Lithium ion battery SOC estimation method and device

Abstract

The embodiment of the invention provides a lithium ion battery SOC estimation method and device. The method comprises the following steps: determining a mapping relationship between each model parameter and an SOC value in a battery equivalent circuit model; running at least two Kalman filters in parallel, and establishing a battery discrete state space model in combination with the battery equivalent circuit model and the mapping relationship between each model parameter and the SOC value; and estimating the SOC value of the battery discrete state space model through Gaussian and Kalman filtering algorithms. According to the lithium ion battery SOC estimation method provided by the embodiment of the invention, a plurality of Kalman filters can be combined in the form of different weight coefficient proportions, and finally, the SOC value of the battery discrete state space model is estimated through Gaussian and Kalman filter algorithms to obtain the optimal SOC estimation value, so that the SOC estimation accuracy of the battery discrete state space model is improved. Equivalently, a plurality of Gaussian density functions are adopted to accurately describe the process noise andthe measurement noise in the whole process according to a certain weight coefficient, so that the accuracy of the SOC estimation result is greatly improved, and the universality is high.

Description

technical field [0001] The invention relates to the technical field of battery management, in particular to a method and device for estimating the SOC of a lithium-ion battery. Background technique [0002] The electric vehicle battery management technology involves many aspects of the battery, among which the estimation of the battery state of charge or remaining capacity (State of Charge, referred to as SOC estimation) plays an important role. The accuracy of battery SOC estimation not only affects the capacity utilization efficiency of the battery and the service life of the battery, but also may directly affect the operation and decision-making of the entire battery management system, thereby affecting the performance of the electric vehicle. [0003] Some scholars have proposed an online SOC measurement method for lithium batteries, which is mainly based on a mixed Gaussian process and a dynamic OCV correction process. This method combines the mixed Gaussian model and ...

AI Technical Summary

Problems solved by technology

In essence, this method still uses a filtering algorithm for SOC estimation. It needs to assume that the process noise and measurement noise are both Gaussian distributions. However, in fact, the probability distribution of noise in real situations is very difficult to determine. It is assumed as Gaussian distribution is a processing method that ignores a lot of interference noise, so the SOC result estimated by this method is still very low in accuracy

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