Use of modern algorithms for multi-parameter optimization and intelligent modelling of sustainable battery performance

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dc.authoridAfzal, Asif/0000-0003-2961-6186en_US
dc.authoridSaboor, Shaik/0000-0002-0490-4766en_US
dc.authoridBURADI, ABDULRAJAK/0000-0001-7776-9238en_US
dc.authoridAlwetaishi, Mamdooh/0000-0001-5053-4324en_US
dc.authoridBURADI, ABDULRAJAK/0000-0001-7776-9238en_US
dc.authorscopusid57057224800en_US
dc.authorscopusid57191913729en_US
dc.authorscopusid56505514100en_US
dc.authorscopusid58529246600en_US
dc.authorscopusid57193789174en_US
dc.authorscopusid57202959651en_US
dc.authorscopusid57190847465en_US
dc.authorwosidAfzal, Asif/U-3071-2017en_US
dc.authorwosidSaboor, Shaik/M-8170-2018en_US
dc.authorwosidAlwetaishi, Mamdooh/M-1322-2016en_US
dc.authorwosidBURADI, ABDULRAJAK/AAE-3904-2021en_US
dc.authorwosidBURADI, ABDULRAJAK/HHN-7207-2022en_US
dc.contributor.authorAfzal, Asif
dc.contributor.authorBuradi, Abdulrajak
dc.contributor.authorJilte, Ravindra
dc.contributor.authorSundara, Vikram
dc.contributor.authorShaik, Saboor
dc.contributor.authorAgbulut, Umit
dc.contributor.authorAlwetaishi, Mamdooh
dc.date.accessioned2024-08-23T16:04:47Z
dc.date.available2024-08-23T16:04:47Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe focus of this computational work is to predict and optimize the battery thermal performance indicators for its sustainable operation using different meta-heuristic optimization algorithms and machine learning models. The contribution of this work is two-fold, first, the heat removal ability from battery indicated by average Nusselt number (Nuavg) and hotspots (MaxT) to avoid battery thermal runaway are optimized as single objective optimization (SOO) and as multi-level objective optimization (MOO) problem. Second, intelligent algorithms: Gradient boosting (GB) algorithm and Gaussian process regressor (GPR) algorithm are used for modelling of Nuavg and MaxT. For SOO, Multi-verse optimization (MVO) and Grey wolf optimization (GWO) algorithms are used for individual battery performance indicators. Similarly, the enhanced version of MVO and GWO for MOO (MMVO and MGWO) algorithms is customized. Each algorithm is operated for five cycles and 100 iterations in each cycle of execution. In GB algorithm the effect of different loss functions and in GPR algorithm the effect of parameter alpha (alpha) is analyzed. SOO gives highest fitness of Nuavg and lowest hotspots occurrence from both the algorithms with same converged positions of operating parameters. MMVO and MGWO relatively provide lower Nuavg with MaxT in the same range of SOO. The MOO provides different set of particle positions compared to SOO. MGWO algorithm has outperformed in providing the best non-dominated solution. The GB and GPR algorithm are good enough for the forecasting of battery thermal parameters. GPR is even accurate, however the range of alpha is important during training and testing. The best Nuavg obtained from SOO using MVO algorithm is around 82.06 while MaxT is 0.34. The same from GWO algorithm is 82.05 and 0.33 respectively. MGWO algorithm in MOO provides Nuavg and MaxT around 75.57 and 0.34 while MMWO provides 66.76 and 0.33 respectively. GPR algorithm gives accuracy as close as 98 % for MaxT while it gives 94 % accuracy for Nuavg. On the other hand GB algorithm gives 99 % and 97.5 % accuracy for MaxT and Nuavg respectively.en_US
dc.description.sponsorshipDeanship of Scientific Research at King Khalid University [RGP 2/567/44]; Deanship of Scientific Research, Taif Universityen_US
dc.description.sponsorshipThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP 2/567/44. The researchers would like to acknowledge Deanship of Scientific Research, Taif University, for funding this work.en_US
dc.identifier.doi10.1016/j.est.2023.108910
dc.identifier.issn2352-152X
dc.identifier.issn2352-1538
dc.identifier.scopus2-s2.0-85170637528en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.est.2023.108910
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14364
dc.identifier.volume73en_US
dc.identifier.wosWOS:001165562100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of Energy Storageen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBattery temperatureen_US
dc.subjectHeaten_US
dc.subjectFitness functionsen_US
dc.subjectModellingen_US
dc.subjectOptimization algorithmsen_US
dc.subjectPredictionsen_US
dc.subjectSupport Vector Machineen_US
dc.subjectAbsolute Error Maeen_US
dc.subjectHealth Estimationen_US
dc.subjectPrognosticsen_US
dc.subjectStateen_US
dc.subjectRmseen_US
dc.titleUse of modern algorithms for multi-parameter optimization and intelligent modelling of sustainable battery performanceen_US
dc.typeArticleen_US

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