Isaac graduated from the University of Bath with a BSc in Natural Sciences (Physics with Chemistry), in which he undertook a year-long placement at Shell Global Solutions as an Electric Mobility intern. For his final year project, Isaac developed a model to predict financial crashes by utilising the self-exciting nature of a financial market, a technique that is used to predict earthquakes and their aftershocks. During his time at Shell, Isaac worked on many projects surrounding electric vehicles and charging infrastructure, including research into smart charging and discharging profiles to reduce battery degradation during DC fast charging, analysing data for the Wireless Charging of Electric Taxis (WiCET) Project, and the creation and management of an electric vehicle database. He also gained considerable expertise in the ISO 15118 charging communication standard, its “Plug and Charge” feature, and the related ecosystem. Outside of university, Isaac is an avid drummer and enjoys powerlifting, swimming, tennis and running.
The recent drive to decarbonise transport is leading to a rapid increase in the number of electric vehicles (EVs). High concentrations of EVs could put excess strain on certain parts of the electrical distribution network, thus requiring expensive grid upgrades. In addition, new public charging infrastructure will need to be installed to meet future demand. However, EV charging demand can be flexible, allowing for the shifting of charging time and power to better align with times of high renewable generation and to perform grid services such as load shifting and frequency response. Understanding where, when and how EVs charge as well as quantifying the flexibility of that demand is imperative in informing the future infrastructure rollout strategy of distribution network operators and local councils to enable cheaper and faster integration of EVs and renewable generation into the grid, thus accelerating the decarbonisation of the automotive and power sectors.
Isaac's PhD research aims to develop high-fidelity spatiotemporal models that leverage data-driven, Bayesian, and agent-based techniques to forecast EV adoption, simulate EV charging behaviour, and quantify the potential impacts of EV charging. These models could be used to identify potential vulnerabilities in the distribution network, optimal locations for new public charging infrastructure, and provide estimates for the financial and carbon savings that smart charging can offer.
© Copyright 2024 AAPS CDT, Centre for Doctoral Training in Advanced Automotive Propulsion Systems at the University of Bath
