Anti-disturbance parameterization method for battery equivalent circuit model

Abstract

The invention discloses an anti-disturbance parameterization method for a battery equivalent circuit model. The method comprises the following steps: S1, establishing the equivalent circuit model of the battery, determining parameters of a model to be identified, and determining a relational expression of a state of charge (SOC) and an open-circuit voltage (OCV) through fitting; S2, carrying out real-time acquisition on the load current and the terminal voltage at the moment k; S3, calculating the SOC of the battery at the moment k, and calculating an OCV value; S4, establishing a discrete domain regression equation for model parameter identification, and updating model parameters online by adopting a recursive least square method (RLS); S5, constructing a tool vector constraint condition,calculating a current noise variance at the moment k on line, and further calculating a voltage noise variance at the moment k according to a FrischScheme method; and S6, correcting the RLS result inthe step S4 according to the estimated values of the current and voltage variances to obtain an unbiased model parameter vector at the moment k. According to the method, current and voltage measurement noise statistical characteristics can be estimated on line, so that model identification deviation in a noise interference environment is compensated, and unbiased model parameter identification isrealized.

Description

technical field [0001] The invention relates to battery model parameter identification, in particular to an on-line, anti-disturbance and unbiased identification method for a battery equivalent circuit model. Background technique [0002] The battery management system (Battery Management System, BMS) is an important guarantee for the safe and efficient operation of the battery system. It needs to complete key tasks such as state estimation, fault diagnosis, life prediction, and charging control. The BMS strategy based on the equivalent circuit model has high precision, strong robustness, and moderate computational complexity, and is the most common type of method in the field of BMS. However, such methods are highly dependent on model accuracy, and model inaccuracy will result in the failure of estimation, diagnosis and control algorithms, leading to major safety accidents. Therefore, accurate equivalent circuit model is one of the difficult issues in BMS design. [0003] ...

AI Technical Summary

Problems solved by technology

It should be pointed out that the existing parameter identification methods rely on high-precision load current and terminal voltage sampling. However, in the real BMS application environment, due to the influence of sensor errors and electromagnetic interference, there are often a lot of noise in the measurement signal, which will cause the parameters of the traditional online model to The identification method fails, and the accuracy of the model drops significantly, which ultimately significantly affects the reliability of the BMS strategy based on the equivalent circuit model

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