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A Predictive Energy Management Strategies for Mining Dump Trucks

Yixuan Yu, Yulin Wang*, Qingcheng Li, Bowen Jiao

College of Mechanical and Electrical Engineering, Qingdao University, Qingdao, 266071, China

* Corresponding Author: Yulin Wang. Email: email

Energy Engineering 2024, 121(3), 769-788. https://doi.org/10.32604/ee.2023.044042

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).

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Cite This Article

Yu, Y., Wang, Y., Li, Q., Jiao, B. (2024). A Predictive Energy Management Strategies for Mining Dump Trucks. Energy Engineering, 121(3), 769–788.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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