Making the Invisible Count : A Design Science Research Approach to Predictive Modelling of Electric Vehicle Workplace Charging Loads

Title data

Seger, Marcel ; Clement, Christoph ; Körner, Marc-Fabian:
Making the Invisible Count : A Design Science Research Approach to Predictive Modelling of Electric Vehicle Workplace Charging Loads.
2025
Event: The 20th International Conference on Design Science Research in Information Systems and Technology (DESRIST) , 02.06.25 -04.06.25 , Ocean Coral Spring, Jamaica.
(Conference item: Conference , Speech with paper )

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Private businesses are facing mounting regulatory pressure to reduce their Scope 1-3 emissions in order to comply with environmental legislation at national- and EU-level. Employees’ daily commute to work in conventional cars with combustion engines, which is accounted for under Scope 3 emissions, can contribute considerably to firms’ emissions profile. As a result, many businesses are now investing into the build-out of large-scale electric vehicle (EV) charging infrastructure at the workplace to incentivise employees to switch to electrified mobility. However, due to limited internal energy management capabilities, firms tend to underestimate the complex impact of the additional electricity demand incurred from EV workplace charging on their bespoke load- and carbon emissions profile. Having identified this organisational problem in a real-world context, we build on contributions from the Green Information Systems (IS) community and aim at developing an open-source web application that empowers businesses executives to plan and operate EV workplace charging infrastructure with greater analytical foresight through predictive, AI-driven electricity load management features. In this research-in-progress paper, we report on the iterative development of the web application using Design Science Research (DSR). Our preliminary results – including four case study partners and qualitative (semistructured interviews) and quantitative (System Usability Scale (SUS) questionnaire) methods – show that our web application is perceived highly useful amongst decision makers, with a SUS score of 86.9%. We anticipate our research to be of high value to researchers in the field of IS and practitioners alike as we provide an actionable solution to an organisational problem in the context of environmental sustainability.