Method for estimating SOC (State of Charge) of lithium ion battery based on gray extended Kalman filtering algorithm

A lithium-ion battery and extended Kalman technology, which is applied in secondary batteries, circuits, and measurement electronics, can solve problems such as non-convergence of estimation results, long-term standing of the open circuit voltage method, and small amount of calculation.

Inactive Publication Date: 2016-08-10
GUANGXI UNIV
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Problems solved by technology

The ampere-hour integration method is simple in principle and easy to calculate, but the accuracy of the ampere-hour method is restricted by accurate initial values, battery aging, and cumulative errors.
The open circuit voltage method can obtain relatively accurate SOC according to the OCV curve, but the open circuit voltage method needs to be left for a long time, which is difficult to meet the online estimation requirements
The extended Kalman filter method is a research hotspot in recent years. It has the characteristics of small amount of calculation and high estimation accuracy. However, the extended Kalman filter method requires that the noise statistics conform to the Gaussian model, which is difficult to meet in real vehicles, which may lead to non-convergence of the estimation results. , affecting the SOC estimation accuracy

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  • Method for estimating SOC (State of Charge) of lithium ion battery based on gray extended Kalman filtering algorithm
  • Method for estimating SOC (State of Charge) of lithium ion battery based on gray extended Kalman filtering algorithm
  • Method for estimating SOC (State of Charge) of lithium ion battery based on gray extended Kalman filtering algorithm

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Embodiment Construction

[0051] The specific steps of the lithium-ion battery SOC estimation method based on extended Kalman are:

[0052] Given the initial value of the algorithm: x(0), P 0 , Q 0 and the covariance R(0) of the initial time ν. where P 0 Estimate error covariance matrix P for polarization voltage and SOC state k Initial time value.

[0053] Measure the lithium-ion battery current and terminal voltage at time k, and identify the first-order RC model parameters of the lithium-ion battery at this moment by the recursive least squares algorithm with forgetting factor: R 0 (k), R 1 (k), C 1 (k), get lithium ion state space expression and observation equation expression coefficient matrix A k , B k , C k ,D k .

[0054] In the practical application of the GM-EKF algorithm, the GM-EKF algorithm is used to estimate the battery SOC after the EKF algorithm converges. According to the historical data of the battery system state quantity after n updates before k time by the gray predic...

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Abstract

The invention discloses a method for estimating the SOC (State of Charge) of a lithium ion battery based on a gray extended Kalman filtering algorithm. The method comprises the steps of firstly predicting prior estimated values of polarization voltage and an SOC state variable of a battery model at the present moment through a gray prediction model and replacing a Jacobian matrix in the extended Kalman filtering algorithm, and then updating and correcting the prior estimated values through observed values by using the extended Kalman filtering algorithm so as to acquire an SOC estimated value of the lithium ion battery at the present moment. The invention provides a lithium ion battery SOC estimation method for an electric automobile battery management system, and the SOC estimation precision of the lithium ion battery can be improved.

Description

technical field [0001] The invention belongs to the technical field of lithium ion batteries, and more specifically relates to a method for estimating the SOC of lithium ion batteries. Background technique: [0002] As the main energy storage device of electric vehicles, power batteries are the core components of the development of electric vehicles. Lithium-ion batteries have become the main direction of electric vehicle power batteries because of their high energy density, long cycle life, and low self-discharge. [0003] The battery state of charge (State of Charge, SOC) is an important indicator of the battery state. Accurate online SOC estimation can effectively prevent battery overcharge and overdischarge, remind users to charge in time, replace batteries in time, etc. At the same time, it can also save battery costs, prolong battery life, and provide a basis for electric vehicle vehicle control. At present, researchers at home and abroad have provided SOC estimation...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
CPCG01R31/387H01M10/48Y02E60/10
Inventor 潘海鸿吕治强陈琳
Owner GUANGXI UNIV
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