Access Type

Open Access Thesis

Date of Award

January 2017

Degree Type

Thesis

Degree Name

M.S.

Department

Mechanical Engineering

First Advisor

Jerry Ku

Abstract

Teams participating in Advanced Vehicle Technology Competitions such as EcoCAR3 are often bound by limited time and resources. Moreover, vehicle and component downtime due to mechanical and electrical issues reduce the time available for testing activities demanded by the Controls/Systems Modeling and Simulation teams. Therefore, the teams would benefit from identifying new approaches and being more pragmatic and productive in order to achieve satisfactory progress in the competition. This thesis summarizes the approach taken to improve the simulation accuracy of the Wayne State University EcoCAR3 team’s Pre-transmission Parallel Hybrid Electric Vehicle plant model and HIL setup. Focus is on testing the Hybrid Supervisory Controller energy management and diagnostic functionality to be successful in the emissions and energy consumption event. After thorough literature research it is determined that a varying fidelity forward dynamic HEV plant model can produce accurate energy consumption simulation results. Initially, data obtained from manufacturers is used to model the components such as IC Engine, Electric Machine, Energy Storage System (ESS), transmission, differential, chassis and the ECUs. Later, test benches are setup to optimize and refine the individual model parameters by comparing the simulated results with the actual results obtained from component testing and on-road vehicle testing. Finally, the total vehicle plant model is validated by comparing the simulated results with the P2 PHEV on-road test data. The accuracy of the plant model determines the ability to optimize the Hybrid Supervisory Controller code to achieve maximum energy efficiency. Apart from model accuracy improvement, the Hardware In Loop (HIL) test setup is also discussed. HIL system is essential for validating the Hybrid Supervisory Controller’s functionalities in real time. The challenges during modeling and HIL setup are discussed and more improvements that can be done during the final year are recommended based on the research.

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