Modified particle swarm optimization with chaotic attraction strategy for modular design of hybrid powertrains

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Modified particle swarm optimization with chaotic attraction strategy for modular design of hybrid powertrains. / Zhou, Quan; He, Yinglong; Zhao, Dezong; Li, Ji; Li, Yanfei; Williams, Huw; Xu, Hongming.

In: IEEE Transactions on Transportation Electrification, 07.08.2020.

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@article{75fa36729aa44eec840e0ab701d21e50,
title = "Modified particle swarm optimization with chaotic attraction strategy for modular design of hybrid powertrains",
abstract = "This paper proposes a new modular design method for hybrid powertrains using a modified accelerated particle swarm optimization (MAPSO) algorithm. The method determines the optimal combination of component specifications and control parameters, where the component specifications include integer variables (e.g. the number of battery modules). A unified chaotic attraction strategy for MAPSO is developed based on a logistic map to improve the probability of achieving the global optimal result. Pareto analysis is carried out to identify the weighting value for the trade-off in modular design. The Comprehensive Reputation Score (CRS), considering both Monte Carlo results and the probability of achieving global optima, is employed to evaluate the advantageous of the MAPSO compared to conventional PSO and four other PSO variants. The MAPSO is verified as the best because it has the highest CRS. Both two-level and simultaneous methods for modular design are developed with the MAPSO, where the former firstly operates component sizing at the level-1 and then conducts control optimization at the level-2, the later optimizes the size and control simultaneously. Compared to the two-level method, the simultaneous method achieves 7% higher cost function value and saves 50% time.",
keywords = "Batteries, Chaotic attraction, Engines, Fuels, Hybrid vehicle, Ice, Integer variables, Mechanical power transmission, Modular design, Optimization, Particle swarm optimization",
author = "Quan Zhou and Yinglong He and Dezong Zhao and Ji Li and Yanfei Li and Huw Williams and Hongming Xu",
year = "2020",
month = aug,
day = "7",
doi = "10.1109/TTE.2020.3014688",
language = "English",
journal = "IEEE Transactions on Transportation Electrification",
issn = "2332-7782",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

}

RIS

TY - JOUR

T1 - Modified particle swarm optimization with chaotic attraction strategy for modular design of hybrid powertrains

AU - Zhou, Quan

AU - He, Yinglong

AU - Zhao, Dezong

AU - Li, Ji

AU - Li, Yanfei

AU - Williams, Huw

AU - Xu, Hongming

PY - 2020/8/7

Y1 - 2020/8/7

N2 - This paper proposes a new modular design method for hybrid powertrains using a modified accelerated particle swarm optimization (MAPSO) algorithm. The method determines the optimal combination of component specifications and control parameters, where the component specifications include integer variables (e.g. the number of battery modules). A unified chaotic attraction strategy for MAPSO is developed based on a logistic map to improve the probability of achieving the global optimal result. Pareto analysis is carried out to identify the weighting value for the trade-off in modular design. The Comprehensive Reputation Score (CRS), considering both Monte Carlo results and the probability of achieving global optima, is employed to evaluate the advantageous of the MAPSO compared to conventional PSO and four other PSO variants. The MAPSO is verified as the best because it has the highest CRS. Both two-level and simultaneous methods for modular design are developed with the MAPSO, where the former firstly operates component sizing at the level-1 and then conducts control optimization at the level-2, the later optimizes the size and control simultaneously. Compared to the two-level method, the simultaneous method achieves 7% higher cost function value and saves 50% time.

AB - This paper proposes a new modular design method for hybrid powertrains using a modified accelerated particle swarm optimization (MAPSO) algorithm. The method determines the optimal combination of component specifications and control parameters, where the component specifications include integer variables (e.g. the number of battery modules). A unified chaotic attraction strategy for MAPSO is developed based on a logistic map to improve the probability of achieving the global optimal result. Pareto analysis is carried out to identify the weighting value for the trade-off in modular design. The Comprehensive Reputation Score (CRS), considering both Monte Carlo results and the probability of achieving global optima, is employed to evaluate the advantageous of the MAPSO compared to conventional PSO and four other PSO variants. The MAPSO is verified as the best because it has the highest CRS. Both two-level and simultaneous methods for modular design are developed with the MAPSO, where the former firstly operates component sizing at the level-1 and then conducts control optimization at the level-2, the later optimizes the size and control simultaneously. Compared to the two-level method, the simultaneous method achieves 7% higher cost function value and saves 50% time.

KW - Batteries

KW - Chaotic attraction

KW - Engines

KW - Fuels

KW - Hybrid vehicle

KW - Ice

KW - Integer variables

KW - Mechanical power transmission

KW - Modular design

KW - Optimization

KW - Particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=85089381344&partnerID=8YFLogxK

U2 - 10.1109/TTE.2020.3014688

DO - 10.1109/TTE.2020.3014688

M3 - Article

JO - IEEE Transactions on Transportation Electrification

JF - IEEE Transactions on Transportation Electrification

SN - 2332-7782

ER -