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To introduce the programme and the courses and the facilities available at Cranfield.

• Team working
• Project management
• Various interpersonal skills: report writing and presentation skills 
• Various MS Office training packages
• 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.

On successful completion of this module you will:

• Have an appreciation of the Automotive Mechatronics Masters programme and course philosophy, structure, content, teaching methods, staff and administration.
• Be familiar with key facilities (internal and external to Cranfield) and resources such as the library, computer network and Careers Service.
• Have essential, fundamental knowledge prior to the study including a range of computing-related skills.
• Have experienced team building and other interpersonal skills including written and verbal communication skills.
• Appreciate the importance of time/project management throughout the study and Health and Safety at workplace.

• To equip you with the skills needed to understand, design and assess single-variable feedback control algorithms using classical control techniques for use in automotive systems.
• To introduce you to MATLAB and Simulink, industry-standard CAD tools for control system design.

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, students will be expected to ensure that they are familiar with the following key concepts:

o Representation of mechanical and electrical systems using differential equations.

o Representation of linear systems and signals in the time domain, including convolution.

o Use of Laplace transform methods for solving ordinary differential equations.

o Transfer functions, poles and transmission zeros, and frequency responses.

The core syllabus is as follows:
• Creating computer models in MATLAB and Simulink
o Introduction to MATLAB programming
o Introduction to modelling and simulation in Simulink
o Linear system analysis in MATLAB
o Finding operating points and linear models using MATLAB and Simulink

• Classical control concepts
o Key feedback concepts: stability, tracking performance, noise/disturbance rejection
o Relationships between closed-loop functions S(s), T(s) and loop-gain L(s)
o Nyquist stability criterion for stable systems, gain/phase margin and Bode diagram

• Classical control design
o Frequency-domain loop-shaping
o PID design and its relationship to frequency-domain loop-shaping
o Introduction to prefilter design and ‘feed-forward’
o Actuator saturation, noise and integrator wind-up

• Estimator (state observer) design
o Introduction to linear state-space representations
o Estimator design using pole-placement methods

On successful completion of this module you should be able to:
1. Evaluate an automotive system (in terms of its performance, robustness, and sensitivity to noise and disturbances) using classical control concepts and methods.
2. Design feedback control algorithms to meet specified performance requirements using frequency-domain ‘loop-shaping’ methods and PID techniques, and to understand trade-offs and limitations on what can be achieved.
3. Create Simulink simulations of multi-domain automotive systems suitable for performance analysis and control system design, and assess control systems with these models.
4. Evaluate operating points and construct linearized state-space and transfer function modules using MATLAB and Simulink.
5. Construct a linear observer (state estimator) using pole-placement methods, and inspect its behaviour using MATLAB and Simulink.

• To provide a fundamental understanding of vehicle dynamics as applied to wheeled vehicles.


• To introduce students to road vehicle ride and handling, from requirements to analytical modelling and practical viewpoints.

• To link understanding of vehicle dynamics, ride and handling to the practical implications for suspension and steering system design.

The module will provide knowledge in vehicle dynamics ride and handling from subjective and objective requirements to analytical methods in developing passive ride and handling models. 

Core topics:

1. Vehicle ride, ride modelling and terrain modelling 
2. Vehicle handling, steady-state and transient handling
3. Tyre characteristics and tyre modelling 
4. Suspension system types, typical designs and practical implications
5. Kinematics, wheel motion control, instantaneous centres of rotation
6. Steering system, steering kinematics and compliance.

On successful completion of this module you will be able to:

1. Demonstrate understanding of vehicle dynamics models, including first principles, associated assumptions and implications to numerical simulations.
2. Critically evaluate vehicle ride and handling performance and the role of tyre and suspension characteristics.
3. Demonstrate an understanding of the fundamentals and practical issues of vehicle suspension and steering systems and their influence on ride and handling. 
4. Critically evaluate suspension and steering designs, including layout, geometry and materials.

• 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.

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

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.

• 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.

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. Students who have not taken this module should ensure they do sufficient pre-work prior to the module.

• 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)

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.

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.

• Motor control
• Engine control
• Battery state estimation and control
• Electric and hybrid-electric powertrain control systems
• Vehicle steering control
• Vehicle suspensions and chassis control
• ADAS and autonomous driving

On successful completion of this module you should be able to:

1. 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.
2. 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.
3. Analyse and evaluate different simulation models including vehicle control architectures.

Within the context of modern automotive control system, the aim of this module is for you to critically evaluate the different technologies and methods required for the efficient vehicle implementation, validation and verification of the automotive mechatronic system.

Course content includes:

• A review of modern automotive control hardware requirements and architectures.
• The evaluation of current and future vehicle networking technologies including, CAN, LIN, MOST and Flex-ray.
• The evaluation of control rapid prototyping techniques to design and calibrate the control algorithm.
• The use of modern validation and verification methods, such as software-in-the-loop, and hardware-in-the-loop techniques.
• The role of Functional Safety and ISO26262 within the overall control system life-cycle.
• The evaluation of the interdependency between software engineering and control system design within the automotive industry including the use of software auto-coding techniques for production and the use of advanced test methods for the validation of safety-critical systems.

On successful completion of this module you should be able to:

1. 1. Analyse the components of an automotive control systems and its implementation.
2. 2. Design and implement a digital controller.
3. 3. Evaluate the effect of sampling times, communication delays and quantization errors in a feedback loop.
4. 4. Construct efficient Matlab code for data coding/decoding and control algorithm implementation.
5. 5. Appraise the purpose of the ISO26262 functional safety standard and the AUTOSAR standardized automotive software design.

The aim of this module is to empower you with the capability to analysis, synthesis, and evaluate various technologies and integration challenges associated with the electric and hybrid vehicles. The module is structured to provide an in-depth knowledge and expertise for design and development of the main systems, components, architectures of the Hybrid and Electric Vehicles. The module includes case-studies of commercially available Hybrid and Electric vehicles and current research projects.

Course content includes:
• Introduction to Hybrid and Electric Vehicles systems and powertrain architectures.
• Introduction to Electric Motors, Power Electronics and Electric Drives, and Motor Control.
• High voltage electrical architectures and the integration of power electronics systems
• Automotive energy storage systems:
  o Batteries, ultracapacitors, flywheels and hydraulic accumulators
  o System design, integration and energy management
• The integration of electrical machines and their electric drive systems
  o Technology options
  o System design and sizing
• The mechanical integration of the hybrid propulsion system including the use of split-path transmissions
• Energy Management and supervisory control for CO2 reduction, fuel saving, vehicle performance and driveability
• The role of energy recovery systems including regenerative brake strategies and vehicle integration challenges
• Modelling, simulation and analysis of Hybrid & Electric Vehicles and its sub-systems, using model based approach, including Mil, SiL, and HiL
• Recent Electric and Hybrid vehicle technologies case studies

On successful completion of this module you should be able to:
1. Evaluate the different Hybrid & Electric powertrain architecture options and be able to propose appropriate solutions within realistic performance, fuel economy, emission and commercial constraints.
2. Evaluate energy storage and energy management technology options for a hybrid or electric vehicle and be able to judge between different technologies relative to a given vehicle application and overall system design.
3. Demonstrate an ability to design and/or size different Hybrid and Electric Vehicle sub-systems, within the context of vehicle usage, weight, packaging, and range constraints.

• Provide deep understanding of vehicle propulsion options and driveline.
• Establish approaches and procedures to analysing and predicting vehicle performance.
• Provide a framework for the appreciation of the interdependency of vehicle systems.
• Critically evaluate the integration of different alternative powertrain options and be able to select appropriate solutions within legislation framework.
• Evaluate vehicle emissions and control systems to identify appropriate solutions.

Basic vehicle characteristics: Vehicle concepts, centre of gravity position, static and dynamic loads and weight distributions, front, rear and all wheel drive. Adhesion coefficient and influencing factors. Traction, braking and resistance to motion.

Fuel consumption: Engine characteristics & fuel maps. Determination of fuel consumption. Energy aspects. Legislative Drive Cycles.

Off-Road: Introduction to off road vehicle design characteristics.

Autonomy: Review of the current technologies surrounding Vehicle Autonomous Driving.

Braking performance: Influence of resistances and inertia. Brake force distribution. ECE 13 legislation. Calculation of required braking characteristics. Stopping distance.

Safety: Principles of passenger restraints, elastic/plastic restraints, energy dissipation, rebound energy (whiplash). Vehicle restraint systems and safety features. Hybrid and electric vehicle safety considerations.

Legislation: Introduction to regulations, European directories, USA federal motor vehicle safety standards. Understanding the influence of relevant legislation on vehicle systems design.

Driveline components: Driveline components: Friction clutches, Final drives, Differentials including e-Diff

Manual & automatic transmissions: Description of gearbox layout and gear change mechanisms.

Hybrid and electric vehicles: Basic definitions, HEV and EV architectures, advantages and disadvantages. Electrical and mechanical energy storage technologies including battery management considerations.

Brakes and braking systems: Disc and drum brakes, braking systems – design, dimensioning and evaluation. Materials, manufacturing methods and testing.

Vehicle refinement: Basic details of noise vibration and harshness and attributes for Driveline refinement.