Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles PDF Download

Author: Tunan Shen
Publisher:
ISBN: 9783658369934
Category :
Languages : en
Pages : 0

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Book Description
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. Contents Background and State of the Art Diagnosis of Electrical Faults in Electric Machines Diagnosis of Mechanical Faults in Electric Machines Target Groups Researchers and students of mechanical engineering, especially automotive powertrains in electric vehicles Research and development engineers in this field About the Author Tunan Shen did his PhD project at the Institute of Automotive Engineering (IFS), University of Stuttgart, Germany. Currently he is Software Developer for Cross Domain Computing Solutions at a German automotive supplier.

Author: Tunan Shen
Publisher: Springer Nature
ISBN: 3658369922
Category : Technology & Engineering
Languages : en
Pages : 144

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Book Description
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.

Author: Bryce Antony Hosking
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 93

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Book Description
Designing an autonomous vehicle system architecture requires extensive vehicle simulation prior to its implementation on a vehicle. Simulation provides a controlled environment to test the robustness of an autonomous architecture in a variety of driving scenarios. In any autonomous vehicle project, high-fidelity modelling of the vehicle platform is important for accurate simulations. For power-split hybrid electric vehicles, modelling the powertrain for autonomous applications is particularly difficult. The mapping from accelerator and brake pedal positions to torque at the wheels can be a function of many states. Due to this complex powertrain behavior, it is challenging to develop vehicle dynamics control algorithms for autonomous power-split hybrid vehicles. The 2015 Lincoln MKZ Hybrid is the selected vehicle platform of Autonomoose, the University of Waterloo's autonomous vehicle project. Autonomoose required high-fidelity models of the vehicle's power-split powertrain and braking systems, and a new longitudinal dynamics vehicle controller. In this thesis, a grey-box approach to modelling the Lincoln MKZ's powertrain and braking systems is proposed. The modelling approach utilizes a combination of shallow neural networks and analytical methods to generate a mapping from accelerator and brake pedal positions to the torque at each wheel. Extensive road testing of the vehicle was performed to identify parameters of the powertrain and braking models. Experimental data was measured using a vehicle measurement system and CAN bus diagnostic signals. Model parameters were identified using optimization algorithms. The powertrain and braking models were combined with a vehicle dynamics model to form a complete high-fidelity model of the vehicle that was validated by open-loop simulation. The high-fidelity models of the powertrain and braking were simplified and combined with a longitudinal vehicle dynamics model to create a control-oriented model of the vehicle. The control-oriented model was used to design an instantaneously linearizing model predictive controller (MPC). The advantages of the MPC over a classical proportional-integral (PI) controller were proven in simulation, and a framework for implementing the MPC on the vehicle was developed. The MPC was implemented on the vehicle for track testing. Early track testing results of the MPC show superior performance to the existing PI that could improve with additional controller parameter tuning.

Author: Ullekh Raghunatha Gambhira
Publisher:
ISBN:
Category : Automated vehicles
Languages : en
Pages : 117

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Book Description
In this thesis, a novel approach is presented for the powertrain optimization of electric autonomous transportation vehicles. Two applications of a Level 5 Autonomous Vehicle (AV) are investigated; pure Autonomous Driving (AD) and mixed Human and Autonomous Driving (HAD). The powertrain-system design process is demonstrated for both the applications. In addition, the tradeoffs in using a HAD optimized powertrain for autonomous driving, compared to an AD optimized powertrain is addressed. A system level model is developed in MATLAB and Simulink to predict low-frequency dynamics. Two architectures of the electric powertrain, single (Front Wheel Drive) and double motor (All Wheel Drive) are considered. The optimization problem is set up, which includes defining the objective function, design space, static constraints and inputs for AD and HAD. Comfort requirements for AVs constrain the permissible acceleration while driving. Autonomous drive cycles are represented by optimal trajectories subject to constraints. Optimal powertrains for both the applications are expressed as Pareto fronts. It is observed that there isn’t a particular trend between energy consumption and powertrain power, and even higher powered powertrains provide similar energy consumption values. The energy savings in driving autonomously is primarily attributed to the optimized trajectory, rather than optimized powertrain.

Author: Gerasimos Rigatos
Publisher: Elsevier
ISBN: 0443288550
Category :
Languages : en
Pages : 0

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Book Description
The integration of ground-breaking technologies, such as next-generation batteries and AI-powered systems, promises to reshape the way we commute, transport goods, and navigate our cities. Autonomous Electric Vehicles: Nonlinear Control, Traction, and Propulsion offers sought-after, specialized know-how on robotized electric vehicles (ground, surface, underwater, aerial). The book builds on theoretical fundamentals to then comprehensively cover the very latest research advances in nonlinear control, estimation, and fault diagnosis for autonomous navigation and electric traction systems. Part I investigates nonlinear optimal control and estimation of a specific class of vehicle per chapter, while part II control and dynamic modeling of a specific type of electric motor per chapter. Furthermore, the methodological analysis conducted is not constrained by the shortcomings of global linearization-based control algorithms, is computationally easy to implement, and is also corroborated by global robustness and stability proofs. Case studies and other practical application discussions exemplify these methods’ potential prospects if adopted at commercial scale. Readers from a wide range of related disciplines will benefit from the structured, modular approach of the volume, which was written by a group of experts with backgrounds both in academia and industry, whose aim is also to contribute transformative solutions to accelerate the global low-carbon power transition as well as smart energy management systems for the continuing shift to renewables. Investigates control and estimation methods which enable key developments in vehicle engineering (ground, surface, underwater, aerial) that are of interest on a global scale Presents real-life case studies and application examples, facilitating understanding of the related challenges for the maturation of scalable solutions matching the needs of the industry Accompanied by a companion site which includes videos of simulation tests of the control and estimation methods discussed

Author: Yueying Zhu
Publisher: Springer
ISBN: 9789811648533
Category : Technology & Engineering
Languages : en
Pages : 0

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Book Description
This book is intended for engineer’s in automotive industry and in research community of electrical machines. This book systematically focus on all the major aspects of switched reluctance motor for intelligent electric vehicle applications, including optimization design, drive system control, regenerative braking control, and motor-suspension system control, which is particularly suited for readers who are interested to learn the theory of the motor used for intelligent electric vehicles.The comprehensive and systematic treatment of practical issues around switched reluctance motor considering vehicle requirments is one of the major features of the book. The book can benefit researchers, engineers, and graduate students in fields of switched reluctance motor, electric vehicle drive system, regenerative braking system, motor-suspension system, etc.

Author: J. Jones
Publisher: Routledge
ISBN: 1136899456
Category : Technology & Engineering
Languages : en
Pages : 294

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Book Description
This book gives a sufficient grounding in mechanics for engineers to tackle a significant range of problems encountered in the design and specification of simple structures and machines. It also provides an excellent background for students wishing to progress to more advanced studies in three-dimensional mechanics.

Author: Chen Lv
Publisher: Springer Nature
ISBN: 3031015045
Category : Technology & Engineering
Languages : en
Pages : 78

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Book Description
This book studies the design optimization, state estimation, and advanced control methods for cyber-physical vehicle systems (CPVS) and their applications in real-world automotive systems. First, in Chapter 1, key challenges and state-of-the-art of vehicle design and control in the context of cyber-physical systems are introduced. In Chapter 2, a cyber-physical system (CPS) based framework is proposed for high-level co-design optimization of the plant and controller parameters for CPVS, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. In Chapter 3, an Artificial-Neural-Network-based estimation method is studied for accurate state estimation of CPVS. In Chapter 4, a high-precision controller is designed for a safety-critical CPVS. The detailed control synthesis and experimental validation are presented. The application results presented throughout the book validate the feasibility and effectiveness of the proposed theoretical methods of design, estimation, control, and optimization for cyber-physical vehicle systems.

Author: Miroslaw Staron
Publisher: Springer
ISBN: 3319586106
Category : Computers
Languages : en
Pages : 248

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Book Description
This book introduces the concept of software architecture as one of the cornerstones of software in modern cars. Following a historical overview of the evolution of software in modern cars and a discussion of the main challenges driving that evolution, Chapter 2 describes the main architectural styles of automotive software and their use in cars’ software. In Chapter 3, readers will find a description of the software development processes used to develop software on the car manufacturers’ side. Chapter 4 then introduces AUTOSAR – an important standard in automotive software. Chapter 5 goes beyond simple architecture and describes the detailed design process for automotive software using Simulink, helping readers to understand how detailed design links to high-level design. Next, Chapter 6 presents a method for assessing the quality of the architecture – ATAM (Architecture Trade-off Analysis Method) – and provides a sample assessment, while Chapter 7 presents an alternative way of assessing the architecture, namely by using quantitative measures and indicators. Subsequently Chapter 8 dives deeper into one of the specific properties discussed in Chapter 6 – safety – and details an important standard in that area, the ISO/IEC 26262 norm. Lastly, Chapter 9 presents a set of future trends that are currently emerging and have the potential to shape automotive software engineering in the coming years. This book explores the concept of software architecture for modern cars and is intended for both beginning and advanced software designers. It mainly aims at two different groups of audience – professionals working with automotive software who need to understand concepts related to automotive architectures, and students of software engineering or related fields who need to understand the specifics of automotive software to be able to construct cars or their components. Accordingly, the book also contains a wealth of real-world examples illustrating the concepts discussed and requires no prior background in the automotive domain.

Author: Teng Liu
Publisher: Springer Nature
ISBN: 3031015037
Category : Technology & Engineering
Languages : en
Pages : 90

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Book Description
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application. In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.