• Charlie Gaylard

  • Theme:Digital Systems, Optimisation and Integration
  • Project:Beyond Predictive Energy Management
  • Supervisor: Nic Zhang ,Chris Brace
  • Industry Partner: AVL
  • The Gorgon's Head - Bath University Logo

Bio

After graduating in Motorsport Technology in 2011, Charlie held several operational roles in the motorsport industry including Production Controller and Design Coordinator for Formula 1 teams and supplier companies with composites. In 2017 he returned to study for a BSc Top-Up in Automotive engineering at the University of Brighton where he worked on the design and manufacture of a Formula Student Chassis with a focus on ergonomics and regulatory compliance. Following his graduation, Charlie joined the Formula E team Mahindra Racing as a Design Engineer, working on the design of the cooling system and inboard suspension components among other projects. Charlie has joined the AAPS CDT hoping to apply his experience in design and manufacturing while widening his knowledge and expertise to help improve efficiency and sustainability in vehicle design. Outside of University he enjoys surfing, playing the guitar and motorsport.

FunFacts

  • Won paper airplane contest at village fete. 1st Prize was a box of wine gums
  • I race a Mazda MX5 in Hillclimb Sprint competitions
  • I worked on a cattle ranch in Australia
  • I have been down a luge on a skeleton bobsleigh in Austria
  • I design and make guitars from recycled items such as boxes, biscuit tins and oil cans

Beyond Predictive Energy Management

Automotive vehicles are designed to work in a wide range of conditions, however they may operate in certain conditions better than others.  These vehicles are relatively unintelligent in preparing for variations of driving conditions including changes in the external environment, terrain, and congestion.

This project aims to identify and investigate applicable uses of predictive control to be applied in the optimization of various attributes of an X-EV with a view to develop and demonstrate predictively optimized control strategies for chosen attributes.
The objectives are divided into 6 key work packages which will include a scoping exercise to  define the problem and conduct a market study and literature review. This will inform a high-level simulation study utilizing an offline global optimization method which will then lead into development of a functional online predictive control strategy. The strategy will be subject to validation and demonstration, where the results and findings for which will be summarized and reported in the final PhD Thesis. Throughout the project objectives, progress will be monitored and recorded as part of the overall management and administrative work package.
This project will be conducted with support from AVL, who have already identified four key opportunities for use of look-ahead functions including Predictive Routing, Predictive Velocity Control, Predictive Powertrain Control and Predictive Thermal Management.
It will be within the scope of this project to select and investigate two or more key attributes which could benefit from using predictive energy management and apply the relevant modelling and control methods as well as a working demonstration of the system. Attributes which may be considered include energy consumption, hardware specification, component life and journey time.

This piece of research will enhance industrial understanding of predictive control strategies for automotive applications and provide a demonstrable predictive control strategy for further research.

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