Numerical Assessment of Auto-Adaptive Energy Management Strategies Based on SOC Feedback, Driving Pattern Recognition and Prediction Techniques

Alessandro Zanelli, Emanuele Servetto, Philippe De Araujo, Sujeet Nagaraj Vankayala and Adam Vondrak
Additional contact information
Alessandro Zanelli: POWERTECH Engineering S.r.l., 10127 Torino, Italy
Emanuele Servetto: POWERTECH Engineering S.r.l., 10127 Torino, Italy
Philippe De Araujo: Garrett Motion France, 88150 Capavenir Vosges, France
Sujeet Nagaraj Vankayala: Garrett Motion Engineering Solutions Private Ltd., Bangalore 560103, India
Adam Vondrak: Garrett Motion s.r.o., 627 00 Slatina, Czech Republic

Energies, 2022, vol. 15, issue 11, 1-22

Abstract: The Equivalent Consumption Minimization Strategy (ECMS) is a well-known control strategy for the definition of optimal power-split in hybrid-electric vehicles, because of its effectiveness and reduced calibration effort. In this kind of Energy Management Systems (EMS), the correct identification of an equivalence factor ( K ), which translates electric power in equivalent fuel consumption, is of paramount importance. To guarantee charge sustaining operation, the K factor must be adjusted to different mission profiles. Adaptive ECMS (A-ECMS) techniques have thus been introduced, which automatically determine the optimal equivalence factor based on the vehicle mission. The aim of this research activity is to assess the potential in terms of fuel consumption and charge sustainability of different A-ECMS techniques on a gasoline hybrid-electric passenger car. First, the 0D vehicle and powertrain model was developed in the commercial CAE software GT-SUITE. An ECMS-based EMS was used to control the baseline powertrain and three alternative versions of an auto-adaptive algorithm were implemented on top of that. The first A-ECMS under study was based on feedback from the battery State of Charge, while the second and third on a Driving Pattern Recognition/Prediction algorithm. Fuel consumption was assessed using the New European Driving Cycle (NEDC), the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Real Driving Emissions (RDE) driving cycles by means of numerical simulation. A potential improvement of up to 4% Fuel Economy was ultimately achieved on an RDE driving cycle with respect to the baseline ECMS.

Keywords: CAE; vehicle simulation; Energy Management Systems; ECMS; RDE; machine learning; Driving Pattern Recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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