KIT | KIT-Bibliothek | Impressum | Datenschutz

A Generic Prediction Approach for Optimal Control of Electrified Vehicles Using Artificial Intelligence

Deufel, Felix; Gießler, Martin ORCID iD icon 1; Gauterin, Frank ORCID iD icon 1
1 Institut für Fahrzeugsystemtechnik (FAST), Karlsruher Institut für Technologie (KIT)

Abstract:

n order to further increase the efficiency of electrified vehicle drives, various predictive energy management strategies (driving strategies) have been developed. Therefore, a generic prediction approach is worked out in this paper, which enables a robust prediction of all traction torque-relevant variables for such strategies. It is intended to be useful for various types of electrification; however, the focus of this work is to the application in hybrid electric vehicles. In contrast to other approaches, no additional information (e.g., telemetry data) is required and thus a reliable prediction is guaranteed at all times. In particular, approaches from the fields of stochastics and artificial intelligence have proven to be effective for such purposes. Within the scope of this work, both so-called Markov Chains and Neural Networks are applied to predict real driving profiles within a required time horizon. Therefore, at first, a detailed analysis of the driver-specific ride characteristics is performed to ensure that real-world operation is represented appropriately. Next, the two models are implemented and the calibration is further discussed. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000149954
Veröffentlicht am 15.08.2022
Originalveröffentlichung
DOI: 10.3390/vehicles4010012
Scopus
Zitationen: 5
Dimensions
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Fahrzeugsystemtechnik (FAST)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2624-8921
KITopen-ID: 1000149954
Erschienen in Vehicles
Verlag MDPI AG (MDPI AG)
Band 4
Heft 1
Seiten 182–198
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 01.03.2022
Schlagwörter artificial neural networks; Markov chains; electrified powertrains; model predictive control; real driving cycles
Nachgewiesen in Scopus
Dimensions
Globale Ziele für nachhaltige Entwicklung Ziel 7 – Bezahlbare und saubere Energie
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page