Lithium battery SOC estimation method based on adaptive weighted volume particle filter

An adaptive weighting and particle filter technology, applied in the direction of measuring electricity, measuring electrical variables, instruments, etc., can solve the problems of large amount of calculation, low SOC estimation accuracy, poor robustness, etc., achieve strong tracking ability and improve estimation accuracy Effect

Pending Publication Date: 2022-06-24
CHANGAN UNIV
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a lithium battery SOC estimation method based on adaptive weighted volumetric particle filter to solve the problems of low SOC estimation accuracy and poor robustness existing in the prior art question
Particle filter mainly has the advantages of high filtering precision and strong anti-noise ability, but has the disadvantages of large amount of calculation and poor samples.

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 battery SOC estimation method based on adaptive weighted volume particle filter
  • Lithium battery SOC estimation method based on adaptive weighted volume particle filter
  • Lithium battery SOC estimation method based on adaptive weighted volume particle filter

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0038] This embodiment provides a lithium battery SOC estimation method based on adaptive weighted volume particle filtering, such as figure 1 As shown, the method includes the following steps:

[0039] Step 1, establish the equivalent circuit model of the battery:

[0040] The first-order RC circuit model of the battery is established, and the parameter relationship between its components is:

[0041]

[0042] where:

[0043] is the derivative of polarization voltage with respect to time;

[0044] U d is the terminal polarization voltage of the RC circuit;

[0045] U t is the terminal voltage of the battery;

[0046] U OCV is the open circuit voltage of the battery;

[0047] I L is the current flowing through the circuit;

[0048] C d are the polarized capacitance model parameters;

[0049] R d are the parameters of the polarization internal resistance model;

[0050] R i is the ohmic internal resistance model parameter;

[0051] Step 2: Use the FFRLS alg...

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 provides a lithium battery SOC (State of Charge) estimation method based on adaptive weighted volume particle filtering, which comprises the following steps: firstly, according to battery current and voltage data acquired by a vehicle-mounted sensor, identifying battery model parameters by using a recursive least square algorithm with a forgetting factor; and finally, estimating the SOC by using a self-adaptive weighted volume particle filter algorithm. According to the lithium battery SOC estimation method based on the adaptive weighted volume particle filter, the PF algorithm and the adaptive weighted volume Kalman filter (AWCKF) algorithm are fused to estimate the SOC. Compared with a traditional PF algorithm, the algorithm can effectively solve the problem of particle degradation in a particle filter algorithm, the estimation precision of the algorithm is effectively improved, and it can be guaranteed that the SOC estimation precision is within 1%. Meanwhile, in the SOC estimation process, the algorithm is extremely high in abrupt change state tracking capacity, and rapid convergence can be achieved in the abrupt change state.

Description

technical field [0001] The invention belongs to the technical field of electric vehicle power battery management systems, relates to lithium batteries, and in particular relates to a lithium battery SOC estimation method based on adaptive weighted volume particle filtering. Background technique [0002] In recent years, lithium-ion power batteries have been widely used in various electric vehicles due to their high energy density and long service life. The battery management system (BMS) plays an important role in improving the performance of the power battery and ensuring the safe and reliable operation of the battery. Accurate estimation of battery state of charge (SOC) is one of the core functions of BMS. SOC cannot be measured directly and must be estimated from several measurable signals, such as voltage, current and temperature. Researchers have proposed many methods for estimating SOC. For example: ampere-hour integration method, open circuit voltage method, fuzzy ...

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 & AuthorityApplications(China)
IPC IPC(8): G01R31/367G01R31/387
CPCG01R31/367G01R31/387
Inventor赵轩张凯何宗科王姝马建贺伊琳王静周猛
OwnerCHANGAN UNIV