内阻
电池(电)
健康状况
人工神经网络
均方误差
计算机科学
磷酸铁锂
降级(电信)
汽车工程
工程类
人工智能
统计
数学
电信
功率(物理)
物理
量子力学
作者
Pannawat Peanjad,Chaitouch Manee-Inn,Surin Khomfoi
标识
DOI:10.1109/ecti-con54298.2022.9795407
摘要
Studying 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.
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