Introductory description

Modelling and simulation are essential stages in engineering, particularly in modern vehicular systems, where the key attributes result from the synergetic integration of different sub-systems. These techniques allow engineers to evaluate various system configurations and predict their behaviour in different operating environments.

In this module, we will focus on dynamical systems derived from various physical domains, exploring their modelling and control, particularly in the context of electric vehicles' propulsion systems. Learners will develop an understanding of how to model such systems and assess their validity for different applications.

This module will also equip students with the skills to apply a model-based approach to designing controllers at various levels of automotive abstraction. Additionally, students will gain hands-on experience using MATLAB/Simulink to model these systems and develop control strategies.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• Lumped Multi-Physics Systems within electrical, mechanical, and hydraulic domains.
• Mathematical Modelling of Physical Systems using ordinary differential equations (ODEs), state-variable representation, and block diagram modelling for both linear and nonlinear systems.
• System Linearisation and Operating Points, including methods for linearising nonlinear systems around their operating points.
• Time-Domain and Frequency-Domain Representation and Analysis, utilising transfer functions and state-space methods to evaluate system behaviour and stability.
• Empirical Modelling Techniques for developing data-driven models of physical systems.
• Controller Design for Dynamical Systems, employing both classical and modern techniques for linear control system development.
• Modelling and Simulation for Performance and Energy Analysis of electrified powertrains.
• Model-Based Control Design for Automotive Applications, including practical implementations in vehicle .

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate a comprehensive understanding of the practical application of mathematical modelling of one-dimensional physical systems, using ordinary differential equations and empirical models to develop lumped models of multi-physics systems. [AHEP:4-M1]
• Analyse the dynamic behaviour of linear systems using time-domain and frequency-domain techniques, assessing stability, performance, and system characteristics. [AHEP:4-M1,M3]
• Demonstrate an understanding of model linearization and validate linearized models, with specific applications in automotive systems. [AHEP:4-M1]
• Develop and implement control systems for dynamical systems in automotive applications, utilising both classical and modern model-based techniques. [AHEP:4-M1, M3]
• Construct integrated models of automotive systems to gain practical insights into multi-physics simulation techniques. [AHEP:4-M3]

Indicative reading list

Ehsani, M., Gao, Y., Longo, S., & Ebrahimi, K. (2019). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles (3rd ed.). CRC Press.
Nise, N. S. (2019). Control Systems Engineering (8th ed.). Wiley.
Yang, B., & Abramova, I. (2022). Dynamic Systems: Modeling, Simulation, and Analysis. Cambridge University Press.

View reading list on Talis Aspire

Subject specific skills

• Understand dynamical systems.
• How to model electrical, mechanical, thermal, fluid systems as analogous systems.
• Numerical/block diagram methods to solve ordinary differential dynamical systems.
• Implement data-driven and empirical modelling in automotive applications.
• Validating and verification of linearised dynamical models.
• Development of controllers for various sub-systems and components within electrified powertrains.
• How the automotive industry use such models and model based design methods.
• MATLAB programming/Simulink Modelling.

Transferable skills

• Technology literacy
• Dependability
• Communication
• Adaptability