Multi-objective Slime Mold Algorithm: A Slime Mold Approach Using Multi-objective Optimization for Parallel Hybrid Power System

Owing to the importance of the fuel economy and emission performance of parallel hybrid electric vehicles (PHEVs), parallel hybrid systems are a critical research topic in the vehicle industry. However, previous research has endeavored to reflect the real situation of optimization objectives, which...

Full description

Saved in:
Bibliographic Details
Published in:Sensors and materials 2022-01, Vol.34 (10), p.3837
Main Authors: Zhu, Tianjun, Wan, Hegao, Ouyang, Zhuang, Wu, Tunglung, Liang, Jianguo, Li, Weihao, Li, Bin, Han, Shiting
Format: Article
Language:English
Subjects:
Algorithms
Carbon monoxide
Consumption
Emissions
Fuel consumption
Fuel economy
Hybrid electric vehicles
Hybrid systems
Mold
Multiple objective analysis
Nitrogen oxides
Optimization
Slime
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Owing to the importance of the fuel economy and emission performance of parallel hybrid electric vehicles (PHEVs), parallel hybrid systems are a critical research topic in the vehicle industry. However, previous research has endeavored to reflect the real situation of optimization objectives, which tends to result in low-quality solutions. To address this issue, a novel multi-objective optimization approach based on the multi-objective slime mold algorithm (MOSMA) is used to optimize the hybrid power system of a PHEV. Then, with the objectives of reducing fuel consumption, CO emission, and the sum of HC and NOx emissions, a mathematical model for three-objective optimization is established. The six parameters affecting the hybrid system performance are optimized by considering the dynamic power performance and battery charge state balance constraints. Finally, ADVISOR is used as a simulation platform to verify the optimization results. The results before and after optimization demonstrate that MOSMA can effectively address the multi-objective optimization of hybrid vehicles; concretely, the fuel consumption and the sum of HC and NOx emissions are reduced by 9.5 and 7.4%, respectively. More notably, the decrease in CO emission is as much as 34.4%.
ISSN:0914-4935
2435-0869
DOI:10.18494/SAM4020