Search Results

This work details the process employed to design the 2009 Cooper Union FSAE® suspension system, spanning the overarching design philosophy, configuration selection, analysis, fabrication, and implementation, while offering recommendations to those especially new to the field. The design methodology illustrated here provides a systematic approach to suspension geometry, material selection, packaging, and construction. Though this paper serves as a starting point for FSAE® suspension designers, it provides a succinct overview for those interested in general suspension design fundamentals. The design process began with the selection of a suspension configuration, geometries, and kinematics, which were driven in part by tire data, desired bulk vehicle dynamics characteristics, and overall geometric variability. The springs and adjustable dampers were then selected as the front and rear anti-roll bar properties were concurrently designed.

A low-cost, high precision strain gauge data acquisition system was designed and implemented to aid in optimizing the design of suspension and steering members in an FSAE vehicle. The primary focus of the project was to capture load limits in A-arms, steering tie-rods, and toe control linkages and to extract the dynamic response of the suspension system when subjected to steady-state cornering and bump scenarios. These data are critical considerations needed to systematically and aggressively address suspension material selection and fabrication, vehicle dynamic response, and weight savings. In addition, the data from this system were intended to enhance the accuracy of imposed FEA boundary conditions, corroborate on-road system responses to simulated data, and provide a cost-effective, wireless alternative for a wide range of low-level electrical signals throughout the vehicle.

The target cascading methodology is applied to the conceptual design of an advanced heavy tactical truck. Two levels are defined: an integrated truck model is represented at the top (vehicle) level and four independent suspension arms are represented at the lower (system) level. Necessary analysis models are developed, and design problems are formulated and solved iteratively at both levels. Hence, vehicle design variables and system specifications are determined in a consistent manner. Two different target sets and two different propulsion systems are considered. Trade-offs between conflicting targets are identified. It is demonstrated that target cascading can be useful in avoiding costly design iterations late in the product development process.

A critical limitation preventing newer FSAE teams from improving in the international rankings is that of the person-machine interface, where driver inexperience and lack of training lead to loss of traction. The Traction Control System (TCS) described here uses closed-loop control of available engine power via spark retardation. Two distinct, driver-selectable algorithms were developed which govern TCS operation for either 1) launch control for the straight line acceleration event, or 2) full traction control for all other dynamic events. Launch control uses a spark retard rev limit to allow the driver to hold the engine at the ideal RPM for easy rev matching via flat foot shifting. Wheel speeds are simultaneously monitored to achieve ideal tire slip ratios. The full traction control algorithm uses the launch control method as a basis, but also addresses potential need for corner exit oversteer or engine braking.

A regenerative braking model for a parallel Hybrid Electric Vehicle (HEV) is developed in this work. This model computes the line and pad pressures for the front and rear brakes, the amount of generator use depending on the state of deceleration (i.e. the brake pedal position), and includes a wheel lock-up avoidance algorithm. The regenerative braking model has been developed in the symbolic programming environment of MATLAB/SIMULINK/STATEFLOW for downloadability to an actual HEV's control system. The regenerative braking model has been incorporated in NREL's HEV system simulation called ADVISOR. Code modules that have been changed to implement the new regenerative model are described. Resulting outputs are compared to the baseline regenerative braking model in the parent code. The behavior of the HEV system (battery state of charge, overall fuel economy, and emissions characteristics) with the baseline and the proposed regenerative braking strategy are first compared.