Improving the estimation of the hydrogen storage density in a cryo-compressed hydrogen tank using an unscented Kalman filter

Responding to the urgency to reduce emissions, there is a need to transition toward sustainable fuels. hydrogen ( ) is a promising alternative, particularly for heavy-duty mobility, such as long-haul trucks. Therefore, cryo-compressed hydrogen ( ), where hydrogen ( ) is stored at high pressure and cryogenic temperatures, offers high density, which extends driving ranges. During driving, exact knowledge of the remaining is crucial for scheduling trips. Due to the system’s working conditions, this remains challenging, as neither level nor pressure measurement can indicate the remaining amount. Available sensors for temperature and pressure can be applied, but may lead to significant errors. The presented study addresses this unique challenge resulting from the use of cryogenic in mobility. Therefore, soft-sensing methods are studied to estimate the storage density in the tank. The measurement accuracy is significantly improved by using flow sensors or by combining all available sensors and applying a square root unscented Kalman filter (SRUKF) that fuses pressure, temperature, and flow measurements. Due to a lack of experimental data, a previously developed thermodynamic model is employed to simulate typical drive cycles and generate generic sensor data for proof-of-concept evaluation of the estimation methods. While flow-based methods show good performance, they rely on accurate sensors. Thus, this paper first applies Kalman filters to this novel challenge, demonstrating robust performance and enabling accurate estimation of the storage density. This approach enables precise knowledge of the driving range and supports optimal drive cycle strategies, potentially boosting -powered heavy-duty transportation. • High accuracy density estimation of onboard cryo-compressed hydrogen storage • Implementation of a robust square root unscented Kalman filter • Estimation enhancement by combining thermodynamic modeling and measurements • Simulation-driven insights into sensor inaccuracies and system reliability