Predictive operation and maintenance-oriented power battery health prediction method

Abstract

The invention relates to a predictive operation and maintenance-oriented power battery health prediction method, and belongs to the technical field of battery management. According to the method, a usefulness evaluation system is used for evaluating health factors, a Bayesian optimization method is used for optimizing a threshold value, transfer learning is used for transmitting most similar attenuation battery model training information to a test single body, early attenuation data is used for fine adjustment of a model, and finally, the trained model is used for health prediction. The methodcomprises one-step attenuation prediction and extrapolation residual capacity prediction, and model self-correction is carried out by utilizing online extracted features. According to the method, theproblem that the on-line capacity is unmeasurable when the battery capacity is traditionally used for prediction is avoided, prediction of retirement points is provided for predictive operation and maintenance of the battery, self-correction can be carried out in real time in the using process, and the prediction precision is improved.

Description

technical field [0001] The invention belongs to the technical field of battery management, and relates to a power battery health prediction method for predictive operation and maintenance. Background technique [0002] The electrification of transportation is a new trend in the industry at home and abroad. Lithium-ion batteries have the characteristics of long life, low self-discharge rate, and high energy density, making them good energy storage devices. Predictive operation and maintenance of batteries is an important way to manage batteries in a refined and effective manner, and battery health prediction is the basis and key step for predictive operation and maintenance. Battery health prediction usually refers to battery capacity estimation and remaining life prediction. However, due to the limitation of unmeasurable capacity in actual use, battery health prediction has become an important challenge in the industry and a key technology to be solved urgently. [0003] B...

AI Technical Summary

Problems solved by technology

However, whether the mapping relationship is established between health factors and capacity or through the capacity sequence, in actual use, there is a defect that the capacity cannot be measured and model correction cannot be performed.

In addition, the mapping relationship between the establishment of the health factor and the capacity is also affected by the difference in the trend and range of the health factor of different batteries, which leads to the problem of inaccurate capacity estimation.

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