State of health prediction of lithium-ion batteries using particle swarm optimization with Levy flight and generalized opposition-based learning

Zhang, Bide; Liu, Wei; Cai, Yongxiang; Zhou, Zhongkang; Wang, Luyan; Liao, Qiangqiang; Fu, Zaiguo; Cheng, Zhiyuan

doi:10.1016/j.est.2024.110816

Cited by 8 publications

(1 citation statement)

References 37 publications

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“…Characterized by random steps, the application of LF enables optimization algorithms to navigate the vicinity of existing solutions, while intermittent significant leaps mitigate the risk of entrapment within local minima. For instance, Zhang et al [ 56 ] recently demonstrated that LF enhances particle diversity within PSO, thereby refining the accuracy of lithium-ion battery State-of-Health prediction. Similarly, Hussien et al [ 57 ] noted the efficacy of LF-based Transient Search Optimization in augmenting the transient response of terminal voltages within islanded microgrids.…”

Section: Related Workmentioning

confidence: 99%

A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems

Hussien,

Pop,

Kumar

et al. 2024

Biomimetics

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The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb’s law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Lévy flights, simulated annealing, and the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive s-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC’17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC’17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems.

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Section: Related Workmentioning

confidence: 99%