Online lithium ion battery SOC (state of charge) estimation method based on extended Kalman filter

A lithium-ion battery, extended Kalman technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve the problem of low reliability

Inactive Publication Date: 2014-01-22
HARBIN INST OF TECH
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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 problem of low reliability of the existing online estimation of lithium-ion battery SOC due to the influence of initial value selection, and provides an online estimation method of lithium-ion battery SOC based on extended Kalman filter

Method used

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  • Online lithium ion battery SOC (state of charge) estimation method based on extended Kalman filter
  • Online lithium ion battery SOC (state of charge) estimation method based on extended Kalman filter
  • Online lithium ion battery SOC (state of charge) estimation method based on extended Kalman filter

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specific Embodiment approach 1

[0104] Specific implementation mode one: the following combination figure 1 , figure 2 with Figure 24 Describe this embodiment, this embodiment is based on the lithium-ion battery SOC online estimation method of extended Kalman filter, and it comprises the following steps:

[0105] Step 1: Collect the battery terminal voltage U of the lithium-ion battery under test o (t) and polarization voltage, establish the voltage-current relational expression of the first-order RC equivalent circuit of the lithium-ion battery under test and the voltage-current relational expression of the second-order RC equivalent circuit;

[0106] Step 2: Carry out charge and discharge experiments on the tested lithium-ion battery, and record each corresponding initial value of the battery SOC and the initial value of the battery open circuit voltage U ocv (0), and establish the Kalman filter initial value SOC of the lithium-ion battery under test 0 The polynomial fitting function of ;

[0107] S...

specific Embodiment approach 2

[0109] Specific implementation mode two: the following combination figure 1 with figure 2 Describe this embodiment, this embodiment will further explain Embodiment 1. In step 1 of this embodiment, the voltage-current relationship of the first-order RC equivalent circuit of the lithium-ion battery under test is:

[0110] u o (t)=U ocv (t)-U p (t)-i 1 (t)R d1 (t);

[0111] where t is time, U ocv (t) is the open circuit voltage of the tested lithium-ion battery, U p (t) is the polarization voltage of the RC link in the first-order RC equivalent circuit, i 1 (t) is the loop current of the first-order RC equivalent circuit, R d1 (t) is the lithium-ion battery resistance measured in the first-order RC equivalent circuit;

[0112] After the discretization of the voltage-current relationship of the above-mentioned first-order RC equivalent circuit is:

[0113] u o,k =U ocv,k -U p,k -i 1,k R d1,k ,

[0114] Among them, U o,k is the terminal voltage U of the lithium-i...

specific Embodiment approach 3

[0122] Specific Embodiment Three: This embodiment further explains Embodiment Two. In step two of this embodiment, the Kalman filter initial value SOC of the lithium-ion battery under test 0 The polynomial fitting function for is:

[0123] SOC 0 =H(U ocv (0));

[0124] In the formula, H is the initial value SOC according to the Kalman filter 0 The established least squares fitting function.

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Abstract

The invention discloses an online lithium ion battery SOC estimation method based on extended Kalman filter, belongs to the technical field of SOC prediction of a lithium ion battery, and aims to solve the problem that the reliability of the online estimation of a conventional lithium ion battery SOC is low due to influence of initial value selection. The method comprises the steps as follows: a voltage and current relation of a first-order RC (resistance/capacitance) equivalent circuit of a detected lithium ion battery and a voltage and current relation of a second-order RC equivalent circuit are established firstly; a charge-discharge experiment is performed on the detected lithium ion battery to establish a polynomial fitting function of a Kalman filter initial value SOC 0 of the detected lithium ion battery; a covariance P (0) of the Kalman filter initial value SOC 0 and a Kalman filter initial error of the detected lithium ion battery is obtained; and then, battery SOC estimation based on extended Kalman filter is performed, so that the online estimation of the lithium ion battery SOC is realized. The method is used for online estimation of the lithium ion battery SOC.

Description

technical field [0001] The invention relates to an online SOC estimation method of a lithium-ion battery based on an extended Kalman filter, and belongs to the technical field of state-of-charge prediction of a lithium-ion battery. Background technique [0002] Among many parts of the battery management system, the prediction of the battery state of charge SOC (State of Charge) is the most basic and most important task. The accuracy of the prediction will affect the control strategy of the battery management system, thereby affecting the performance of the battery play. At the same time, SOC is also an important parameter to guide the charging or discharging process of the battery, which can prevent irreversible damage caused by overcharging and overdischarging of the battery, and can better protect the battery. For the power battery used in electric vehicles, by correctly estimating the SOC of the battery and making full use of the electric energy of the battery, the cruis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
Inventor 刘丹刘昕张岩王启松孙金玮朱春波
Owner HARBIN INST OF TECH
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