An integrated quantitative-qualitative study to monitor the utilization and assess the perception of hydrogen fueling stations

Zero-emission vehicle (ZEV) adoption is one of the critical solutions to decarbonize the transportation sector. Among the ZEV fleet in the US, battery electric vehicles (BEV) have been leading the market penetration. However, hydrogen fuel cell electric vehicles (FCEV) have also been increasingly adopted in recent years. Although both technologies have challenges with infrastructure, unlike BEVs that have multiple venues for charging (home, work or public), FCEVs rely solely on fueling at public hydrogen stations, and their availability is a significant factor before the vehicle purchase. Therefore, for the success of FCEV adoption, a need to monitor and understand the driver satisfaction of these stations is extremely critical. This research project introduces a quantitative-qualitative approach for continuous monitoring of hydrogen stations based on the station utilization patterns and to assess their preferability based on driver experiences. To illustrate a proof-of-concept, we collected the hourly utilization data of all the hydrogen fueling stations in California for three months. The time-series data was used to develop a capacity-independent term called “Normalized Relative Utilization Index” (NRUI) that encapsulates the utilization pattern of each station to a single metric. We spatially regressed this metric over the number of FCEVs present in the neighborhood to deduce the relationship. We designed a survey to obtain the refueling experiences of FCEV drivers, where about 100 participants responded with their station preferences. Their answers were used to validate the quantitative approach and identify a “Satisfactory Utilization Range” (SUR) of stations which are preferred by most drivers. Though this project illustrates the analysis of data collected over a small period, this approach is easily scalable with new station installations and can be implemented as a continuous monitoring system with real-time station utilization data. We believe this demand-focused approach could complement the existing supply-side monitoring methods on station performance to provide a smoother fueling experience to drivers. We are also releasing the hourly station capacity dataset that was collected as a part of this study to the research community.