A DataDriven Method for Predicting Capacity Degradation of Rechargeable Batteries Rechargeable batteries supply numerous devices with electric power and are critical part in a variety of applications. An accurate monitoring and prediction of capacity degradation is directly related to making timely decision as to when a battery should be replaced, so that power disruption of the system it supplies power to is avoided. We propose a methodology for predicting capacity of a battery over future time horizon. The proposed method is based on training data consisting of occasional measurements, taken under the same conditions, of capacity and chargedischarge voltagecurrent of a certain number of batteries sharing the same chemistry and manufacturer, that otherwise undergo different usage patterns. In the operationalonline stage, capacity degradation over future time horizon of a test battery cell of unknown state of health and previous usage pattern is predicted based on its capacity and voltagecurrent measurements over one chargedischarge cycle and the training dataset. The experimental validation reveals that the proposed method predicts capacity of a test battery cell over prediction time horizon of few hundred days of batteryxe2x80x99s operation with relative prediction error below 1%.

Artificial Neural Network enabled P2D Model Deployment for EndofLine Battery Cell Characterization The production chain of lithiumion battery cells is an intricate process with manifold processproduct interdependencies leading to a complex product, the battery cell. In order to assess the quality and the performance of the battery cell in the endofline characterization, battery cell models need to be fit to experimental data in order to estimate unmeasured physical parameters of the cell. This procedure is laborious and error prone to the model complexity and the large number of physical parameters and is therefore presently not suitable as an endof line test in a largescale battery cell production. In this paper, a battery cell model is used to generate data by varying measured and unmeasured parameters in order to train an artificial neural network model for a fast and reliable diagnostics. The presented artificial neural network model is capable of fitting the battery cell model in less than a second to make it more suitable for endofline characterization.