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Lithium-ion battery remaining life prediction method

A lithium-ion battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of falling into local extremum, battery prediction accuracy and speed influence, and achieve the effect of high prediction accuracy

Inactive Publication Date: 2019-07-12
GUIZHOU UNIV
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Problems solved by technology

However, due to the problem that the PSO algorithm is easy to fall into local extreme values, it will have a certain impact on the prediction accuracy and speed of the battery.

Method used

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  • Lithium-ion battery remaining life prediction method
  • Lithium-ion battery remaining life prediction method
  • Lithium-ion battery remaining life prediction method

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Embodiment 1

[0020] Embodiment 1: a kind of lithium-ion battery residual life prediction method, this method comprises the following steps:

[0021] Step 1: Initialize chaotic particles. A particle is composed of two parameters c and g of SVR, where c is the factor, g is the width of the Gaussian radial basis kernel function, and any initial value z 0 ∈[0,1], according to formula z n+1 =μz n (1-z n ), n=0, 1, 2, ... 1000 Logistic equation can obtain a definite sequence z 1 ,z 2 ,z 3 ,…, where μ is the Logistic mapping parameter, Z n is the chaotic variable, Z 0 is the initial value of the chaotic variable;

[0022] Step 2: Fitness evaluation, calculate the fitness function value of each particle, the function value is the fitness function value of the particle, if the current fitness value of the particle is better than the individual extreme value, use the current position of the particle and The fitness value is updated to the current value; if the fitness of the particle with th...

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Abstract

The invention discloses a lithium-ion battery remaining life prediction method. The method comprises the steps that (1) chaos particles are initialized; (2) fitness assessment is performed; (3) individual optimal values and a global optimal value of the particles are updated; (4) the velocities and positions of the particles are updated according to a PSO algorithm formula; (5) the optimal position is subject to chaos optimization; (6) whether each iteration meets an ending requirement or not is judged; and (7) a CPSO-SVR model is used to perform training and prediction on a dataset. Accordingto the method, a chaos algorithm is adopted to improve a PSO algorithm, the particle appearing at the optimal position in a generated chaos sequence randomly replaces a certain particle in a currentparticle swarm, so that inert particles are urged to get rid of local optimum, jump out of constraint and are initialized again according to the chaos sequence, and an optimal solution is quickly found through search in space. An improved CPSO-SVR algorithm is adopted to predict the remaining life of a lithium-ion battery, and compared with a PSO-SVR algorithm, higher prediction precision is achieved.

Description

technical field [0001] The invention relates to a method for predicting the remaining life of a lithium ion battery, and belongs to the technical field of remaining life prediction of the lithium ion battery. Background technique [0002] With the advantages of long life, low pollution, strong safety, high energy density, etc., lithium-ion batteries have been widely used in many new energy fields in recent years. However, due to the material characteristics of lithium-ion batteries, the external environment temperature is too high or Too low, that is, the depth of discharge and other problems will affect the life of lithium-ion batteries. Therefore, in order to ensure the safe and reliable operation of lithium batteries in life and work, and reduce accidents, the prediction of the remaining service life of lithium batteries becomes Hot issues of current research. Support Vector Regression (SVR) has a strong nonlinear estimation ability and is applied to the prediction of th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367
Inventor 王一宣李泽滔
Owner GUIZHOU UNIV
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