The mass adoption of Electric Vehicles (EVs) might raise pressure on the power system, especially during peak hours. Therefore, there is a need for delayed charging. However, to optimize the charging system, the progression of charging from an empty battery until a full battery of the EVs based on realworld data needs to be analyzed. Many researchers currently view this charging profile as a static load and ignore the actual charging behavior during the charging session. This study investigates how different factors influence the charging profile of individual EVs based on real-world data of charging sessionsin the Netherlands, enabling optimization analysis of EV smart charging schemes.
Electric vehicles have penetrated the Dutch market, which increases the potential for decreased local emissions, the use and storage of sustainable energy, and the roll-out and use of electric car-sharing business models. This development also raises new potential issues such as increased electricity demand, a lack of social acceptance, and infrastructural challenges in the built environment. Relevant stakeholders, such as policymakers and service providers, need to align their values and prioritize these aspects. Our study investigates the prioritization of 11 Dutch decision-makers in the field of public electric vehicle charging. These decision-makers prioritized different indicators related to measurements (e.g., EV adoption rates or charge point profitability), organization (such as fast- or smart-charging), and developments (e.g., the development of mobility-service markets) using the best-worst method. The indicators within these categories were prioritized for three different scenario's in time. The results reveal that priorities will shift from EV adoption and roll-out of infrastructure to managing peak demand, using more sustainable charging techniques (such as V2G), and using sustainable energy towards 2030. Technological advancements and autonomous charging techniques will become more relevant in a later time period, around 2040. Environmental indicators (e.g., local emissions) were consistently valued low, whereas mobility indicators were valued differently across participants, indicating a lack of consensus. Smart charging was consistently valued higher than other charging techniques, independent of time period. The results also revealed that there are some distinct differences between the priorities of policymakers and service providers. Having a systematic overview of what aspects matter supports the policy discussion around EVs in the built environment.
The transition towards electric mobility is expected to take off the coming years, as more EV car models access the market and charging infrastructure is being expanded. The expansion of charging infrastructure will have to accelerate to keep pace with the fast-growing need for charging. The coming years will be marked by uncertainty regarding technological developments (batteries, range), charging technologies (e.g. fast charging, inductive), growth of car sharing and autonomous driving and impact on user preferences and charging behaviour Data management is key to the EV market and public parties involved: to be able to adapt quickly to changes and to reduce risks and costs. This paper describes the five most important preconditions for effective data management that allows stakeholders to monitor the performance of their charging infrastructure and to take informed decisions on rollout strategies based on data science research results.
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