Lithium-ion battery SoC (State-of-Charge) estimation method based on improved and self-adaptive dual unscented-Kalman-filters

A technology of unscented Kalman and power battery, applied in the direction of instruments, measuring electricity, measuring electric variables, etc., can solve the problems of algorithm influence, update and increase the amount of calculation, etc.

Active Publication Date: 2019-11-01
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Not only that, each update and iteration of the covariance matrix greatly increases the amount of calculations, which puts high demands on the

Method used

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  • Lithium-ion battery SoC (State-of-Charge) estimation method based on improved and self-adaptive dual unscented-Kalman-filters
  • Lithium-ion battery SoC (State-of-Charge) estimation method based on improved and self-adaptive dual unscented-Kalman-filters
  • Lithium-ion battery SoC (State-of-Charge) estimation method based on improved and self-adaptive dual unscented-Kalman-filters

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Experimental program
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Effect test

Embodiment 1

[0126] See Figure 1 to Figure 4 , The SoC estimation method of lithium-ion power battery based on improved adaptive dual unscented Kalman filter mainly includes the following steps:

[0127] 1) Obtain the model and operating parameters of the lithium-ion power battery to be tested, and establish an equivalent circuit model of the lithium-ion power battery.

[0128] The operating parameters mainly include nominal capacity C, charging cut-off voltage V c And discharge cut-off voltage V d .

[0129] The equivalent circuit model of the lithium-ion power battery is a second-order RC equivalent circuit model.

[0130] The circuit structure of the second-order RC equivalent circuit model is as follows:

[0131] Remember that the end of the power supply's positive pole is S, and the end of the power supply's negative pole is W. S end in series with resistance R 1 , Resistance R 2 And resistance R 0 . S terminal is connected in series with capacitor C 1 And resistance R 2 . Resistance R 1 C...

Embodiment 2

[0235] The SoC estimation method of lithium-ion power battery based on improved adaptive dual unscented Kalman filter mainly includes the following steps:

[0236] 1) Obtain the model and operating parameters of the lithium-ion power battery to be tested, and establish an equivalent circuit model of the lithium-ion power battery.

[0237] 2) Determine the characteristic parameters of the equivalent circuit model of the lithium-ion power battery.

[0238] 3) Establish the state filter and parametric filter of the lithium-ion power battery based on the equivalent circuit model.

[0239] 4) Couple the state filter and parametric filter of the equivalent circuit model of the lithium-ion power battery to establish a dual unscented Kalman filter.

[0240] 5) Input the operating parameters of the lithium-ion power battery to be tested into the dual unscented Kalman filter to perform parameter correction of the lithium-ion power battery equivalent circuit model and SoC estimation of the state o...

Embodiment 3

[0242] The method for estimating the SoC of a lithium-ion power battery based on an improved adaptive dual unscented Kalman filter, the main steps are the same as in embodiment 2, wherein the operating parameters mainly include the nominal capacity C and the charge cut-off voltage V c And discharge cut-off voltage V d .

[0243] Take the AYP110161227N50 ternary material battery cell produced by Zhejiang Aoyou Power System Co., Ltd. as an example. Three basic operating parameters of its nominal capacity C (54Ah, 0.3C), charge cut-off voltage Vc (4.2V), and discharge cut-off voltage Vd (2.75V) are obtained.

[0244] The lithium ion power battery equivalent circuit model is a second-order RC equivalent circuit model, and a Rint model, a first-order model, a multi-order model, etc. can also be selected.

[0245] The circuit structure of the second-order RC equivalent circuit model is as follows:

[0246] Remember that the end of the power supply's positive pole is S, and the end of the po...

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Abstract

The invention discloses a lithium-ion battery SoC (State-of-Charge) estimation method based on improved and self-adaptive dual unscented-Kalman-filters. The method mainly comprises the following steps: 1) establishing a state filter and a parameter filter of a lithium ion power battery based on an equivalent circuit model; 2) coupling the state filter and the parameter filter of the lithium ion power battery based on the equivalent circuit model, and building dual unscented-Kalman-filters; and 3) inputting operation parameters of a to-be-detected lithium ion power battery into the dual unscented-Kalman-filters, and carrying out parameter correction and stage-of-charge (SoC) estimation on a lithium ion power battery equivalent circuit model. According to the method in the invention, the effectiveness of cholesky decomposition is guaranteed, the problem that iteration is stopped due to the fact that a covariance matrix is indefinite caused by reasons such as an initial value error, noisedisturbance, calculation module floating point error and the like is overcome, and the numerical stability of the filtering process and the robustness of the algorithm are enhanced.

Description

Technical field [0001] The invention relates to the field of state-of-charge prediction, in particular to a lithium ion power battery SoC estimation method based on an improved adaptive dual unscented Kalman filter. Background technique [0002] The State-Of-Charge (SoC, State-Of-Charge) of the power battery of an electric vehicle is an important parameter of the running state of the power battery, and it is also a basic parameter for related control of the power battery in the battery management system (BMS, Battery-Management-System) . The estimation accuracy of SoC will directly affect the control effect of BMS. [0003] In the existing SoC estimation methods based on the equivalent circuit model, the Kalman filter algorithm is widely used in microcontrollers with its tracking characteristics and real-time performance. Due to the nonlinear characteristics of the state equation and observation equation established based on the equivalent circuit model, the traditional Kalman fi...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/3842
CPCG01R31/367G01R31/3842
Inventor 余传祥谢延敏桑曌宇杨诗雅刘和平黄鹏黄远胜董治平游逍遥杨生博
Owner CHONGQING UNIV
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