[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120160-en":3,"doc-seo-120160-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120160,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","To Accelerate the Battery Simulation Process for Crash and Impact Tests using Machine Learning","Prediction of thermal runaway in lithium-ion batteries under abusive mechanical loading serves as a critical safety measure comparable to crash or impact testing. The thesis investigates an explicit crash simulation approach for indentation test modelling, while addressing its substantial time requirements. A cell-level modelling workflow is combined with machine learning to predict thermal runaway, capturing internal short-circuit behavior and validating against published experimental data.","Master Thesis  \nTo Accelerate the Battery Simulation Process for Crash and Impact Tests using Machine Learning  \nMayur S. Talele  \nTo Accelerate the Battery Simulation Process for Crash and Impact Tests using Machine Learning  \nby  \nMayur S. Talele  \nedited from  \nMay 2023 to October 2023  \n(Date of Submission: 27.10.2023)  \nin the study program  \nComputational Mechanics of Materials and Structures, COMMAS (M.Sc.)  \nunder the supervision of  \nTarun Kumar Mitruka Vinod Kumar Mitruka, M.Sc and  \nDr. math. Christian Alscher(Altair Engineering GmbH)  \nDeclaration  \n• I hereby declare that I have independently written the thesis presented here.  \n• Only the sources and aids mentioned explicitly in the thesis have been used. I have marked as such any ideas taken over verbatim or in spirit.  \n• The submitted thesis was not and is not the subject of any other examination procedure, neither in its entirety nor in substantial parts.  \n• Likewise, I have not already published the work, either in full or in part.  \n• I certify that the electronic copy matches the other copies.  \nStuttgart, October 26, 2023    \n(Signature Student)  \nMayur Santosh Talele  \nDigitally signed by Mayur Santosh Talele Date: 2023.10.27  \n12:36:01 +02'00'  \nMaster Thesis  \nTo Accelerate the Battery Simulation Process for Crash and Impact Tests using Machine Learning.  \nSimulation of battery packs can be a tedious task given the complexity and the failure modes. In order to analyze it with ease the most trending technology i.e., AI/ML can be used to optimize, predict the results and accelerate the pace of simulations.  \nThe work in the thesis is based on a Radioss FE model of a cylindrical “jelly roll” battery with homogenized material/failure law that can capture the main Multiphysics aspects. This model will be validated with test results to calculate the risks of thermal runaway (Jia et al.). With the help of Radioss model, we will perform an indentation test simulation (or compression test / 3-point bending test, etc.) with different load cases and analyze the results.  \nNumerous simulations with the variation of numerical & physical parameters that define the system under simulation yield a wide range of results related to the evolution of the system under observation. These results will train a Neural Network (NN) and develop its own algorithm that can be used to predict results. These results will be verified with the test data and the CAE simulation. The greater the number of simulations, the better the model adapts this information with minimum errors. The trained network will be used to predict the results for new data at a significantly faster speed compared to the conventional simulation process. With this we will further create a package for battery modelling.  \nAnother important aspect will be the automation of the process to create sampling data, train the AI, validate and optimize the quality of the prediction.  \nTasks:  \n• Literature study on Batteries and their failure modes.  \n• Investigation of mesh size dependency, number of tests needed for the training, impact angle in the indentation test.  \n• Creating custom simulation datasets.  \n• Learning Hyperworks, Radioss, Physics AI & Altair Pulse and test setup modelling for battery simulations.  \n• Case studies of experimental test setups and validation techniques.  \n• Follow up on the Modern techniques used in AI & ML to automate a process.  \nReferences:  \nJia, Yikai; Gao, Xiang; Mouillet, Jean B.; Terrier, Jean M.; Lombard, Patrick; Xu, Jun: Effective thermo-electro-mechanical modeling framework of lithium-ion batteries based on a representative volume element approach. In: Journal of Energy Storage 33 (2021), 1.–  \nISSN 2352152X, [https://doi.org/10.1016/j.est.2020.102090](https://doi.org/10.1016/j.est.2020.102090)  \nAbstract  \nAn investigation into the prediction of thermal runaway in lithium-ion batteries subjected to abusive mechanical loading is comparable to a crash or impact test and a cru","cbCainMEuL7Zcqe8","https://ap.wps.com/l/cbCainMEuL7Zcqe8","pdf",4641763,1,64,"English","en",105,"# Declaration\n# Thesis Overview\n## Battery Pack and Radioss Cell Model\n## Neural Network Training and Validation\n## Automation and Data Sampling Workflow\n# Tasks\n# References","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis targets the slow, time-intensive process of simulating battery behavior under crash/impact-like mechanical loading, especially to predict thermal runaway safely.\"},{\"question\":\"How does the proposed method speed up predictions?\",\"answer\":\"It couples a Radioss-based indentation/cell model with a neural network trained on simulation data, enabling faster prediction on new cases than conventional simulation.\"},{\"question\":\"How is the machine learning model validated?\",\"answer\":\"Validation is performed using experimental data reported in existing literature, and the outcomes are further checked against CAE simulation results and the trained model’s prediction accuracy.\"}]","To Accelerate the Battery Simulation Process for Crash and Impact Tests using Machine Learning | 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problem does the thesis address?","Question",{"text":75,"@type":76},"The thesis targets the slow, time-intensive process of simulating battery behavior under crash/impact-like mechanical loading, especially to predict thermal runaway safely.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method speed up predictions?",{"text":80,"@type":76},"It couples a Radioss-based indentation/cell model with a neural network trained on simulation data, enabling faster prediction on new cases than conventional simulation.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the machine learning model validated?",{"text":84,"@type":76},"Validation is performed using experimental data reported in existing literature, and the outcomes are further checked against CAE simulation results and the trained model’s prediction 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