@Article{ee.2023.044042,
AUTHOR = {Yixuan Yu, Yulin Wang, Qingcheng Li, Bowen Jiao},
TITLE = {A Predictive Energy Management Strategies for Mining Dump Trucks},
JOURNAL = {Energy Engineering},
VOLUME = {121},
YEAR = {2024},
NUMBER = {3},
PAGES = {769--788},
URL = {http://www.techscience.com/energy/v121n3/55504},
ISSN = {1546-0118},
ABSTRACT = {The plug-in hybrid vehicles (PHEV) technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks. Meanwhile, plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies (EMS). Therefore, a series hybrid system is constructed based on a 100-ton mining dump truck in this paper. And inspired by the dynamic programming (DP) algorithm, a predictive equivalent consumption minimization strategy (P-ECMS) based on the DP optimization result is proposed. Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm, the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor (EF). Finally, applying the equivalent consumption minimization strategy (ECMS) realizes real-time control. The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km, which is 10.9% less than that of the common CDCS strategy (169.3 L/100 km), and achieves 99.47% of the fuel saving effect of the DP strategy(150 L/100 km).},
DOI = {10.32604/ee.2023.044042}
}