[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128067-en":3,"doc-seo-128067-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128067,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Properties of Storage Batteries Using GPR-based Machine Learning Methods - Thesis","Accurate battery performance prediction is essential for planning projects that rely on storage systems, yet high-fidelity physics-based models are computationally costly and struggle with complex phenomena like battery degradation. This thesis introduces two Gaussian process regression (GPR) machine-learning approaches. A multifidelity physics-informed CoKriging model predicts VRFB charge-discharge curves using constrained 0D physics and limited initial voltage measurements. An ensemble GPR model forecasts capacity decay and cycle life for LFP/graphite cells under fast charging, with uncertainty from conditional variance.","© 2023 Tong Yu  \nPREDICTING PROPERTIES OF STORAGE BATTERIES USING GPR-BASED  \nMACHINE LEARNING METHODS  \nBY  \nTONG YU  \nTHESIS  \nSubmitted in partial fulfillment of the requirements for the degree of Master of Science in Civil Engineering in the Graduate College of the University of Illinois Urbana-Champaign, 2023  \nUrbana, Illinois  \nAdviser:  \nProfessor Alexandre Tartakovsky  \nAbstract  \nAccurately predicting the performance and properties of batteries, such as voltages at different states of charge (SOCs) within one cycle and discharge capacities at different cycles, is critical for planning projects that require the application of the batteries. The existing high-fidelity physics-based models are computationally expensive. Also, due to the complexity of the involved physics, certain processes (e.g. , battery degradation), cannot be accurately predicted by these models. To overcome these challenges, two machine-learning methods based on the Gaussian process regression (GPR) model are introduced in this thesis.  \nThe first model is a multifidelity model to predict the charge-discharge curve within one charge-discharge cycle. In this model, the physics-informed CoKriging (CoPhIK) machine learning method is trained on experimental data collected at the Pacific Northwest National Laboratory (PNNL) for vanadium redox flow batteries (VRFBs) . The physics in this model is constrained by the VRFB zero-dimensional physics-based model (0D model) . Our results show that a small amount of experimental data is needed for the range of parameters in the 0D model, including current density, flow rate, and initial concentrations, to train the model. To accurately predict the charge-discharge curve, only one initial measurement of voltage in the unknown charge-discharge curve is needed. The model is tested to be robust since the predictions based on this model show good agreement with the experimental results.  \nThe second model is an ensemble Gaussian process regression (ensemble GPR) model to predict discharge capacities at different cycles and the cycle lives for commercial lithium iron phosphate/graphite cells under fast-charging conditions before their degradation. In this model, the properties (mean and covariance) of the prior distribution of discharge capacities are calculated from the measured discharge capacities of many batteries. Then, the discharge capacity of the modeled battery is predicted based on the discharge capacity of this battery at the first N cycles as the GPR conditional mean. Uncertainty in the prediction is given by the conditional variance. Our results show that the ensemble GPR model is capable of predicting capacity decay and the life of the battery, i.e., the number of cycles after which the battery capacity drops below the critical value.  \nThe significance of the GPR method as a basis for the derived machine learning models is shown for the  \nphysics-informed CoKriging model with physics embedded in or in the purely data-driven ensemble GPR model. Both GPR-based models demonstrate great performance in predicting the properties of storage batteries.  \nAcknowledgments  \nI would like to express profound gratitude to my advisor Dr. Alexandre M. Tartakovsky for giving me the opportunity to be a part of a project funded by the Pacific Northwest National Laboratory(PNNL) and all the guidance and support he has given me along the way for all the research tasks and coursework. I am inspired by his dedication to the profession, his attention to detail, and his intense commitment to his work. Through the process of working on the battery project and taking numerical modeling and physics-informed machine-learning course from him, I learned a lot. This process broadens my horizon, making me realize the power of combining physics learn from major courses, data science, and computational mathematics in solving problems within complex systems, which sparks my interest in doing research with this framework. I appreciate all ","cbCairdadRj8hMnK","https://ap.wps.com/l/cbCairdadRj8hMnK","pdf",4085494,4,1,50,"English","en",105,"# Chapter 1 Introduction\n# Chapter 2 VRFB and Commercial Lithium Iron Phosphate/Graphite (LFP) Batteries\n# Chapter 3 GPR-based Machine Learning Methods\n# Chapter 4 Physics-informed CoKriging Model of VRFB’s Charge-discharge Curves\n# Chapter 5 Ensemble GPR model for Predicting Discharge Capacity and Cycle Life of LFP Cells","[{\"question\":\"Why are physics-based battery models difficult to use for predicting performance and degradation?\",\"answer\":\"High-fidelity physics-based models are computationally expensive, and their complexity limits accurate prediction of processes such as battery degradation.\"},{\"question\":\"How does the multifidelity CoKriging model for VRFBs work?\",\"answer\":\"It trains a physics-informed CoKriging method on experimental data while constraining the model with a VRFB zero-dimensional (0D) physics model, using experimental voltage measurements to predict the full charge-discharge curve.\"},{\"question\":\"What does the ensemble GPR model predict for lithium iron phosphate/graphite cells?\",\"answer\":\"It predicts discharge capacity decay and cycle life under fast-charging conditions, determining the number of cycles until capacity drops below a critical threshold, with uncertainty provided by conditional variance.\"}]","Predicting Properties of Storage Batteries Using GPR-based Machine Learning Methods - 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