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Method for estimating state of charge (SOC) of lithium ion battery based on IPF

A lithium-ion battery and particle technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problems of long battery charging time, low effective energy storage density of batteries, and expensive batteries, so as to improve the accuracy of SOC estimation, Avoid numerically sensitive problems and solve the effect of particle degradation problems

Active Publication Date: 2019-02-19
WENZHOU UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

At present, the industrialization process of electric vehicles is facing the following core problems: 1. The effective energy storage density of the battery is low, and it is difficult to effectively increase it in a short period of time; 2. The battery is expensive and the production and maintenance costs are high; 3. The battery charging time is long
To solve the above problems, we can only start with the research and development of the battery management system

Method used

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  • Method for estimating state of charge (SOC) of lithium ion battery based on IPF
  • Method for estimating state of charge (SOC) of lithium ion battery based on IPF
  • Method for estimating state of charge (SOC) of lithium ion battery based on IPF

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Embodiment

[0078] Embodiment: a kind of lithium-ion battery SOC estimation method based on IPF (improved Particle Filter improved particle filter), comprises the following steps:

[0079] S1. Establish a mathematical model of the lithium-ion battery system, obtain the state equation and observation equation of the system, and discretize them;

[0080] The stated equation of state is

[0081]

[0082] The observation equation is

[0083] u L (k)=UOC(k)-i(k)×R 0 (k)-U 1 (k)+v(k)

[0084] In the formula: SOC(k+1) is the state of charge of the lithium-ion battery at time k+1 of the system, U 1 (k+1) is the polarization voltage value of the system at time k+1, Δt is the sampling time, R1 is the polarization resistance, R0 is the ohmic internal resistance, τ1 is the polarization time, η is the charge-discharge efficiency, and QN is the actual Capacity, i(k) represents the discharge current of the system at time k, w(k) and v(k) represent the state and measurement noise of the system a...

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Abstract

The invention discloses a method for estimating the state of charge (SOC) of a lithium ion battery based on IPF. The method comprises the following steps that a mathematic model of a lithium ion battery system is established, and a state equation and an observation equation of the system are obtained and discretized; the lithium ion battery is subjected to a constant-current pulse discharge experiment, and the open-circuit voltage of the lithium ion battery and the residual current SOC of the lithium ion battery are obtained; a relation curve between the open-circuit voltage of the lithium ionbattery and the residual current SOC of the lithium ion battery is fitted on a MATLAB, battery model parameters are identified, and thus an equivalent model of the lithium ion battery is established;and the SOC of the lithium ion battery is estimated through an improved particle filter. The SOC of the lithium ion battery is estimated through the mathematic model of the lithium ion battery systemand the improved particle filter, the characteristics of high estimation precision and fast estimation are achieved, and accordingly, the service life of the lithium ion battery is ensured.

Description

technical field [0001] The invention relates to a lithium ion battery SOC estimation method, in particular to an IPF-based lithium ion battery SOC estimation method. Background technique [0002] In recent years, due to many factors such as energy crisis, environmental pollution and energy security, the electric vehicle industry has ushered in a booming development. At present, the industrialization process of electric vehicles is facing the following core problems: 1. The effective energy storage density of the battery is low, and it is difficult to effectively increase it in a short period of time; 2. The battery is expensive and the production and maintenance costs are high; 3. The charging time of the battery is long. To solve the above problems, we can only start with the research and development of the battery management system. As an important part of electric vehicles, the battery management system (BMS) can monitor the voltage, temperature, current, SOC, etc. of th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/387
Inventor 玄东吉侍壮飞钱潇赵晓波
Owner WENZHOU UNIVERSITY
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