Developers of charging infrastructure, be it public or private parties, are highly dependent on accurate utilization data in order to make informed decisions where and when to expand charging points. The Amsterdam University of Applied Sciences, in close cooperation with the municipalities of Amsterdam, Rotterdam, The Hague, Utrecht, and the Metropolitan Region of Amsterdam Electric, developed both the back- and front-end of a charging infrastructure assessment platform that processes and represents real-life charging data. Charging infrastructure planning and design methods described in the literature use geographic information system data, traffic flow data of non-EV vehicles, or geographical distributions of, for example, refueling stations for combustion engine vehicles. Only limited methods apply real-life charging data. Rolling out public charging infrastructure is a balancing act between stimulating the transition to zero-emission transport by enabling (candidate) EV drivers to charge, and limiting costly investments in public charging infrastructure. Five key performance indicators for charging infrastructure utilization are derived from literature, workshops, and discussions with practitioners. The paper describes the Data Warehouse architecture designed for processing large amounts of charging data, and the web-based assessment platform by which practitioners get access to relevant knowledge and information about the current performance of existing charging infrastructure represented by the key performance indicators developed. The platform allows stakeholders in the decision-making process of charging point installation to make informed decisions on where and how to expand the already existing charging infrastructure. The results are generalizable beyond the case study regions in the Netherlands and can serve the roll-out of charging infrastructure, both public and semi-public, all over the world.
During the COVID-19—related lockdowns (2020–2022), mobility patterns and charging needs were substantially affected. Policies such as work from home, lockdowns, and curfews reduced traffic and commuting significantly. This global pandemic may have also substantially changed mobility patterns on the long term and therefore the need for electric vehicle charging infrastructure. This paper analyzes changes in electric charging in the Netherlands for different user groups during different phases of the COVID-19 lockdown to assess the effects on EV charging needs. Charging needs dropped significantly during this period, which also changed the distribution of the load on the electricity grid throughout the day. Curfews affected the start times of charging sessions during peak hours of grid consumption. Infrastructure dedicated to commuters was used less intensively, and the charging needs of professional taxi drivers were drastically reduced during lockdown periods. These trends were partially observed during a post–lockdown measuring period of roughly 8 months, indicating a longer shift in mobility and charging patterns.
This paper presents data-driven insights from a case study that was carried out in an University EV charging plaza where EV charging demand is met with the combination of the University campus grid and installed solar capacity. First, we assessed the plaza dependency on the grid for meeting EV charging demand and intake of excess solar energy using the available dataset. By modifying the plaza network to accommodate a small approx. 50 kWh battery storage can significantly reduce the grid dependency of the plaza by approx. 30% compared to the present situation and can also increase the green energy utility for EV charging by 10-20%. Having an battery storage could also help overcome the limitations due to the campus grid capacity during EV charging peak demand by means of scheduling algorithms. Second, we assessed the utility rate of the plaza which indicated that the average utility of charging infrastructure is about 30% which has an increasing trend over the analysed period. The low utility and EV charging peak demand may be the result of current EV user behavior where the average idle time during charging sessions is found to be approx. 90 minutes. Reduction in idle time by one third may increase the capacity and utility of plaza by two to two and half times the forecasted daily demand. By having the campus grid capacity and user information may further help with effect EV demand forecasting and scheduling.
