An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

Antonio Galvagno, Umberto Previti, Fabio Famoso and Sebastian Brusca
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Antonio Galvagno: Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy
Umberto Previti: Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy
Fabio Famoso: Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy
Sebastian Brusca: Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy

Energies, 2021, vol. 14, issue 6, 1-16

Abstract: The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.

Keywords: HEV; WLTC; Google Maps traffic levels; driving cycles; passenger car; numerical model (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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