{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:18:17Z","timestamp":1783570697010,"version":"3.55.0"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Apple"},{"name":"IBM"},{"name":"Southern University"},{"name":"A&amp;M College"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Especially NMC-LCO 18650 cells, lithium-ion batteries are essential parts of electric vehicles (EVs), where their dependability and performance directly affect operating efficiency and safety. Predictive maintenance, cost control, and increasing user confidence in electric vehicle technology depend on accurate Remaining Useful Life (RUL) forecasting of these batteries. Using advanced machine learning models, this research uses past usage data and essential performance characteristics to forecast the RUL of NMC-LCO 18650 batteries. The work creates a scalable and web-based application for RUL prediction by utilizing predictive models like Long Short-Term Memory (LSTM), Linear Regression (LR), Artificial Neural Network (ANN), and Random Forest with Extra Trees Regressor (RF with ETR) with results in Mean Square Error (MSE) as accuracy as 96%, 97%, 98% and 99% respectively. This research also emphasizes the importance of algorithm design that can provide reliable RUL predictions even in cases when cycle count data is lacking by properly using alternative features. On further investigation, our findings highlighted that the introduction of cycle count as a feature is critical for significantly reducing the mean squared error (MSE) in all four models. When the cycle count is included as a feature, the MSE for LSTM decreases from 12,291.69 to 824.15, the MSE for LR decreases from 3363.20 to 51.86, the MSE for ANN decreases from 2456.65 to 1858.31, and finally, the RF with ETR decreases from 384.27 to 10.23, which makes it the best performing model considering these two crucial performance metrics. Apart from forecasting the remaining useful life of these lithium-ion batteries, the web application gives options for selecting a model amongst these models for prediction and further classifies battery condition and advises best use practices. Conventional approaches for battery life prediction, such as physical disassembly or electrochemical modeling, are resource-intensive, ecologically destructive, and unfeasible for general use. On the other hand, machine learning-based methods use extensive real-world data to generate scalable, accurate, and efficient forecasts.<\/jats:p>","DOI":"10.3390\/a18060303","type":"journal-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T06:16:03Z","timestamp":1747980963000},"page":"303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models\u2014A Web-Based Application"],"prefix":"10.3390","volume":"18","author":[{"given":"Chisom","family":"Onyenagubo","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6648-6275","authenticated-orcid":false,"given":"Yasser","family":"Ismail","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5892-3225","authenticated-orcid":false,"given":"Radian","family":"Belu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7851-439X","authenticated-orcid":false,"given":"Fred","family":"Lacy","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Southern University and A&M College, Baton Rouge, LA 70807, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"ref_1","unstructured":"U.S. Department of Energy (2015). Battery Life and Performance Testing Methodology: PEV Battery Test Manual, Revision 3, INL\/EXT-13-30323."},{"key":"ref_2","unstructured":"(2017). Secondary Lithium Cells and Batteries for Portable Applications\u2014Part 3: Prismatic and Cylindrical Lithium Secondary Cells and Batteries Made from Them (Standard No. IEC 61960-3)."},{"key":"ref_3","first-page":"54","article-title":"A Review of State-of-Health Estimation for Lithium-Ion Batteries","volume":"391","author":"Li","year":"2018","journal-title":"J. Power Sources"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","article-title":"A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance","volume":"20","author":"Jardine","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.jpowsour.2012.05.012","article-title":"Parameterization of a Physico-Chemical Model for Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy","volume":"215","author":"Ecker","year":"2012","journal-title":"J. Power Sources"},{"key":"ref_6","first-page":"114151","article-title":"Hybrid Models for Lithium-Ion Battery RUL Prediction","volume":"259","author":"Wang","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5049","DOI":"10.1109\/TTE.2023.3323976","article-title":"Remaining Useful Life Prediction of Lithium-ion Batteries with Limited Degradation History Using Random Forest","volume":"10","author":"Yang","year":"2023","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.ensm.2017.05.013","article-title":"Thermal Runaway Mechanism of Lithium Ion Battery for Electric Vehicles: A Review","volume":"10","author":"Li","year":"2018","journal-title":"Energy Storage Mater."},{"key":"ref_9","first-page":"102","article-title":"Early Prediction of Battery Lifetime via a Machine Learning-Based Framework","volume":"19","author":"Fei","year":"2021","journal-title":"Battery Res. Adv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"114047","DOI":"10.1109\/ACCESS.2023.3304699","article-title":"Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM","volume":"11","author":"Jafari","year":"2023","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sekhar, J.N.C., Domathoti, B., and Gonzalez, E.D.R.S. (2023). Prediction of Battery Remaining Useful Life Using Machine Learning Algorithms. Sustainability, 15.","DOI":"10.3390\/su152115283"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.30574\/wjarr.2024.21.2.0553","article-title":"Machine Learning-Based Prediction of Lithium-Ion Battery Life Cycle for Capacity Degradation Modelling","volume":"21","author":"Kumarapp","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_13","first-page":"301","article-title":"Battery health prognosis for electric vehicles using data-driven approaches","volume":"75","author":"Hu","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_14","first-page":"420","article-title":"Cost analysis of predictive maintenance in lithium-ion batteries","volume":"18","author":"He","year":"2019","journal-title":"Energy Storage Mater."},{"key":"ref_15","first-page":"100905","article-title":"Advances in battery safety through RUL prediction: A review","volume":"85","author":"Kim","year":"2021","journal-title":"Prog. Energy Combust. Sci."},{"key":"ref_16","first-page":"129870","article-title":"Sustainability-driven battery management systems: Benefits and challenges","volume":"331","author":"Zhang","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_17","first-page":"45","article-title":"Democratizing Predictive Analytics: The Role of Web Applications","volume":"12","author":"Smith","year":"2020","journal-title":"J. Anal. Technol."},{"key":"ref_18","unstructured":"White, A. (2019). Centralized Data Management for Predictive Analytics, Springer."},{"key":"ref_19","unstructured":"(2025, March 18). Amazon Web Services. Predictive Maintenance with Amazon Monitron. Available online: https:\/\/aws.amazon.com\/solutions\/guidance\/predictive-maintenance-with-amazon-monitron\/."},{"key":"ref_20","first-page":"210","article-title":"Real-Time Monitoring and Predictive Analytics in IoT Systems","volume":"8","author":"Li","year":"2021","journal-title":"IoT Anal. Rev."},{"key":"ref_21","first-page":"300","article-title":"Smart Grids: Real-Time Optimization through Predictive Tools","volume":"15","author":"Khan","year":"2021","journal-title":"IEEE Trans. Energy Syst."},{"key":"ref_22","first-page":"50","article-title":"Frameworks for Developing Interactive Dashboards","volume":"14","author":"Johnson","year":"2022","journal-title":"Int. J. Data Vis."},{"key":"ref_23","first-page":"120","article-title":"User-Centered Design in Predictive Analytics Applications","volume":"53","author":"Patel","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_24","first-page":"67","article-title":"Enhancing User Experience with Data Visualization","volume":"10","author":"Williams","year":"2020","journal-title":"Data Sci. J."},{"key":"ref_25","first-page":"90","article-title":"Enterprise Integration with Predictive Systems","volume":"19","author":"Martinez","year":"2022","journal-title":"Int. J. Syst. Archit."},{"key":"ref_26","unstructured":"Zhou, H., and Lee, C. (2020). Machine Learning Algorithms for Predictive Maintenance Applications, Elsevier."},{"key":"ref_27","first-page":"48","article-title":"Improving Decision-Making with Web-Based Analytics","volume":"99","author":"Baker","year":"2021","journal-title":"Harv. Bus. Rev."},{"key":"ref_28","first-page":"88","article-title":"Standardizing Integration Frameworks for Predictive Analytics","volume":"31","author":"Stevens","year":"2022","journal-title":"Softw. Eng. Q."},{"key":"ref_29","unstructured":"Hosseini, M. (2025, March 18). Optimizing Energy Storage Systems with Predictive Maintenance. Available online: https:\/\/arshon.com\/blog\/optimizing-energy-storage-systems-with-predictive-maintenance\/."},{"key":"ref_30","first-page":"1","article-title":"Predictive Analytics in Healthcare: Enhancing Patient Outcomes through Data-Driven Forecasting and Decision-Making","volume":"8","author":"Bandi","year":"2024","journal-title":"J. Mach. Learn. Robot."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1287\/mnsc.43.4.546","article-title":"Information Distortion in a Supply Chain: The Bullwhip Effect","volume":"43","author":"Lee","year":"1997","journal-title":"Manag. Sci."},{"key":"ref_32","unstructured":"GDPR Advisor (2025, March 18). GDPR and Cloud Computing: Safeguarding Data in the Digital Cloud. Available online: https:\/\/www.gdpr-advisor.com\/gdpr-and-cloud-computing-safeguarding-data-in-the-digital-cloud\/."},{"key":"ref_33","first-page":"210","article-title":"Model Interpretability in Machine Learning for Non-Experts","volume":"17","author":"Park","year":"2020","journal-title":"Artif. Intell. J."},{"key":"ref_34","first-page":"228806","article-title":"Battery Prognostics under Uncertainty: A Comparative Study and New Prediction Horizon Metric","volume":"479","author":"Zhang","year":"2020","journal-title":"J. Power Sources"},{"key":"ref_35","first-page":"78","article-title":"A Review of RUL Prediction Models for Variable Operational Environments","volume":"10","author":"Kim","year":"2021","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_36","first-page":"1234","article-title":"Integrating Predictive Analytics with Practical Systems: Barriers and Opportunities","volume":"16","author":"Li","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_37","first-page":"34","article-title":"Autonomous Fleet Management Enabled by Predictive Maintenance","volume":"8","author":"Williams","year":"2023","journal-title":"Fleet Insights"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1038\/s41597-024-03831-x","article-title":"Comprehensive Battery Aging Dataset: Capacity and Impedance Fade Measurements of a Lithium-Ion NMC\/C-SiO Cell","volume":"11","author":"Luh","year":"2024","journal-title":"Sci. Data"},{"key":"ref_39","first-page":"89","article-title":"Developing Open-Access Battery Datasets for Predictive Modeling","volume":"9","author":"Gupta","year":"2022","journal-title":"Renew. Energy Syst. J."},{"key":"ref_40","first-page":"150","article-title":"Simplifying Predictive Analytics Tools for Non-Experts","volume":"12","author":"Thomas","year":"2020","journal-title":"J. Hum.-Comput. Interact."},{"key":"ref_41","first-page":"44","article-title":"Intuitive Dashboards for RUL Prediction: Design and Implementation","volume":"7","author":"Johnson","year":"2023","journal-title":"Predict. Insights Q."},{"key":"ref_42","first-page":"100","article-title":"Improving Interpretability in Deep Learning Models for Predictive Analytics","volume":"5","author":"Sun","year":"2022","journal-title":"AI Ethics"},{"key":"ref_43","unstructured":"Paneru, B., Thapa, B., Mainali, D.P., Paneru, B., and Shah, K.B. (2024). Remaining Useful Life Prediction for Batteries Utilizing an Explainable AI Approach with a Predictive Application for Decision-Making. arXiv, Available online: https:\/\/arxiv.org\/abs\/2409.17931."},{"key":"ref_44","first-page":"134","article-title":"Data Security in IoT-Based Predictive Systems: A Review","volume":"11","author":"Martin","year":"2023","journal-title":"Cybersecur. Priv. J."},{"key":"ref_45","first-page":"67","article-title":"Balancing Accessibility and Security in Predictive Analytics Platforms","volume":"8","author":"Kumar","year":"2020","journal-title":"Cloud Comput. Insights"},{"key":"ref_46","first-page":"90","article-title":"RUL Prediction for Emerging Battery Chemistries","volume":"15","author":"Stevens","year":"2021","journal-title":"Adv. Battery Technol."},{"key":"ref_47","first-page":"320","article-title":"Next-Generation Batteries: Challenges in Prognostics and RUL Prediction","volume":"13","author":"Wang","year":"2023","journal-title":"Energy Storage Futures"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-P\u00e9rez, N., Domingo, J.M., and L\u00f3pez, G.L. (2024). ICT Scalability and Replicability Analysis for Smart Grids: Methodology and Application. Energies, 17.","DOI":"10.3390\/en17030574"},{"key":"ref_49","unstructured":"Taylor, B. (2019). Big Data Processing in Cloud Computing Environments, Wiley."},{"key":"ref_50","first-page":"100","article-title":"Advancements in AI for Energy Management","volume":"15","author":"Doe","year":"2023","journal-title":"Energy Technol. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.patrec.2024.02.008","article-title":"Machine learning for low signal-to-noise ratio detection","volume":"179","author":"Lacy","year":"2024","journal-title":"Pattern Recognit. Lett."},{"key":"ref_52","unstructured":"Ding, T., Xiang, D., Sun, T., Qi, Y., and Zhao, Z. (2025). AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"50587","DOI":"10.1109\/ACCESS.2018.2858856","article-title":"Remaining useful life prediction for lithium-ion battery: A deep learning approach","volume":"6","author":"Ren","year":"2018","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/TVT.2018.2805189","article-title":"Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/6\/303\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:38:52Z","timestamp":1760031532000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/6\/303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,23]]},"references-count":54,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["a18060303"],"URL":"https:\/\/doi.org\/10.3390\/a18060303","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,23]]}}}