STUDY OF BATTERY HEALTH CONSCIOUS POWERTRAIN ENERGY MANAGEMENT STRATEGIES FOR HYBRID ELECTRIC VEHICLES PDF Download
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Languages : en
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Abstract : The goal of this research is to study the battery aging pattern for the application of hybrid electric vehicles (HEV) and advanced control algorithm to improve the performance of HEV energy management controller by maximizing fuel efficiency and minimizing battery aging speed at the same time. To achieve the combined goals, the tasks of this research can be laid out as follows. The first part studies the HEV model provided by Autonomie software and the electrochemical battery model to be built and integrated with the whole vehicle model. The battery model integrated is an averaged single particle model with the battery thermal aging features added. The battery aging will be quantified as the increasing of SEI layer and decreasing of battery capacity. The battery model was able to simulate the aging performance under different temperature, charge current, SOC and other operational conditions. The simulation results of the vehicle following certain driving cycles and the simulation results of battery voltage output are presented. The second part investigates the feasibility of the entire system to be running in a real-time hardware-in-the-loop system. The vehicle model together with the electrochemical battery model is built and loaded to the dSPACE simulator. The hybrid controller model is built and loaded to the dSPACE MicroAutoBox. The hybrid controller and dSPACE simulator communicate in real-time with vehicle components information coming from plant model and the control signals coming from the MicroAutoBox. The vehicle model together with the battery model is able to be running in Simulator with the battery model simulated correctly and providing battery aging features in real-time. The third part of the research looks into the application of nonlinear model predictive control (NMPC) in the hybrid controller. To meet the goal of minimizing fuel consumption and battery aging speed, the nonlinear model predictive control without concern of battery aging is first studied. The predictive model is built to predict the dynamic performance of battery pack, the E-motors, the engine and the vehicle powertrain key part - planetary gear set. A cost function is built to provide the best control performance for our case. The performance of the NMPC is compared with the rule-based controller. And the performance of NMPC with different weighting factors is compared and analyzed. Following the previous part, the NMPC with the concern of battery aging is also studied and simulated using the vehicle and battery model built and integrated into the first part. By changing the cost function of the NMPC, the battery aging performance is greatly improved compared with that of the previous part. The studied NMPC is able to maintain the fuel economy at similar or even better level compared with the NMPC without battery aging concern. The last part of the research studies the modeling of a single shaft parallel hybrid electric vehicle built from the dSPACE Automotive Simulation Model (ASM) and the AutoLion-ST battery simulation software. Both commercial software packages provide solid physics-based modeling of HEV components such as E-motor, the lithium-ion battery pack, the engine etc., the entire vehicle model is built using these individual models to study the battery performance under different environmental and operational conditions.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Abstract : The goal of this research is to study the battery aging pattern for the application of hybrid electric vehicles (HEV) and advanced control algorithm to improve the performance of HEV energy management controller by maximizing fuel efficiency and minimizing battery aging speed at the same time. To achieve the combined goals, the tasks of this research can be laid out as follows. The first part studies the HEV model provided by Autonomie software and the electrochemical battery model to be built and integrated with the whole vehicle model. The battery model integrated is an averaged single particle model with the battery thermal aging features added. The battery aging will be quantified as the increasing of SEI layer and decreasing of battery capacity. The battery model was able to simulate the aging performance under different temperature, charge current, SOC and other operational conditions. The simulation results of the vehicle following certain driving cycles and the simulation results of battery voltage output are presented. The second part investigates the feasibility of the entire system to be running in a real-time hardware-in-the-loop system. The vehicle model together with the electrochemical battery model is built and loaded to the dSPACE simulator. The hybrid controller model is built and loaded to the dSPACE MicroAutoBox. The hybrid controller and dSPACE simulator communicate in real-time with vehicle components information coming from plant model and the control signals coming from the MicroAutoBox. The vehicle model together with the battery model is able to be running in Simulator with the battery model simulated correctly and providing battery aging features in real-time. The third part of the research looks into the application of nonlinear model predictive control (NMPC) in the hybrid controller. To meet the goal of minimizing fuel consumption and battery aging speed, the nonlinear model predictive control without concern of battery aging is first studied. The predictive model is built to predict the dynamic performance of battery pack, the E-motors, the engine and the vehicle powertrain key part - planetary gear set. A cost function is built to provide the best control performance for our case. The performance of the NMPC is compared with the rule-based controller. And the performance of NMPC with different weighting factors is compared and analyzed. Following the previous part, the NMPC with the concern of battery aging is also studied and simulated using the vehicle and battery model built and integrated into the first part. By changing the cost function of the NMPC, the battery aging performance is greatly improved compared with that of the previous part. The studied NMPC is able to maintain the fuel economy at similar or even better level compared with the NMPC without battery aging concern. The last part of the research studies the modeling of a single shaft parallel hybrid electric vehicle built from the dSPACE Automotive Simulation Model (ASM) and the AutoLion-ST battery simulation software. Both commercial software packages provide solid physics-based modeling of HEV components such as E-motor, the lithium-ion battery pack, the engine etc., the entire vehicle model is built using these individual models to study the battery performance under different environmental and operational conditions.
Author: Yongqiang Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 162
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Book Description
There have been many efforts to combat climate change to provide a sustainable future. Fuel cell vehicles can reduce emissions for the transportation sector as their only by-product is water. One major barrier towards commercialization is the cost of fuel cell stacks. Extending the operating life of fuel cells can lower their overall cost. Thus it is important to study the effects of degradation when designing fuel cell/battery hybrid vehicles. We have collected test data from the University of Delaware's latest fuel cell bus by implementing two-way on-board communication systems. A vehicle model was built in MATLAB based on the collected data. Various options including health conscious energy management strategies(offline and real-time) and system sizing were explored in this study to reduce the degradation of fuel cell stacks and batteries to improve their lifetime.
Author: Sheldon S. Williamson
Publisher: Springer Science & Business Media
ISBN: 1461477115
Category : Technology & Engineering
Languages : en
Pages : 263
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Book Description
This book addresses the practical issues for commercialization of current and future electric and plug-in hybrid electric vehicles (EVs/PHEVs). The volume focuses on power electronics and motor drives based solutions for both current as well as future EV/PHEV technologies. Propulsion system requirements and motor sizing for EVs is also discussed, along with practical system sizing examples. PHEV power system architectures are discussed in detail. Key EV battery technologies are explained as well as corresponding battery management issues are summarized. Advanced power electronic converter topologies for current and future charging infrastructures will also be discussed in detail. EV/PHEV interface with renewable energy is discussed in detail, with practical examples.
Author: Simona Onori
Publisher: Springer
ISBN: 1447167813
Category : Technology & Engineering
Languages : en
Pages : 121
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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: Teng Liu
Publisher: Morgan & Claypool Publishers
ISBN: 1681736195
Category : Technology & Engineering
Languages : en
Pages : 99
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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.
Author: Rui Xiong
Publisher: John Wiley & Sons
ISBN: 1119481643
Category : Technology & Engineering
Languages : en
Pages : 292
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Book Description
A comprehensive examination of advanced battery management technologies and practices in modern electric vehicles Policies surrounding energy sustainability and environmental impact have become of increasing interest to governments, industries, and the general public worldwide. Policies embracing strategies that reduce fossil fuel dependency and greenhouse gas emissions have driven the widespread adoption of electric vehicles (EVs), including hybrid electric vehicles (HEVs), pure electric vehicles (PEVs) and plug-in electric vehicles (PHEVs). Battery management systems (BMSs) are crucial components of such vehicles, protecting a battery system from operating outside its Safe Operating Area (SOA), monitoring its working conditions, calculating and reporting its states, and charging and balancing the battery system. Advanced Battery Management Technologies for Electric Vehicles is a compilation of contemporary model-based state estimation methods and battery charging and balancing techniques, providing readers with practical knowledge of both fundamental concepts and practical applications. This timely and highly-relevant text covers essential areas such as battery modeling and battery state of charge, energy, health and power estimation methods. Clear and accurate background information, relevant case studies, chapter summaries, and reference citations help readers to fully comprehend each topic in a practical context. Offers up-to-date coverage of modern battery management technology and practice Provides case studies of real-world engineering applications Guides readers from electric vehicle fundamentals to advanced battery management topics Includes chapter introductions and summaries, case studies, and color charts, graphs, and illustrations Suitable for advanced undergraduate and graduate coursework, Advanced Battery Management Technologies for Electric Vehicles is equally valuable as a reference for professional researchers and engineers.
Author: Li Yeuching
Publisher: Springer Nature
ISBN: 3031792068
Category : Technology & Engineering
Languages : en
Pages : 123
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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: Li Tang
Publisher:
ISBN:
Category : Hybrid electric cars
Languages : en
Pages : 213
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Book Description
The dissertation offers a systematic analysis on the interdependency between fuel economy and battery capacity degradation in hybrid electric vehicles. Optimal control approaches including Dynamic Programming and Pontryagin's Minimum Principle are used to develop energy management strategies, which are able to optimally tradeoff fuel consumption and battery aging. Based on the optimal solutions, a real-time implementable battery-aging-conscious Adaptive Equivalent Consumption Management Strategy is proposed, which is able to achieve performance that is comparable to optimal results. In addition, an optimal control based charging strategy for plug-in hybrid electric vehicles and battery electric vehicles is developed, which minimizes battery capacity degradation incurred during charging by optimizing the charging current profile. Combining a generic control-oriented vehicle cabin thermal model with the battery aging model, the benefit of this strategy in terms of decreasing battery aging is significant, when compared with the existing strategies, such as the widely accepted constant current constant voltage (CC-CV) protocol. Thus this dissertation presents a complete set of optimal control solutions related to xEVs with consideration of battery aging.
Author: Bruno Scrosati
Publisher: Woodhead Publishing
ISBN: 1782423982
Category : Technology & Engineering
Languages : en
Pages : 547
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Book Description
Advances in Battery Technologies for Electric Vehicles provides an in-depth look into the research being conducted on the development of more efficient batteries capable of long distance travel. The text contains an introductory section on the market for battery and hybrid electric vehicles, then thoroughly presents the latest on lithium-ion battery technology. Readers will find sections on battery pack design and management, a discussion of the infrastructure required for the creation of a battery powered transport network, and coverage of the issues involved with end-of-life management for these types of batteries. - Provides an in-depth look into new research on the development of more efficient, long distance travel batteries - Contains an introductory section on the market for battery and hybrid electric vehicles - Discusses battery pack design and management and the issues involved with end-of-life management for these types of batteries
Author: Chris Mi
Publisher: John Wiley & Sons
ISBN: 111897056X
Category : Technology & Engineering
Languages : en
Pages : 611
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Book Description
The latest developments in the field of hybrid electric vehicles Hybrid Electric Vehicles provides an introduction to hybrid vehicles, which include purely electric, hybrid electric, hybrid hydraulic, fuel cell vehicles, plug-in hybrid electric, and off-road hybrid vehicular systems. It focuses on the power and propulsion systems for these vehicles, including issues related to power and energy management. Other topics covered include hybrid vs. pure electric, HEV system architecture (including plug-in & charging control and hydraulic), off-road and other industrial utility vehicles, safety and EMC, storage technologies, vehicular power and energy management, diagnostics and prognostics, and electromechanical vibration issues. Hybrid Electric Vehicles, Second Edition is a comprehensively updated new edition with four new chapters covering recent advances in hybrid vehicle technology. New areas covered include battery modelling, charger design, and wireless charging. Substantial details have also been included on the architecture of hybrid excavators in the chapter related to special hybrid vehicles. Also included is a chapter providing an overview of hybrid vehicle technology, which offers a perspective on the current debate on sustainability and the environmental impact of hybrid and electric vehicle technology. Completely updated with new chapters Covers recent developments, breakthroughs, and technologies, including new drive topologies Explains HEV fundamentals and applications Offers a holistic perspective on vehicle electrification Hybrid Electric Vehicles: Principles and Applications with Practical Perspectives, Second Edition is a great resource for researchers and practitioners in the automotive industry, as well as for graduate students in automotive engineering.
