The understanding of charging behavior has been recognized as a crucial element in optimizing roll out of charging infrastructure. While current literature provides charging choices and categorizations of charging behavior, these seem oversimplified and limitedly based on charging data.

In this research we provide a typology of charging behavior and electric vehicle user types based on 4.9 million charging transactions from January 2017 until March 2019 and 27,000 users on 7079 Charging Points the public level 2 charging infrastructure of 4 largest cities and metropolitan areas of the Netherlands.

We overcome predefined stereotypical expectations of user behavior by using a bottom-up data driven two-step clustering approach that first clusters charging sessions and thereafter portfolios of charging sessions per user. From the first clustering (Gaussian Mixture) 13 distinct charging session types were found; 7 types of daytime charging sessions (4 short, 3 medium duration) and 6 types of overnight charging sessions. The second clustering (Partition Around Medoids) clustering result in 9 user types based on their distinct portfolio of charging session types. We found (i) 3 daytime office hours charging user types (ii) 3 overnight user types and (iii) 3 non-typical user types (mixed day and overnight chargers, visitors and car sharing). Three user types show significant peaks at larger battery sizes which affects the time between sessions. Results show that none of the user types display solely stereotypical behavior as the range of behaviors is more varied and more subtle. Analysis of population composition over time revealed that large battery users increase over time in the population. From this we expect that shifts charging portfolios will be observed in future, while the types of charging remain stable.

对充电行为的了解已被认为是优化充电基础设施推出的关键要素。尽管当前的文献提供了充电选择和充电行为的分类，但这些似乎过于简单化并且有限地基于充电数据。

在此研究中，我们提供了充电行为和电动汽车用户类型的分类学，该数据基于2017年1月至2019年3月的490万次充电交易，以及7079个充电点上的27,000名用户，这些充电点是荷兰4个最大城市和大城市地区的公共2级充电基础设施。

通过使用自下而上的数据驱动的两步群集方法，我们克服了预先定义的用户行为定型预期，该方法首先对计费会话进行聚类，然后对每个用户的计费会话组合进行聚类。从第一个聚类（高斯混合）中，发现了13种不同的计费会话类型。7种类型的日间充电会话（4个简短，3个中等持续时间）和6种类型的隔夜充电会话。第二个聚类（围绕Medoids进行分区）聚类基于9个用户类型（基于他们不同的计费会话类型组合）。我们发现（i）3个白天办公时间对用户类型收费（ii）3个隔夜用户类型和（iii）3个非典型用户类型（混合日间和隔夜充电器，访客和汽车共享）。三种用户类型在较大的电池尺寸下显示出明显的峰值，这会影响会话之间的时间。结果表明，没有一种用户类型仅显示刻板印象的行为，因为行为的范围更加多样且更加微妙。随时间推移对人口构成的分析表明，随着时间的推移，大型电池用户会增加。因此，我们预计将来会观察到转变的充电产品组合，同时充电类型保持稳定。