Power battery SOC estimation method based on backward difference discrete model and system thereof

Abstract

Provided is a power battery SOC estimation method based on backward difference discrete model and a system thereof; the method comprises the following steps: step one, establishing a backward difference discrete model of a power battery, identifying parameters of the backward difference discrete model by a least square method including forgetting factors; step two, on the basis of the backward difference discrete model of the power battery obtained in step one, using self-adaptive extended Kalman filter in combination with a non-linear relationship between an open-circuit voltage and the SOC to complete an effective estimation of the power battery SOC. In the system, voltage and current sensors connected with the power battery are connected with an embedded microcontroller via an analog-digital conversion module. The microcontroller comprises a low-pass filter pre-processing module, a backward difference discrete battery model parameter online identification module, and an AEKF algorithm SOC estimation module. The obtained SOC result is sent to a CAN network of a display device. The power battery SOC estimation system based on backward difference discrete model is simple in structure; the parameter identification speed and precision are increased; the affection to the identification caused by history data is reduced; the calculation is convenient; and the SOC estimation precision is high.

Description

technical field [0001] The invention relates to the technical field of charge state estimation of automobile power batteries, in particular to a power battery SOC estimation method and system based on a backward differential discrete model. Background technique [0002] In recent years, as a power battery, compared with traditional lead-acid batteries and nickel-metal hydride batteries, lithium batteries have the characteristics of high energy density, no memory effect, long cycle life, and environmental friendliness. Therefore, lithium batteries have become the preferred choice for electric vehicle power batteries. main body. [0003] In electric vehicles, accurately estimating the state-of-charge (SOC) of the battery, that is, the remaining power of the battery, is the premise and key to the good operation of the BMS (battery management system) of electric vehicles. [0004] The accurate estimation of battery state of charge SOC mainly includes two parts: parameter identi...

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Problems solved by technology

[0006] However, some of the existing SOC estimation methods are not high in accuracy, and some are complex in calculation and slow in speed, and none of them can meet the online accurate estimation of the state of charge of the lithium battery in the automotive power battery.

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