Research on Multi-Objective Parameter Matching and Stepwise Energy Management Strategies for Hybrid Energy Storage Systems

Electric vehicle technologies present promising solutions for achieving energy conservation and emission reduction goals. However, efficiently distributing power across hybrid energy storage systems (HESSs) remains a major challenge in enhancing overall system performance. To address this, this pape...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Energies (Basel) Ročník 18; číslo 6; s. 1354
Hlavní autori: Xu, Wenna, Huang, Hao, Wang, Chun, Hu, Yixin, Gao, Xinmei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.03.2025
Predmet:
Accuracy
Adaptability
Air quality management
Algorithms
Artificial intelligence
Batteries
Dynamic programming
Electric vehicles
Energy efficiency
Energy management
Energy management systems
Energy storage
Energy use
Environmental aspects
Flexibility
Genetic algorithms
hybrid power system
Lithium
Mathematical models
Mathematical optimization
Methods
Optimization
parameter matching method
stepwise-rule optimization energy management strategy
China
ISSN:1996-1073, 1996-1073
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Electric vehicle technologies present promising solutions for achieving energy conservation and emission reduction goals. However, efficiently distributing power across hybrid energy storage systems (HESSs) remains a major challenge in enhancing overall system performance. To address this, this paper proposes an energy management strategy (EMS) based on stepwise rules optimized by Particle Swarm Optimization (PSO). The approach begins by applying a multi-objective optimization method, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to fine-tune the parameters of lithium-ion batteries and ultracapacitors for an optimal balance in system performance. Additionally, an innovative stepwise-based EMS has been designed using adaptive PSO. This strategy builds a real-time control mechanism by dynamically adjusting the power distribution gradient threshold, taking into account the compensation for the state of charge (SOC). Comparative analysis across three typical operating conditions—urban, suburban, and highway—demonstrates that the stepwise-rule optimized strategy reduces the energy consumption of the HESS by 3.19%, 7.9%, and 5.37%.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en18061354