📖Program Curriculum
Course modules
Compulsory modules
All the modules in the following list need to be taken as part of this course.
Induction and Introduction to Motorsport
Aim
As an introductory non-assessed module it sets the scene for both the Advanced Motorsport Engineering and Advanced Motorsport Mechatronics courses, providing the context for motorsport engineering and mechatronics in relation to the sport and the industry behind it.
Syllabus
The Advanced Motorsport Engineering and the Advanced Motorsport Mechatronics with reference to its key components: the taught modules, the group design project (GDP) and the individual thesis projects as well as considerations such as ethics, health and safety and the environment.
Introduction to aspects of motorsport which will be developed during the course such as the design of competition vehicles.
History of motorsport including the evolution of competition vehicles.
Competition vehicle categories.
Sporting and technical regulations.
Library sessions with the Information Team covering qualitative information, referencing, ethics and plagiarism.
Careers sessions including CV writing, preparation for interviews and assessment centres, interview techniques, using LinkedIn.
Group exercise, ‘The Cranfield Challenge’, which relates to the use of materials and structural integrity.
Intended learning outcomes
On successful completion of this module you will be able to:
1. Place the elements of the courses in relation to the award. This includes understanding the variety of assessments, the Group Design Project, and the individual research project.
2. Relate the courses to the practice of motorsport engineering.
3. Illustrate the historical development of motorsport and competition vehicles.
4. Assess the design and categorisation of competition vehicles.
5. Evaluate the criticality of the technical and sporting regulations and what these mean to motorsport engineers.
Motorsport Electronics and Data Acquisition
Aim
Provides an understanding of the electronic and data acquisition systems that are integral to the modern motorsport vehicle. Appreciation of principles of data acquisition to “get good data” on track or in test environments.
Provides methodologies for the analysis and interpretation of the data acquired, and how this underpins all performance optimisation.
Syllabus
Electrical circuit issues, sensors, signal conditioning
Sampling issues in amplitude and frequency domain
Data communications on car and test cell
Data processing and analysis techniques
Introduction to realtime software
Practical system packaging
Build of a basic embedded control and data acquisition system
Intended learning outcomes
On successful completion of this module you will be able to:
Examine the fundamental role electronic systems and acquired data have on and off vehicle throughout motorsport.
Design, evaluate and optimise data systems based on fundamental principles of electrical and digital information transfer.
Propose and apply suitable data analysis techniques to tackle particular engineering questions in a motorsport context.
Analyse data in the context of a chosen field, maximising the result from a particular test (vehicle dynamics used as an example with direct involvement in configuration and calibration of instrumentation on a vehicle for a track test)
Approach or challenge a basic embedded control system.
Motorsport Vehicle Dynamics
Aim
To provide you with fundamental information on vehicle dynamics focussing on limit behaviour with explanations and derivations from first principles, using simplified physical models. To provide experience of a computer based dynamics simulation package of industrial standard, and to provide experimental exercises to illustrate major physical concepts.
Syllabus
• Minimum time optimisation
• Tyre shear force development, measurement and characterisation
• Suspension geometry description and analysis – important properties
• Steady turning equilibrium states; suspension/chassis interactions; roll angles, load transfers, jacking
• Yaw/sideslip handling dynamics; steady turn responses, understeer and oversteer; stability and controllability (a) small perturbations from straight running (b) small perturbations from cornering trim
• Limit behaviour and design aspects; differentials and brake balancing
• Simulation tools and model building
• Vibration behaviour of car and wheels; springs; dampers; track roughness and the use of electro-hydraulic shaker rigs for setup.
Intended learning outcomes
On successful completion of this module you will be able to:
1. Appraise the performance limits of a competition vehicle and the sources of such limitations.
2. Evaluate the interactions of competition vehicle and participant and discuss intelligently the requirements on the competition vehicle from a controllability point of view.
3. Distinguish the complex relationships between competition vehicle design aspects and competition vehicle performance.
4. Examine simulation and optimisation methods for improving design and performance.
Vehicle Control Applications
Aim
The aim of this module is to cover a range of applications of Control Theory and Artificial Intelligence techniques in different components of a modern vehicle including engines, electric motors, energy storage, steering, chassis, suspensions, advanced driver-assistance systems, etc.
Syllabus
Motor control
Engine control
Battery state estimation and control
Electric and hybrid-electric powertrain control systems
Vehicle steering control
Vehicle suspensions and chassis control
An introduction to automated driving and autonomous land vehicles
Intended learning outcomes
On successful completion of this module you should be able to:
Critically evaluate the physical configuration of a vehicle sub-system and be able to formulate new control design solutions appropriate for integration within a vehicle.
Appraise a set of vehicle performance targets for higher levels of automation and safety, more energy conservation and less environmental impacts and be able to select the most appropriate control methods and design techniques to meet the vehicle specification.
Analyse and evaluate different simulation models including vehicle control architectures.
The Business of Motorsport
Aim
To provide you with a series of learning activities during which they will acquire an understanding of how to apply management techniques to the context of motorsport and thus building an awareness of the specific management challenges faced in this sector. The course aims to encourage you to acquire skills in information gathering, the processing of information, analysis and communication and these skills will be evaluated and assessed by group presentation and by written group assignment.
Syllabus
• The business environment in general.
• The business context for motorsport organisations.
• Managing motorsport businesses strategically.
• Creating and sustaining competitive advantage in motorsport.
• Commercial aspects of motorsport management.
• Marketing and motorsport including branding, media and sponsorship.
• Financing motorsport businesses and their on-going financial management.
• Project management and motorsport.
• Managing technical knowledge and expertise in motorsport.
• Technology transfer and opportunities for diversification.
• Appreciate environmental and sustainability considerations where motorsport is concerned.
Intended learning outcomes
On successful completion of this module you will be able to:
1. Appraise the specific management challenges facing the motorsport sector.
2. Distinguish the motorsport environment and the influences on its development.
3. Assess the potential sources of competitive advantage for an organisation in the motorsport sector and the steps needed to both create and sustain such an advantage
4. Evaluate the particular issues relating to the commercial aspects of motorsport management. These would include raising and sustaining sponsorship, media relations, raising capital, diversification through technology transfer.
5. Examine the particular issues relating to the management of technical expertise and knowledge in motorsport and its exploitation.
Motorsport Powertrains
Aim
To provide you with a series of learning activities during which they will acquire an understanding of the engineering principles on which engine design and development depend. Some activities will be classroom based, some reliant on group work and some requiring active learning by the student. The course aims to encourage you to acquire skills in information gathering, the processing of information, analysis and communication and these skills will be tested by written assignments.
Syllabus
Gasoline engine performance characteristics: performance indices.
Idealised thermodynamic cycles and the limits to ideal behaviour.
Maximising power output using high engine speeds: thermo-fluid implications.
Maximising the air/fuel charge in every cylinder: intake system design, supercharging & turbo-charging.
Fuel systems, combustion control and engine management systems.
Mechanical design of high performance two and four stroke petrol and diesel motorsport engines.
The matching of engine, transmission and vehicle.
The design of high performance vehicle transmission systems.
Hybrid and electric powertrains as used in motorsport.
Intended learning outcomes
On successful completion of this module you should be able to:
Understand what counts as excellent engine performance and how to use engine simulation techniques to find such levels of performance.
Test and evaluate the physical processes at work during the preparation of the fuel & air mixture and its eventual combustion and emission with particular reference to high engine speeds.
Evaluate the matching of engine, transmission and vehicle chassis for motorsport applications.
Appraise the operation of high performance vehicle transmission systems.
Examine hybridisation and electrification of motorsport powertrains.
Mechatronics Modelling for Vehicle Systems
Aim
• To provide a fundamental understanding of physical modelling applied to vehicles mechatronic systems.
• To introduce you to modelling techniques, from basic methodology to graphical modelling and practical viewpoints.
• To illustrate the role of first principle and data-driven modelling.
Syllabus
Course content includes:
• Introduction to mathematical modelling
• Modelling from first principle
• Newtonian and Lagrangian modelling
• Electric circuits and networks
• Modelling from data and system identification
• Modelling of delays
• Block diagram reduction
• Powertrain backward and forward modelling
• Modelling for vehicle dynamics and tyre-surface interaction
• Modelling with Matlab/Simulink
Intended learning outcomes
On successful completion of this module you should be able to:
1. Compare and criticise the different analogies that can be made between all system dynamics.
2. Experiment with fundamental concepts of mechatronics systems to design simplified system dynamics models.
3. Evaluate and construct mechatronics models using state-space models derived from system identification, Newtonian equation and Lagrangian equations.
4. Construct state-space equations for the purpose of control system design.
5. Appraise mechatronics models and the simulations results obtained within the context of practical automotive design concepts, performance and constraints.
Advanced Control and Optimisation
Aim
• To provide knowledge of advanced control engineering theory and techniques and their application to automotive control.
• To introduce students to the tools and methodology associated with multivariable control design techniques.
• To provide students with practical experience in designing and simulating advanced modern controllers within the context of multi-domain automotive systems.
Syllabus
The module will provide knowledge in advanced control design tools and techniques and advance analytical methods in designing multivariable controllers with applications in the automotive engineering area. The theory of the multivariable controls will be introduced and then their use will be illustrated and developed by example applications. The theory and applications will be interleaved with selected associated topics (listed below) as appropriate through the module.
The material will be addressed theoretically and practically: all lecture-based teaching will be supported by practical exercises using MATLAB and Simulink.
Prior to the start of the module, you are expected to have reached a high standard of expertise in advanced classical control and the use of MATLAB and Simulink for control system design. As a guideline, you should have met the intended learning outcomes for the module ‘Automotive Control and Simulation’ before commencing this course.
• Modelling multivariable systems
o Describing multivariable systems using state-space representations
o Using norms to describe the sizes and behaviours of signals and systems
o Modelling uncertainty, noise and nonlinearities
o The Nyquist stability criterion and robustness
• Using optimisation in multivariable control
o Representing feedback using state-space techniques
o Pole-placement techniques
o Optimal control using the Linear-Quadratic Regulator (LQR)
o Introduction to Model-Predictive Control (MPC)
• Estimator design
o Multivariable estimator design using pole-placement techniques
o Optimal estimator design for linear systems using the Kalman Filter
o Introduction to optimal control using Linear-Quadratic-Gaussian (LQG) techniques
o Introduction to nonlinear Kalman filtering techniques
• Neoclassical control
o SISO design using the Youla parameter technique
o Direct shaping of S(s) and T(s) and the associated stability criteria
• Robust control
o H∞ control methods: ‘mixed sensitivity’ and ‘H∞ loop-shaping’
o Estimating robust performance using the v-gap metric
• Reference conditioning using prefilters and two degree-of-freedom compensators (covered in outline only)
Intended learning outcomes
On successful completion of this module you should be able to:
1. Create theoretical and computer models of multivariable automotive systems suitable for use in control design.
2. Apply different advanced control techniques to automotive control problems.
3. Design control algorithms for automotive systems using MATLAB and Simulink (commercial software packages).
4. Design state estimators for multivariable automotive control systems using established techniques.
5. Judge the suitability of a given control technique to a particular application in the context of automotive control.
Embedded Vehicle Control Systems
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