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    RESEARCH  |  IMPACT SERIES  |  SOLVING SUSTAINABLE

    The data science behind our EV modelling

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    Contributor: Ryan Preclaw
    12 Jul 2023

    For important disclosures, including analyst certifications and to access the full report, EV Transition, please log into Barclays Live 

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    Examining how we manoeuvre the world’s transition to electric vehicles (EVs) isn’t just prescient, it’s critical. For policymakers, these choices are imminent and must be carefully considered as they may be irreversible. The decisions made today could have significant ramifications on the way societies operate, the health of the planet and the future of economies.

    Answering some of the crucial questions around EV adoption’s effect on energy grids has been a key motivator for Ryan Preclaw, Global Head of Investment Sciences, which is why he and his multi-disciplinary team created a highly advanced artificial intelligence (AI)-powered model, revealing the implications of various EV transition scenarios. For example, what would happen if we had a completely green transition, or one with a minimum level of investment?

    The team behind the model

    From PhD physicists to social scientists, operational research specialists to ex-army apprentices, the team spans a variety of academic backgrounds, areas of expertise and life experiences. Between them, they write 500+ papers a year using data science and statistical modelling.

    The science powering the analysis

    The team’s sophisticated data-modelling tool is akin to a powerful AI chatbot – only on a much grander scale, employing significant processing power. Using it, the team can conduct fast, highly complex analysis on millions of data points, highlighting the consequences of multiple hypothetical scenarios.

    In a short space of time, the model has the ability to answer extremely complicated questions on a given scenario. For instance, what is the difference in impact between switching to renewable power sources and encouraging people to drive less, as their charging behaviour changes with dynamic pricing.

    The model is built on hard data to be as accurate as possible. Throughout the process, the team has rejected using hypothetical inputs. Instead, the model uses hourly data of California driving behaviour such as miles travelled or cars parked, and 25 years’ worth of highly detailed variables such as weather patterns and power generation.

    Looking to the future

    The team chose California as the initial focus of the model because the state is leading the electrification of vehicles in the US, where a law requires 100% of new cars to be EVs by 2035. 

    One area of interest could be understanding how the change in driving behaviour is dependent on other factors such as reduced fuel costs. Would this encourage people to drive more, or have some other unintended consequences?

    Thanks to the team’s tenacity and rigour, and the speed of the data modelling tool they’ve created, questions that might have taken months or years to resolve can now be answered in minutes.

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    Contact the team

    Find out more about how the Barclays Investment Sciences Team can help you and your business.

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    About the expert

    Ryan Preclaw

    Global Head of Investment Sciences

    Ryan Preclaw is the head of Investment Sciences, a group that creates investment insights by combining alternative data, data science, and traditional research. Previously, he was a Director in Credit Strategy, where he focused on special situations, event-driven strategies, and industries facing fundamental transitions. Ryan has also worked as a coverage banker in Barclays' Communications and Media group. Prior to joining Barclays, Ryan worked as an economist at NERA Economic Consulting and London Economics International. Ryan received his M.B.A. from the University of Chicago in 2008, his M.A. from Western University in 2001, and his B.A. from the University of Alberta in 2000.

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