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2022 | Buch

Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles

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SUCHEN

Über dieses Buch

Tunan Shen aims to increase the availability of powertrain systems for autonomous electric vehicles by improving the diagnostic capability for critical faults. Following the fault analysis of powertrain systems in battery electric vehicles, the focus is on the electrical and mechanical faults of the electric machine. A multi-level diagnostic approach is proposed, which consists of multiple diagnostic models, such as a physical model, a data-based anomaly detection model, and a neural network model. To improve the overall diagnostic capability, a decision making function is designed to derive a comprehensive decision from the predictions of various operating points and different models.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Zusammenfassung
Recently, electric vehicles and automated driving have attracted strong interests in the field of automotive industry. Many researches focus on autonomous electric vehicles and aim to enhance the performance as well as the reliability of these vehicles, which will be used in highly automated driving applications [2, 3]. These autonomous electric vehicles will run much more over lifetime than today’s privately owned vehicles and require much higher availability.
Tunan Shen
Chapter 2. Fault Analysis
Zusammenfassung
The powertrain system of a battery electric vehicle (BEV) consists of a battery system, inverter, electric machine, gearbox and other mechanical parts. The powertrain will be shut down if any subsystem or component fails. In this chapter, the failure mechanisms of each subsystem are summarized.
Tunan Shen
Chapter 3. Background and State of the Art
Zusammenfassung
In this chapter, the background of fault diagnostic methods are firstly introduced, followed by introduction of signal processing techniques used for feature engineering. In the end, the principles of machine learning algorithms employed in the present study are briefly explained.
Tunan Shen
Chapter 4. Diagnosis of Electrical Faults in Electric Machines
Zusammenfassung
In this chapter, a multi-stage diagnostic method to classify five electrical faults in electric machines is proposed. At first, related works concerning fault detection in electric machines are briefly reviewed in Section 4.1. Then, the current challenges of fault detection are pointed out. After that, the main contributions of this thesis are summarized in Section 4.2. In this thesis, simulation data are used.
Tunan Shen
Chapter 5. Diagnosis of Mechanical Faults in Electric Machines
Zusammenfassung
In this chapter, diagnosis of the most common mechanical fault in electric machine i.e. bearing fault is focused. At first, different fault mechanisms of bearing will be briefly introduced in Section 5.1. After that, related works of bearing fault diagnosis are researched. The current challenges are summarized as following: limited data, limited label and knowledge transfer from laboratory to real products.
Tunan Shen
Chapter 6. Conclusion and Outlook
Zusammenfassung
The goal of this thesis is to enhance the availability of the powertrain for autonomous electric vehicles by improving the diagnostic capability. Once a fault can be detected at an early stage, critical failures can be avoided by taking proper fault reactions such as operating in degradation mode until the degraded part is repaired. The powertrain of a battery electric vehicle consists of four main components: battery, inverter, electric motor and gearbox. To find out the suitable candidate for diagnosis, the most critical faults in each component are analyzed at first.
Tunan Shen
Backmatter
Metadaten
Titel
Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles
verfasst von
Tunan Shen
Copyright-Jahr
2022
Electronic ISBN
978-3-658-36992-7
Print ISBN
978-3-658-36991-0
DOI
https://doi.org/10.1007/978-3-658-36992-7

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