Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs

Jiajun Liu, Tianxu Jin, Li Liu, Yajue Chen and Kun Yuan
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Jiajun Liu: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Tianxu Jin: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Li Liu: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Yajue Chen: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Kun Yuan: School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Sustainability, 2017, vol. 9, issue 10, 1-18

Abstract: Energy storage systems (ESS) play an important role in the performance of mining vehicles. A hybrid ESS combining both batteries (BTs) and supercapacitors (SCs) is one of the most promising solutions. As a case study, this paper discusses the optimal hybrid ESS sizing and energy management strategy (EMS) of 14-ton underground load-haul-dump vehicles (LHDs). Three novel contributions are added to the relevant literature. First, a multi-objective optimization is formulated regarding energy consumption and the total cost of a hybrid ESS, which are the key factors of LHDs, and a battery capacity degradation model is used. During the process, dynamic programming (DP)-based EMS is employed to obtain the optimal energy consumption and hybrid ESS power profiles. Second, a 10-year life cycle cost model of a hybrid ESS for LHDs is established to calculate the total cost, including capital cost, operating cost, and replacement cost. According to the optimization results, three solutions chosen from the Pareto front are compared comprehensively, and the optimal one is selected. Finally, the optimal and battery-only options are compared quantitatively using the same objectives, and the hybrid ESS is found to be a more economical and efficient option.

Keywords: load-haul-dump vehicle; multi-objective optimization; hybrid energy storage system; energy management strategy; parameter sizing; battery capacity loss (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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