Search Results

Southwest Research Institute developed an Autonomous Ground Vehicle (AGV) capable of navigating in urban environments. The paper first gives an overview of hardware and software onboard the vehicle. The systems onboard are classified into perception, intelligence, and command and control modules to mimic a human driver. Perception deals with sensing from the world and translating it into situation awareness. This awareness is then fed into intelligence modules. Intelligence modules take inputs from the user to understand the need to navigate from its current location to another destination and, then, generate a path between them on urban, drivable surfaces using its internal urban database. Situational awareness helps intelligence to update the path in real time by avoiding any static/moving obstacles while following traffic rules.

This paper describes the development of a generic test cell software designed to overcome many vehicle-component testing difficulties by introducing modern, real-time control and simulation capabilities directly to laboratory test environments. Successfully demonstrated in a transmission test cell system, this software eliminated the need for internal combustion engines (ICE) and test-track vehicles. It incorporated the control of an advanced AC induction motor that electrically simulated the ICE and a DC dynamometer that electrically replicated vehicle loads. Engine behaviors controlled by the software included not only the average crankshaft torque production but also engine inertia and firing pulses, particularly during shifts. Vehicle loads included rolling resistance, aerodynamic drag, grade, and more importantly, vehicle inertia corresponding to sport utility, light truck, or passenger cars.

Vehicle designers make use of vehicle performance programs such as RAPTOR™ to predict the performance of concept vehicles over ranges of industry standard drive cycles. However, the accuracy of such predictions may be greatly influenced by factors requiring more specialist simulation capabilities. For example, fuel economy prediction will be heavily influenced by the performance of the engine cooling system and its impact on the vehicle's aerodynamic drag, and the load from the air-conditioning system. To improve the predictions, specialist simulation capabilities need to be applied to these aspects, and brought together with the vehicle performance calculations through co-simulation. This paper describes the approach used to enable this cosimulation and the benefits achieved by the vehicle designer.