An Environment for Model-Based Development of Ground Vehicles
Date Published: 2005-04-11
Paper Number:2005-01-0935
DOI: 10.4271/2005-01-0935
Citation:
Boggavarapu, R., Dixit, R., Sasena, M., and Sivashankar, S., "An Environment for Model-Based Development of Ground Vehicles," SAE Technical Paper 2005-01-0935, 2005, doi:10.4271/2005-01-0935.
Author(s):
Rao Boggavarapu - General Dynamics Land Systems
Ravi Dixit - Emmeskay, Inc.
Michael J. Sasena - Emmeskay, Inc.
Shiva Sivashankar - Emmeskay, Inc.
Model-based simulation and analysis tools have been used in the design of both commercial and military hybrid ground vehicles. The goal of the US Army's Future Combat Systems (FCS) is to develop a number of vehicles, called variants, fulfilling different missions on the battlefield. Design goals include reduced weight for air transport and increased agility, while maintaining a common chassis, power generator and energy storage system.
In this paper we describe a unique tool that facilitates the development of such a series of vehicles. The modeling and analysis environment is flexible, modular and reconfigurable. It has been developed on commercially available MathWorks tool suite consisting of MATLAB
®
1
, Simulink
®
and Stateflow
®
. A library of loads representing different energy usage events is first created. With the common generator and energy storage system, a different variant model is easily generated by selecting the appropriate mission loads. A series of simulations for each of the variants can then be performed to determine the generator and energy storage system required to meet all the variants and their missions.
The tool has the ability to randomly initiate the application of mission loads to simulate the battlefield scenarios and to perform robust design analysis. In addition, the tool has Smart Energy Management strategies on the vehicle performance by assigning priorities to different loads. Analyses to study (i) the impact of electric loads on the vehicle mobility performance and (ii) the impact of battery capacity using Monte Carlo analysis are presented to illustrate the utility and flexibility of this tool.
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