SOC estimation of lithium battery based on the combination of electrical parameters and FBG non-electrical parameters and using NGO-BP model

Abstract

Accurately estimating of the state of charge (SOC) of lithium batteries is of great significance for improving the service life and utilization efficiency of lithium batteries. In this paper, fiber Bragg grating (FBG) sensors are used to capture the offset of the FBG central wavelength caused by strain and temperature during the charging and discharging process of lithium batteries, and the current state of charge of lithium batteries is obtained through the combination of electrical parameters and FBG non-electrical parameters.A battery monitoring experimental system was established, and the comparison of wavelength offset collected by PDMS packaged FBG and bare FBG during the charging and discharging process of lithium batteries was discussed. The prediction results were compared by temperature compensating the central wavelength offset collected during the charging and discharging of lithium batteries, then introduced BP neural network and optimized NGO- BP neural network regression models for training to compare the prediction effects, the results of which point to the fact that in terms of regression model performance and improvement of FBG non-electrical parameters on model performance, the various indicators predicted by NGO-BP are stronger than those predicted by BP. After adding FBG non-electrical parameters, both the BP regression model and the NGO-BP regression model showed remarkable improvement in RMSE, MAE, and R2 compared to the purely electrical parameters, and the best prediction method for the NGO-BP non-purely electrical parameters, with RMSE of 0.0201 and MAE of 0.0154, which decreased by 77.94% and 73.12% respectively compared to the BP pure electrical parameter prediction method, and R2 of 0.9988, which increased by 5.88%, thus the joint accurate estimation of lithium battery SOC by electrical measurement characteristics and FBG non-electrical parameters is basically realized, which brings values for the design and development of battery management system.