Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques

Early Prediction of the Health Conditions for Battery Cathodes Assisted by the Fusion of Feature Signal Analysis and Deep-Learning Techniques

Blog Article

With rapid development of clean energy vehicles, the health diagnosis and Motion Sensor prognosis of lithium batteries remain challenging for practical applications.Accurate state-of-health (SOH) and remaining useful life (RUL) estimation provides crucial information for improving the safety, reliability and longevity of batteries.In this paper, a fusion of deep-learning model and feature signal analysis methods are proposed to realize accurate and fast estimation of the health conditions for battery cathodes.Specifically, the long short-term memory (LSTM) network and differential thermal voltammetry (DTV) are utilized to verify our fusion method.Firstly, the DTV feature Cyclist Accessories - Helmets - BMX signal analysis is executed based on battery charging and discharging data, based on which useful feature variables are extracted with Pearson correlation analysis.

Next, the deep-learning model is constructed and trained with the LSTM as the core based on timeseries datasets constructed with features.Finally, the validation and error analysis of proposed model are provided, showing a max mean absolute error of 0.6%.The proposed method enables highly accurate models for SOH and RUL estimation that can be potentially deployed on cloud-end for offline battery degradation tracking.