Research

The deployment of connected and automated vehicles (CAVs) on roads offers an inspiring opportunity of improving traffic throughput, driving comfort, safety and energy efficiency. Cooperative adaptive cruise control (CACC) will play a key role to explore the economical and social potential of CAVs. However, challenges are found in implementing CACC on CAVs, particularly in vehicular communication, vehicle platooning and mixed traffic flow managements. We aim to develop innovative vehicular communication and control strategies to realise safe and smooth platooning of mixed CAVs and HDVs. This project is underpinned by developing cooperative vehicular communication, distributed robust platoon control, and design verification and validation.

There is an ongoing revolution in developing autonomous vehicles to greatly improve safety, traffic efficiency, environment, and passenger comfort. In March 2017, the House of Lords reported that the UK would establish global leadership in connected and autonomous vehicles. The driverless technology market is set to be worth up to £50 billion to the UK economy by 2035. This general trend is greatly impacted by vehicle hybridisation and advances in internal combustion engine leading to improved vehicle fuel economy. These themes will shape the future of the automotive industry, and their intersection will represent a new potential for economic growth. This project will create a cloud-computing-aided testing platform and a real-time optimisation strategy to develop energy efficient autonomous vehicles. It aims to address key challenges in enhancing vehicles safety and energy efficiency including sensing, modelling and control.

The fast development of transportation systems has led to a significant increase in energy use and air pollutant emissions. Transportation systems have consumed more than 60% of the fossil energy globally. Therefore, improving energy efficiency in transportation systems has become essential. It is generally recognised that an eco-driving strategy can substantially improve vehicle efficiency. A study indicated that eco-driving strategies can improve vehicle fuel economy by 5-15%. Eco-driving strategies can assist vehicle to operate in energy efficient conditions by running under optimised speed profiles. Most eco-driving strategies have been developed for connected and automated vehicles (CAVs). CAVs are based on vehicle-to-vehicle (V2V) communications, vehicle-to-infrastructure (V2I) communications and intelligent transportation systems (ITS). They can access more traffic information than conventional vehicles. Therefore, CAVs can acquire traffic flow rates, traffic densities, and average speeds of future routes. This research aims to develop decision making and control solutions for the ecological driving of CAVs in the co-existence with human-driven vehicles.

The ecological (eco-) driving strategies can assist vehicles to operate in fuel efficient conditions by running under optimal speed profiles. It is generally accepted that eco-driving strategies can substantially improve the efficiency of vehicles. Some studies indicated that eco-driving strategies can improve vehicle fuel economy up to 25%. Connected and auto mated vehicles (CAVs) have access to more and further road information than conventional vehicles using advanced technologies, such as V2V, V2I and ITS. These technologies enable CAVs to acquire the future route’s traffic information, in terms of traffic flow rate, traffic density and traffic signal timing. The traffic density and traffic flow rate information can be utilised to provide the average speed in optimising the vehicle’s future speed. The most significant benefit of considering the average speed of an area is that it considers the real traffic condition and avoids selfish optimisations which can create traffic waves and reduces capacity.

How should the increasingly interconnected and intelligent autonomous systems and infrastructure cooperate with humans for trustworthy joint decisions? To achieve ultra-reliable (and real-time) operations, what are the roles of each individual in the human-autonomy-infrastructure teaming? Among the stakeholders including the autonomous systems’ owners, network/computing infrastructure providers, and the human operators, who will be responsible when harmful or disastrous consequences happen in such interconnected systems? To answer the fundamental questions above, the overarching goal of this research is to establish ultra-reliable and trustworthy connected human-autonomy teaming assisted by infrastructure. Four pillar techniques: causal inference, human-machine interaction, distributed consensus and communication/ computing infrastructure are identified for jointly designing.


This project is to procure a flagship platform to support and encourage the multi-theme and multi-discipline collaboration with the focus of Robotics and Networked Autonomous Systems. The equipment would be placed at Glasgow and connected for remote access from our students and staff in TNE. The involvement of research students, undergraduate students and industry partners are expected.

This project is funded by Innovate UK and Advanced Propulsion Centre (APC). This project is to develop a low carbon engines with the targets of 5% fuel economy improvement and 40% reduction on particulate number. Based on the physical insights into the formation of particulate number within the engine cylinder, the characterization process would be modeled physically and analytically. Based on the characterization, a global mathematical model would be built. Finally, an online dynamic optimization solution would be designed to minimize both fuel consumption and exhaust emissions.

This project is funded by Ford and APC. The objective is to establish an integrated communication and control platform for the development of hybrid electric vehicles. In modern automotive industry, most of the projects are finished by spatially distributed institutes. The data sharing and delivery is working in series and therefore time consuming. This project is to break the bottleneck by sharing the data on the same platform, such that all the partners are working in parallel. The time efficient solution would significantly reduce the development effort of hybrid vehicles. The gained expertise can also be generalized into multiple applications.

This project is funded by Digital Engineering and Test Centre (DETC) of the APC. The objective is to develop an optimal control solution for conventional powertrain vehicles. Using advanced modelling methods such as the local linear model tree (LOLIMOT) or hierarchical local model tree (HILOMOT), the vehicle model in both steady states and transients can be precisely built.  Following the modelling work, a dynamic optimization problem would be formulated and solved. In practical environments, diverse terrain information would be considered and therefore a huge number of possible decisions would be made. The computational effort is so big such that the decisions are made offline using high performance computing. 

Homepage: http://tc48.co.uk/

This project is funded by Innovate UK (previously the Technology Strategy Board, UK). The objective is to develop a lightweight hybrid powertrain vehicle. The engine and motors will be configured in a parallel way to drive the vehicle, where the engine, motors, power electronics, and control systems are all self-defined to achieve the highest efficiency. The project aims are:

This project is co-funded by Caterpillar Inc. and Loughborough University. The objective is to demonstrate the practical benefits of the Electric Turbo Assist (ETA) on:

This project is funded by Innovate UK, finished by Caterpillar, BorgWarner, Loughborough University, and Imperial College London. In this project, an ETA technology developed and validated for heavy-duty diesel engines providing fuel economy improvements of 5 - 10% over a target transient cycle. Simulation tool and development process improvements enabling rapid deployment of ETA technology into production models across multiple engine applications. Model-based controls that optimize performance of the engine and ETA over many diverse machine applications. Heat resistant power electronics. Durable and reliable turbocharger integration. This will include acceptable shaft motion performance, motor/generator temperatures at worst-case turbine temperatures and reliable oil seals.

The critical achievements of the project include:

Global Nonlinear Control of Electrified Turbocharged Diesel Engines (PI, 2013 - 2014, £30k from Caterpillar Inc.)

This project is funded by Caterpillar. The ETA is novel because the motor accelerates the turbocharger during transients to improve engine response in assist mode, or provides steady state boost pressure to enhance low speed torque. When transient response is the objective, this method is more efficient than adding energy to the flywheel. The research is to build an explicit modelling strategy for the ETA equipped diesel engine, and a global nonlinear control method rather than previous piecewise linear control approaches. The modelling and control methods can be generalized across heavy duty and light duty vehicles.