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.
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.
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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