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Fault Diagnosis for Functional Safety in Electrified and Automated Vehicles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fault Diagnosis for Functional Safety in Electrified and Automated Vehicles./
作者:	Li, Tianpei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	333 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Automotive engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28642404
ISBN:	9798516074240

Fault Diagnosis for Functional Safety in Electrified and Automated Vehicles.
Li, Tianpei.

Fault Diagnosis for Functional Safety in Electrified and Automated Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 333 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--The Ohio State University, 2020.

This item must not be sold to any third party vendors.

Vehicle safety is one of the critical elements of modern automobile development. With increasing automation and complexity in safety-related electrical/electronic (E/E) systems, and given the functional safety standards adopted by the automotive industry, the evolution and introduction of electrified and automated vehicles had dramatically increased the need to guarantee unprecedented levels of safety and security in the automotive industry.The automotive industry has broadly and voluntarily adopted the functional safety standard ISO 26262 to address functional safety problems in the vehicle development process. A V-cycle software development process is a core element of this standard to ensure functional safety. This dissertation develops a model-based diagnostic methodology that is inspired by the ISO-26262 V-cycle to meet automotive functional safety requirements. Specifically, in the first phase, system requirements for diagnosis are determined by Hazard Analysis and Risk Assessment (HARA) and Failure Modes and Effect Analysis (FMEA). Following the development of system requirements, the second phase of the process is dedicated to modeling the physical subsystem and its fault modes. The implementation of these models using advanced simulation tools (MATLAB/Simulink and CarSim in this dissertation) permits quantification of the fault effects on system safety and performance. The next phase is dedicated to understanding the diagnosability of the system (given a sensor set), or the selection of a suitable sensor set to achieve the desired degree of diagnosability, using a graph-theoretic method known as structural analysis. By representing a system in directed-graph or incidence-matrix form, structural analysis allows the determination of analytical redundancy in the system and of the detectability and isolability of individual faults. Further, it provides a logical computation sequence for solving for system unknowns, by identifying analytical redundant relations (ARRs) that can be used to design diagnostic algorithms. The design of residual generation based on ARRs is linked to state estimation and system identification methods, including state observers and parameter estimation. The later phases of the V-diagram address the development of Model-In-the-Loop, Software -In-the-Loop, Hardware-In-the-Loop, in-vehicle calibration and validation. For the purposes of this dissertation we limit our demonstration of the methods to Model-In-the-Loop validation for two of the case studies and Hardware-In-the-Loop validation for a third.In addition to developing a process-oriented methodology, this dissertation also addresses trade-offs in selecting different methods in terms of computational causality and robustness to noise and uncertainty in compliance with diagnostic requirements. Further, when dealing with state estimation and system identification in nonlinear systems, system observability can change with operating conditions. This dissertation also introduces a novel nonlinear system observability index to quantify system observability under different operating conditions. This index helps determine proper scenarios to apply state estimation and system identification approaches for fault diagnosis. That is, the system observability and fault detectability may be enhanced in some operating conditions.The effectiveness of the methodology is demonstrated in three case studies: i) the diagnosis of electric traction drive resolver faults in all-wheel drive battery electric vehicles; ii) resolver fault diagnosis in a P-2 configuration hybrid-electric powertrain; and iii) fault diagnosis in the automated vehicle steering system. While ISO 26262 applies to E/E systems, mechanical and electromechanical systems are also susceptible to safety-related degradation and failure. Thus, in the third case study, we extend the scope of functional safety problems addressed by ISO 26262 to mechanical faults in vehicle steering system. That is, this dissertation addresses functional safety issues related to both E/E systems and mechanical systems in electrified and autonomous vehicles.

ISBN: 9798516074240Subjects--Topical Terms:

2181195
Automotive engineering.
Subjects--Index Terms:

Model-based

Fault Diagnosis for Functional Safety in Electrified and Automated Vehicles.
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