{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:53:31Z","timestamp":1774940011901,"version":"3.50.1"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003662","name":"KEIT","doi-asserted-by":"publisher","award":["RS2024\\u201300431095"],"award-info":[{"award-number":["RS2024\\u201300431095"]}],"id":[{"id":"10.13039\/501100003662","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Chemical Engineering"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.compchemeng.2026.109632","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T16:07:00Z","timestamp":1773158820000},"page":"109632","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Prediction of VRFB state of health in PV-coupled systems using PSO-optimized ensemble and neural models"],"prefix":"10.1016","volume":"210","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3386-3477","authenticated-orcid":false,"given":"Yonghae","family":"Jang","sequence":"first","affiliation":[]},{"given":"Yosoon","family":"Choi","sequence":"additional","affiliation":[]},{"given":"Shubhashish","family":"Bhakta","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compchemeng.2026.109632_bib0028","first-page":"11","article-title":"Comparative modeling of vanadium redox flow batteries using multiple linear regression and random forest algorithms","volume":"2","author":"Ali","year":"2025","journal-title":"Energy Storage Appl."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0013","doi-asserted-by":"crossref","DOI":"10.1002\/est2.70087","article-title":"Machine-learning-based accurate prediction of vanadium redox flow battery temperature rise under different charge\u2013discharge conditions","volume":"6","author":"Anirudh Narayan","year":"2024","journal-title":"Energy Storage"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0036","unstructured":"Artificial neural network. [Available online: https:\/\/www.mathworks.com\/help\/stats\/fitrnet.html."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0005","article-title":"A comprehensive review of vanadium redox flow batteries: principles, benefits, and applications","volume":"100767","author":"Barzigar","year":"2025","journal-title":"Next Res."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0023","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3390\/batteries10010008","article-title":"Optimization of a redox-flow battery simulation model based on a deep reinforcement learning approach","volume":"10","author":"Ben Ahmed","year":"2023","journal-title":"Batteries. (Basel)"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0008","doi-asserted-by":"crossref","DOI":"10.1002\/batt.202400737","article-title":"New non-invasive method to monitor and reverse faradaic imbalance in redox flow batteries","author":"Cantera","year":"2025","journal-title":"Batter. Supercaps e202400737"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0011","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.3390\/su8121320","article-title":"Sustainable development of abandoned mine areas using renewable energy systems: a case study of the photovoltaic potential assessment at the tailings dam of abandoned Sangdong mine","volume":"8","author":"Choi","year":"2016","journal-title":"Korea. Sustain."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.jelechem.2020.114145","article-title":"The Butler-Volmer equation in electrochemical theory: origins, value, and practical application","volume":"872","author":"Dickinson","year":"2020","journal-title":"J. Electroanal. Chem."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.125321","article-title":"Surrogate model-based parameter estimation framework of physics-based model for vanadium redox flow batteries","volume":"383","author":"Ha","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0037","unstructured":"k-Nearest neighbor algorithm. [Available online: https:\/\/www.mathworks.com\/help\/stats\/fitcknn.html."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0040","series-title":"Proceedings of ICNN'95-International Conference on Neural Networks","first-page":"1942","article-title":"Particle swarm optimization","volume":"4","author":"Kennedy","year":"1995"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.electacta.2023.143709","article-title":"Analytical current-voltage formulas in electrodes and concentration differences for VRFB","volume":"476","author":"Krowne","year":"2024","journal-title":"Electrochim. Acta"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0042","first-page":"23","article-title":"Aspect-based opinion ranking framework for product reviews using a Spearman's rank correlation coefficient method","volume":"460","author":"Kumar","year":"2018","journal-title":"Inf. Sci."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.flatc.2023.100606","article-title":"Thermo-electro-rheological properties of graphene oxide and MXene hybrid nanofluid for vanadium redox flow battery: application of explainable ensemble machine learning with hyperparameter optimization","volume":"43","author":"Kumar","year":"2024","journal-title":"FlatChem"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0038","unstructured":"Least-squares boosting (LSBoost). <Available online: https:\/\/www.mathworks.com\/help\/stats\/fitrensemble.html>."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.electacta.2023.141998","article-title":"A novel U-net based data-driven vanadium redox flow battery modelling approach","volume":"444","author":"Li","year":"2023","journal-title":"Electrochim. Acta"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.111790","article-title":"Long term performance evaluation of a commercial vanadium flow battery system","volume":"90","author":"Li","year":"2024","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0024","series-title":"2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2)","first-page":"2967","article-title":"State of charge estimation of all-vanadium redox battery based on BIGRU-UKF network","author":"Lin","year":"2024"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0001","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1007\/s11814-021-0819-z","article-title":"Techno-economic feasibility evaluation of a standalone solar-powered alkaline water electrolyzer considering the influence of battery energy storage system: a Korean case study","volume":"38","author":"Niaz","year":"2021","journal-title":"Korean J. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0026","series-title":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","first-page":"1289","article-title":"State of charge prediction study of vanadium redox-flow battery with BP neural network","author":"Niu","year":"2020"},{"issue":"7","key":"10.1016\/j.compchemeng.2026.109632_bib0029","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1039\/D0MH01632B","article-title":"Trust is good, control is better: a review on monitoring and characterization techniques for flow battery electrolytes","volume":"8","author":"Nolte","year":"2021","journal-title":"Mater. Horiz."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0031","doi-asserted-by":"crossref","first-page":"409","DOI":"10.3390\/batteries9080409","article-title":"Redox flow batteries: recent development in main components, emerging technologies, diagnostic techniques, large-scale applications, and challenges and barriers","volume":"9","author":"Olabi","year":"2023","journal-title":"Batteries"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.117689","article-title":"Investigation of the positive electrode and bipolar plate degradation in vanadium redox flow batteries","volume":"132","author":"Oreiro","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.117689","article-title":"Investigation of the positive electrode and bipolar plate degradation in vanadium redox flow batteries","volume":"132","author":"Oreiro","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122271","article-title":"Vanadium redox flow battery capacity loss mitigation strategy based on a comprehensive analysis of electrolyte imbalance effects","volume":"355","author":"Puleston","year":"2024","journal-title":"Appl. Energy"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0020","doi-asserted-by":"crossref","first-page":"24441","DOI":"10.1002\/er.8757","article-title":"Prediction of vanadium redox flow battery storage system power loss under different operating conditions: machine learning based approach","volume":"46","author":"Ra","year":"2022","journal-title":"Int. J. Energy Res."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0003","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.enconman.2019.04.025","article-title":"Optimal design and implementation of solar PV-wind-biogas-VRFB storage integrated smart hybrid microgrid for ensuring zero loss of power supply probability","volume":"191","author":"Sarkar","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0030","article-title":"A low-cost amperometric sensor for the combined state-of-charge, capacity, and state-of-health monitoring of redox flow battery electrolytes","volume":"14","author":"Stolze","year":"2022","journal-title":"Energy Convers. Manag.: X"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0039","unstructured":"Support vector regression. <https:\/\/kr.mathworks.com\/help\/stats\/fitrsvm.html>."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0010","unstructured":"System Advisor Model (SAM). [Available online: <Available online: https:\/\/sam.nrel.gov\/>."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.126485","article-title":"Insights into energy efficiency for vanadium redox flow battery (VRFB) using the artificial intelligence technique","volume":"399","author":"Talebian","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2025.120059","article-title":"Design and technical assessment of photovoltaic and vanadium redox flow battery systems for residential buildings based on time-of-use electricity pricing strategy","volume":"341","author":"Tang","year":"2025","journal-title":"Energy Convers. Manag."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.116780","article-title":"Ensemble and deep learning based prediction of vanadium redox flow battery system power loss and a precision equivalent circuit model for parameter benchmarking","volume":"123","author":"Verma","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.115349","article-title":"Hybrid nanophotonic-microfluidic sensor integrated with machine learning for operando state-of-charge monitoring in vanadium flow batteries","volume":"111","author":"Vlasov","year":"2025","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2024.234428","article-title":"A new zero-dimensional dynamic model to study the capacity loss mechanism of vanadium redox flow batteries","volume":"603","author":"Wang","year":"2024","journal-title":"J. Power. Sources."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0009","article-title":"A review of capacity decay studies of all-vanadium redox flow batteries: mechanism and state estimation","volume":"17","author":"Wang","year":"2024","journal-title":"ChemSusChem."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2020.228725","article-title":"Enhanced cycle life of vanadium redox flow battery via a capacity and energy efficiency recovery method","volume":"478","author":"Wei","year":"2020","journal-title":"J. Power. Sources."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0027","series-title":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","first-page":"1","article-title":"Machine learning based data-driven approach for state-of-charge estimation in redox flow battery","author":"Yadav","year":"2024"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.111768","article-title":"Validation of 3D multi-physics equivalent resistance network model with flow field for VRFB stack and battery scale-up analysis","volume":"90","author":"Zhang","year":"2024","journal-title":"J. Energy Storage"},{"key":"10.1016\/j.compchemeng.2026.109632_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpowsour.2025.237087","article-title":"A robust machine learning-based SOC estimation approach for vanadium redox flow battery","volume":"645","author":"Zheng","year":"2025","journal-title":"J. Power. Sources."},{"key":"10.1016\/j.compchemeng.2026.109632_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.114029","article-title":"A critical review on operating parameter monitoring\/estimation, battery management and control system for redox flow batteries","volume":"102","author":"Zhu","year":"2024","journal-title":"J. Energy Storage"}],"container-title":["Computers &amp; Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426000852?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135426000852?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:23:34Z","timestamp":1774934614000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135426000852"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":41,"alternative-id":["S0098135426000852"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109632","relation":{},"ISSN":["0098-1354"],"issn-type":[{"value":"0098-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Prediction of VRFB state of health in PV-coupled systems using PSO-optimized ensemble and neural models","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2026.109632","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109632"}}