Optimization of Fuel Cell Hybrid System Configuration via Modified Multi-Objective Particle Swarm Algorithm

This paper presents a novel approach to the capacity allocation problem in fuel cell hybrid vehicles. It introduces a multi-objective evolutionary algorithm nested Dynamic Programming (DP) strategy aimed at minimizing both manufacturing and operating costs. The outer loop employs a fast-converging M...

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Bibliographic Details
Published in2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA) pp. 499 - 503
Main Authors Liu, Yingfang, Sun, Zhendong, Chen, Zonghai
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.07.2024
Subjects
configuration optimisation
Convergence
Costs
Energy management
fuel cell hybrid power systems
Fuel cells
Manufacturing
Mechanical power transmission
MOPSO based on competitive mechanisms
Optimization
Particle swarm optimization
Resource management
Systems simulation
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Summary:This paper presents a novel approach to the capacity allocation problem in fuel cell hybrid vehicles. It introduces a multi-objective evolutionary algorithm nested Dynamic Programming (DP) strategy aimed at minimizing both manufacturing and operating costs. The outer loop employs a fast-converging Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on competitive mechanisms for parameter matching, while the inner loop employs DP for energy management. The effectiveness of the proposed method is validated through simulation under specific operating conditions. Comparative analysis with traditional MOPSO demonstrates superior performance in terms of solution set diversity and convergence, affirming the efficacy of the proposed approach.
DOI:10.1109/CCSSTA62096.2024.10691839