Modeling, Dynamics, and Control of Electrified Vehicles

Haiping Du, Dongpu Cao, Hui Zhang

• Publisher: Woodhead Publishing
• Year: 2017
• Print ISBN: 9780128127865
• eBook ISBN: 9780128131091
• Topic: Technology & Engineering
• Subtopic: Automotive Transportation & Engineering
• Index: Technology & Engineering

Modelling, Dynamics and Control of Electrified Vehicles provides a systematic overview of EV-related key components, including batteries, electric motors, ultracapacitors and system-level approaches, such as energy management systems, multi-source energy optimization, transmission design and control, braking system control and vehicle dynamics control. In addition, the book covers selected advanced topics, including Smart Grid and connected vehicles. This book shows how EV work, how to design them, how to save energy with them, and how to maintain their safety.The book aims to be an all-in-one reference for readers who are interested in EVs, or those trying to understand its state-of-the-art technologies and future trends.- Offers a comprehensive knowledge of the multidisciplinary research related to EVs and a system-level understanding of technologies- Provides the state-of-the-art technologies and future trends- Covers the fundamentals of EVs and their methodologies- Written by successful researchers that show the deep understanding of EVs

Chapter 1

Modeling, Evaluation, and State Estimation for Batteries

Hao Mu and Rui Xiong, Beijing Institute of Technology, Beijing, China

Abstract

State estimation of the lithium-ion battery has been the focus of many researchers, and the consensus is that the model-based method is an effective tool for state of charge (SoC) estimation. In this chapter, we start with battery modeling. Several modeling approaches are presented and the their advantages and disadvantages are discussed. Moreover, the balance problem between model accuracy and complexity of an nth order RC networks model is tackled using an evaluation index of terminal voltages. Finally, the adaptive extended Kalman filter algorithm is proposed to estimate the SoC and its validity is confirmed.

Keywords

Electric vehicles; evaluation of models; lithium-ion battery; modeling; state of charge; dual timescales; AEKF algorithm

1.1 Introduction

Currently, hybrid electric vehicles (HEVs) and electric vehicles (EVs) promise a future of green travel in which fuel-consuming engines are replaced with electric motors, thus reducing our dependence on fossil energy and ultimately producing less harmful emissions. Such vehicles can be plugged in at home overnight or at the office or in a parking space during the day, using electricity that is generated at a centralized power station or even by renewable sources. The key component to the achievement of these electrical systems is the energy storage system, namely, the battery technology.

The lithium-ion (Li-ion) battery, as depicted in Fig. 1.1, is the most common choice for phone communication and portable appliances because of its many advantages, such as high energy-to-weight and power-to-weight ratios (180 Wh/kg and 1500 W/kg, respectively) and low self-discharge rate (Linden and Reddy, 2002; Capasso and Veneri, 2014). In addition, among all rechargeable electrochemical systems, Li-ion technology is the first-choice candidate as a power source for HEVs/EVs. However, this technology is still delicate and affected by numerous limitations, such as issues of safety (Doughty and Roth, 2012), cost (Lajunen and Suomela, 2012), recycling (Gaines, 2011), and charging infrastructure (Veneri et al., 2012).

Figure 1.1 Different types of Li-ion batteries.

To ensure the power battery works safely and reliably, which is a function of the battery management system (BMS), the temperature, voltage, and current of the batteries should be monitored and the st...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of Contributors
• Chapter 1. Modeling, Evaluation, and State Estimation for Batteries
• Chapter 2. High-Power Energy Storage: Ultracapacitors
• Chapter 3. HESS and Its Application in Series Hybrid Electric Vehicles
• Chapter 4. Transmission Architecture and Topology Design of EVs and HEVs
• Chapter 5. Energy Management of Hybrid Electric Vehicles
• Chapter 6. Structure Optimization and Generalized Dynamics Control of Hybrid Electric Vehicles
• Chapter 7. Transmission Design and Control of EVs
• Chapter 8. Brake-Blending Control of EVs
• Chapter 9. Dynamics Control for EVs
• Chapter 10. Robust Gain-Scheduling Control of Vehicle Lateral Dynamics Through AFS/DYC
• Chapter 11. State and Parameter Estimation of EVs
• Chapter 12. Modeling and Fault-Tolerant-Control of Four-Wheel-Independent-Drive EVs
• Chapter 13. Integrated System Design and Energy Management of Plug-In Hybrid Electric Vehicles
• Chapter 14. Integration of EVs With a Smart Grid
• Index