Development of System Analysis Methodologies and Tools for Modeling andOptimizing Vehicle System Efficiency.

Vehicle Modeling and Simulation;Powertrain Control;Dynamic Optimization;Systems Analysis;Fuel Efficiency;Mechanical Engineering;Engineering;Mechanical Engineering

Baglione, Melody L.Thompson, Jr., Levi T. ;

University of Michigan

关键词: Vehicle Modeling and Simulation;    Powertrain Control;    Dynamic Optimization;    Systems Analysis;    Fuel Efficiency;    Mechanical Engineering;    Engineering;    Mechanical Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/57640/mpapke_1.pdf?sequence=2&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Optimizing the vehicle system is essential for achieving higher fuel efficiency.This dissertation addresses the need to better understand energy demand from a vehicle subsystem standpoint and tackles the challenge of optimal hardware and control system design.An energy analysis methodology and Matlab®/Simulink® based tool are developed to account for where the fuel energy supplied to a vehicle system is demanded.A hybrid semi-empirical and analytical approach that combines first principles with detailed component speed and load data is proposed.The methodology and tool are applied to account for the instantaneous and accumulated vehicle subsystem energy usage over a given drive cycle.A comparison of the prevailing fuel economy factors for city and highway driving are presented.Incremental vehicle subsystem changes that account for a fraction of the total energy demand are analyzed to determine individual effects on overall fuel economy.A reverse dynamic optimization methodology is proposed for optimal powertrain integration and control design.A reverse tractive road load demand model developed in Matlab®/Simulink® propagates the required wheel torque and speed derived from vehicle speed and road grade through the powertrain system to determine the required fuel flow for all possible states within the hardware constraints.The control strategy is treated as a multi-stage, multi-dimension decision process, where dynamic programming is applied to find an optimal control policy that minimizes the accumulated fuel flow over a drive cycle.The reverse dynamic optimization methodology and tool are used to assess and develop transmission gear shift, torque converter lock-up clutch, and pedal control strategies that are catered to specific vehicle applications.The reverse model and dynamic optimization technique are extended to virtually optimize variable displacement engine operation taking gear and clutch control interaction effects into account.The reverse model is used for establishing design criteria, such as minimum engine part throttle torque requirements, by determining the required speeds and loads to traverse drive cycles.The advantages of the reverse dynamic optimization approach are demonstrated by performing powertrain matching analyses (i.e., vehicle attribute sensitivity analysis; optimal engine, transmission and axle selection; and variable displacement effects) and key system integration concepts are revealed.