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Author: Zhengyu Yao Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 0
Book Description
Due to increased environmental and economic considerations, energy-efficient vehicles such as Hybrid Electric Vehicles (HEVs) have received great attention from the general public as well as automotive research community. HEVs achieve better fuel economy than conventional vehicles by employing two different power sources: a mechanical engine and an electrical motor. By controlling the two power sources optimally based on the current system state under different driving conditions, they can achieve much improved fuel economy compared to conventional vehicles powered by internal combustion engines only. These power sources have conventionally been controlled by rule-based or optimization-based control algorithms. Rule-based algorithms utilize a well-defined and easy-to-understand control logic while optimization-based control algorithms employ a mathematical function that is minimized by a controller during the vehicle operation. Besides these two conventional approaches, recent advancements in machine learning offer new opportunities in optimal control of multiple power sources in unprecedent ways. Therefore, in order to investigate benefits offered by the new machine learning-based powertrain control paradigm, different machine learning approaches are studied for a HEV in this dissertation.
Author: Zhengyu Yao Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 0
Book Description
Due to increased environmental and economic considerations, energy-efficient vehicles such as Hybrid Electric Vehicles (HEVs) have received great attention from the general public as well as automotive research community. HEVs achieve better fuel economy than conventional vehicles by employing two different power sources: a mechanical engine and an electrical motor. By controlling the two power sources optimally based on the current system state under different driving conditions, they can achieve much improved fuel economy compared to conventional vehicles powered by internal combustion engines only. These power sources have conventionally been controlled by rule-based or optimization-based control algorithms. Rule-based algorithms utilize a well-defined and easy-to-understand control logic while optimization-based control algorithms employ a mathematical function that is minimized by a controller during the vehicle operation. Besides these two conventional approaches, recent advancements in machine learning offer new opportunities in optimal control of multiple power sources in unprecedent ways. Therefore, in order to investigate benefits offered by the new machine learning-based powertrain control paradigm, different machine learning approaches are studied for a HEV in this dissertation.
Author: Teng Liu Publisher: Morgan & Claypool Publishers ISBN: 1681736195 Category : Technology & Engineering Languages : en Pages : 99
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.
Author: Li Yeuching Publisher: Springer Nature ISBN: 3031792068 Category : Technology & Engineering Languages : en Pages : 123
Book Description
The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.
Author: Stephan Rinderknecht Publisher: MDPI ISBN: 3039437534 Category : Technology & Engineering Languages : en Pages : 264
Book Description
Among the various factors greatly influencing the development process of future powertrain technologies, the trends in climate change and digitalization are of huge public interest. To handle these trends, new disruptive technologies are integrated into the development process. They open up space for diverse research which is distributed over the entire vehicle design process. This book contains recent research articles which incorporate results for selecting and designing powertrain topology in consideration of the vehicle operating strategy as well as results for handling the reliability of new powertrain components. The field of investigation spans from the identification of ecologically optimal transformation of the existent vehicle fleet to the development of machine learning-based operating strategies and the comparison of complex hybrid electric vehicle topologies to reduce CO2 emissions.
Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Abstract : This dissertation focuses on advancing Predictive Energy Management (PrEM) functions applied to modern connected and automated vehicles (CAV) cohorts. PrEM aims to utilize connectivity and ADAS functions to adaptively minimize vehicle energy consumption in a wide array of operations, extending the original control designed around a reduced set of test cycle procedures to adapt to real-world stochastic operating conditions. This research document is built upon three journal publications covering two PrEM schemes; the global cohort and local vehicle optimization paths. Both optimal control solutions are generated using various Neuroevolution centric processes. Chapter 1 discusses the methods and reasoning behind the need to increase the development speed of readily implementable optimal control functions for both complex and system-of-systems (SoS) applications. Neuroevolution allows for fast development time, optimal design space exploration, high-fidelity modeling usage, and seamless integration with data science processes. It additionally enables real-time implementation without modification and requires a low compute footprint. This provides a new paradigm for future automotive product development where conventional adaptive and optimal techniques deployment is still lagging due to their complexity and shortcomings. At the global level, vehicle energy consumption is minimized by optimally controlling vehicle speed in diverse environments. Chapters 2 and 3 relate to connected traffic lights and uncontrolled intersection operations respectively. In the first study, the CAV cohort optimizes its velocity based on connected traffic light information. Thanks to the Traffic Technology Services (TTS) network, this information is shared via cellular communication. Energy consumption reduction of up to 22\% is reported using simulation and during closed-loop track testing. In the second study, no such timing information exists, and the cohorts must collaborate to enable safe operation at uncontrolled intersections. Here, the cohorts share states' information to minimize deceleration and acceleration events for comfort and energy savings, primarily focusing on safety. Simulation demonstrates that effective collaboration can be achieved with cohorts' lengths of up to 100 meters in congested environments. At the local PrEM level, additional energy savings can be achieved for each specific cohort's vehicle based on its powertrain architecture. One of the more complex and relevant architectures to apply localized PrEM to are hybrid electric vehicles (HEV), where two sources of energy can be blended optimally based on a vehicle's predicted speed profile, which is directly controlled by the global PrEM optimization function. In Chapter 4, Neuroevolution and vehicle speed profile classification is applied to a P3 HEV in demonstrating significant additional energy consumption improvements.
Author: John G. Hayes Publisher: John Wiley & Sons ISBN: 1119063647 Category : Technology & Engineering Languages : en Pages : 564
Book Description
The why, what and how of the electric vehicle powertrain Empowers engineering professionals and students with the knowledge and skills required to engineer electric vehicle powertrain architectures, energy storage systems, power electronics converters and electric drives. The modern electric powertrain is relatively new for the automotive industry, and engineers are challenged with designing affordable, efficient and high-performance electric powertrains as the industry undergoes a technological evolution. Co-authored by two electric vehicle (EV) engineers with decades of experience designing and putting into production all of the powertrain technologies presented, this book provides readers with the hands-on knowledge, skills and expertise they need to rise to that challenge. This four-part practical guide provides a comprehensive review of battery, hybrid and fuel cell EV systems and the associated energy sources, power electronics, machines, and drives. Introduces and holistically integrates the key EV powertrain technologies. Provides a comprehensive overview of existing and emerging automotive solutions. Provides experience-based expertise for vehicular and powertrain system and sub-system level study, design, and optimization. Presents many examples of powertrain technologies from leading manufacturers. Discusses the dc traction machines of the Mars rovers, the ultimate EVs from NASA. Investigates the environmental motivating factors and impacts of electromobility. Presents a structured university teaching stream from introductory undergraduate to postgraduate. Includes real-world problems and assignments of use to design engineers, researchers, and students alike. Features a companion website with numerous references, problems, solutions, and practical assignments. Includes introductory material throughout the book for the general scientific reader. Contains essential reading for government regulators and policy makers. Electric Powertrain: Energy Systems, Power Electronics and Drives for Hybrid, Electric and Fuel Cell Vehicles is an important professional resource for practitioners and researchers in the battery, hybrid, and fuel cell EV transportation industry. The resource is a structured, holistic textbook for the teaching of the fundamental theories and applications of energy sources, power electronics, and electric machines and drives to engineering undergraduate and postgraduate students.
Author: Amir Khajepour Publisher: John Wiley & Sons ISBN: 111840310X Category : Technology & Engineering Languages : en Pages : 506
Book Description
An advanced level introductory book covering fundamental aspects, design and dynamics of electric and hybrid electric vehicles There is significant demand for an understanding of the fundamentals, technologies, and design of electric and hybrid electric vehicles and their components from researchers, engineers, and graduate students. Although there is a good body of work in the literature, there is still a great need for electric and hybrid vehicle teaching materials. Electric and Hybrid Vehicles: Technologies, Modeling and Control – A Mechatronic Approach is based on the authors’ current research in vehicle systems and will include chapters on vehicle propulsion systems, the fundamentals of vehicle dynamics, EV and HEV technologies, chassis systems, steering control systems, and state, parameter and force estimations. The book is highly illustrated, and examples will be given throughout the book based on real applications and challenges in the automotive industry. Designed to help a new generation of engineers needing to master the principles of and further advances in hybrid vehicle technology Includes examples of real applications and challenges in the automotive industry with problems and solutions Takes a mechatronics approach to the study of electric and hybrid electric vehicles, appealing to mechanical and electrical engineering interests Responds to the increase in demand of universities offering courses in newer electric vehicle technologies
Author: Xiangrui Zeng Publisher: ISBN: Category : Languages : en Pages :
Book Description
One of the main goals of hybrid electric vehicle technology is to improve the energy efficiency. In industry and most of academic research, the powertrain control is designed and evaluated under standard driving cycles. However, the situations that a vehicle may encounter in the real world could be quite different from the standard cycles. Studies show that the human drivers have a great influence on the vehicle energy consumptions and emissions. The actual operating conditions that a vehicle faces are not only dependent on the roads and traffic, but also dependent on the drivers. A standard driving cycle can only represent the typical and averaged driving style under the typical driving scenarios, therefore the control strategies designed based on a standard driving cycle may not perform well for all different driving styles. This motivates the idea to design optimally-personalized hybrid electric vehicle control methods that can be adaptive to individual human driving styles and their driving routes. Human-subject experiments are conducted on a driving simulator to study the driving behaviors. A stochastic driver pedal model that can learn individual driver’s driving style is developed first. Then a theoretic investigation on worst-case relative cost optimal control problems, which is closely related to vehicle powertrain optimal control under real-world uncertain driving scenarios, is presented. A two-level control structure for plug-in hybrid electric vehicles is proposed, where the parameters in the lower-level controller can be on-line adjusted via optimization using historical driving data. The methods to optimize these parameters are designed for fixed-route driving first, and then extended to multi-routes driving using the idea similar to the worst-case relative cost optimal control. The performances of the two proposed methods are shown through simulations using human driving data and stochastic driver model data respectively. The energy consumption results in both situations are close to the posteriori optimal result and outperform other existing methods, which show the effectiveness of applying optimally-personalized energy management strategy on hybrid electric vehicles. Finally, a route-based global energy-optimal speed planning method is also proposed. This off-line method provides a useful tool to evaluate the potential of other speed planning methods, for either eco-driving guidance applications or future automated vehicle controls. The contributions of this dissertation include 1) a novel stochastic driver pedal behavior model which can learn independent drivers’ driving styles is created, 2) a new worst-case relative cost optimal control method is proposed, 3) a real-time implementable stochastic optimal energy management strategy for hybrid electric vehicles running on fixed routes is designed using the statistics of history driving data, 4) the fix-route strategy is extended to the multi-route situation, and 5) an off-line global energy-optimal speed planning solution for road vehicles on a given route is presented.
Author: Simona Onori Publisher: Springer ISBN: 1447167813 Category : Technology & Engineering Languages : en Pages : 121
Book Description
This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. The brief is intended as a straightforward tool for learning quickly about state-of-the-art energy-management strategies. It is particularly well-suited to the needs of graduate students and engineers already familiar with the basics of hybrid vehicles but who wish to learn more about their control strategies.
Author: Jili Tao Publisher: Elsevier ISBN: 0443131902 Category : Science Languages : en Pages : 348
Book Description
Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state-of-the-art in hybrid electric vehicle system modelling and management. With a focus on learning-based energy management strategies, the book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.The book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multi objective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, the book also introduces State of Charge and State of Health prediction methods and real time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modelling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering. Provides a guide to the modeling and simulation methods of hybrid electric vehicle energy systems, including fuel cell systems Describes the fundamental concepts and theory behind CNN, MPC, fuzzy control, multi objective optimization, fuzzy Q-learning and DDPG Explains how to use energy management methods such as parameter estimation, Q-learning, and pattern recognition, including battery State of Health and State of Charge prediction, and vehicle operating conditions
Author: Zhengyu Yao
Author: Zhengyu Yao
Author: Teng Liu
Author: Li Yeuching
Author: Stephan Rinderknecht
Author: 
Author: John G. Hayes
Author: Amir Khajepour
Author: Xiangrui Zeng
Author: Simona Onori
Author: Jili Tao