Online SOC measurement method for lithium battery

Abstract

The invention provides an SOC online measurement method for a lithium battery based on the Gaussian mixture process and the dynamic OCV correction. According to the invention, the Gaussian mixture regression (GMR) process is integrated with a Gaussian mixture model and a Gaussian process regression model, so that the time series of dynamic non-linearity can be effectively represented. The dynamic OCV correction method can calibrate an OCV-SOC curve according to external factors, so that the accurate OCV is obtained. Therefore, the SOC is corrected, and the accumulative error is eliminated. In this way, a battery model can be updated in real time according to the appropriate algorithm difficulty under the complex working condition of an automobile. Meanwhile, battery characteristics can be accurately tracked and the accumulated estimation error is corrected. The long-term precision is guaranteed.

Description

technical field [0001] The invention relates to a method for online measurement of lithium battery SOC, in particular to an online measurement method for lithium battery SOC based on mixed Gaussian process and dynamic OCV correction. Background technique [0002] In recent years, people have gradually realized the importance of environmental protection, and new energy vehicles have become more and more popular among consumers. Lithium batteries are widely used in new energy vehicles because of their high energy density and long service life. In order to prevent overcharge and overdischarge of lithium battery packs, and perform battery balancing, improve battery efficiency and estimate accurate remaining mileage, battery load State of Charge, hereinafter referred to as SOC, has become the most concerned parameter in the battery management system. SOC cannot be measured directly, but can only be estimated by corresponding algorithms. [0003] Due to the variable working cond...

AI Technical Summary

Problems solved by technology

However, due to the variable working conditions of the car and the dynamic nonlinearity of the lithium battery, the offline data-driven model cannot accurately represent the characteristics of the battery. As time goes by, the model will become invalid, and the online dynamic update process of the data-driven model is not considered.

In addition to the dynamic changes of the model, due to running on new energy vehicles, considering practical applications, the computing power and storage capacity of embedded systems are limited, and the computational complexity is called another challenge for SOC estimation based on data-driven models

In addition, there are also many previous works based on Kalman filter and OCV-SOC curve to estimate SOC, but on the one hand, it is difficult to obtain the open circuit voltage in a completely balanced state during driving. The resulting open circuit voltage will appear falsely high or falsely low. On the other hand, due to the hysteresis effect of lithium batteries, there are obvious differences in the OCV-SOC curves of charging and discharging.

[0004] Many existing technologies use static battery models, or the running algorithms are too complex to guarantee real-time performance, or there are cumulative errors in the online data model, and some do not take into account the complex working conditions of the car, making the battery SOC value inaccurate

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