Introduction
You will pursue experimental research on connected and autonomous systems to reduce the energy consumption of electric vehicles (EVs).
The overall aim is to transform the way we travel from Point A to Point B so that it is as energy efficient as possible without compromising travel time, while ensuring user acceptance and feasibility.
Reduction of EV energy consumption is crucial for addressing limitations in renewable electricity generation, charging infrastructure and grid capacity. In 2025, more than 50% of electricity in the UK was not generated from renewable sources. As the number of EVs increases, electricity demand will continue to rise. This challenge has been highlighted by recent energy crises and increasing pressure on electricity networks. Improving energy efficiency of EVs is therefore an important sustainability challenge.
Connected and autonomous driving systems have the potential to reduce EV energy consumption by exploiting vehicle, infrastructure and traffic information that is not readily available to human drivers [1, 2].
Such information can be used to make driving more energy efficient, e.g. using speed planning, acceleration and braking strategies, and interaction with traffic control systems. In addition to saving driving time and increasing productivity, driving automation could therefore improve the sustainability of future transport systems.
Although this topic has received growing attention, existing studies often neglect important practical considerations such as powertrain characteristics, vehicle-to-infrastructure communication, realistic onboard computing constraints, cabin comfort, experimental validation and user acceptance. As a result, it remains unclear which energy efficient autonomous driving strategies are both effective and practically deployable in production vehicles.
Many advanced energy efficient driving strategies rely on computationally demanding optimisation and control methods such as model predictive control [3, 4]. Historically, deployment has been limited by the processing capabilities of conventional automotive electronic control units (ECUs). Recent advances in automotive edge computing platforms provide new opportunities to implement sophisticated real time optimisation and control algorithms directly within vehicles. This project will investigate whether such capabilities can enable feasible and effective energy efficient connected and autonomous driving systems. The research will involve a combination of simulation studies, hardware-in-the-loop testing and real-world experiments using our instrumented Kia Niro EV.
Your objectives will be to:
Research and develop connected and autonomous driving strategies that improve EV energy efficiency using machine learning and advanced control techniques
Analyse and compare the performance and feasibility of different systems for different driving scenarios using traditional ECUs and modern edge computers
Analyse the effects of these energy-efficient driving strategies on energy consumption, travel time and user acceptance
If you would like to know more about this opportunity, feel free to drop a line at anil@soton.ac.uk or ss2@ecs.soton.ac.uk.
References:
[1] A. Vahidi and A. Sciarretta, “Energy saving potentials of connected and automated vehicles,” Transportation Research Part C: Emerging Technologies, vol. 95, pp. 822–843, 2018.
[2] J. Han, D. Shen, D. Karbowski, and A. Rousseau, “Leveraging multiple connected traffic light signals in an energy-efficient speed planner,” IEEE Control System Letters, vol. 5, no. 6, pp. 2078–2083, 2021.
[3] C. Pan et al., “Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control,” Energy, vol. 241, no. 122793, p. 122793, 2022.
[4] B. HomChaudhuri, A. Vahidi, and P. Pisu, “Fast model predictive control-based fuel efficient control strategy for a group of connected vehicles in urban road conditions,” IEEE Transactions on Control System Technologies, vol. 25, no. 2, pp. 760–767, 2017.