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Advanced ECMS for hybrid electric heavy-duty trucks with predictive battery discharge and adaptive operating strategy under real driving conditions

  • To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.

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Author:Sven Schulze, Günter Feyerl, Stefan Pischinger
Parent Title (English):Energies
Place of publication:Basel
Document Type:Article
Year of Completion:2023
Date of the Publication (Server):2023/07/24
Tag:CO2 emission reduction targets; Driving cycle recognition; ECMS; Energy management strategies; Predictive battery discharge
Length:29 Seiten, Art. Nr.: 5171
The article belongs to the Special Issue "Energy Management Strategies of Electrified Vehicles toward the Real-World Driving".
Institutes:FH Aachen / Fachbereich Luft- und Raumfahrttechnik
FH Aachen / ECSM European Center for Sustainable Mobility
collections:Verlag / MDPI
Open Access / Gold
Licence (German):License LogoCreative Commons - Namensnennung