An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm
Yongbing Xiang and
Xiaomin Yang
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Yongbing Xiang: School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
Xiaomin Yang: School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China
Energies, 2021, vol. 14, issue 4, 1-21
Abstract:
In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved ant colony optimization (ACO) algorithm and the equivalent fuel consumption minimization strategy (ECMS) considering mode switching. The proposed strategy adopts a two-layer structure. In the inner layer, the ECMS considering mode switching was adopted to optimize the working mode and working point, so as to achieve the goal of reducing fuel consumption. In the outer layer, aiming at the shortcomings of traditional ACO, the heuristic factor and adaptive volatilization factor were introduced. An improved ACO method was proposed to optimize the equivalent factor, so as to achieve the goal of reducing the deviation between the final value of SOC and the target value. In order to verify the effectiveness of the proposed algorithm, it is compared with the traditional ECMS strategy and the rule-based (RB) ECMS strategy. The simulation results show that the proposed energy management strategy combining an improved ACO algorithm with ECMS considering mode switching can reduce the energy consumption of the whole ship and control the battery power.
Keywords: diesel electric hybrid; ECMS; ACO; multi-objective optimization; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)
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