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Languages : en
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Abstract : The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. The objective of this dissertation is to develop a real-time implementable optimal energy management strategy which improves both the fuel economy and battery aging for Hybrid Electric Vehicles by using ECMS. This work introduces a new optimal control problem where the cost function includes terms for both fuel consumption and battery aging. The Ah-throughput method is used to quantify battery aging. ECMS (with the appropriate equivalence factor) is shown to also minimize the cost function that incorporates battery aging. Finding the appropriate equivalence factor often required prior knowledge of the entire drive cycle. While using the appropriate equivalence factor might miss the opportunities for fuel savings under certain conditions. Therefore, an adaptive control law of equivalence factor called Catch Energy Saving Opportunity (CESO) has been introduced in this work to make the proposed aging ECMS real-time implementable. In order to better understand the impact of the developed optimal strategies on battery aging in HEVs, systematic analysis has been performed to find relations between fuel economy, battery aging and the optimization decisions when using ECMS. Therefore, the varies equivalence factors, state of charge constraints and battery temperatures are observed and analyzed under different Combined Drive-cycles (CDs). The CDs are formulated to test the energy management strategy and battery aging with weights on city and highway drive. In addition, rule-based control in charge-depletion mode aimed to improve battery aging has been simulated in a HEV truck. The simulation results show that, the fuel consumed and battery aging degradation during varied operation could be significantly improved by using a simple control rule in charge-depletion mode. This further indicates the benefits of implementing a battery aging term which impacts the control decision in charge-sustaining ECMS. Based on the analysis results, an aging ECMS has been developed by adding a battery aging term as a cost to the battery. The simulation results showed that this optimal energy management strategy improves battery aging significantly with little or no penalty in fuel economy. In addition, aging CESO ECMS, a real-time optimal strategy, has been developed based on the proposed aging ECMS. The simulation results show that aging CESO ECMS improvs upon the basic aging ECMS performance.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Abstract : The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. The objective of this dissertation is to develop a real-time implementable optimal energy management strategy which improves both the fuel economy and battery aging for Hybrid Electric Vehicles by using ECMS. This work introduces a new optimal control problem where the cost function includes terms for both fuel consumption and battery aging. The Ah-throughput method is used to quantify battery aging. ECMS (with the appropriate equivalence factor) is shown to also minimize the cost function that incorporates battery aging. Finding the appropriate equivalence factor often required prior knowledge of the entire drive cycle. While using the appropriate equivalence factor might miss the opportunities for fuel savings under certain conditions. Therefore, an adaptive control law of equivalence factor called Catch Energy Saving Opportunity (CESO) has been introduced in this work to make the proposed aging ECMS real-time implementable. In order to better understand the impact of the developed optimal strategies on battery aging in HEVs, systematic analysis has been performed to find relations between fuel economy, battery aging and the optimization decisions when using ECMS. Therefore, the varies equivalence factors, state of charge constraints and battery temperatures are observed and analyzed under different Combined Drive-cycles (CDs). The CDs are formulated to test the energy management strategy and battery aging with weights on city and highway drive. In addition, rule-based control in charge-depletion mode aimed to improve battery aging has been simulated in a HEV truck. The simulation results show that, the fuel consumed and battery aging degradation during varied operation could be significantly improved by using a simple control rule in charge-depletion mode. This further indicates the benefits of implementing a battery aging term which impacts the control decision in charge-sustaining ECMS. Based on the analysis results, an aging ECMS has been developed by adding a battery aging term as a cost to the battery. The simulation results showed that this optimal energy management strategy improves battery aging significantly with little or no penalty in fuel economy. In addition, aging CESO ECMS, a real-time optimal strategy, has been developed based on the proposed aging ECMS. The simulation results show that aging CESO ECMS improvs upon the basic aging ECMS performance.
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: 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: 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.
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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: 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: M. Taha Demirkan
Publisher: BoD – Books on Demand
ISBN: 1789856930
Category : Technology & Engineering
Languages : en
Pages : 184
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Book Description
Energy storage will be a very important part of the near future, and its effectiveness will be crucial for most future technologies. Energy can be stored in several different ways and these differ in terms of the type and the conversion method of the energy. Among those methods; chemical, mechanical, and thermal energy storage are some of the most favorable methods for containing energy. Current energy storage devices are still far from meeting the demands of new technological developments. Therefore, much effort has been put to improving the performance of different types of energy storage technologies in the last few decades.
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: Jili Tao
Publisher: Elsevier
ISBN: 0443131902
Category : Science
Languages : en
Pages : 348
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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: 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
