Back to Our Community

Charlie Gaylard

Theme

Digital Systems, Optimisation and Integration

Project

Predictive Integrated Energy and Thermal Management for Advanced Propulsion Systems

Supervisor(s)

Dr Nic Zhang, Prof Chris Brace

Industry Partner

AVL
View research portal View LinkedIn
View publications

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.

Fun Facts

  • 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

Predictive Integrated Energy and Thermal Management for Advanced Propulsion Systems

Predictive energy management has gained interest in automotive research, primarily aimed at optimising power delivery between energy sources, since the introduction of hybrid electric powertrains. Predictive Energy Management uses knowledge or predictions of future driving conditions to better inform control systems with a potential to improve aspects including efficiency, driveability, comfort and range. 
Battery electric vehicles (BEV) and fuel cell hybrid electric vehicles (FCHEV) emerge as leading alternatives to replace internal combustion engines (ICE) and reduce the transport sector's contribution to climate change. However both these propulsion alternative introduce distinct thermal challenges that have spurred deeper interest in predictive thermal management. However, fewer studies have explored integrated strategies that jointly optimise propulsion and thermal energy within a unified framework.

This PhD project investigates integrated predictive thermal and energy management through two case studies focused on FCHEV and BEV applications. A Dynamic Programming (DP) framework is used to determine globally optimal control strategies in offline simulations over given driving conditions, then adapted to shorter, rolling prediction horizons to assess suitability for online application.
The first case study extends the classic hybrid power split problem by applying predictive control to a hydrogen fuel cell bus model in MATLAB, incorporating battery state of charge (SOC) and fuel cell temperature as system states. The DP controller targets optimal fuel cell control by balancing energy deployment between the fuel cell and battery. The optimisation  aims to minimise hydrogen consumption while avoiding thermal violations of +80°C using a multi-objective cost function which can then be compared to a non-predictive rule-based control strategy.

The second case study applies the DP framework to a BEV holistic thermal management system (HTMS) model provided by AVL GmbH. Thermal energy is distributed across the cabin, battery, and motor using multi-mode control. The DP control selects the optimal thermal modes and heater power across varying road and temperature conditions to optimise thermal energy management for overall efficiency and passenger comfort. This model also uses a multi-objective cost function to balance the priority between passenger comfort and overall system efficiency to improve range. 

This research explores the feasibility and limitations of applying DP for integrated predictive thermal and energy management where prior studies have only addressed propulsion and thermal energy separately. The results obtained from this research highlight the practical challenges of implementing thermal models for predictive control and the challenge of balancing trade-offs between efficiency and thermal performance. The learning gained supports a foundation for future work toward real-time, holistic vehicle control.

"The AAPS CDT offers a unique opportunity to learn from leading academics and work with experts from a range of backgrounds, while applying your own skills in new contexts on collaborative projects. This provides an excellent framework and support structure to prepare oneself for working in forward thinking, innovative and multidisciplinary environments which are key to solving some of the biggest challenges faced by the automotive and transport sector today."

Charlie Gaylard Cohort 2
Model Based Development Summer Masterclass with AVL
News
Model Based Development Summer Masterclass with AVL

Next generation vehicles are becoming increasingly complex, meaning the development processes used in their development must evolve in parallel to ensure that the ever-increasing customer needs can be fulfilled. 

25 Aug 22

Community,Events,Training
AAPS researchers first trip to AVL in Austria
News
AAPS researchers first trip to AVL in Austria

In November of 2021, five of our PhD students here at AAPS embarked on a trip to Graz in Austria, where their sponsor company AVL is based. 

14 Dec 21

Collaboration,Events,Research Updates