive cycles that would closely emulate the true demands on a rickshaw operated in an urban environment were developed. A model of an FC hybrid rickshaw was created and tested. The effects of FC and battery capacities as well as the electric motor type on performance were studied. A comparison between ICE and FC hybrid rickshaw configurations was done using a realistic drive cycle. The ICE and the FC hybrid rickshaw models were created and assessed using the Powertrain System Analysis Toolkit software package. Copyright © 2011 John Wiley & Sons, Ltd.

Comparisons of the simulated emissions and fuel efficiencies of diesel and gasoline hybrid electric vehicles

This paper presents results from gasoline- and diesel-powered hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) that account for the interaction of drive cycle transients and engine start–stop events with after-treatment devices and their associated fuel penalties. These simulations were conducted using the Powertrain Systems Analysis Toolkit software combined with after-treatment component models developed at Oak Ridge National Laboratory. The present authors employed a three-way catalyst model for gasoline emissions control and a lean nitrogen oxide (NO x) trap model to simulate diesel exhaust NO x reduction. A previously reported methodology based on experimentally calibrated corrections to steady state maps was used to simulate engine-out emissions and thermal variations. As expected, the simulations indicate a higher baseline fuel efficiency for diesel-powered hybrid vehicles, but this advantage is reduced by about a third for both HEVs and PHEVs when the fuel penalty for the lean NO x trap is included. These preliminary studies demonstrate that existing engine and exhaust systems models can capture important features of the highly transient engine operation in hybrid vehicles and can provide useful comparisons between advanced hybrid vehicle engine options.

Characterizing One-day Missions of PHEVs Based on Representative Synthetic Driving Cycles

This paper investigates series plug-in hybrid electric vehicle (PHEV) behavior during one-day with synthesized representative one-day missions. The amounts of electric energy and fuel consumption are predicted to assess the PHEV impact on the grid with respect to the driving distance and different charging scenarios: (1) charging overnight, (2) charging whenever possible. The representative cycles are synthesized using the extracted information from the real-world driving data in Southeast Michigan gathered through the Field Operational Tests (FOT) conducted by the University of Michigan Transportation Research Institute (UMTRI). The real-world driving data include 4,409 trips covering 830 independent days and temporal distributions of departure and arrival times. The sample size is large enough to represent real-world driving. The driving cycle synthesis approach proposed by Lee, and Filipi 2,3 based on a stochastic process and subsequent validation procedure is used to create real-world driving cycles. To cover the entire range of real-world driving distance, ten synthetic cycles are created ranging from 4.78 miles to 40.71 miles following the real-world driving distance distribution. The PHEV behavior over one-day is characterized through a simulation based approach. The PHEV simulation is executed using Matlab simulink based Powertrain System Analysis Toolkit (PSAT) developed by Argonne National Laboratory (ANL) and in-house Matlab codes. The amounts of the electricity and fuel consumptions over one-day are predicted under different driving distances and different charging scenarios. The prediction of the PHEV behavior can be directly linked to the loads on the local distribution network. © 2011 SAE International.

Impact of SiC Devices on Hybrid Electric and Plug-In Hybrid Electric Vehicles

The application of silicon carbide (SiC) devices as battery interface, motor controller, etc., in a hybrid electric vehicle (HEV) will be beneficial due to their high-temperature capability, high-power density, and high efficiency. Moreover, the light weight and small volume will affect the whole powertrain system in a HEV and, thus, the performance and cost. In this paper, the performance of HEVs is analyzed using the vehicle simulation software Powertrain System Analysis Toolkit (PSAT). Power loss models of a SiC inverter based on the test results of latest SiC devices are incorporated into PSAT powertrain models in order to study the impact of SiC devices on HEVs from a system standpoint and give a direct correlation between the inverter efficiency and weight and the vehicle's fuel economy. Two types of HEVs are considered. One is the 2004 Toyota Prius HEV, and the other is a plug-in HEV (PHEV), whose powertrain architecture is the same as that of the 2004 Toyota Prius HEV. The vehicle-level benefits from the introduction of SiC devices are demonstrated by simulations. Not only the power loss in the motor controller but also those in other components in the vehicle powertrain are reduced. As a result, the system efficiency is improved, and vehicles that incorporate SiC power electronics are predicted to consume less energy and have lower emissions and improved system compactness with a simplified thermal management system. For the PHEV, the benefits are even more distinct; in particular, the size of the battery bank can be reduced for optimum design.

On Conversion of Hybrid Electric Vehicles to Plug-In

The retrofit conversion of currently available hybrid electric vehicles (HEVs) to plug-in HEVs (PHEVs) is studied in this paper through experiments and simulations using the powertrain system analysis toolkit (PSAT). First, a rule-based fuzzy controller of the battery energy-management unit is developed to simulate different energy-management policies. Second, by modifying the energy-control strategy, the model of the conversion PHEV (C-PHEV) is verified with experiments. Finally, the C-PHEV model is used to simulate different battery energy-management control strategies. The results show improvement in fuel economy, whereas the energy-management controller discharges the power through the plug-in battery pack only when the state of charge of the base vehicle battery is close to its minimum value. This method keeps the advantage of driving in electric mode using a combination of two batteries and optimizing the use of regenerative braking capabilities, which is the main advantage of HEVs. It is also found that increasing the power threshold of the internal combustion engine (ICE) improves the performance of C-PHEV. Increasing the ICE power threshold increases the engine efficiency by running the engine in its efficient points. It also drives the vehicle in electric mode in higher power demands.

A proposed methodology for estimating transient engine-out temperature and emissions from steady-state maps

Many vehicle systems simulations utilize engine maps constructed from steady-state dynamometer measurements to estimate exhaust temperature and emissions as functions of engine speed and load. Unfortunately, steady-state engine behaviour is often significantly different from actual behaviour under transient driving conditions. This is particularly true for vehicles that undergo repeated engine shutdown and restart (e.g. electric hybrids). The authors propose a methodology for estimating transient engine exhaust properties and fuel economy based on corrections to steady-state maps. The suggested methodology has been implemented in the Powertrain Systems Analysis Toolkit (PSAT) and this implementation is used to demonstrate good agreement with experimental measurements for both a light-duty diesel and a flex-fuel gasoline/ethanol engine. Specific procedures are also recommended for setting key parameters required by the proposed methodology and possible directions for further improvements are suggested.

Intelligent Vehicle Power Control Based on Machine Learning of Optimal Control Parameters and Prediction of Road Type and Traffic Congestion

Previous research has shown that current driving conditions and driving style have a strong influence over a vehicle's fuel consumption and emissions. This paper presents a methodology for inferring road type and traffic congestion (RT&TC) levels from available onboard vehicle data and then using this information for improved vehicle power management. A machine-learning algorithm has been developed to learn the critical knowledge about fuel efficiency on 11 facility-specific drive cycles representing different road types and traffic congestion levels, as well as a neural learning algorithm for the training of a neural network to predict the RT&TC level. An online University of Michigan-Dearborn intelligent power controller (UMD_IPC) applies this knowledge to real-time vehicle power control to achieve improved fuel efficiency. UMD_IPC has been fully implemented in a conventional (nonhybrid) vehicle model in the powertrain systems analysis toolkit (PSAT) environment. Simulations conducted on the standard drive cycles provided by the PSAT show that the performance of the UMD_IPC algorithm is very close to the offline controller that is generated using a dynamic programming optimization approach. Furthermore, UMD_IPC gives improved fuel consumption in a conventional vehicle, alternating neither the vehicle structure nor its components.

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 vehicle's 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 PHEV's 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.

Plug-in Hybrid Electric Vehicle Control Strategy Parameter Optimization

Plug-in Hybrid Electric Vehicles (PHEVs) offer a great opportunity to significantly reduce petroleum consumption. The fuel economy of PHEV is highly dependent on All-Electric-Range (AER) and control strategy. Previous studies have shown that in addition to parameters influencing Hybrid Electric Vehicles (HEVs), control strategies of PHEVs are also influenced by the trip distance. This additional parameter makes it even more difficult to manually tune the parameters that minimize fuel consumption. This study uses a pre-transmission parallel PHEV model developed with the Powertrain System Analysis Toolkit (PSAT). A non-derivative based algorithm called DIRECT (for DIvided RECTangles) is used to optimize the main control strategy parameters. The fuel economy and main performance criteria of the PHEVs are compared for the initial design and final optimal design. An optimal control solution resulting from an extensive search of the entire design space can provide physical insight into the PHEV operation.

Modeling and Simulation of Electric and Hybrid Vehicles

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Different modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced VehIcle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed. </para>