Battery SOC online estimation method based on double Kalman filtering algorithm

A filtering algorithm and extended Kalman technology, applied in the field of battery identification and estimation, can solve problems such as cumulative error, large battery model dependence, and high accuracy requirements of battery model parameters, and achieve the effect of reducing current accuracy requirements

Inactive Publication Date: 2017-06-09
深圳市麦澜创新科技有限公司
View PDF4 Cites 41 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method is simple to implement, but prone to cumulative errors
The open circuit voltage refers to the voltage value of the battery after a long period of standing and stabilizing. The open circuit voltage is close to the electromotive force of the battery in value, but the open circuit voltage needs to be obtained after a long period of standing, and the general battery SOC estimation is a dynamic estimation; the Kalman filter method is Considering the battery as a power system, and mak

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Battery SOC online estimation method based on double Kalman filtering algorithm
  • Battery SOC online estimation method based on double Kalman filtering algorithm
  • Battery SOC online estimation method based on double Kalman filtering algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0049] A battery SOC online estimation method based on double Kalman filter algorithm, refer to figure 1 and figure 2 , figure 1 It is a flow chart of the steps of a battery SOC online estimation method based on a double Kalman filter algorithm in the present invention, figure 2 It is an algorithm schematic diagram of a battery SOC online estimation method based on a double Kalman filter algorithm of the present invention, comprising the following steps:

[0050] S1. Obtain the initial value of the battery SOC;

[0051] S2. Establish a battery equivalent circuit model, and obtain the state equation and output equation of the battery;

[0052] S3. Using the initial value of the battery SOC as the input state quantity, and the voltage equation corresponding to the battery equivalent circuit model as th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a battery SOC online estimation method based on double Kalman filtering algorithm, comprising: S1) obtaining the initial value of the battery SOC; S2) creating a battery equivalent circuit model; obtaining the state equation and the output equation of the battery; S3) using the initial value of the battery SOC as the input state amount and the voltage equation corresponding to the battery equivalent circuit model as the output equation; and utilizing the expanded Kalman filtering algorithm to perform battery SOC estimation; S4) using the battery SOC estimated by the expanded Kalman filtering algorithm as the input state amount and the amper-hour integral method as the output equation; and performing battery SOC estimation through the use of the Kalman filtering algorithm for the estimation value of the battery SOC. The battery SOC online estimation method based on double Kalman filtering algorithm can obtain the SOC estimation value more accurately. Without its excessive reliance on a battery model, the requirement of the method on the current accuracy is also reduced. The Battery SOC online estimation method based on double Kalman filtering algorithm of the invention can find wide applications in the battery identification and estimation field.

Description

technical field [0001] The invention relates to the field of battery identification and estimation, in particular to a battery SOC online estimation method based on a double Kalman filter algorithm. Background technique [0002] In recent years, pure electric vehicles and hybrid vehicles with batteries as the main power source have been widely put into use. This emerging means of transportation has attracted people's attention for its low-carbon emission, energy-saving and light-weight features. However, the development of electric vehicles at this stage is largely limited by factors such as the stability, safety and service life of power batteries. During the operation of the electric vehicle, the driver needs to know the remaining battery power at any time to judge the cruising range. The battery state of charge (SOC) is the ratio of the available energy to the total energy of the battery. It is an important parameter that cannot be directly measured by the battery. Its ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01R31/36
CPCG01R31/367G01R31/387
Inventor 刘博洋吕洲杨林
Owner 深圳市麦澜创新科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products