A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization DOI Creative Commons
Wuke Li, Ying Xiong, Shiqi Zhang

и другие.

World Electric Vehicle Journal, Год журнала: 2025, Номер 16(5), С. 268 - 268

Опубликована: Май 14, 2025

This paper shows that lithium-ion battery model parameters are vital for state-of-health assessment and performance optimization. Traditional evolutionary algorithms often fail to balance global local search. To address these challenges, this study proposes the Elite Opposition-Based Learning Snake Optimization (EOLSO) algorithm, which uses an elite opposition-based learning mechanism enhance diversity a non-monotonic temperature factor exploration exploitation. The algorithm is applied parameter identification of second-order RC equivalent circuit model. EOLSO outperforms some traditional optimization methods, including Gray Wolf Optimizer (GWO), Honey Badger Algorithm (HBA), Golden Jackal (GJO), Enhanced (ESO), (SO), in both standard functions HPPC experiments. experimental results demonstrate significantly SO, achieving reductions 43.83% Sum Squares Error (SSE), 30.73% Mean Absolute (MAE), 25.05% Root Square (RMSE). These findings position as promising tool modeling state estimation. It also potential applications management systems, electric vehicle energy management, other complex problems. code available on GitHub.

Язык: Английский

An efficient enhanced exponential distribution optimizer: applications in global, engineering, and combinatorial optimization problems DOI Creative Commons

Marwa M. Emam,

Mohammed R. Saad,

Mina Younan

и другие.

Journal Of Big Data, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 13, 2025

Язык: Английский

Процитировано

0
Balancing the trade-off between quad-factors in construction management: a opposition-based Giant Pacific Octopus optimizer method DOI
Vu Hong Son Pham, Luu Ngoc Quynh Khoi

Cluster Computing, Год журнала: 2025, Номер 28(5)

Опубликована: Апрель 28, 2025

Язык: Английский

Процитировано

0
A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization DOI Creative Commons
Wuke Li, Ying Xiong, Shiqi Zhang

и другие.

World Electric Vehicle Journal, Год журнала: 2025, Номер 16(5), С. 268 - 268

Опубликована: Май 14, 2025

This paper shows that lithium-ion battery model parameters are vital for state-of-health assessment and performance optimization. Traditional evolutionary algorithms often fail to balance global local search. To address these challenges, this study proposes the Elite Opposition-Based Learning Snake Optimization (EOLSO) algorithm, which uses an elite opposition-based learning mechanism enhance diversity a non-monotonic temperature factor exploration exploitation. The algorithm is applied parameter identification of second-order RC equivalent circuit model. EOLSO outperforms some traditional optimization methods, including Gray Wolf Optimizer (GWO), Honey Badger Algorithm (HBA), Golden Jackal (GJO), Enhanced (ESO), (SO), in both standard functions HPPC experiments. experimental results demonstrate significantly SO, achieving reductions 43.83% Sum Squares Error (SSE), 30.73% Mean Absolute (MAE), 25.05% Root Square (RMSE). These findings position as promising tool modeling state estimation. It also potential applications management systems, electric vehicle energy management, other complex problems. code available on GitHub.

Язык: Английский

Процитировано

0