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Lithium battery pack parameter identification method based on multi-constraint condition particle swarm optimization algorithm

A technology of particle swarm optimization and parameter identification, which is applied in the field of parameter identification of the electrochemical model of lithium-ion battery packs, can solve problems such as inability to predict the state of the battery pack

Active Publication Date: 2021-03-09
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL +1
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  • Summary
  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the existing problem that only the behavior of battery cells can be identified, but the overall state of the battery pack cannot be predicted.

Method used

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  • Lithium battery pack parameter identification method based on multi-constraint condition particle swarm optimization algorithm
  • Lithium battery pack parameter identification method based on multi-constraint condition particle swarm optimization algorithm
  • Lithium battery pack parameter identification method based on multi-constraint condition particle swarm optimization algorithm

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specific Embodiment approach 1

[0082] Specific implementation mode one: refer to figure 1 Specifically explain this embodiment, the lithium battery pack parameter identification method based on the multi-constraint condition particle swarm optimization algorithm described in this embodiment, the method includes the following steps:

[0083] Step 1, establish the electrochemical model of lithium-ion single battery;

[0084] Step 2, using the stimulus response method to identify the electrochemical model of the lithium-ion battery cell, and obtain the model parameter values;

[0085] Step 3, according to the model parameter value obtained in step 2, set the parameter value range of the lithium-ion battery pack electrochemical model;

[0086] Step 4, using the particle swarm optimization algorithm with multi-constraints to obtain the model parameter vector of the lithium-ion battery pack from the parameter value range of the electrochemical model of the lithium-ion battery pack.

[0087] This application doe...

specific Embodiment approach 2

[0095] Specific embodiment 2: This embodiment is to further explain the lithium battery pack parameter identification method based on the multi-constraint condition particle swarm optimization algorithm described in the specific embodiment 1. In this embodiment, in step 1, the lithium-ion single battery The electrochemical model is:

[0096] u app (t k ) = U p (y surf (t k ))-U n (x surf (t k ))-R ohm I(t k ) Formula 1,

[0097] In the formula, U app (t k ) for t k Theoretical terminal voltage of lithium-ion battery cell at time, U p and U n are positive open circuit potential and negative open circuit potential respectively, y surf (t k ) and x surf (t k ) are respectively t k Lithium ion concentration on the surface of the positive solid phase at time and t k Lithium ion concentration on the solid surface of negative electrode at time, R ohm is the equivalent ohmic internal resistance of the lithium-ion battery, I(t k ) for t k time external current, ...

specific Embodiment approach 3

[0103] Specific embodiment three: This embodiment is to further explain the lithium battery pack parameter identification method based on the multi-constraint condition particle swarm optimization algorithm described in specific embodiment two. In this embodiment, in step 2, the excitation response method is used to The electrochemical model of the lithium-ion battery cell is identified, and the parameter values ​​of the model are obtained, including: the initial lithium intercalation amount of the positive electrode y 0 , initial lithium intercalation amount of negative electrode x 0 , positive electrode capacity Q p and negative electrode capacity Q n ;

[0104] The initial lithium intercalation amount of positive electrode y 0 and the initial lithium intercalation amount of the negative electrode x 0 The acquisition process is:

[0105] Carry out 0.02C small rate discharge test on the battery, obtain the voltage and current I data corresponding to the battery from full...

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Abstract

The invention discloses a lithium battery pack parameter identification method based on a multi-constraint condition particle swarm optimization algorithm, and relates to the field of lithium ion battery pack electrochemical model parameter identification. The invention aims to solve the problems that only the behavior of a battery monomer can be identified and the overall state of a battery packcannot be predicted in the prior art. The method comprises the steps of 1, establishing an electrochemical model of a lithium-ion single battery; 2, identifying the lithium ion battery monomer electrochemical model by adopting an excitation response method to obtain a model parameter value; 3, setting a parameter value range of the electrochemical model of the lithium ion battery pack according tothe model parameter value obtained in the step 2; and 4, obtaining a model parameter vector of the lithium ion battery pack from a set parameter value range of the electrochemical model of the lithium ion battery pack by adopting a multi-constraint condition particle swarm optimization algorithm. The method is used for detecting the state of the lithium-ion battery pack.

Description

technical field [0001] The invention relates to an online acquisition method for electrochemical model parameters of a battery pack. It belongs to the field of parameter identification of electrochemical model of lithium-ion battery pack. Background technique [0002] With the destruction of the environment and the increasing consumption of resources, new energy has become the trend of future development, and battery energy storage plays a vital role in renewable energy. Compared with other batteries, lithium-ion batteries have the advantages of low energy density, no memory effect, long life, and low cost. They are widely used in energy storage, military, electronic industry and other types of batteries, and have great application prospects. and huge market demand. The study of high-performance lithium-ion batteries has great scientific significance and economic benefits. The reliability and safety of lithium-ion batteries are still the key factors restricting the develo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/00G06F111/04
CPCG06F30/27G06N3/006G06F2111/04
Inventor 王立欣李俊夫冀禹昆刘能锋于全庆王宇海楚潇
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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