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.
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An electric vehicle simulation code which can be used with any IBM compatible personal computer was written. This general purpose simulation program is useful for performing parametric studies of electric vehicle performance on user input driving cycles. The program is run interactively and guides the user through all of the necessary inputs. Driveline components and the traction battery are described and defined by ASCII files which may be customized by the user. Scaling of these components is ...
Abstract This paper summarizes battery modeling capabilities in ADVISOR—the National Renewable Energy Laboratory’s advanced vehicle simulator written in the Matlab/Simulink environment. ADVISOR’s Matlab-oriented battery models consist of the following: (1) an internal resistance model, (2) a resistance–capacitance ( RC ) model, (3) a PNGV capacitance model, (4) a neural network (nnet) lead acid model, and (5) a fundamental lead acid battery model. For the models, the electric schematics (where a...
ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approachADVISOR 2.1 is the latest version of the National Renewable Energy Laboratory's advanced vehicle simulator. It was first developed in 1994 to support the US Department of Energy hybrid propulsion system program and is designed to be accurate, fast, flexible, easily sharable, and easy to use. This paper presents the model, focusing on its combination of forward- and backward-facing simulation approaches, and evaluates the model in terms of its design goals. ADVISOR predicts acceleration time to w...
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PowerManagements of a Hybrid Electric Propulsion System Powered by Solar Cells, Fuel Cells, and Batteries for UAVs
This chapter describes the power management of a UAV hybrid electric propulsion system. Three electric propulsion systems with different power sources, i.e., solar cells, fuel cells, and batteries, are considered. The power sources are designed and constructed to share the same operation voltage range and connect to the power bus without the need for additional converters or controllers. Each power source is modeled in MATLAB/Simulink, and the component models are verified with published data fr...
Many efforts have been made in recent years to address issues surrounding the use of fossil fuels for energy. However, it must be conceded that world’s dependence on fossil fuels cannot cease overnight. In reality, the switch is expected to be a relatively slow migration of technologies over many decades. During this transition period the world will need bridging technologies to aid in the transition to alternate energy sources. One such technology, which shows much promise in boosting energy ef...
This chapter focuses on fundamentals and basic concepts of vehicle power management. Several aspects are involved in this chapter as different sections, containing energy consuming effects and vehicle performance, introduction to drive cycles and discussion of vehicle power demand, current power management research in various types of vehicles, and description of major software tools (i.e., MATLAB/Simulink, ADVISOR and PSAT) for structural optimization and verification of control strategies in v...
INTEGRATION OF SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND BAYESIAN METHODS TO PREDICT PRODUCTION THROUGHPUT UNDER RANDOM VARIABLESAnalysing and modelling efforts on production throughput are getting more complex due to random variables in today’s dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of prediction. In the dynamic manufacturing environment, production lines have to cope with changes in set-up time, machinery breakdown, lead time of manufacturing, demand, and scrap. This study applied a...
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A major portion of current research in the automotive industry involves the study of the overall efficiency of advanced vehicular power trains. The improvement of overall energy efficiency is one of the most important subjects for developing hybrid electric, fuel cell, and battery electric vehicle (HEV, FCV, and BEV) technologies. This paper aims at developing a basis for comparison of overall efficiencies of advanced vehicular topologies for the above-mentioned advanced vehicular systems.
In order to analyse and improve the energy efficiency of electric vehicles (EVs), an efficient, effective and accurate simulation model of vehicular systems is established from the energy flow point of view. The proposed model includes sub-systems of energy storage, energy consumption, energy transmission, vehicle dynamics, driver model, and vehicle controller. A case study, based on Nissan Leaf, is implemented for validation of the proposed model. Finally, the energy flow and consumption distri...
The control of fuel cell systems was studied in Chaps. 3, 6 analyzing the system composed by the fuel cell stack and its auxiliary subsystems (e.g., compressor, valves, etc.), with the following objectives: achieve high efficiency, reduce the hydrogen consumption, improve the dynamic behavior and guarantee its safe operation. We continue in this chapter and Chap. 8 with the study of fuel cell-based systems approaching the fuel cell hybrid systems with some energy storage. Hybridization has impor...