Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Lithium ion power battery state-of-charge estimation method and device, medium and equipment

A technology of power battery and state of charge, which can be used in measuring devices, measuring electricity, measuring electrical variables, etc., and can solve the problem of low estimation accuracy.

Inactive Publication Date: 2021-02-02
GUANGZHOU YIWEI ELECTROMOTION AUTOMOBILE CO LTD
View PDF1 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when these existing SOCs are estimated, when the system model is uncertain, the accuracy of the model's SOC estimation will be relatively low

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
  • Lithium ion power battery state-of-charge estimation method and device, medium and equipment
  • Lithium ion power battery state-of-charge estimation method and device, medium and equipment
  • Lithium ion power battery state-of-charge estimation method and device, medium and equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0173] This embodiment discloses a method for estimating the state of charge of a lithium-ion power battery, which optimizes the nonlinear system filtering technology, and based on the Kalman filter method, realizes real-time dynamic estimation of the state of charge of a lithium-ion power battery, as shown in figure 1 As shown, the method includes:

[0174] S1. The SOC value algorithm estimation procedure starts.

[0175] S2. Importing the battery model, performing parameter identification on the imported battery model.

[0176] In this example, the battery model as figure 2 as shown in figure 2 Among them, it represents the electromotive force (EMF) of the battery, and the unit is volts (V). It has a certain functional relationship with the SOC of the power battery. Among them, R o Represents the ohmic internal resistance inside the power battery, the unit is ohm (Ω); the resistance-capacitance network parameter (R p1 ,C p1 ) is used to describe the first polarizatio...

Embodiment 2

[0259] This embodiment discloses a device for estimating the state of charge of a lithium-ion power battery, including a parameter identification module, an algorithm fusion module, an initialization module, an acquisition module, and an SOC value estimation module; the functions of each module are as follows:

[0260] The parameter identification module is used for parameter identification of the imported battery model;

[0261] The algorithm fusion module is used to construct the basic formula of power battery SOC estimation according to the identified model parameters, and integrate the basic formula of power battery SOC estimation into the Kalman filter algorithm;

[0262] The initialization module is used for the initialization assignment of Kalman filter state vector and error covariance matrix parameters;

[0263] The obtaining module is used to obtain the measured value of the power battery current collected in real time as the input vector of the Kalman filter;

[02...

Embodiment 3

[0267] This embodiment discloses a storage medium, which stores a program. When the program is executed by a processor, the method for estimating the state of charge of a lithium-ion power battery described in Embodiment 1 is implemented, specifically as follows:

[0268] Battery model import, parameter identification for the imported battery model;

[0269] According to the identified model parameters, the basic formula for power battery SOC estimation is constructed, and the basic formula for power battery SOC estimation is integrated into the Kalman filter algorithm;

[0270] Initial assignment of Kalman filter state vector and error covariance matrix parameters;

[0271] Obtain the measured value of the power battery current collected in real time as the input vector of the Kalman filter;

[0272] The fading factor is introduced into the fused Kalman filter, and the SOC value of the power battery is estimated based on the Kalman filter introduced with the fading factor. ...

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 lithium ion power battery state-of-charge estimation method and device, a medium and equipment, and the method comprises the steps: firstly carrying out the importing of a battery model, and carrying out the parameter recognition of the imported battery model; constructing a power battery SOC estimation basic formula according to the identified model parameters, and fusing the power battery SOC estimation basic formula into a Kalman filtering algorithm; initializing assignment of Kalman filtering state vectors and error covariance matrix parameters; during SOC actualestimation, collecting open-circuit voltage and load current of the power battery in real time; introducing a fading factor into the fused Kalman filtering, and estimating the SOC value of the power battery based on the Kalman filtering introduced with the fading factor. According to the method, estimation of the state of charge of the battery is realized based on an optimized nonlinear system filtering technology, the method has excellent characteristics of a strong tracking filter when a system model is uncertain, and real-time, accurate and dynamic estimation of the state of charge of the lithium ion power battery can be realized.

Description

technical field [0001] The invention belongs to the technical field of lithium-ion power battery management, and in particular relates to a method, device, medium and equipment for estimating the state of charge of a lithium-ion power battery. Background technique [0002] The world's proven oil reserves are decreasing day by day, and energy shortage will be the biggest problem facing the automotive industry in the world today. In order to meet the requirements of energy saving and emission reduction, the promotion of new energy vehicles (especially electric vehicles) is expected to solve this problem. Intelligent, Networking, electrification, and sharing not only change people's traditional transportation modes, but also affect people's lifestyles. The development and extension of new energy transportation technologies represented by electric vehicles constitute the future of intelligent transportation and smart cities. The cornerstone has become an inevitable development t...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/388
CPCG01R31/367G01R31/388
Inventor 李菁欧阳剑
Owner GUANGZHOU YIWEI ELECTROMOTION AUTOMOBILE CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com