Battery SOC online estimation method based on double Kalman filtering algorithm

Abstract

The invention discloses a battery SOC online estimation method based on double Kalman filtering algorithm, comprising: S1) obtaining the initial value of the battery SOC; S2) creating a battery equivalent circuit model; obtaining the state equation and the output equation of the battery; S3) using the initial value of the battery SOC as the input state amount and the voltage equation corresponding to the battery equivalent circuit model as the output equation; and utilizing the expanded Kalman filtering algorithm to perform battery SOC estimation; S4) using the battery SOC estimated by the expanded Kalman filtering algorithm as the input state amount and the amper-hour integral method as the output equation; and performing battery SOC estimation through the use of the Kalman filtering algorithm for the estimation value of the battery SOC. The battery SOC online estimation method based on double Kalman filtering algorithm can obtain the SOC estimation value more accurately. Without its excessive reliance on a battery model, the requirement of the method on the current accuracy is also reduced. The Battery SOC online estimation method based on double Kalman filtering algorithm of the invention can find wide applications in the battery identification and estimation field.

Description

technical field [0001] The invention relates to the field of battery identification and estimation, in particular to a battery SOC online estimation method based on a double Kalman filter algorithm. Background technique [0002] In recent years, pure electric vehicles and hybrid vehicles with batteries as the main power source have been widely put into use. This emerging means of transportation has attracted people's attention for its low-carbon emission, energy-saving and light-weight features. However, the development of electric vehicles at this stage is largely limited by factors such as the stability, safety and service life of power batteries. During the operation of the electric vehicle, the driver needs to know the remaining battery power at any time to judge the cruising range. The battery state of charge (SOC) is the ratio of the available energy to the total energy of the battery. It is an important parameter that cannot be directly measured by the battery. Its ...

AI Technical Summary

Problems solved by technology

This method is simple to implement, but prone to cumulative errors

The open circuit voltage refers to the voltage value of the battery after a long period of standing and stabilizing. The open circuit voltage is close to the electromotive force of the battery in value, but the open circuit voltage needs to be obtained after a long period of standing, and the general battery SOC estimation is a dynamic estimation; the Kalman filter method is Considering the battery as a power system, and making an optimal estimate of the state of the power system in the minimum sense, this method can filter out the fixed noise and obtain an accurate estimate of the SOC, although the extended Kalman filter algorithm is less sensitive to input disturbances. Good filterability, strong correction of the initial state error, and can quickly converge the estimated value to near the true value, but it is highly dependent on the battery model and requires high accuracy of the battery model parameters

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