Lithium battery SOC evaluation method of mixed expansion Kalman filtering

Abstract

A lithium battery SOC evaluation method of mixed expansion Kalman filtering includes the step of the establishment of a lithium battery equivalent model. In the lithium battery equivalent model, the open circuit voltage (OCV) of the battery is expressed as an electrochemical model, R refers to the battery internal resistance, which is different during charging and discharging and is set to be R+ during charging and R- during discharging, and p1, p2, p3, p4, p5, p6, p7 are the fitting parameters of the lithium battery model. According to the battery charge and discharge experiment data, the battery equivalent circuit model parameters can be estimated. The lithium battery SOC evaluation method also includes the steps of establishing a discrete state space model of the battery system, and using the improved EKF algorithm to estimate the battery SOC. The lithium battery SOC evaluation method overcomes the problems of low filtering precision or generation of filtering divergence in the EKF algorithm due to the uncertainty of the battery model, the unknown noise statistical characteristics and the Taylor expansion linearization.

Description

【Technical field】 [0001] The invention belongs to the technical field of lithium batteries, and in particular relates to a method for estimating the SOC of a lithium battery using a hybrid extended Kalman filter. 【Background technique】 [0002] Batteries are the main energy carrier and power source of electric vehicles, and they are also the main components of the electric vehicle body. Accurate estimation of battery SOC can not only improve the capacity utilization efficiency of batteries, but also prolong the service life of batteries. Due to the complex electrochemical properties and physical reactions inside the battery, SOC cannot be measured directly, and is generally estimated through external parameters of the battery, such as voltage and operating current. [0003] SOC is one of the most important parameters of the battery management system. Accurate estimation of SOC not only provides accurate remaining power for electric vehicle drivers, but also provides a basis ...

AI Technical Summary

Problems solved by technology

[0011] The present invention proposes a hybrid extended Kalman filter lithium battery SOC estimation method, which overcomes the uncertainty of the battery model in the EKF algorithm, the unknown statistical characteristics of the noise, and the linearization of the Taylor expansion that may cause the filter accuracy to decrease or even cause filter divergence. question

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