Lithium ion battery SOC estimation method based on deep-transfer learning

Abstract

The invention relates to a lithium ion battery SOC estimation method based on deep-transfer learning. The method comprises the steps of obtaining a source domain training set, a target domain trainingset and a test set; constructing a lithium ion battery SOC estimation source domain model based on deep learning, training the lithium ion battery SOC estimation source domain model by using the source domain training set, and storing model training data parameters; constructing a lithium ion battery SOC estimation target domain model based on deep learning, transferring lithium ion battery SOC estimation source domain model training data parameters to the lithium ion battery SOC estimation target domain model by adopting a transfer learning method, and sharing model weight parameters to perform initialization setting; and importing the lithium ion battery target domain training set into the lithium ion battery SOC estimation target domain model to perform fine adjustment training processing, and further importing the target domain training set into a target domain test set to predict the SOC value of the lithium ion battery. According to the method, the training time of the SOC estimation model of the lithium ion battery is shortened, and a large amount of time and capital investment consumed in the experimental data collection process are reduced.

Description

technical field [0001] The invention relates to the technical field of battery SOC estimation, in particular to a lithium-ion battery SOC estimation method based on depth-transfer learning. Background technique [0002] Lithium-ion power battery is currently a commonly used power source for electric vehicles. Due to its advantages of high energy density, high-rate charging and low-temperature resistance, it has become the focus of this research field. [0003] The State of Charge (SOC) estimation of lithium-ion batteries, that is, the calculation of the ratio of the remaining power of the battery to the rated capacity under the same conditions, is one of the important functions in the battery management system; accurate estimation of the state of charge of lithium-ion batteries can Provide the driver with accurate information on the remaining power of the vehicle and the mileage reference to prevent the life of the battery from being affected by overcharging and over-dischar...

AI Technical Summary

Problems solved by technology

The use of deep learning methods requires a large amount of experimental data support, and the experimental process requires a lot of time and cost. Considering the correlation between lithium-ion batteries, ...

Images