SOC prediction method and system based on strong tracking algorithm and adaptive Kalman filtering

An adaptive Kalman and prediction method technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve problems such as inability to predict with high precision, lack of fast tracking ability, and inability to quickly track battery status.

Inactive Publication Date: 2021-04-20
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] Most of the lithium battery SOC prediction methods disclosed in the prior art do not have fast tracking capabilities, and cannot quickly track the bat

Method used

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  • SOC prediction method and system based on strong tracking algorithm and adaptive Kalman filtering
  • SOC prediction method and system based on strong tracking algorithm and adaptive Kalman filtering
  • SOC prediction method and system based on strong tracking algorithm and adaptive Kalman filtering

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Experimental program
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Embodiment 1

[0047] In one or more embodiments, a lithium battery SOC prediction method based on strong tracking algorithm and adaptive Kalman filter is disclosed, refer to figure 1 , including the following procedures:

[0048] (1) Carry out charge and discharge experiments on the battery to be tested under different working conditions, record the experimental data, and obtain the SOC-OCV curve of the battery.

[0049] Among them, the experimental data recorded includes: obtaining the open circuit voltage OCV curve under different SOC conditions when the battery is charged and discharged, and then summing the two curves and taking the average value, and defining this curve as the SOC-OCV of the battery curve.

[0050] (2) Carry out parameter identification to described experimental data, construct SOC estimation state equation and measurement equation;

[0051] Specifically, parameter identification is carried out on the processed experimental data, and the influencing factors of SOC pr...

Embodiment 2

[0087] In one or more embodiments, a lithium battery SOC prediction system based on a strong tracking algorithm and an adaptive Kalman filter is disclosed, including:

[0088] The data acquisition module is used to conduct charge and discharge experiments of the battery under test under different working conditions, and record the experimental data;

[0089] A prediction model building module, used for parameter identification of the experimental data, constructing a SOC prediction state equation and measurement equation;

[0090] Algorithm optimization module, for adopting strong tracking algorithm to optimize the improved adaptive Kalman filter algorithm;

[0091] A noise correction module, configured to correct the noise of the state model and the measurement model by using an optimized adaptive Kalman filter method;

[0092] The prediction module is used to predict the SOC of the battery by using the corrected state model and the measurement model.

[0093] The specific ...

Embodiment 3

[0095] In one or more embodiments, a terminal device is disclosed, including a server, the server includes a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor executes the The program realizes the lithium battery SOC prediction method based on the strong tracking algorithm and the adaptive Kalman filter in the first embodiment. For the sake of brevity, details are not repeated here.

[0096] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

[0097] T...

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PUM

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Abstract

The invention discloses an SOC prediction method and system based on a strong tracking algorithm and adaptive Kalman filtering. The method comprises the following steps: carrying out the charging and discharging experiment of a to-be-tested battery under different working conditions, and recording the experiment data; performing parameter identification on the experimental data, and constructing an SOC estimation state equation and a measurement equation; optimizing the improved adaptive Kalman filtering algorithm by adopting a strong tracking algorithm; correcting the noise of the state model and the measurement model by adopting an optimized adaptive Kalman filtering method; and predicting the SOC of the battery by using the corrected state model and the measurement model. According to the invention, the adaptive Kalman filtering algorithm can overcome the working condition that the environmental noise is non-Gaussian white noise; and the strong tracking algorithm is applied to SOC calculation, so that an estimation result can be quickly followed under an emergent working condition.

Description

technical field [0001] The invention relates to the technical field of battery power management, in particular to a lithium battery state of charge (hereinafter referred to as SOC) prediction method based on a strong tracking algorithm and an adaptive Kalman filter. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] As a new type of transportation, the development and use of electric vehicles will gradually become a mainstream way of life. As the core of electric vehicles, power lithium batteries need to have relatively better charging and discharging performance, and need to have a higher energy ratio and high power tolerance. [0004] The battery management system (BMS) in electric vehicles is the guarantee for the safe and reliable operation of the battery. In the actual application of the battery management system, the working state of t...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/388
Inventor 孙赛赛
Owner SHANDONG UNIV
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