Delivered Hydrogen Pressure

Fuel cell electric vehicles (FCEVs) are an emerging technology that may help increase energy security and sustainability and decrease air pollution. These vehicles are now commercially available, but some obstacles must be overcome for them to be a viable alternative for consumers. Since FCEVs operate on hydrogen, a robust hydrogen refueling network will be essential to commercial acceptance, and standardization of key vehicle and infrastructure characteristics will help facilitate its development. One such attribute is delivered hydrogen pressure (DHP), which is the pressure at which hydrogen fuel is pumped from a refueling station into vehicles.

Hydrogen is not energy dense and therefore must be stored on board at high pressure to provide adequate driving range for FCEVs to compete with conventional vehicles. Higher storage pressures provide greater driving range and decrease refueling frequency. However, hydrogen refueling station capital costs—and, ultimately, the price of hydrogen to the consumer—increases with the pressure at which it is delivered. FCEV fuel tanks are currently designed to store hydrogen fuel safely up to a nominal working pressure (NWP) of 700 bar. However, it is less expensive to deliver hydrogen at lower pressures, such as 500 or 350 bar. So, it is important to determine the optimal delivered hydrogen pressure when deploying refueling infrastructure, so that capital costs are minimized while meeting consumer refueling requirements. The optimal pressure can depend on many variables such as vehicle fuel economy, refueling time, the number of refueling stations, and base hydrogen cost, and these factors evolve over time. This makes determining optimal DHP a complex calculation.

Hydrogen Optimal Pressure (HOP) Model

The Transportation Energy Evolution Modeling (TEEM) program at Oak Ridge National Laboratory (ORNL) has developed a model to determine the optimal delivered hydrogen pressure for refueling FCEV under prevailing conditions. The Hydrogen Optimal Pressure (HOP) model is an Excel-based model that attempts to minimize the sum of three perceived cost components:

  1. Delivered H2 cost
  2. Refueling inconvenience cost
  3. Range limitation cost

These costs are estimated based on three parameter types:

  1. Infrastructure: refueling speed, fuel availability, number of stations, station size, station utilization, etc.
  2. Vehicle-driver: fuel economy, annual miles driven, vehicle lifetime, annual fuel use, value of time, etc.
  3. Fleet-city: number of gasoline stations, number of vehicles in operation, hydrogen vehicle fleet penetration, hydrogen demand, city type, etc.

The HOP model is a flexible tool that can be used to analyze how hydrogen station profitability can be affected by station size, utilization, hydrogen pricing, and market penetration of FCEVs.

The HOP model was used in a study to (1) determine the optimal DHP by considering both infrastructure (supply) and consumer (demand) factors that are of stakeholder interest and (2) conduct case studies to provide useful insights into DHP strategies that reduce infrastructure cost, increase market acceptance, or both. The model and the study are described in detail in A Method for Determining the Optimal Delivered Hydrogen Pressure for Fuel Cell Electric Vehicles. This study is a collaboration among ORNL, Argonne National Laboratory, Chevron Corporation, and Ford Motor Company.

Determining Optimal DHP for Two Infrastructure Strategies

Fuel availability was modeled for two station deployment strategies, a cluster strategy and a region strategy.

  • Cluster strategy: A number of stations are deployed in a small city or geographic area that is projected to have a high concentration of FCEV drivers. Santa Monica, California, was used as an example of a cluster strategy in this study.
  • Region strategy: Stations are spread around a large region with the intention to benefit a large potential consumer base and provide greater station coverage for FCEV drivers. The region encompassing Los Angeles, Long Beach, and Santa Ana, California, was used in this study.

The study suggests that optimal DHP is very sensitivity to fuel availability, fuel economy, driving patterns, and value of time. Several of these factors are discussed below.

Effect of Fuel Availability and Driver Type

Drivers in the study were categorized by driving intensity and daily commute distance:

  • Frequent drivers (top 5%) with long commutes (FLC)
  • Frequent drivers with short commutes (FSC)
  • Average drivers (middle 50%) with long commutes (ALC)
  • Average drivers with short commutes (ASC)
  • Moderate drivers (bottom 45%) with long commutes (MLC)
  • Moderate drivers with short commutes (MSC)

DHP perceived cost was estimated for these six driver types for delivered pressures of 350, 500, and 700 bar. The study results indicate that, in the early market, refueling inconvenience is the biggest concern in the region strategy, while delivered hydrogen cost is a larger barrier in the cluster strategy. In both strategies, delivered hydrogen cost increases with DHP but decreases with annual driving distance. This means that selling FCEVs to frequent drivers in the early stage can increase hydrogen demand and station utilization and reduce hydrogen costs. A higher DHP reduces the refueling inconvenience cost more in the region strategy. Range limitation cost is a concern only for frequent short commute (FSC) drivers who often travel long distances away from hydrogen stations.

For the cluster strategy, the optimal DHP is 700 bar for frequent drivers, 500 bar for average intensity drivers, and 350 bar for moderate intensity drivers. However, in the region strategy, 700 bar is the optimal DHP for all six driver types.

Effect of Time Value

The value of travel time varies significantly among consumers. To examine its effect on the optimal DHP, the value of time was assumed to vary between $30 and $90 per hour, and the resulting marginal consumer benefit (MCB) was compared to the marginal delivered hydrogen cost. As shown, the benefit curve shifts up as value of time increases, leading to higher optimal DHP. This means that higher time value consumers demand a higher DHP for a longer driving range. For the cluster strategy, a change in time value from $30 to $90 per hour increases optimal DHP from just under 350 bar to over 550 bar. This is a significant effect.

This impact is even more significant for the region strategy. However, given the practical pressure cap at 700 bar, time value spread is less important, since a DHP of 700 bar is optimal for cases. This suggests a need to investigate higher density onboard storage technologies, even though they can be more costly.

Effect of Fuel Economy

Better fuel economy reduces the optimal DHP in both strategies but for different reasons. In the cluster strategy, higher fuel economies lead to less fuel demand and a lower utilization of refueling stations, and thus higher delivered hydrogen cost, making pressure upgrades more expensive. With the region strategy, higher fuel economies reduce refueling frequency and the need to maximize driving range, making pressure upgrades less worthwhile.

Fuel economy affects the regret value of the 700-bar DHP, which is defined as the amount by which the total perceived cost of 700-bar hydrogen exceeds that of the theoretical DHP. As fuel economy increases (see figure), the regret value decreases in the region strategy and increases in the cluster strategy.

The 700-bar regret values for the cluster strategy are overall much smaller than for the region strategy, reflecting the more urgent need for better fuel economies to extend the driving range and reduce the much larger refueling inconvenience cost in the region strategy than in the cluster strategy.

Optimal DHP Over Time

The optimal DHP depends on a range of factors, all of which evolve over time. Therefore, it is important to understand this evolution of optimal DHP in scenarios where those factors are reasonably projected. A scenario of three three-year phases of market penetrations was constructed for both the cluster and region strategies, and these scenarios analyzed by estimating non-optimality regret. Non-optimality regret is the regret of choosing one the three DHP alternatives (350, 500, or 700 bar) relative to the optimal DHP.

As shown in the two graphs below, a DHP of 700 bar is the best choice (indicated by lowest regret cost) for most of the market phases for both the cluster and region strategies, while 500 bar is the best choice for the earliest phase in the cluster strategy.

Key Findings

  • A DHP of 700 bar is a robustly better choice than 350 or 500 bar for the region strategy, regardless of fuel availability, FCEV adoption, driver types (driving intensity and commute length), time values, and fuel economies.
  • A DHP of 350 or 500 bar can be the best option for a cluster strategy under certain assumptions of driving patterns and time value.
  • Although a higher DHP can be more desirable for the cluster strategy under some circumstances, the urgency for pressure upgrades is significantly less than in the region strategy. The cluster strategy allows a small number of stations to achieve a high level of refueling convenience and thus increases consumer tolerance for decreased driving range. It also avoids the uneconomical situation of having many underutilized or small-size stations.
  • Optimal DHP is very sensitive to fuel availability, fuel economy, driving patterns, and time value.
  • The appeal of 700-bar (or even higher) DHP is more obvious during the early market stages, when the number of hydrogen stations is limited and early FCEV consumers likely have higher time value and may be willing to pay more for the increased range with higher DHP.

Media Coverage

Determining Optimal Delivered Hydrogen Pressure. Science Trends. 4 April 2018.