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M. Fallmann, A. Poks, M. Kozek:
"Hybrid model-based online estimation of air temperature in mobile small-scale cooling chambers";
Applied Thermal Engineering, 208 (2022), 118147.

Mobile, small-scale refrigeration applications for last-mile deliveries have gained increased importance in
recent years. As they face disturbances and frequent door openings more extensively than long-distance
transport, reliable temperature information is crucial for control concepts to comply with temperature
regulations and increase efficiency. For desired ruggedness, sensors are typically integrated within the cooling
unit, yielding substantial deviations between actual air temperature in the cooling chamber and measured one
- particularly in periods of altered airflow conditions resulting from fan switching and the actual door opening
status. This article introduces and compares two hybrid, online estimation procedures to overcome this issue:
firstly, a Kalman-filter approach based on a simple lumped physical heat transfer model, and secondly, a graybox-
model approach resulting from a realizable inversion of the physical model. Experimental investigations
of typical operating profiles provide 14 h of real-world data to parameterize (11.75 h data length), validate
(2.25 h data length), and compare both estimators. The proposed concepts are based on a sensor setup available
in state-of-the-art system architectures and provide satisfactory temperature estimates with more than 83%
overall fit. As the algorithms provide comprehensive process insight independent of the actual operating
condition, sophisticated control schemes can be built upon the concept.

Model-based estimation Kalman filter Hybrid system Physical modeling Gray-box modeling Refrigeration

http://dx.doi.org/10.1016/j.applthermaleng.2022.118147

https://publik.tuwien.ac.at/files/publik_303519.pdf