Battery remaining life prediction method considering recovery effect based on uncertain process

Abstract

The present invention provides a method for predicting the remaining life of a battery based on the recovery effect based on an uncertain process, which includes the following steps: S1 obtaining step, obtaining degradation data; S2 establishing an uncertain process model step, using the Liu process in the uncertain process to Degradation incremental data modeling; S3 parameter initialization step, use the uncertain least squares method to estimate the estimated value of the parameter; S4 parameter update step, use the method of weighted least square estimation to update the parameter; S5 denoising step, based on the uncertain Liu process model and updated parameters, denoising the degraded data; S6 parameter re-estimation and re-updating steps, using the denoised degraded data to re-estimate and update the parameters; S7 prediction step, using uncertain simulation to obtain Prediction of remaining life. The invention improves the cognitive uncertainty of the remaining life prediction model, and considers the recovery phenomenon in degradation, thereby improving the accuracy of degradation prediction.

Description

technical field [0001] The invention relates to the technical field of component remaining life prediction, in particular to a battery remaining life prediction method based on an uncertain process and considering recovery effects. Background technique [0002] Remaining life prediction refers to the length of the time period between the current operating time and failure time of individual components. Remaining life prediction plays an important role in condition-based maintenance, and condition-based maintenance is an effective maintenance strategy. The method is based on historical information of similar components and real-time information of components to be predicted. Condition-based maintenance has been widely used in the fields of ships, aviation and transportation. This maintenance strategy has attracted widespread attention in recent years because it can reduce unnecessary maintenance costs while ensuring product safety and reliability. It is worth noting that th...

AI Technical Summary

Problems solved by technology

First, the current methods based on statistical models are all based on probability theory. In probability theory and data statistics, according to the theorem of large numbers, when the detection samples are sufficient, the frequency approaches probability. In the degradation of actual equipment, The data that can be monitored is often limited, so the cognitive uncertainty of the parameters will be introduced; second, for some practical application scenarios, the recovery phenomenon in the degradation process must be considered, but this phenomenon is in the existing In the degradation model, it is often ignored. A common example of the recovery phenomenon in the degradation process is the degradation of the battery, including the degradation of the lithium-ion battery and the degradation of the proton exchange membrane fuel cell. In the lithium-ion battery, when the battery is charging Pausing for a period of time during the discharge cycle, the chemical reaction inside the battery will increase the available capacity of the battery in the next cycle, and there are many cognitive uncertainties about the occurrence of this recovery phenomenon; third, in the remaining life prediction, due to Models are built based on historical information about similar parts, so there is cognitive uncertainty about the degree of similarity between individual parts and population parts

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