Adaptive energy management strategy for a heavy-duty truck with a P2-hybrid topology

Schulze, Sven; Mühleisen, Martin; Feyerl, Günter; Pischinger, Stefan

doi:10.1007/978-3-658-21194-3_9

Cited by 1 publication

(4 citation statements)

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“…The SOC trajectories indicated that the useable depth of discharge (DOD) of the battery was not utilized. This system behavior was also observed in [24]. However, under real driving conditions, the battery will experience the total DOD several times per driving cycle due to the height profile [25].…”

Section: Introductionsupporting

confidence: 55%

“…This system behavior was also observed in [24]. However, under real driving conditions, the battery will experience the total DOD several times per driving cycle due to the height profile [25]. In [26], a predictive EMS based on a global optimal approach was tested under real driving conditions.…”

Section: Introductionmentioning

confidence: 66%

“…The increased power demand shifts the operating points of the ICE close to, or even above, the sweet spot regarding efficiency. The advantage of the optimization-based EMS diminishes in these regions of operation, as shown in [25]. A final comparison of the Prd-AECMS against DP shows that only 0.3% (Vb) further fuel saving potential is achievable for high vehicle masses.…”

Section: Impact Of Predictive Discharge-pbdismentioning

confidence: 99%

“…A fuel saving potential of up to 2% was obtained compared with a heuristic approach. In [25], it was shown that approaches based on ECMS lack the ability to optimize the efficiency of the ICE and maximize the recuperated energy at the same time for HD application. In addition to that, it was observed that an adaptive EMS, as formulated in [27], is required due to distinct changes of the height profile.…”

Section: Introductionmentioning

confidence: 99%

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Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions

2023

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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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Section: Introductionsupporting

confidence: 55%