• 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 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

• 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 

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

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

• 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