Discrete variable structure observer-based lithium battery SOC (state of charge) estimation method

Abstract

The invention discloses a discrete variable structure observer-based lithium battery SOC (state of charge) estimation method. The method includes the following steps that: a rapid calibration test is performed on a lithium battery, a relation curve of SOC and open-circuit voltage (OCV) is obtained; a lithium battery discrete state space model for SOC estimation is established; a pulse discharge test is performed on the lithium battery, the parameters of the lithium battery model are identified; the terminal voltage and charge-discharge current of the lithium battery under a real-time collection condition are obtained; and a discrete variable structure observer is constructed to estimate the SOC of the lithium battery accurately. The method not only has a good SOC estimation effect, but also can ensure convergence strictly, and exhibits strong robustness to lithium battery modeling error, internal parameter perturbation and external disturbance.

Description

technical field [0001] The invention belongs to the field of estimating the state of charge of a lithium battery for vehicles, and in particular relates to a method for estimating the SOC of a lithium battery based on a discrete variable structure observer. Background technique [0002] The battery management system (BMS) is an important part of electric vehicles, and has basic functions such as battery state detection, battery state estimation, battery safety protection, and energy control management. [0003] Battery SOC estimation is the heart of the battery management system. SOC is an important parameter to characterize the remaining capacity of the battery. Accurate SOC value is an important basis for battery charge and discharge control, balance control, and formulation of energy management strategies. The estimation accuracy directly affects the service life and cost of the battery. Therefore, accurate estimation of SOC is the key to BMS. The essential. [0004] SO...

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Problems solved by technology

[0008] 1) The algorithm requires that the noise is white noise and the statistical properties such as the mean and variance of the noise are known, which is difficult to meet in practical applications, not only because the statistical properties of noise are difficult to obtain, but also white noise is only available under ideal conditions exist;

[0009] 2) The algorithm has high requirements on the accuracy of the lithium-ion battery performance model. When the model accuracy is low, it will cause a large SOC estimation error;

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