Lithium iron phosphate battery SOC estimation method based on dynamic optimization forgetting factor recursive least square online identification

A technology of recursive least squares and lithium iron phosphate batteries, which is applied in the direction of testing electrical devices in transportation, measuring electricity, measuring electrical variables, etc.

Inactive Publication Date: 2021-08-03
NANJING FORESTRY UNIV
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  • Abstract
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Problems solved by technology

Offline identification has a relatively simple least squares fitting method, but it generally requires appropriate initial parameters, b

Method used

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  • Lithium iron phosphate battery SOC estimation method based on dynamic optimization forgetting factor recursive least square online identification
  • Lithium iron phosphate battery SOC estimation method based on dynamic optimization forgetting factor recursive least square online identification
  • Lithium iron phosphate battery SOC estimation method based on dynamic optimization forgetting factor recursive least square online identification

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Experimental program
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Embodiment Construction

[0030] Create a second-order equivalent circuit model:

[0031] see figure 1 , U oc (t) is the open circuit voltage; R 0 (t) is the internal resistance in ohms; I(t) is the working current, positive for discharge; R 1 (t), R 2 (t) are electrochemical polarization internal resistance and concentration polarization internal resistance respectively, C 1 (t), C 2 (t) are electrochemical polarization capacitance and concentration polarization capacitance respectively [11] ; 1 (t) is the electrochemical polarization ring terminal voltage; U 2 (t) is the terminal voltage of the concentration polarization ring; U t (t) is the battery terminal voltage. According to Kirchhoff's voltage law and Kirchhoff's current law, the electrical relationship of the equivalent circuit model can be obtained as:

[0032]

[0033] Further, the open circuit voltage model is established:

[0034] In view of the fact that the open circuit voltage at the middle value of the SOC of the lithium ...

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Abstract

The invention provides a lithium iron phosphate battery state of charge (SOC) estimation method based on dynamic optimization forgetting factor recursive least square online identification. The method comprises the steps: carrying out segmented equal-interval discharge standing test on a lithium iron phosphate battery to obtain the curve relation between the open-circuit voltage and the state of charge of the lithium iron phosphate battery, carrying out particle swarm optimization on a forgetting factor, recurring a forgetting factor of a least square method, and estimating the SOC of the battery by combining optimized least square online identification and extended Kalman filtering. According to the method, the forgetting factor selected in real time can enable the terminal voltage following error to be extremely small so as to ensure the accuracy of parameter online identification, and the optimized forgetting factor recursive least square and extended Kalman filtering are combined to estimate the SOC of the lithium iron phosphate battery; and the combined estimation precision is high, and the dynamic requirements of combined estimation on forgetting factor values under different batteries and different battery use environments can be adapted.

Description

technical field [0001] The invention relates to a method for estimating the state of charge (SOC) of a power battery. It provides a method for estimating the SOC of a lithium iron phosphate battery based on a dynamic optimal forgetting factor recursive least squares online identification. SOC estimation accuracy. Background technique [0002] In recent years, the development of electric vehicles has become increasingly rapid, and the role of power batteries as the power source of electric vehicles is self-evident. SOC is an important parameter of power batteries, and researchers are eager to estimate SOC more accurately to serve vehicle energy management and safety. [0003] The equivalent circuit model observation method is currently the mainstream battery SOC estimation method. Based on the circuit model, the battery SOC is estimated by using the charging and discharging data to identify the parameters and combining with the filtering algorithm. Model parameters affect ...

Claims

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Application Information

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IPC IPC(8): G01R31/367G01R31/36G01R31/388G01R31/00
CPCG01R31/367G01R31/3648G01R31/388G01R31/006
Inventor 葛才安郑燕萍王浩
Owner NANJING FORESTRY UNIV
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