[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126057-en":3,"doc-seo-126057-105":31,"detail-sidebar-cat-0-en-105":93},{"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},126057,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning-Driven Optimisation of Spent Lithium Iron Phosphate Regeneration","The growing volume of spent lithium-ion batteries requires efficient recovery and regeneration to mitigate environmental impacts. Solid-state direct regeneration of spent electrodes has attracted strong interest, yet practical deployment still depends on extensive process optimisation. This study applies machine learning to build accurate models that predict regenerated lithium iron phosphate (LFP) cathode performance using five ML approaches across three regeneration case studies. ANN-optimised conditions improve specific discharge capacity by 6.2% versus experimental settings and suggest longer cycle life with capacity retention after 1,147 cycles, reducing time and resources while enabling generalisation to other battery materials.","1 Machine Learning-Driven Optimisation of Spent Lithium Iron Phosphate Regeneration  \n2 Mohammed Alyoubi1,2, Imtiaz Ali3, and AmrM. Abdelkader*1  \n3 1 Department of Design and Engineering, Faculty of Science & Technology, Bournemouth  \n4 University, Poole, Dorset BH12 5BB, United Kingdom  \n5 2 Department of Chemical and Materials Engineering, King Abdulaziz University, Rabigh, 6 21911, Saudi Arabia  \n7 3 Department of Chemical Engineering, College of Engineering, Prince Mohammad Bin Fahd  \n8 University, Al Khobar, 31952, Saudi Arabia  \n9  \n10 Abstract  \n11 The increasing number of spent lithium-ion batteries demands efficient recovery or  \n12 regeneration to address the associated environmental challenges. Solid-state direct regeneration  \n13 of spent electrodes is a promising technique that has received significant attention recently.  \n14 However, the process still requires considerable optimisation before being commercially  \n15 applied. This study leverages machine learning (ML) to develop highly accurate models that  \n16 characterise the performance of regenerated lithium iron phosphate (LFP) cathodes through  \n17 three case studies focused on direct regeneration methods. Five different ML models, including  \n18 Artificial Neural Network (ANN), Advanced Classification and Regression Trees (C&RT), 19 Boosted Regression Trees (BRT), Support Vector Machine (SVM), and K-Nearest Neighbours  \n20 (KNN), were trained using the collected data. The optimised regeneration conditions identified  \n21 by the ANN model indicate that a 6.2% increase in specific discharge capacity can be achieved  \n22 compared to the conditions determined experimentally. The results also showed a possible  \n23 increase in cycle life, with higher capacity retention after 1,147 cycles. These findings highlight  \n24 the efficacy of ANN models in predicting and optimising the performance of regenerated  \n25 batteries, offering significant reductions in time and resources compared to traditional 26 laboratory methods. Moreover, the concept demonstrated in this study shows strong potential 27 for generalisation to other battery materials, enabling the optimisation of regeneration 28 processes across a broader range of battery chemistry. While most research emphasises using 29 Support Vector Machines (SVM) for modelling newly manufactured batteries, this study 30 demonstrates that ANN models provide superior accuracy for regenerated batteries, paving the 31 way for more sustainable energy storage solutions.  \n32  \n33 Keywords:  \n34 Spent lithium iron phosphate batteries; batteries direct regeneration; machine learning;  \n35 predictive models  \n36  \n37 List of Acronyms  \n38 Artificial Neural Network ANN  \n39 Boosted Regression Trees BRT  \n40 Classification and Regression Trees C&RT  \n41 Density Functional Theory DFT  \n42 Deep Learning DL  \n43 Dimethylacetamide DMAC  \n44 Fully Convolutional Network FCN  \n45 K-Nearest Neighbours KNN  \n46 Lithium iron phosphate LFP  \n47 Lithium-Ion Batteries LIB  \n48 Mean Absolute Error MAE  \n49 Machine Learning ML  \n50 Neural Networks NN  \n51 Polyvinylidene fluoride PVDF  \n52 Regenerated Lithium Iron Phosphate RLFP  \n53 Root-Mean-Square Error RMSE  \n54 State of Charge SoH  \n55 Remaining Useful Life RUL  \n56 Support Vector Machine SVM 57  \n58  \n59 TOC graphics  \n60  \n61 Accelerating research and identifying more effective regeneration processes for lithium iron 62 phosphate (LFP) electrodes contribute to enhancing the sustainability of battery materials and 63 promoting resource efficiency.  \n64 Introduction  \n65 Lithium-ion batteries (LIBs) have become the most attractive energy storage solution  \n66 due to their high energy density and long-life cycle.1 It has become the core of empowering  \n67 handheld devices and electric vehicles (EVs) .2 The global market for LIBs by the end of 2017  \n68 was $29.86 billion, which is expected to grow to $139.36 billion by 2026.3 Lithium iron  \n69 phosphate (LFP) is increasingly employed as the ","cbCaipZvcSTqeuy5","https://ap.wps.com/l/cbCaipZvcSTqeuy5","pdf",2560447,10,1,32,"English","en",105,"# Abstract\n# Introduction\n## Recycling and regeneration of LFP batteries","[{\"question\":\"Why is regeneration of spent lithium-ion batteries important?\",\"answer\":\"Regeneration is needed because increasing numbers of spent batteries create environmental challenges and contribute to resource depletion when not recovered or regenerated.\"},{\"question\":\"What machine learning models are used in the study?\",\"answer\":\"The study trains five ML models, including ANN, C\\u0026RT, BRT, SVM, and KNN, to characterise performance of regenerated LFP cathodes.\"},{\"question\":\"How does the ANN model improve regeneration outcomes?\",\"answer\":\"ANN-identified optimal conditions yield a 6.2% increase in specific discharge capacity compared with experimental conditions and indicate possible improvement in cycle life.\"}]","Machine Learning-Driven Optimisation of Spent Lithium Iron Phosphate Regeneration | 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is regeneration of spent lithium-ion batteries important?","Question",{"text":77,"@type":78},"Regeneration is needed because increasing numbers of spent batteries create environmental challenges and contribute to resource depletion when not recovered or regenerated.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What machine learning models are used in the study?",{"text":82,"@type":78},"The study trains five ML models, including ANN, C&RT, BRT, SVM, and KNN, to characterise performance of regenerated LFP cathodes.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the ANN model improve regeneration outcomes?",{"text":86,"@type":78},"ANN-identified optimal conditions yield a 6.2% increase in specific discharge capacity compared with experimental conditions and indicate possible improvement in cycle 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