Power lithium battery SOC estimation method based on self-adaptive Kalman filtering method

Abstract

The invention discloses a power lithium battery SOC estimation method based on the self-adaptive Kalman filtering method. The power lithium battery SOC estimation method comprises the following steps:at first, according to the dynamic characteristics of a lithium ion battery, establishing a dual-polarization equivalent circuit model of the battery; then, obtaining data through testing the performance of the composite pulse power, identifying the characteristic parameter of the model, and adopting the least squares fit to obtain a relation curve of the open-circuit voltage and SOC; based on the relation curve of the open-circuit voltage and SOC and the discrete equation of a DP model, establishing a state equation and an observation equation, and substituting the state equation and the observation equation into the EFK algorithm to obtain a system matrix; and finally, adopting the modified self-adaptive extended Kalman filtering algorithm to estimate the battery SOC. With adoption of the power lithium battery SOC estimation method, the problems that the filtering results diffuse and the operation is not stable when the traditional self-adaptive Kalman filtering method or the EFK algorithm is adopted for SOC estimation are effectively solved, and the speed that the SOC estimated value is convergent to the truth value is increased.

Description

technical field [0001] The invention relates to the technical field of battery management systems for new energy vehicles, in particular to a power lithium battery SOC estimation method based on an adaptive Kalman filter method. Background technique [0002] As the key technology of the power system of new energy vehicles, the battery management system (Battery Management System, BMS) takes power battery state estimation as the core, and formulates corresponding control strategies by obtaining the state of the battery to make the battery work efficiently and safely. Its functions mainly include estimating battery state (state of charge SOC, state of health SOH, power state SOP), monitoring battery working state, battery balance control, thermal management and information interaction functions. The estimation of battery state of charge (State of Charge, SOC) has always been the core work of BMS. It can reflect the remaining power of the battery for estimating the mileage. It ...

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Problems solved by technology

[0007] In view of this, the present invention provides a power lithium battery SOC estimation method based on an adaptive Kalman filter method to solve or at least partially solve the technical problems of filter divergence and low stability in the prior art method

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