State of Health Estimation of LFP Batteries Using DC Internal Resistance and Neural Network

dc.contributor.authorPannawat Peanjad
dc.contributor.authorChaitouch Manee-Inn
dc.contributor.authorSurin Khomfoi
dc.date.accessioned2026-05-08T19:22:54Z
dc.date.issued2022-5-24
dc.description.abstractStudying the state of health estimation of lithium-ion phosphate batteries (LFP) using an Artificial neural network (ANN). This research examines the relationship between DC internal resistance and the state of health (SoH) of batteries. The advantage of DC internal resistance measurement is that it does not require battery removal from the system. Analysis of degradation patterns in the application of several cycles. Then apply the previously studied relationship to train the ANN to design and test the model with other battery packs. As a result, the error value is acceptable. ( MAE = 9.06%, MSE = 1.23% and RMSE = 11.11% ). Thus, this ANN Model can assist in the early detection of a potential battery failure due to battery degradation.
dc.identifier.doi10.1109/ecti-con54298.2022.9795407
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18793
dc.publisher2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
dc.subjectAdvanced Battery Technologies Research
dc.subjectAdvancements in Battery Materials
dc.subjectAdvanced Battery Materials and Technologies
dc.titleState of Health Estimation of LFP Batteries Using DC Internal Resistance and Neural Network
dc.typeArticle

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