Advanced Battery Management Technologies for Electric Vehicles

 
 
Automotive Series (Verlag)
  • 1. Auflage
  • |
  • erschienen am 21. Dezember 2018
  • |
  • 280 Seiten
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-1-119-48168-3 (ISBN)
 
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.
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Preface


Electric vehicles (EVs) have been widely recognized as the most environmentally friendly form of road transport. Over the past decade, there have been significant advancements in EV technologies. Such advancements have seen EVs gradually replace conventional internal combustion engine vehicles (ICEVs). Some experts foresee sale volumes of EVs will surpass those of ICEVs in the next 10-20?years. In the current global EV market, China has played a dominant role in EV manufacturing and sales. China's EV sales have topped the world for the third consecutive year since 2015 and are predicted to account for 57% of the world's EV sales by 2035.

With the rapid uptick in EV sales, EV technologies have been developed at an accelerated pace. Among these EV technologies, researchers have been conducting studies on battery management technologies for many years. Weixiang Shen is a leader of applied battery research for EVs and renewable energy systems at Swinburne University of Technology. He is a pioneer researcher on battery management technologies for EVs and has been working in the area for more than 20 years. Rui Xiong is a leader of advanced energy storage and application at Beijing Institute of Technology. He has been working on battery management technologies for about 10 years. They both have published numerous conference and journal papers, successfully completed many industrial projects and engaged in consultancy work on the topic of battery management technologies for EVs. While conducting research, the authors have found that although there is a wealth of information for battery management technologies in the public domain literature, there is not a comprehensive and specific book focusing on battery management technologies in EVs as yet. The aim of this book is to bridge this gap.

Lithium-ion (Li-ion) batteries have been widely used in EVs due to their high energy density, high power density, high voltage, low self-discharge, and long cycle life in comparison with other secondary batteries. However, behaviors of Li-ion batteries are greatly affected by their working environment. In particular, Li-ion battery systems in EVs operate in a more dynamic working environment than those in portable electronic devices such as laptops and mobile phones. The charge and discharge currents and thus voltages of the battery systems fluctuate significantly when EVs are in regenerative braking and acceleration and their operation temperatures vary greatly when EVs are driving during different seasons in various locations. The battery systems under such large current and temperature variations along with rapid charge-discharge cycles require sophisticated battery management systems (BMSs). The purpose of BMSs is to regulate the operation of the battery systems within allowable voltage, current and temperature ranges and to estimate battery states for EV optimal operations. This leads to the development of advanced battery management technologies, which will be presented in this book.

To make this book self-contained, vehicle dynamics and standard EV driving cycles are introduced to provide the basis to discuss power and energy requirements of EVs and to evaluate EV performances. Also included is an introduction to the electrochemistry of different battery systems applicable to EVs. The key battery management technologies and BMSs have also been introduced. Beyond the basics, the book focuses on battery modeling technique and estimation techniques of battery state of charge, state of energy, state of health and state of power. For each of these techniques, there is the detailed description of the techniques accompanied with step-by-step mathematical equations. Case studies are provided with experimental and simulation results to demonstrate the applications of these techniques in EV working conditions. Furthermore, this book discusses battery charging and balancing techniques. The application of all the above-mentioned techniques to BMSs and the key technologies of BMSs in future generation are also discussed. In addition to referencing the relevant work of other researchers, a large portion of the materials presented in this book is the collection of many years of research and development by the authors.

This book consists of eight chapters. In Chapter 1, the fundamental knowledge about EVs, requirements of battery systems for EVs and battery systems applicable to EVs such as lead-acid, nickel-cadmium, nickel-metal hydride and Li-ion batteries are introduced. The overview of the key battery management technologies, the BMS structures and BMS functions are presented.

In Chapter 2, the classification of battery modeling techniques is provided. Then, the chapter goes on to focus on the explanation of battery equivalent circuit models in terms of the model structure, open circuit voltage, polarization and hysteresis characteristics. Based on the equivalent circuit models, offline parameter identification methods and online parameter identification methods are introduced, followed by case studies showing the application of each method. Furthermore, the influences of battery aging, battery type and battery temperature on accuracies of equivalent circuit models are discussed in detail.

In Chapter 3, the classification of battery state of charge estimation methods is introduced. This includes look-up table methods, an ampere-hour integral method, data-driven estimation methods, and model-based estimation methods. Following this, a detailed explanation is given to model-based state of charge estimation methods with constant model parameters using Kalman filter and an H infinity filter. The influences of the uncertainties on the state of charge estimation methods are discussed. For real EV applications, model-based state of charge and state of energy estimation methods with identified model parameters in real-time are emphasized. The MATLAB codes and Simulink models used in the case studies to implement these estimation methods are provided to users.

In Chapter 4, battery state of health estimation is divided into two categories: experimental methods; and model-based methods. The experimental methods include direct measurement methods and indirect analysis methods. The model-based methods include adaptive state estimation methods and data-driven methods. For real EV applications, the focus is on model-based methods including the joint estimation method and dual estimation method which allow the estimation of state of health together with state of charge. Readers should be able to follow the examples of MATLAB codes and Simulink models to design a state of health estimator for their EV applications.

In Chapter 5, battery state of power estimation has been grouped into instantaneous state of power estimation and continuous state of power estimation. The instantaneous state of power estimation includes the hybrid pulse power characterization method, the state of charge-limited method, voltage-limited methods and multi-constraint dynamic methods. For realistic state of power estimation in EVs, this chapter mainly addresses the continuous state of power estimation methods. Again, examples of MATLAB codes and Simulink models are provided for readers to implement these estimation methods.

In Chapter 6, basic terms are defined to evaluate the performances of charging methods. With these basic terms, different charging methods based on pre-set charging profiles and charging termination methods for Li-ion batteries are reviewed. The qualitative comparison of different charging methods for Li-ion batteries is also discussed. Subsequently, the latest development of charging methods is introduced in terms of two directions. One direction is the optimization of the charging profile during the charging process to minimize charging time or maximize charging efficiency or achieve the balance between charging time and charging efficiency. The other direction is the development of a new battery technology (i.e. lithium titanate oxide battery) to allow for high charge acceptance, speeding up the charging process.

In Chapter 7, several battery balancing techniques are introduced, including battery sorting, battery passive balancing and battery active balancing. Then, this chapter focuses on battery active balancing through detailed discussions of battery balancing criterion, balancing control and balancing circuits. Two examples of active battery balancing systems are provided to demonstrate their applications in EVs.

In Chapter 8, basic BMS functions are introduced. Thereafter, the typical BMS structures and representative BMS products are discussed. The hardware examples of BMS implementation are provided to demonstrate the applications of battery management technologies in EVs. This chapter also covers the discussion of key insights into future generations of BMSs including self-heating and safety management as well as the application of cloud computing and big data in the state of health estimation and battery life prediction.

The material in this book is recommended for a graduate or senior-level undergraduate course. Depending on the background of the students in different disciplines, course instructors have the flexibility to choose those chapters from the book that are most suitable for their students. This book is also an in-depth source and comprehensive reference in advanced battery management technologies for engineers and researchers working in EV-related industries, government...

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