A levy chaotic horizontal vertical crossover based artificial hummingbird algorithm for precise PEMFC parameter estimation

作     者：Jangir, Pradeep Ezugwu, Absalom E. Saleem, Kashif Arpita Agrawal, Sunilkumar P. Pandya, Sundaram B. Parmar, Anil Gulothungan, G. Abualigah, Laith 

作者机构：Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Graph Era Hill Univ Dept CSE Dehra Dun 248002 Uttar Pradesh India Graph Era Dept CSE Dehra Dun 248002 Uttarakhand India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan North West Univ Unit Data Sci & Comp 11 Hofman St ZA-2520 Potchefstroom South Africa King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh 11362 Saudi Arabia Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 Tamil Nadu India Govt Engn Coll Dept Elect Engn Gandhinagar 382028 Gujarat India Shri KJ Polytech Dept Elect Engn Bharuch 392001 India SRM Inst Sci & Technol Dept Elect & Commun Engn Chengalpattu 603203 Tamil Nadu India Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Jadara Univ Res Ctr POB 733 Irbid Jordan 

出 版 物：《SCIENTIFIC REPORTS》 (Sci. Rep.)

年 卷 期：2024年第14卷第1期

页      面：1-26页

核心收录：

基　　金：North-West University [RSPD2024R697] King Saud University, Riyadh, Saudi Arabia 

主　　题：PEM fuel cell Optimal parameter estimation Electrical Engineering Optimization Artificial hummingbird algorithm LCAHA 

摘      要：In this research, enhanced versions of the Artificial Hummingbird Algorithm are used to accurately identify unknown parameters in Proton Exchange Membrane Fuel Cell (PEMFC) models. In particular, we propose a multi strategy variant, the L & eacute;vy Chaotic Artificial Hummingbird Algorithm (LCAHA), which combines sinusoidal chaotic mapping, L & eacute;vy flights and a new cross update foraging strategy. The combination of this method with PEMFC parameters results in a significantly improved performance compared to traditional methods, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA), which we use as baselines to validate PEMFC parameters. The quantitative results demonstrate that LCAHA attains a minimum Sum of Squared Errors (SSE) of 0.0254 and standard deviation of 4.59E-08 for the BCS 500W PEMFC model, which is much lower than the SSE values obtained for PSO (0.1924) and GWO (0.0364), thereby validating the superior accuracy and stability of LCAHA. Moreover, LCAHA converges faster than DE and SSA, reducing runtime by about 47%. The robustness and reliability of LCAHA-simulated and actual I-V curves across six PEMFC stacks are shown to be in close alignment.