Preface

This book provides a comprehensive overview of hybrid and electric vehicles. It explores various aspects of current research in the field, such as design, modeling of Li-ion battery management systems, state-of-charge (SOC) estimation algorithms, and the most used electric motors. It also discusses new trends in electric vehicle automation as well as different control strategies. Almost all simulations presented were performed in the MATLAB and Simulink environment or other specialized software.

The book begins with Chapter 1, which presents a case study of investigations of different approaches to nonlinear speed control methods and SOC estimation techniques applied to a rechargeable Li-ion battery adapted to power the electrical motor of an electric vehicle. The investigations use the most suitable design approaches for the real-time implementation of the most advanced state estimators based on intelligent neural networks and neural control strategies.

Chapter 2 proposes an intelligent controller for a hydrogen-powered, plug-in fuel cell hybrid electric vehicle that integrates a fuel cell with two energy storage systems (ultracapacitor and battery), resulting in a high dynamic response while maintaining efficient use of resources for energy storage.

Chapter 3 introduces a battery SOC management technique designed for an electric vehicle traction system that incorporates an indirect field-oriented induction motor drive. The primary goal of this technique is to restrict the change in battery SOC from exceeding a maximum limit by compensating the motor speed tracking performance, dealing with a fuzzy-tuned model predictive controller.

Chapter 4 proposes a nonlinear robust H-infinity control approach to enhance the trajectory-following capabilities of autonomous ground electric vehicles. Given the inherent influence of driving maneuvers and road conditions on vehicle trajectory dynamics, the primary objective is to address the control challenges associated with trajectory following, including parametric uncertainties, system nonlinearities, and external disturbance.

Chapter 5 discusses vehicle system dynamics, torque vector control, and stability performance analysis.

Finally, Chapter 6 presents the main typical topologies of hybrid energy storage systems for electric vehicles and reviews different electrochemical energy storage technologies by highlighting their pros and cons.

It is my great pleasure to acknowledge the contributing authors and the staff at IntechOpen, especially Publishing Process Manager Mr. Dominik Samardzija, for their tremendous efforts, excellent collaboration, support, and guidance. I would also like to express my gratitude to my research team collaborators Dr. Mohammed Zaheeruddin from Concordia University of Montreal, Canada; Dr. Sorin-Mihai Radu and Dr. Roxana-Elena Tudoroiu from the University of Petroșani, Romania; and Dr. Stefan Tiberiu Letia and Dr. Adina Astilean from the Technical University of Cluj-Napoca, Romania. Finally, I wish to thank my teammates, Prof. Šilerová Roberta, Prof. Mark Ewanchyna, Dr. Demartonne Ramos França, Dr. Huang Shiwei, Prof. Evgeni Kiriy, and Prof. Hana Chammas from the Engineering Technologies Department, John Abbott College, Sainte-Anne-de-Bellevue, Montreal, Canada, for their valuable support, ideas, and encouragement.

> **Nicolae Tudoroiu** Engineering Technologies Department, John Abbott College, Sainte-Anne-de-Bellevue, Montreal, Quebec, Canada

**Chapter 1**

**Abstract**

**1. Introduction**

Investigations of Different

Li-ion Battery – Case Study

*Roxana-Elena Tudoroiu, Mohammed Zaheeruddin,*

*Nicolae Tudoroiu, Sorin Mihai Radu and Hana Chammas*

This research investigated different nonlinear models, state estimation techniques and control strategies applied to rechargeable Li-ion batteries and electric motors powered and adapted to these batteries. The finality of these investigations was achieved by finding the most suitable design approach for the real-time implementation of the most advanced state estimators based on intelligent neural networks and neural control strategies. For performance comparison purposes, was chosen as case study an accurate and robust EKF state of charge (SOC) estimator built on a simple second-order RC equivalent circuit model (2RC ECM) accurate enough to accomplish the main goal. An intelligent nonlinear autoregressive with exogenous input (NARX) Shallow Neural Network (SSN) estimator was developed to estimate the battery SOC, predict the terminal voltage, and map the nonlinear open circuit voltage (OCV) battery characteristic curve as a function of SOC. Focusing on nonlinear modeling and linearization techniques, such as partial state feedback linearization, for "proof concept" and simulations purposes in the case study, a third order nonlinear model for a DC motor (DCM) drive was selected. It is a valuable research support suitable to analyze the performance of state feedback linearization, system singularities, internal and zero dynamics, and solving reference tracking problems.

**Keywords:** Li-ion battery, SOC, Simscape generic model, PID control, state feedback

Clean and efficient transportation across the planet is only possible if governments and scientists focus on stimulating and sustaining the automotive industry of electric vehicles (EVs) by developing and deploying the most advanced battery technologies. Nowadays, Li-ion battery technologies have made significant progress and have

linearization, NARX shallow neural network, NARMA-L2 neuro controller

Approaches for Controlling the

Speed of an Electric Motor with

Nonlinear Dynamics Powered by a
