Method for estimating charge state of power cell

Abstract

The invention discloses a method for estimating the charge state of a power cell, which comprises the following steps that: 1. a continuous state space model capable of expressing the relationship of each element in an expression circuit is obtained by an equivalent circuit model of a power cell; the relationship between the open-circuit voltage and the cell charge state is obtained through the standing experiment of the power cell, and the cell charge state is introduced into the continuous state space model of the power cell as a state variable; the power battery model under the noise environment is obtained by combining the noise information; and finally the continuous state space model is subjected to linearization and discretization so as to obtain a linear discrete state space model; 2. a relationship curve of the open-circuit voltage and the cell charge state is obtained through the standing experiment of the power cell, and a parameter k and a parameter d in the power cell model are obtained approximately; 3. parameters of the power cell model are obtained through the identification of current and voltage data collected by a data collecting system; and 4. the power cell charge state is estimated by utilizing a moving horizon estimation method based on the determined power cell model.

Description

technical field [0001] The present invention applies to the technical field of electric vehicle power batteries, and more specifically, the present invention relates to an optimized method for estimating the state of charge of a power battery. Background technique [0002] The battery state of charge (State of Charge, SOC) is used to represent the remaining power of the battery, that is, the percentage of the remaining power to the rated capacity, and its value is theoretically in the range of 0% to 100%. The state of charge (SOC) of the battery cannot be obtained directly from the battery itself, but can only be estimated indirectly by measuring the external characteristic parameters of the battery pack (such as voltage, current, internal resistance, temperature, etc.). During the use of electric vehicle power batteries, due to the internal complex electrochemical reaction phenomenon, the battery characteristics show a high degree of nonlinearity, which makes it very diffic...

AI Technical Summary

Problems solved by technology

During the use of electric vehicle power batteries, due to the internal complex electrochemical reaction phenomenon, the battery characteristics show a high degree of nonlinearity, making it very difficult to accurately estimate the battery state of charge (SOC)

[0003] Traditional battery state of charge (SOC) estimation methods, such as discharge test method, internal resistance method, open circuit voltage method, etc., although the estimation results are relatively accurate, are not suitable for the actual driving of electric vehicles; Although the current measurement method is simple to implement, it will produce cumulative errors due to the influence of current acquisition accuracy, and the improper selection of the initial value of the battery state of charge (SOC) will also lead to inaccurate estimation results.

In recent years, the estimation algorithms studied by people, such as Kalman filter, can estimate the battery state of charge (SOC) online, but also solve the error caused by the initial value, and have a good effect on the complex working conditions of electric vehicles. Adaptability, but the estimated results sometimes appear unreasonable (such as SOC>100% or SOC<0%); and the method of using neural networks requires a large amount of reference data, and the amount of calculation is too large Big

[0004] In view of the problems existing in the above methods, a rolling time-domain estimation method is proposed to solve the problem that the estimated value of the battery state of charge (SOC) may appear due to the large-scale changes in the battery charging and discharging current when the vehicle is running under complex conditions. Unreasonable circumstances beyond the constraints

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