A Combined Energy Management Strategy for Heavy-Duty Trucks Based on Global Traffic Information Optimization

Haishan Wu (), Liang Li and Xiangyu Wang
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Haishan Wu: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Liang Li: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Xiangyu Wang: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

Sustainability, 2025, vol. 17, issue 14, 1-24

Abstract: As public concern over environmental pollution and the urgent need for sustainable development grow, the popularity of new-energy vehicles has increased. Hybrid electric vehicles (HEVs) represent a significant segment of this movement, undergoing robust development and playing an important role in the global transition towards sustainable mobility. Among the various factors affecting the fuel economy of HEVs, energy management strategies (EMSs) are particularly critical. With continuous advancements in vehicle communication technology, vehicles are now equipped to gather real-time traffic information. In response to this evolution, this paper proposes an optimization method for the adaptive equivalent consumption minimization strategy (A-ECMS) equivalent factor that incorporates traffic information and efficient optimization algorithms. Building on this foundation, the proposed method integrates the charge depleting–charge sustaining (CD-CS) strategy to create a combined EMS that leverages traffic information. This approach employs the CD-CS strategy to facilitate vehicle operation in the absence of comprehensive global traffic information. However, when adequate global information is available, it utilizes both the CD-CS strategy and the A-ECMS for vehicle control. Simulation results indicate that this combined strategy demonstrates effective performance, achieving fuel consumption reductions of 5.85% compared with the CD-CS strategy under the China heavy-duty truck cycle, 4.69% under the real vehicle data cycle, and 3.99% under the custom driving cycle.

Keywords: hybrid electric vehicles; energy management; parameter optimization; traffic information; Nutcracker Optimization Algorithm; connected vehicles (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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