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Intelligent Control of HVAC Systems in Electric Buses

Sommer, Martin; Junk, Carolin; Rösch, Tobias; Sax, Eric

Abstract (englisch):
Battery electric buses (BEB) will increasingly replace buses with internal combustion engines in the fleets of transport companies. However, range prevents the application of BEB on all bus routes. Auxiliary consumers highly affect the range and the heating, ventilation and air conditioning (HVAC) system plays a major role within all. The high energy consumption of the HVAC system can possibly be reduced with intelligent control methods since their conventional counterparts guarantee compliance with specifications but do not consider energy consumptions. Thus, an energy-saving control is desired, which considers the minimization of energy consumption, but simultaneously complies with given specifications. To meet these requirements, following controllers were implemented: (1) model predictive control (MPC) and (2) reinforcement learning (RL) based control. This paper describes the implementation and application of both controllers on a Simulink model of a modern heat pump HVAC system and compares the results with PID control.



Originalveröffentlichung
DOI: 10.1007/978-3-030-74009-2_9
Zugehörige Institution(en) am KIT Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISBN: 978-3-030-74009-2
ISSN: 2194-5357, 2194-5365
KITopen-ID: 1000131797
Erschienen in Human Interaction, Emerging Technologies and Future Applications IV : Proceedings of the 4th International Conference on Human Interaction and Emerging Technologies: Future Applications (IHIET – AI 2021), April 28-30, 2021, Strasbourg, France. Ed.: T. Ahram
Veranstaltung 4th International Conference on Human Interaction and Emerging Technologies (IHIET 2021), Straßburg, Frankreich, 28.04.2021 – 30.04.2021
Auflage 1st ed.
Verlag Springer International Publishing
Seiten 68–75
Serie Advances in Intelligent Systems and Computing ; 1378
Vorab online veröffentlicht am 16.04.2021
Schlagwörter HVAC, Reinforcement Learning, Model predictive control
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