Battery SOC estimation method based on fusion of multi-scale Kalman filtering and unscented Kalman filtering

Abstract

The invention discloses a battery SOC estimation method based on the fusion of multi-scale Kalman filtering and unscented Kalman filtering. The method comprises a first step of performing charging anddischarging tests on a to-be-tested battery at different temperatures and under different working conditions through a battery charging and discharging test system and a temperature box; a second step of constructing a battery actual capacity calculation model according to the sample parameters of the to-be-tested battery collected in the test process, and obtaining the actual capacity of the to-be-tested storage battery through the calculation of the calculation model; a third step of establishing a first-order RC equivalent circuit model of the storage battery to be tested according to thesample parameters of the battery collected in the test process, and obtaining a state and observation equation of the first-order equivalent circuit model according to the Kirchhoff law; and a fourthstep of performing state estimation by using a multi-scale adaptive unscented Kalman filter algorithm, inputting the test current and voltage, and obtaining an optimal estimated value of the SOC valueof the storage battery to be measured by taking the minimum error between the voltage of the actual measurement end and the estimated value as a target, namely, the SOC estimated value of the storagebattery.

Description

technical field [0001] The invention relates to the field of electric vehicle battery management, in particular to a method for estimating battery SOC by combining multi-scale Kalman filtering and unscented Kalman filtering. Background technique [0002] The battery management system is one of the key parts of the electric vehicle, which can effectively manage the working state of the power battery pack and provide safety guarantee for the normal driving of the electric vehicle. One of the main functions of the battery management system is to obtain the state of charge (SOC) of each single battery, and judge whether it needs a balancing strategy to ensure that the entire battery pack is in a stable working state. [0003] However, during the driving process of the vehicle, the internal working state of the battery is a nonlinear electrochemical reaction, and is easily affected by the external environment temperature and its own cycle life, so it is very difficult to obtain a...

AI Technical Summary

Problems solved by technology

However, the initial parameter value in the least squares method is not easy to determine, which will lead to a large error in the initial stage of the algorithm. If it cannot continue to converge, it will affect the SOC estimation accuracy of the entire working condition experiment.

Therefore, some dual Kalman filter algorithms have been proposed. The principle is to use a Kalman filter observer to update the model parameters, and then pass the updated parameters to another Kalman filter observer to estimate the battery SOC state, and finally update the The SOC is passed to the previous Kalman filter to update the state, and the model parameters and SOC of the entire working condition are updated alternately, although the DPF and improved DAPF algorithms are more accurate than the Kalman filter algorithm in the case of non-Gaussian noise However, it is very difficult to select the number of particles in the algorithm. Using the particle filter algorithm to update the model parameters with slow time-varying characteristics may not necessarily improve the SOC accuracy. On the contrary, the increase in the number of particles will greatly increase Calculations of the algorithm

Since battery model parameters have slow time-varying characteristics, while battery SOC has fast time-varying characteristics, frequent parameter updates on the same scale sometimes not only fail to improve the estimation accuracy of SOC, but increase the amount of calculation

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