Cycle life prediction

　　Due to the long time and high cost of battery cycle life testing, the establishment of life model and the evaluation and prediction of life have become the research focus of scholars at home and abroad. The life prediction methods of lithium batteries can be divided into three categories according to information sources: prediction based on capacity decline mechanism, prediction based on characteristic parameters and prediction based on data drive.

　　1 Prediction based on capacity decline mechanism

　　Mechanism based prediction is to predict the battery life according to the internal structure and aging and fading mechanism of materials during the cycle. This method requires the use of basic models to describe the physical and chemical reaction processes occurring inside the battery, such as Ohm's law, electrochemical polarization, concentration polarization and internal diffusion of electrode materials.

　　Based on the loss of active lithium during the battery cycle, Ning et al. used the first principle to simulate the capacity decline model of lithium cobalt oxide battery. The influencing parameters include exchange current density, DOD, interface facial mask impedance, and charging cut-off voltage. The author compares the life prediction model with the measured data, and finds that the model is very close to the actual test results.

　　Virkar proposed a battery degradation model based on non-equilibrium thermodynamics, taking into account the influence of chemical potential, SEI film and other factors on the capacity decline, and pointed out that there would be unbalanced monomer in the series battery pack, and SEI film might also be generated at the interface between the positive electrode and electrolyte, leading to the capacity decline.

　　2 Prediction based on characteristic parameters

　　Prediction based on characteristic parameters refers to the prediction of battery life by using the changes of some characteristic factors during battery aging. At present, researchers pay most attention to the relationship between EIS and cycle life. Li et al. studied the change of impedance spectrum of commercial lithium cobalt oxide battery during the 1C charge discharge cycle, and observed the change of electrode materials by XRD, TEM and SEM. It was found that in the Nyquist curves of the positive and negative electrodes of the lithium battery, the size of the semicircle in the low frequency region corresponding to the impedance of the interface facial mask increased with the increase of the number of cycles, from which the cycle life of the battery could be inferred.

　　EIS can give a more detailed description of the battery impedance, but the test instrument is vulnerable to external interference and it is difficult to effectively analyze the complex spectrum. In contrast, the measurement of pulse impedance is simple and easy, and online monitoring can be realized quickly.

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