• Importance of mathematical modelling
• Block diagram approach & model-based design for engineering systems
• Introduction to Simulink environment
• Creating a simple model
• Obtaining results
• Introduction to various Simulink toolboxes

• How does a model run in Simulink?
• Types of solvers
• Model configuration
• Continuous & discrete-time systems
• Timestep
• Solving ODE
• Introduction to physical modelling using simscape

• Using a script file with Simulink models
• Creating a subsystem
• Variant subsystem
• Projects
• Template
• lookup tables
• Running simulation in steps

• Control logic for engineering applications
  
• Using a finite state machine
• Making logic diagrams

In this module, we will introduce the ideas of Statistics, machine learning and artificial intelligence. 

⮚ Basics of Probability

⮚ Basics of Statistics

⮚ What is ML & AI

In this section, we start with supervised learning

⮚ Introduction to normal distribution & standard normal distribution

⮚ Introduction to business moments

⮚ Artificial Intelligence

In this section, we start with supervised learning

⮚ Introduction to supervised learning

⮚ What is linear regression

⮚ One hot encoding

⮚ Cost function and gradient descent

In this section, we start with classification algorithm

⮚ Introduction to classification problems

⮚ What is logistic regression

⮚ Cost function and gradient descent

In this module, we will introduce some more classification algorithms

⮚ Decision tree

⮚ Entropy

⮚ Information gain

In this section, we will assess the algorithms

⮚ Random Forest

⮚ Bootstrapping and majority rule

⮚ Evaluation of classifiers

In this module, we will introduce SVM

⮚ Support Vector Machines

⮚ Mathematical intuition behind SVM 

⮚ How SVM is different from other classifiers

In this section, we will introduce knn

⮚ K-Nearest Neighbour

⮚ Lazy Algorithm

⮚ Single layer Neural Network

In this section, we will introduce clustering

⮚ What is clustering

⮚ Why clustering is important

⮚ Kmeans and elbow curve

In this section, we will introduce another type of clustering

⮚ Hierarchical Clustering

⮚ Dendrogram

⮚ Evaluation of clustering algorithms

In this module, we will introduce some feature selection techniques

⮚ Feature Selection

⮚ Principal Component Analysis

⮚ Mathematical intuition behind PCA

In this section, we will introduce neural network

⮚ Artificial Neural Network

⮚ Deep learning

⮚ Different activation functions

⮚ Understanding back propagation

• Vehicle power requirement calculations: Rolling resistance, air drag, hill climbing and acceleration forces
• Driving patterns and vehicle testing standards, important terms and definitions related to driving cycle, Energy expenditure calculation

• Different configurations of power train components: Battery Electric Vehicle, Hybrid Electric Vehicle: Series, Parallel and Series-Parallel 
• MATLAB Simulation: ADVISOR and Powertrain block set
• Various modelling options, Control strategies 

In a motor vehicle, the powertrain or powerplant comprises the main components that generate power and deliver that power to the road. In hybrid powertrains, the torque generated by the combustion engine and the electric motor has to be brought together and distributed to the wheels. The control of this power flow is discussed along with the working of electric motors.

Electrical engineering terminologies, Fundamentals of DC and AC motors: working principle, characteristics, and control.

• Understanding power electronics, basic DC converter circuit operation, power devices, switching losses 
• Power electronic control of PMDC, PMBLDC and induction motor
• Inverter operation, Sine PWM, Variable frequency control
• Review of induction motor controller, connections and features, limitations of variable frequency control, introduction to vector control 

Moving vehicles have a lot of kinetic energy, and when brakes are applied to slow a vehicle, all of that kinetic energy has to go somewhere. Regenerative braking uses an electric vehicle’s motor as a generator to convert much of the kinetic energy lost when decelerating back into stored energy in the vehicle’s battery. To evaluate regenerative braking, we really need to look at two different parameters, efficiency and effectiveness.

• Braking requirements of vehicle, Methods of braking of DC & Induction motors: regenerative braking and dynamic braking  
• Coordinating electrical and mechanical brakes, braking control strategies

• Fundamentals of control engineering
• Important terms
• Feedback system
• Transfer functions
• Poles zeros
• State-space
• Laplace transform

This section dealt with the stability analysis of control systems. It is a prerequisite for any of the systems to be stable to give satisfactory behaviour. Students can learn about stability theories and how to implement the same using the MATLAB command.

• Stability
• Bode plot and MATLAB tools for control system engineering

A PID controller is considered to be very important in the industrial standpoint. This section helps to learn the significance of the PID controller and how to auto-tune the gain parameters of the PID controller in MATLAB simulation environment.

• PID control actions and simulations for step response

In an Electric Vehicle, the motor plays a crucial part to propel the vehicle. The motor characteristic needs to be known to control it efficiently. This section helps to learn about the transfer function of DC motors and control of DC-DC power converter.

• DC motor transfer function
• Setpoint tracking and disturbance rejection

This section briefs the difference between analogue and digital systems. The significance of discrete control systems and the domain that is to be defined for the discrete control system. Student can get an insight on the advantage of using Discrete control system over the Analog control system

• Limitations of analog control
• Discrete control system
• Z-domain analysis

• Digital power control
• Microcontroller fundamentals
• Programming and interface

• Intelligent and connected vehicle system
• Master controller and its interfacing with the local controller

• Sources of energy for propulsion & their comparison: Net Calorific Value, Conversion efficiency
• History and background of battery technology
• Electrochemistry fundamentals & terminologies
• Lithium ion battery and different chemistries

• Portable power applications and electrical load requirements

• Factors affecting the choice of EV battery systems

• Commercially available lithium ion cells

• Electrical characteristics of battery: Capacity, C-rate, impedance, DOD, SOC, SOH, Life cycles, Mechanical characteristics, Form factor, Safety

• Reading cell manufacturer’s specifications
• Cell characterization
• Tools and standard testing
• Battery capacity estimation algorithms
• Electrical equivalent circuit & mathematical model in MATLAB

• Battery modules and complete battery pack system
• Assembly methods
• Electrical connections
• Cell level protection system
• battery pack level protection system
• Understanding laptop battery pack system

• Other energy sources: New battery technologies, Ultracapacitor, Fuel cell, Flywheel

• Hybridization of energy storage systems

• Battery pack requirements: Measurement

• Protection and management

• Cell balancing

• Battery pack electronics

• Battery Management System (BMS): Functionality, technology and topology (centralized, modular, master-slave, distributed)

• BMS Application Specific Integrated Circuit (ASIC) selection

• Analog BMS design

• Digital BMS design

• BMS deploying: Installing, testing and troubleshooting

• Battery charge management

• MATLAB simulation of battery charging circuit

• Charger circuit overview

• EV charging technology review

• Charging behavior: life cycle & safety

• Types of temperature sensors

• thermal management system

• Thermal model of battery pack

• Drive cycle simulation and vehicle range estimation

• Cooling materials and methods

• Communication systems for battery pack

• Review of electric car battery pack

• Important considerations

• Recent trends: Grid level energy storage

• Solar & wind integration

• Recycling and pricing

Introduction to the fuel cell, fuel cell stack system, current development in the market.

Internal structure of PEM fuel cell, losses in fuel cell that affect performance.

Fuel cell operation, starvation and its control parameters

Details of these components. Modelling of these components in Matlab/Simulink

Going over the basics of programming in Matlab and Simulink that will help with the modelling assignments to follow.

Understanding different energy management strategies like Load following, rule based

Using Matlab/Simulink going over the basics of creating vehicle model

Using Matlab/Simulink going over the basics of creating

Using Matlab/Simulink creating the basic strategy for power management

A well to wheel analysis for fuel cell vehicles to be covered.

Concluding lecture with a summary of topics taught so far. Going over the project and integration of different models created before

Conducting a live webinar to help students with their questions regarding. 

Fundamentals & Basics of MATLAB scripting is covered in this section. If you have no prior MATLAB scripting knowledge, you can learn the fundamentals of it from this course. Topics ranging from “Introduction to using the MATLAB software” to “programmatically controlling a Simulink model” are discussed here.

Here is a detailed list of the key concepts that you will learn from this course.

• Introduction to MATLAB scripting and Syntax Basics
• Vectors, Matrices & Data Types in m-scripting
• MATLAB Operators, Decision Making Statements & Strings in m-scripting
• MATLAB Data Structures Overview & Cell Arrays in m-scripting
• Tables & Structures, MATLAB Functions & Callbacks in m-scripting
• File Handling Formats, Debugging & Flow Control Logics in m-scripting
• Block properties, Different Workspaces in m-scripting
• Programmatically accessing Simulink in m-scripting

Fundamentals & Basics of MATLAB Simulink is discussed in this section. If you do not have prior MATLAB or Simulink knowledge, you can learn the basic fundamentals from this course.

Topics range from “Opening a Blank Simulink Model to Adding Blocks from Library to Programmatically Change  Block Properties & its Fundamentals” are discussed here.

• Introduction to Simulink & Simulink Toolbars
• Block Settings, Model Annotation, Simulink Solvers
• Sources & Sink Libraries
• Math Operations; Logical & Bit Operations
• Ports & Subsystems; Atomic Subsystems
• Masked Subsystem & Linked Libraries
• Continuous, Discontinuous & Discrete Blocks
• User-Defined Functions & Lookup Tables
• Mathematical Model Representation & System Models Creation in Simulink
• Stateflow-1
• Stateflow-2

In this section, an overview of Automotive Software Industry, the size at which it has grown & its demands in today’s market are discussed briefly. Need for Model Based Development, key concepts in developing a complete MATLAB model from scratch & challenges to be addressed by an MBD engineer are discussed here.

• Overview of Automotive Industry
• Software Development demands of Automotive Industry
• Model Based Development in Automotive Industry & Model Based Development in MATLAB
• Requirement Analysis in Model Based Development
• Model Based Development Configuration Parameters Settings
• Creating Simulink Data Dictionary
• Accessing Simulink Data Dictionary & Port Property Settings
• Signal Names & Signal Property Configuration

In this section, the validation & code generation process for the developed model is discussed. Simulating a model, generating C - code from it & validating both the model & code using concepts like Model in Loop (MIL) & Software in Loop (SIL) are discussed here.

• Model Simulation & Model Advisor Report
• Code Generation Settings, Auto-code Generation
• Overview of Model in Loop, Software in Loop & Hardware in Loop
• Testing Theory
• Test Report Analysis (Coverage Analysis & Different Techniques)
• Model in Loop Testing
• Software in Loop Testing
• Overview of Hardware in Loop Testing

In this section, an Introduction to Advanced Driver Assistance System (ADAS), levels of autonomous driving is discussed. Apart from these, basic level software architecture for some of the features like Traffic Sign Recognition, Adaptive Cruise Control, Anti-Lock Braking System are also discussed. Moreover, you will focus on an ADAS feature - Tilt & Telescopic Switch (this feature is implemented by OEMs like Toyota & Honda) & its Control Model is developed from the beginning using MATLAB and Simulink using MBD concepts.

• Introduction to ADAS & Levels of Autonomous Driving
• Overview to ADAS Features - 1
• Overview to ADAS Features - 2
• ADAS Project - Tilt & Telescopic Steering Column
• Requirement Analysis & Problem Understanding
• MATLAB Model Development of Tilt & Telescopic Function Feature

• Introduction to Automotive Embedded Systems

• Basic overview on different domains in Automotive Systems

• Overview of CAN & Flexray Protocol

• Software Development demands in Automotive Industry

• Introduction to Model based Development in MATLAB Environment

• Requirement Analysis

• Configuration Settings Parameter

• Data Dictionary Creation

• MATLAB Automotive Advisory Board Process Overview

• Simulation & Code Generation

• Model in Loop Testing & Validation

• Software in Loop Testing & Validation

• Hardware in Loop Testing & Validation

• Autosar Basics

• Autosar Software Components & Application Layer

• Autosar basic Software Layer

• Autosar MCAL Layer

• Autosar Services Layer

• Autosar Diagnostics

• Autosar Memstack

• Autosar RTE

• Autosar Complex Drivers

• Autosar OS & C Rules

• Modelling Autosar SWCs in MATLAB

• Embedded Coder vs Autosar Coder

• Autosar Editor – Code Mapping

• Automotive Functional Safety Concepts

• Overview of ISO26262

• Different safety standards & levels

• A brief introduction on the need of switch mode power converters and their everyday applications.

• The performance and analysis of buck DC-DC converters. 

Using simulation tools from different software packages to simulate and perform high-level design of a boost DC-DC converter

• The performance and analysis of boost DC-DC converters. 

• Using simulation tools from different software packages to simulate and perform high-level and gate driver design of a boost converter.

• The performance and analysis of various UP/Down DC-DC converters. • Implement design techniques using simulation tools on the studied Up/Down converters.

•The performance, analysis, and simulation of various DC-DC converters operating in discontinuous conduction mode (DCM). 

• The development of small signal models for various DC-DC converters.

• Build simulation blocks to validate the modeled systems.

• The design of feedback control system using analog circuits for DC-DC converters.

• Simulate a fully designed closed loop DC-DC converter.

• The performance, analysis, and simulation of various isolated DC-DC converters. AC-DC rectifiers performance and simulations

• The performance, analysis, and simulation of various AC-DC rectifiers.

• The performance, analysis, and simulation of various DC-AC inverters.

• Studying the full design or commercial inverters and rectifiers.

• Studying some of the modern applications of power electronic converters in electric vehicles, renewable energy systems and data centers.

Creating and Simulating a Model (1.5 hrs)

• Create a simple Simulink model, simulate it, and analyze the results.
  • Introduction to the Simulink interface
  • Potentiometer system
  • System inputs and outputs
  • Simulation and analysis

Modeling Programming Constructs (1.5 hrs)

• Model and simulate basic programming constructs in Simulink.
  • Comparisons and decision statements
  • Vector signals
  • PWM conversion system
  • Zero crossings
  • MATLAB Function block

Modeling Discrete Systems (2 hrs)

• Model and simulate discrete systems in Simulink.
  
  • Discrete signals and states
  • PI controller system
  • Discrete transfer functions and state-space systems
  • Multirate discrete systems

Modeling Continuous Systems (2 hrs)

• Model and simulate continuous systems in Simulink.
  • Continuous states
  • Throttle system
  • Continuous transfer functions and state-space systems
  • Physical boundaries

Solver Selection (2 hrs)

• Select a solver that is appropriate for a given Simulink model.
  • Solver behavior
  • System dynamics
  • Discontinuities
  • Algebraic loops

Developing Model Hierarchy (1.5 hrs)

• Use subsystems to combine smaller systems into larger systems.
  • Subsystems
  • Bus signals
  • Masks

Modeling Conditionally Executed Algorithms (1 hrs)

• Create subsystems that are executed based on a control signal input.
  • Conditionally executed subsystems
  • Enabled subsystems
  • Triggered subsystems
  • Input validation model

Combining Models into Diagrams (1.5 hrs)

• Use model referencing to combine models.
  • Subsystems and model referencing
  • Model referencing workflow
  • Model reference simulation modes
  • Model workspaces
  • Model dependencies

Creating Libraries (1 hrs)

• Use libraries to create and distribute custom blocks.
  • Creating and populating libraries
  • Managing library links
  • Adding a library to the Simulink Library Browser

Modeling Flow Charts (2 hrs)

• Implement decision flows with flow charts.
  • Junctions and transitions
  • Flow chart behavior
  • Stateflow interface
  • Conditions and condition actions
  • Chart data
  • Common patterns

Modeling State Machines (2 hrs)

• Implement state machines with state transition diagrams.
• State machine behavior
• State and transition actions
• Chart initialization
• Action execution order
• Flow charts within states
• Mealy and Moore charts

Hierarchical State Diagrams (1.5 hrs)

• Implement hierarchical diagrams to improve the clarity of state machine designs.
  • Superstates and substates
  • State data
  • History junction
  • Transition priority
  • Action execution order

Parallel State Diagrams (1.5 hrs)

• Implement parallel states to model multiprocessing designs.
  • Benefits of parallel states
  • Chart/state decomposition
  • Parallel state behavior

Using Events in State Diagrams (2 hrs)

• Use events within a Stateflow diagram to affect chart execution.
  • Using events in state diagrams
  • Broadcasting events
  • Behavior of state diagrams that contain events
  • Implicit events
  • Temporal logic operators

Calling Functions from Stateflow (1.5 hrs)

• Create functions in a Stateflow chart out of Simulink blocks, MATLAB code, and flow charts.
  • Types of functions
  • Simulink functions
  • MATLAB functions
  • Graphical functions

Truth Tables and State Transition Tables (1.5 hrs)

• Create flow charts and state transition diagrams in tabular form.
  • Truth tables
  • Conditions, decisions, and actions
  • State transition tables
  • States, transitions, and actions

Component-Based Modeling in Stateflow (2 hrs)

• Prepare Stateflow designs for component reuse and interact with structured Simulink data.
• Bus signals
• Data types
• Atomic subcharts
• Data mapping

Introduction to Simscape and the Physical Network Approach (1 hrs)

• Become familiar with the Simscape environment by modeling a simple electrical system.
  • Introduction to Simscape
  • Differences between Simulink and Simscape
  • Building and simulating a model in Simscape
  • Guidelines for Simscape modeling

Working with Simscape Components (2 hrs)

• Interpret Simscape block diagrams and identify the physical variables in Simscape by modeling a mechanical
  system.
  • Describing Simscape component fundamentals
  • Using the Simscape Foundation Library
  • Setting initial conditions
  • Logging physical variables

Connecting Physical Domains (1 hrs)

• Connect models from different physical domains to create a single, multidomain model.
  • Creating multidomain physical components
  • Modeling ideal and nonideal connections between physical domains
  • Dividing components into subsystems
  • Parameterizing models

Combining Simscape Models and Simulink Models (1 hrs)

• Add Simulink blocks to a Simscape model to increase modeling flexibility.
  • Connecting physical signals and Simulink signals
  • Performing operations on physical signals
  • Controlling physical models
  • Solving models with Simscape and Simulink blocks

Creating Custom Components with the Simscape Language (2 hrs)

• Leverage the Simscape language to create custom physical components in Simscape.
  • Simscape language
  • Custom component workflow
  • Complete custom component example

• Introduction to Battery models and Battery terminology Simscape language
• Creating Battery Models using Simscape
• Cell model and characterization
• Battery Pack Modeling

• Battery Management System Overview
• SoC Estimation using Extended Kalman Filter
• Cell Balancing using Stateflow Logic
• Testing Battery Management System using Simulink Test

The topics below focuses on modeling electric powertrain systems using Powertrain Blockset. Topics include:
• Construct Entire Vehicle level model using Powertrain block library
• Component Sizing for battery and motor choices
• Range and performance estimation
• Identify powertrain architecture for better performance

This topics below focuses on modeling power electronic systems using Simscape Electrical™. Topics include:
• Modeling single-phase power electronic components
• Controlling the level of fidelity in a model
• Developing controls for power electronics
• Modeling three-phase power electronic components
• Controlling power electronics for motor drive applications