Lithium iron phosphate power battery equivalent circuit model parameter estimation method based on particle swarm algorithm

Abstract

The invention relates to a lithium iron phosphate power battery equivalent circuit model parameter estimation method based on a particle swarm algorithm. The method includes the following steps that 1, modeling of a lithium iron phosphate power battery is performed; 2, decoding is performed, a lithium iron phosphate power battery equivalent circuit model parameter estimation problem is converted into an arrangement problem suitable for particle swarm optimization, and particles are represented by P, wherein Pi is (Roi, Cai, Rpi and Cpi) and represents the parameter information of the ith particle; 3, lithium iron phosphate power battery equivalent circuit model parameter estimation is conducted by the utilization of the global mode particle swarm algorithm; 4, a fitness function is min|Ur-Ue|, wherein Ur is a measured value of the terminal voltage of the power battery, and Ue is an estimated value of the terminal voltage of the power battery; 5, circulation stops when optimization circulation of the particle swarm algorithm reaches the maximum set time or |Ur-Ue| is less than or equal to 0.01. According to the method, the particle swarm algorithm is used for performing power battery equivalent circuit model parameter estimation, and experimental verification shows that the method is low in parameter estimation error and improves the accuracy of model parameter estimation.

Description

technical field [0001] The invention relates to the technical field of electric vehicle power batteries, in particular to a method for estimating parameters of an equivalent circuit model of a lithium iron phosphate power battery based on a particle swarm algorithm. Background technique [0002] The power battery system is widely used in today's hybrid vehicles, fuel cell vehicles and pure electric vehicles. In the selection of power batteries, the most widely used ones are those with high specific energy, small self-discharge, long cycle life, and no Lithium iron phosphate power battery with the characteristics of memory effect and low environmental pollution. However, lithium iron phosphate power batteries also have obvious nonlinear and time-varying characteristics, and some of their characteristics and parameters change with factors such as the battery's charge and discharge current, ambient temperature, and health status during driving. From a theoretical point of view...

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Problems solved by technology

[0004] In order to overcome the deficiency that the parameters of the equivalent circuit model of the existing lithium iron phosphate power battery are difficult to be accurately estimated, the present invention provides a method for estimating the parameters of the equivalent circuit model of the lithium iron phosphate power battery based on the particle swarm algorithm

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