Tony Markel

ADVISOR: a systems analysis tool for advanced vehicle modeling

This paper provides an overview of Advanced Vehicle Simulator (ADVISOR)—the US Department of Energy’s (DOE’s) ADVISOR written in the MATLAB/Simulink environment and developed by the National Renewable Energy Laboratory. ADVISOR provides the vehicle engineering community with an easy-to-use, flexible, yet robust and supported analysis package for advanced vehicle modeling. It is primarily used to quantify the fuel economy, the performance, and the emissions of vehicles that use alternative technologies including fuel cells, batteries, electric motors, and internal combustion engines in hybrid (i.e. multiple power sources) configurations. It excels at quantifying the relative change that can be expected due to the implementation of technology compared to a baseline scenario. ADVISOR’s capabilities and limitations are presented and the power source models that are included in ADVISOR are discussed. Finally, several applications of the tool are presented to highlight ADVISOR’s functionality. The content of this paper is based on a presentation made at the ‘Development of Advanced Battery Engineering Models’ workshop held in Crystal City, Virginia in August 2001. # 2002 Elsevier Science B.V. All rights reserved.

Energy Management Strategies for Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) differ from hybrid vehicles (HEVs) with their ability to use off-board electricity generation to recharge their energy storage systems. In addition to possessing charge-sustaining HEV operation capability, PHEVs use the stored electrical energy during a charge-depleting operating period to displace a significant amount of petroleum consumption. The particular operating strategy employed during the charge-depleting mode will significantly influence the component attributes and the value of the PHEV technology. This paper summarizes three potential energy management strategies, and compares the implications of selecting one strategy over another in the context of the aggressiveness and distance of the duty cycle over which the vehicle will likely operate.

Communication and control of electric drive vehicles supporting renewables

The coming intersection between a growing electrified vehicle fleet and desired growth in renewable electricity generation presents an opportunity for synergistic value. Some types of renewable electricity generation are variable and intermittent, and rarely coincident with utility load pattern. Vehicles are typically parked 90% of the time, and the batteries are a significant capital investment. In this paper we discuss the intersection of these two growth areas, the technology needed for integration, and several potential scenarios highlighting the limitations and opportunities for renewable energy resources to fuel electrified vehicles of the future.

Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) have received considerable recent attention for their potential to reduce petroleum consumption significantly and quickly in the transportation sector. Analysis to aid the design of such vehicles and predict their real-world performance and fuel displacement must consider the driving patterns the vehicles will typically encounter. This paper goes beyond consideration of standardized certification cycless by leveraging state-of-the-art travel survey techniques that use Global Positioning System (GPS) technology to obtain a large set of real-world drive cycles from the surveyed vehicle fleet. This study specifically extracts 24-h, second-by-second driving profiles from a set of 227 GPS-instrumented vehicles in the St. Louis, Missouri, metropolitan area. The performance of midsize conventional, hybrid electric, and PHEV models is then simulated over the 227 full-day driving profiles to assess fuel consumption and operating characteristics of these vehicle technologies over a set of real-world usage patterns. In comparison to standard cycles used for certification procedures, the travel survey duty cycles include significantly more aggressive acceleration and deceleration events across the velocity spectrum, which affect vehicle operation and efficiency. Even under these more aggressive operating conditions, PHEVs using a blended charge-depleting energy management strategy consume less than 50% of the petroleum used by similar conventional vehicles. Although true prediction of the widespread real-world use of these vehicles requires expansion of the vehicle sample size and a refined accounting for the possible interaction of several variables with the sampled driving profiles, this study demonstrates a cutting-edge use of available GPS travel survey data to analyze the (highly drive cycle–dependent) performance of advanced technology PHEVs. This demonstration highlights new opportunities for using innovative GPS travel survey techniques and sophisticated vehicle system simulation tools to guide vehicle design improvements and to maximize the benefits offered by energy efficiency technologies.

Modeling grid-connected hybrid electric vehicles using ADVISOR

The overall system efficiency of a hybrid electric vehicle is highly dependent on the energy management strategy employed. In this paper, an electric utility grid-connected energy management strategy for a parallel hybrid electric vehicle is presented. ADVISOR was used as a modeling tool to determine the appropriate size of the hybrid components and the energy management strategy parameter settings. Simulation results demonstrated that with this strategy it is possible to achieve double the fuel economy of a comparable conventional vehicle while satisfying all performance constraints. In addition, the final vehicle design provides an all-electric range capability in excess of 20 miles.

Modeling and Validation of a Fuel Cell Hybrid Vehicle

This paper describes the design and construction of a fuel cell hybrid electric vehicle based on the conversion of a five passenger production sedan. The vehicle uses a relatively small fuel cell stack to provide average power demands, and a battery pack to provide peak power demands for varied driving conditions. A model of this vehicle was developed using ADVISOR, an Advanced Vehicle Simulator that tracks energy flow and fuel usage within the vehicle drivetrain and energy conversion components. The Virginia Tech Fuel Cell Hybrid Electric Vehicle was tested on the EPA City and Highway driving cycles to provide data for validation of the model. Vehicle data and model results show good correlation at all levels and show that ADVISOR has the capability to model fuel cell hybrid electric vehicles.

Value of plug-in vehicle grid support operation

Plug-in electric hybrids will soon be introduced by several auto manufacturers. While the initial volume of plug-in hybrid electric vehicles will only constitute a very small fraction of total automobiles sold, “clustered” purchasing patterns for these vehicles may result in large localized loads on the power distribution network. Additionally, high-penetration deployment of variable generation sources will decrease flexibility in the power system. Previous research has indicated that plug-ins may be used as grid resources with no significant impact on fuel consumption. Deterministic 1- and 2-way communication architectures were modeled to show how real-time vehicle-grid interactions might occur. This analysis uses GPS travel survey data, grid-wide load data, and distribution-system load data to determine the effective power and energy storage capacity of a charging plug-in fleet, given actual driver behavior. The power utility controls vehicle charging rates based on the area control error and distribution transformer overheating. The cost of regulation and distribution transformer wear was calculated to determine the value potential of different data communication methods controlling a fleet of plug-in hybrids.

Simulated fuel economy and performance of advanced hybrid electric and plug-in hybrid electric vehicles using in-use travel profiles

As vehicle powertrain efficiency increases through electrification, consumer travel and driving behavior have significantly more influence on the potential fuel consumption of these vehicles. Therefore, it is critical to have a good understanding of in-use or “real world” driving behavior if accurate fuel consumption estimates of electric drive vehicles are to be achieved. Regional travel surveys using Global Positioning System (GPS) equipment have been found to provide an excellent source of in-use driving profiles. In this study, a variety of vehicle powertrain options were developed and their performance was simulated over GPS-derived driving profiles for 783 vehicles operating in Texas. The results include statistical comparisons of the driving profiles versus national data sets, driving performance characteristics compared with standard drive cycles, and expected petroleum displacement benefits from the electrified vehicles given various vehicle charging scenarios.

Improving Petroleum Displacement Potential of PHEVs Using Enhanced Charging Scenarios

Describes NRELs RD vehicles charged during the day would save about 5% more fuel than those charged at night.

Vehicle System Impacts of Fuel Cell System Power Response Capability

The impacts of fuel cell system power response capability on optimal hybrid and neat fuel cell vehicle configurations have been explored. Vehicle system optimization was performed with the goal of maximizing fuel economy over a drive cycle. Optimal hybrid vehicle design scenarios were derived for fuel cell systems with 10 to 90% power transient response times of 0, 2, 5, 10, 20, and 40 seconds. Optimal neat fuel cell vehicles where generated for responses times of 0, 2, 5, and 7 seconds. DIRECT, a derivative-free optimization algorithm, was used in conjunction with ADVISOR, a vehicle systems analysis tool, to systematically change both powertrain component sizes and the vehicle energy management strategy parameters to provide optimal vehicle system configurations for the range of response capabilities.