AN EXPERIMENTAL STUDY OF PARTIAL LOW RISK DEPLOYMENT IN A FIXED VEHICLE ENVIRONMENT

This study will look at the passenger air bag (PAB) performance in a fix vehicle environment using Partial Low Risk Deployment (PLRD) as a strategy. This development will follow test methods against actual baseline vehicle data and Federal Motor Vehicle Safety Standards 208 (FMVSS 208). FMVSS 208 states that PAB compliance in vehicle crash testing can be met using one of three deployment methods. The primary method suppresses PAB deployment, with the use of a seat weight sensor or occupant classification sensor (OCS), for three-year old and six-year old occupants including the presence of a child seat. A second method, PLRD allows deployment on all size occupants suppressing only for the presents of a child seat. A third method is Low Risk Deployment (LRD) which allows PAB deployment in all conditions, all statures including any/all child seats. This study outlines a PLRD development solution for achieving FMVSS 208 performance. The results of this study should provide an option for system implementation including opportunities for system efficiency and other considerations. The objective is to achieve performance levels similar too or incrementally better than the baseline vehicles National Crash Assessment Program (NCAP) Star rating. In addition, to define systemic flexibility where restraint features can be added or removed while improving occupant performance consistency to the baseline. A certified vehicles’ air bag system will typically remain in production until the vehicle platform is redesigned. The strategy to enable the PLRD hypothesis will be to first match the baseline out of position occupant performance (OOP) for the three and six-year old requirements. Second, improve the 35mph belted 5th percentile female NCAP star rating over the baseline vehicle. Third establish an equivalent FMVSS 208 certification for the 25mph unbelted 50th percentile male. FMVSS 208 high-speed requirement defines the federal minimum crash performance required for meeting frontal vehicle crash-test compliance. The intent of NCAP 5-Star rating is to provide the consumer with information about crash protection, beyond what is required by federal law. In this study, two vehicles segments were used for testing to compare and contrast to their baseline vehicles performance. Case Study 1 (CS1) used a cross over vehicle platform and Case Study 2 (CS2) used a small vehicle segment platform as their baselines. In each case study, the restraints systems were from different restraint supplier manufactures and each case contained that suppliers approach to PLRD. CS1 incorporated a downsized twins shaped bag, a carryover inflator, standard vents, and a strategic positioned bag diffuser to help disperse the flow of gas to improve OOP. The twin shaped bag with two segregated sections (lobes) to enabled high-speed baseline performance correlation on the HYGE Sled. CS2 used an A-Symmetric (square shape) PAB with standard size vents, including a passive vent, to obtain OOP similar to the baseline. The A-Symmetric shape bag also helped to enabled high-speed baseline performance improvements in HYGE Sled testing in CS2. The anticipated CS1 baseline vehicle-pulse-index (VPI) target was in the range of 65-67. However, actual dynamic vehicle (barrier) testing was overshadowed with the highest crash pulse from the previous tested vehicles with a VPI of 71. The result from the 35mph NCAP Barrier test was a solid 4-Star (4.7 Star) respectfully. In CS2, the vehicle HYGE Sled development VPI range, from the baseline was 61-62 respectively. Actual NCAP test produced a chest deflection result of 26mm versus the anticipated baseline target of 12mm. The initial assessment of this condition was thought to be due to the vehicles significant VPI increase to 67. A subsequent root cause investigation confirmed a data integrity issue due to the instrumentation. In an effort to establish a true vehicle test data point a second NCAP test was performed but faced similar instrumentation issues. As a result, the chest deflect hit the target of 12.1mm; however a femur load spike, similar to the baseline, now skewed the results. With noted level of performance improvement in chest deflection, the NCAP star was assessed as directional for 5-Star capable performance. With an actual rating of 3-Star due to instrumentation, using data extrapolation raised the ratings to 5-Star. In both cases, no structural changes were made to the surrogate vehicle and the results in each case matched their perspective baseline vehicle platforms. These results proved the PLRD is viable for further development and production implementation.

Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.