A method according to one embodiment of the present disclosure comprises the steps of: transmitting a control request regarding the state of hydrogen in a mobility tank of a hydrogen vehicle to the hydrogen vehicle; obtaining on-site data on the change in the state of hydrogen in the mobility tank as feedback in response to the control request; and updating a model of the change in the state of hydrogen in the mobility tank in response to the control request based on the on-site data of the change in the state of hydrogen in the mobility tank in response to the control request.

A space electric propulsion system and a xenon filling method therefor are provided, suitable for a filling process of an electric propulsion system using high-purity xenon as a working medium. By reasonably designing a filling process, the precision of a xenon filling amount is ensured, and xenon filling can be performed without weighing the whole spacecraft. This method features a simple and efficient filling device, short preparation time, precise and controllable filling process, as well as safety and convenience, which can effectively meet the requirements for a high-purity xenon rapid filling task of a satellite.

The method for performing a hydrogen fueling simulation comprises obtaining information on at least one of a capacity of a largest hydrogen tank equipped for hydrogen fueling in a real-case scenario, a total capacity of hydrogen tanks, a supply fuel temperature of a fueling line, a supply fuel pressure, and a pressure of a hydrogen tank; extracting, from a predefined lookup table, a first pressure loss coefficient corresponding to a fueling line pressure loss coefficient that satisfies a predetermined condition in a reference case and a second pressure loss coefficient corresponding to a pressure loss coefficient for SAE test configuration in H2Fills, based on the obtained information; calculating an average density in the fueling line; calculating a third pressure loss coefficient based on the average density, a measured mass flow rate, and a pressure difference between a fuel supply pressure and a hydrogen tank pressure in the fueling line.

Aspects of the disclosure are directed to an optimization model for storing liquid hydrogen to power fuel cells in data centers. The optimization model can be based on hydrogen fuel consumption rates in the data center, refueling rates from vendors, refueling response time, storage tank area constraints in the data center, and/or logistical refueling constraints. The optimization model can allow for providing sufficient fuel within a constrained space for backup power in the data center, such as when an emergency arises.

A method for assisting the management of a vessel comprising at least one tank configured to contain liquefied gas and a vapor phase treatment system capable of sending boil-off gas exiting the tank to a propulsion engine of the vessel or to a gas combustion unit on board the vessel and capable of extracting a portion of the liquid phase contained in the tank and of evaporating this portion in order to send it to the propulsion engine. The method comprises: generating at least one tank management scenario defining an evolution of the pressure of the gas phase contained in the tank along a path of the vessel; computing a cost function that at least depends on a total amount of boil-off gas generated in the tank along the path; and displaying to a user the tank management scenario as a function of the computed cost function.

An on-board device for storing pressurized gas, for example hydrogen. The device includes a tank, a sensor for measuring the pressure of the gas in the tank, a first sensor for measuring the ambient temperature, a computer having an electronic system equipped with a microprocessor for acquiring and processing data. In particular, the computer is configured to: i) receive and process measurements from the sensors, and ii) calculate a theoretical minimum temperature and a theoretical maximum temperature of the gas in the tank on the basis of the measurements from the sensors and by using at least one predefined predictive model, the computer also being configured to approximate the temperature of the gas in the tank to a value of between the theoretical minimum temperature and the theoretical maximum temperature, or to a value equal to one of either the theoretical minimum temperature or the theoretical maximum temperature.