Course

List of experiment with Matlab /Python 45 hours

1. Developing a Reduced-Order Model of a Physical System using Deep Learning
2. Model Predictive Control of a Vehicle using Simulink
3. Characterizing and Modelling a DC Motor using Data-Driven Techniques
4. Battery Modelling and Simulation for Electric Vehicle Performance Analysis
5. PID-Based Driver Model Design and Implementation in MATLAB/Simulink
6. Optimizing Vehicle Dynamics Model Response through Rate Limiters and Saturation
7. From MATLAB Model to Reality: Exploring Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) Simulation
8. Implementing a Simplified Brake Model using Opposing Torque in Simulink
9. Investigating the Impact of Transmission Characteristics on Vehicle Performance

Unit 1

Physical System Modelling: Introduction – Modelling of physical system – Linear System, non – linear system, System governed by PDE and ODE. Reduced Order Modelling – Types of ROM – Data driven models and dynamic ROM using Deep Learning networks. Introduction to Simulink blocks set.

Unit 2

Vehicle Dynamics Modelling – Introduction to governing equations of longitudinal, lateral and vertical dynamics of vehicle. Reduced order modelling of vehicle dynamics equations and improving models using Coast Down Test results. Motor and Transmission Modelling – Introduction to modelling of simple DC motor governing equations. Data driven modelling of generic motor. Maps/Lookup tables relating speed torque, power delivered, current drawn and efficiency of motors for model improvements. Adopting transmission and final drive gear ratios and efficiencies.

Unit 3

Battery Pack, Drive and Brake Modelling: – Introduction to battery parameters – Open circuit voltage, internal resistance and n – RC models of battery packs. Introduction to standard driving cycles and PID controller. Simple driver model using driving cycle and PID controller. Simplified brake modelling – Usage of opposing torque. Introduction to systems engineering- model based systems engineering and its application to automobile systems.

Course Objectives

1. To introduce the fundamental concepts and principles behind the modelling of physical systems,
2. To provide insight on various reduced order modelling techniques and their applications, including data- driven models and dynamic ROM using Deep Learning networks
3. To familiarize the governing equations of vehicle dynamics and apply reduced order modelling techniques to simplify and improve models, leveraging experimental data
4. To familiarize in modelling of motor , battery pack models, HIL, SIL and MIL

Course Outcomes

Skills acquired

Model diverse physical systems, employ reduced order modelling techniques, utilize Simulink blocks, and optimize models for simulation.

Text Books / References

1. “System Dynamics Modeling with MATLAB and Python by Edward O. Pytlak: Chapman and Hall/CRC,
2. Mehrdad Ehsani, Yimi Gao, Sebastian Longo, Kambiz Ebrahimi, “Modern Electric, Hybrid Electric and Fuel Cell vehicles: Fundamentals, Theory and Design”, CRC Press, Third edition, 2022.
3. https://matlabacademy.mathworks.com/details/simulink-onramp/simulink
4. https://matlabacademy.mathworks.com/details/simulink-fundamentals/slbe
5. INCOSE , INCOSE systems engineering handbook. John Wiley & Sons, 2023.
6. Haberfellner, Reinhard, Peter Nagel, Mario Becker, Alfred Büchel, and Heinrich von Systems engineering. Cham: Springer International Publishing, 2019.