CN107831448B - A kind of state-of-charge estimation method of parallel connection type battery system - Google Patents

Abstract

The invention discloses a method for estimating the state of charge of a parallel battery system. The method is as follows: a parallel battery system model is established according to the battery cell equivalent circuit model and the characteristics of the parallel circuit, and each branch of the battery system detected is The battery system current output by the current and battery system model is used as the input of the parameter corrector, and the state of charge compensation value ΔSOCb is obtained through the parameter corrector; at the same time, the system noise estimation value is obtained by using the noise estimator, and the battery system space obtained by the battery system model The state equation, combined with the predicted value of the battery system terminal voltage and the detected value of the battery system terminal voltage, and then using the unscented Kalman filter method to obtain the estimated value of the state of charge of the battery system SOCb; finally, superimpose ΔSOCb and SOCb to obtain the modified positive SOCr, and then use the SOCr to update the battery system model, and get the estimated value of the state of the battery system at the next moment, and update it cyclically to obtain an accurate estimated value of the state of charge of the parallel battery system.

Description

A State of Charge Estimation Method for Parallel Battery System

technical field

The invention belongs to the technical field of design and control of a MW-level battery energy storage system in a smart grid, and relates to a method for estimating the state of charge of a parallel battery system.

Background technique

With the rapid development of wind power, photovoltaic power generation and new energy vehicles, battery cells and their battery systems have made great progress. In order to meet the needs of large-scale wind power and photovoltaic power generation connected to the grid and high-power new energy vehicles, the voltage and current levels of the battery system are also increasing, and the battery cells that make up the battery system are also increasing. However, the charging and discharging process of a battery is a complex electrochemical reaction process, and the power contained in it is difficult to obtain directly through measurement. Usually, the state of charge (SOC) of the battery is used to characterize the power of the battery.

At present, the commonly used SOC estimation methods mainly include: ampere-hour method, open circuit voltage method, impedance method, neural network method, fuzzy logic method, extended Kalman filter (EKF) and standard unscented Kalman filter (UKF) and so on. The shortcomings of the current method: (1) The accuracy of the ampere-hour method is not high due to the shortcomings of accumulation of errors and obtaining a clear initial value of SOC; (2) The open-circuit voltage method is not suitable for online estimation and is time-consuming; (3) Impedance method has the disadvantages of complex algorithm and inconvenient actual operation; (4) Neural network and fuzzy logic method need to obtain a large amount of experimental data, which is difficult to obtain during the actual operation of the battery, and its actual accuracy is not high; (5) ) Extended Kalman filter method (EKF) has the disadvantages of calculating the Jacobian matrix and ignoring high-order terms, and its estimation accuracy is not high. (6) The standard unscented Kalman filter (UKF) has the advantages of not needing to calculate the Jacobian matrix, and the calculation amount is small, but in the actual application process, the statistical information in the standard unscented Kalman filter (UKF) (such as system noise, measurement noise, etc.) are not constant, or even unknown or unclear, resulting in low estimation accuracy and poor robustness. For example, a power battery charge estimation method disclosed in document (CN103675706A). In order to obtain noise statistical information, the literature (CN106443496A) discloses a method for estimating the state of charge of a battery with an improved noise estimator. This method can obtain system noise estimation information through the improved noise estimator, which improves the battery charge state to a certain extent. However, there are still two main disadvantages: First, the prerequisite for improving the SOC accuracy of this method is to require the battery model to be accurate, that is, if the battery model is not accurate, the SOC estimation accuracy will also be limited. However, this method The accuracy of the general battery model used is not high, which will inevitably lead to limited accuracy of battery SOC estimation; the second is that the improved noise estimator used in the method itself is relatively complicated to calculate, specifically see claim 2 of the document (CN106443496A), It will inevitably lead to a large amount of calculation and is not suitable for online estimation. In order to further improve the SOC estimation accuracy of the parallel battery system, the present invention discloses a method for estimating the state of charge of the parallel battery system, which improves the SOC estimation accuracy through two approaches: one is to further improve the noise estimation disclosed in the document (CN106443496A) to make the algorithm simpler and suitable for online estimation; the second is to form a closed-loop control with the battery system SOC _b obtained based on the improved noise estimator and the SOC compensation value obtained based on the parameter corrector to obtain an accurate battery system SOC _r , and then update the battery system model to improve the accuracy of the battery system model, and further improve the estimation accuracy of the battery system SOC _b .

Contents of the invention

The purpose of the present invention is to address the above problems and propose a method for estimating the state of charge of a parallel battery system to obtain an estimated value of the state of charge of a parallel battery system with high precision, small amount of calculation, and online estimation.

The object of the invention is to realize through the following technical solutions:

The present invention provides a method for estimating the state of charge of a battery with an improved noise estimator. The method is as follows: the first step is to determine the equivalent of the parallel battery system from the battery cell model, the characteristics of the parallel circuit and the state of charge SOC of the battery system. Circuit model (1); the second step is to detect the currents I ₁ ～ I _N of N branches in the battery system and the output current of the battery system predicted by the battery system model, and take the two as input quantities, and pass the parameter corrector (2 ) to obtain the battery system charge state compensation value ΔSOC _b ; the third step is to use the noise estimator (3) to obtain the system noise estimate (4), and to establish the battery space state equation (5) by combining the battery system model with the state of charge definition ; In the fourth step, use the system noise estimate (4) to replace the noise statistics in the Unscented Kalman Filter method UKF (6), and use the battery state of charge in the battery space state equation (5) and two RC parallel circuits The terminal voltage is used as the state variable of the unscented Kalman filter method UKF (6), and the input state space equation and the output voltage state space equation of the battery space state equation (5) are respectively used as the unscented Kalman filter method UKF (6) The nonlinear state equation f(·) and the measurement equation g(·), use the predicted value of the battery system terminal voltage and the detected value of the battery system terminal voltage as the input of the filter gain of the unscented Kalman filter method UKF(6); the fifth step , superimpose the battery system state of charge estimated value SOC _b output by the unscented Kalman filter method UKF (6) and the state of charge compensation value ΔSOC _b to obtain the modified positive state of charge SOC _r of the battery system; the sixth step , use SOC _r to update the battery system model, and get the estimated value of the state of the battery system at the next moment, and update it cyclically to get an accurate estimated value of the state of charge of the parallel battery system. Fig. 1 is a structural diagram of a method for estimating the state of charge of a parallel battery system.

The parallel battery system is formed by connecting N battery cells in parallel, where N is a natural number greater than 1, as shown in FIG. 2 . The parallel battery system equivalent circuit model (1) is a second-order equivalent circuit model. The main circuit of the model is composed of two RC parallel circuits, a controlled voltage source U ₀ (SOC) and a battery internal resistance R _b . The circuit diagram is shown in Figure 3. The battery model expression obtained by Kirchhoff's law KVC is: U(t)= _Ub0 [SOC(t)]- _Ib (t) _Zb (t). The basic model for determining the performance parameters of each battery cell and the battery system by using the operating characteristics of the parallel circuit and the screening method is determined as follows: the open-circuit terminal voltage of the battery system in the basic model is calculated as follows: U _b0 (SOC) = U ₀ (SOC), Among them, U ₀ (SOC) is the open-circuit terminal voltage of the battery cell; the impedance of the battery system in the basic model is calculated as follows: Among them, R _b (t) is the internal resistance of the battery system, R _bs (t), R _bl (t) and C _bs (t), C _bl (t) are the resistance and capacitance describing the transient response characteristics of the battery system, respectively. The calculations of R _bs (t), R _bl (t) and C _bs (t), C _bl (t) in the basic model are as follows: C _bs (t) = NC _s (t), C _bl (t) = NC _l (t), where R (t) is the internal resistance of the battery cell, R _s (t), R _l (t) and C _s (t), C _l (t) are the description The resistance and capacitance of the transient response characteristics of the battery cell, and the above performance parameters are all related to the SOC. SOC is defined as: Among them, SOC ₀ is the initial value of the SOC of the battery cell, which is generally a constant between 0 and 1; Qu ( _t ) is the unusable capacity of the battery cell, and Q ₀ is the rated capacity of the battery cell. The calculations of U ₀ (SOC), R _s (t), R _l (t) and C _s (t), C _l (t) are as follows: Among them, a ₀ ~ a ₅ , c ₀ ~ c ₂ , d ₀ ~ d ₂ , e ₀ ~ e ₂ , f ₀ ~ f ₂ , b ₀ ~ b ₅ are all model coefficients, which can be obtained by fitting the battery measurement data have to.

The parameter corrector (2) is designed as follows: the SOC corrector is composed of N PID regulators and a weighting device, and the two inputs of each PID regulator are respectively the i-th branch battery string output current I _i and The battery model outputs 1/N of the total current I _b ; after passing through each PID regulator, the SOC compensation value ΔSOC _i of N branch batteries is obtained, namely In the formula, k _P is a proportional constant, k _I is an integral constant, k _D is a differential constant, s is an integral factor, and i is a natural number greater than 1; after passing through the weighter, the battery system SOC compensation value ΔSOC _b is obtained, namely In the formula, _ki is the weighting coefficient.

The establishment of the battery space state equation (5) is as follows: 1), taking the battery SOC _b and the terminal voltage U _bs (t) and U _bl (t) of the two RCs as the system state variable x _k , taking U _b , I _b is used as the system measurement variable y _k and the system input variable respectively, and the battery space state equation is established according to the equivalent circuit model as

In the formula, U _bs and U _bl are the terminal voltages of two RC parallel circuits, τ ₁ and τ ₂ are time constants, ω _k is the system noise, Δt is the sampling period, and k is a natural number greater than 1; 2), according to Keele Hough's voltage law, combined with the battery equivalent circuit model, the battery output measurement equation can be obtained as: [U _b,k ]=U _b0,k -R _k I _b,k -U _bs,k -U _bl,k + υ _k ＝g _k (x _k )+υ _k ＝y _k , where υ _k is the system measurement noise, and k is a natural number greater than 1.

The main steps of the Unscented Kalman Filter method UKF (6) are: 1) initializing x mean value E ( ) variance and noise statistics: 2) Calculate sampling points x _i,k and corresponding weight ω _i : In the formula, λ=α ² (n+h)-n, n is the dimension of the state variable; ω ^m and ω ^c represent the weight of the variance and the mean value respectively, and the operator is the Cholesky decomposition of a symmetric matrix, and α, β, and h are all constants; 3) Time update of state estimation and mean square error: state estimation time is updated as In the formula, q _k is the noise mean value of the state equation; the mean square error time is updated as Q _k is the noise variance of the state equation; the system output time is updated as In the formula, g _k-1 (·) is the measurement equation, r _k is the noise mean value of the measurement equation; 4) Calculate the gain matrix: In the formula, P _y,k is the autocovariance, P _xy,k is the autocovariance, R _k is the noise variance of the state equation; 5) The state estimation and the measurement update of the mean square error: the state estimation measurement update is The mean squared error measure is updated to

Described improved noise estimator (3) is: In the formula, diag( ) is a diagonal matrix, y _k is the actual measured value of the system output, Respectively represent the estimated value of state equation noise mean value, state equation noise variance estimate value, measurement equation noise mean value estimate value, and measurement equation noise variance estimate value at time k, that is, the noise estimate value at time k (4).

Finally, add the compensation value ΔSOC _b obtained by the parameter corrector (2) to the SOC _b output by the battery system equivalent circuit model (1), and use it as the new input quantity SOC _r of the battery system equivalent circuit model, thereby updating the battery system Performance parameters, and then update the equivalent circuit model of the battery system, such a cycle, to obtain accurate state of charge estimation of the parallel battery system.

The battery type is one of lithium-ion battery or lead-acid battery.

Description of drawings

FIG. 1 is a structural diagram of a state of charge estimation method for a parallel battery system;

Figure 2 is a structural diagram of a parallel battery system containing N battery cells;

Figure 3 is a battery equivalent circuit model diagram containing two RC parallel circuits.

Detailed ways

The present invention will be further described in detail below in conjunction with specific examples, which are for explanation of the present invention rather than limitation.

According to the embodiment of the present invention, as shown in Fig. 1, Fig. 2 and Fig. 3, a method for estimating the state of charge of a parallel battery system is provided. The flow chart of the embodiment is shown in Fig. 1, mainly including the following step:

1. Determine the equivalent circuit model of the parallel battery system

The parallel battery system is formed by connecting three battery cells in parallel. The equivalent circuit model (1) of the parallel battery system is a second-order equivalent circuit model. The main circuit of the model consists of two RC parallel circuits with a controlled voltage Source U ₀ (SOC) and battery internal resistance R _b , etc., as shown in Figure 2. Its circuit diagram is shown in Figure 3. The battery model expression obtained by Kirchhoff's law KVC is: U(t)= _Ub0 [SOC(t)]- _Ib (t) _Zb (t). The basic model for determining the performance parameters of each battery cell and the battery system by using the operating characteristics of the parallel circuit and the screening method is determined as follows: the open-circuit terminal voltage of the battery system in the basic model is calculated as follows: U _b0 (SOC) = U ₀ (SOC), Among them, U ₀ (SOC) is the open-circuit terminal voltage of the battery cell; the impedance of the battery system in the basic model is calculated as follows: Among them, R _b (t) is the internal resistance of the battery system, R _bs (t), R _bl (t) and C _bs (t), C _bl (t) are the resistance and capacitance describing the transient response characteristics of the battery system, respectively. The calculations of R _bs (t), R _bl (t) and C _bs (t), C _bl (t) in the basic model are as follows: C _bs (t) = 3C _s (t), C _bl (t) = 3C _l (t), where R (t) is the internal resistance of the battery cell, R _s (t), R _l (t) and C _s (t), C _l (t) are the description The resistance and capacitance of the transient response characteristics of the battery cell, and the above performance parameters are all related to the SOC. SOC is defined as: Among them, SOC ₀ is the initial value of the SOC of the battery cell, which is generally a constant between 0 and 1; Qu ( _t ) is the unusable capacity of the battery cell, and Q ₀ is the rated capacity of the battery cell. The calculations of U ₀ (SOC), R _s (t), R _l (t) and C _s (t), C _l (t) are as follows: Among them, the values of a ₀ ~ a ₅ are -0.915, 40.867, 3.632, 0.537, 0.499, 0.522 respectively, the values of b ₀ ~ b ₅ are 0.1463, 30.27, 0.1037, 0.0584, 0.1747, 0.1288, c ₀ ~ c ₂ The values are 0.1063, 62.49, 0.0437 respectively, the values of d ₀ ~ d ₂ are -200, 138, 300 respectively, the values of e ₀ ~ e ₂ are 0.0712, 61.4, 0.0288 respectively, and the values of f ₀ ~ f ₂ are respectively 3083, 180, 5088.

2. Design parameter corrector

The parameter corrector (2) is designed as follows: the SOC corrector is composed of three PID regulators and a weighting device, and the two inputs of each PID regulator are respectively the i-th branch battery string output current I _i and 1/3 of the total current I _b output by the battery model; after passing through each PID regulator, the SOC compensation value ΔSOC _i of the three branch batteries is obtained, namely In the formula, k _P is a proportional constant, k _I is an integral constant, k _D is a differential constant, s is an integral factor, and i is a natural number greater than 1; after passing through the weighter, the battery system SOC compensation value ΔSOC _b is obtained, namely In the formula, _ki is the weighting coefficient. At time k, the parameter corrector can obtain the battery system SOC compensation value ΔSOC _b,k

3. Establish the battery space state equation

1) Take the battery SOC _b and the terminal voltages U _bs (t) and U _bl (t) of the two RCs as the system state variable x _k , and take U _b and I _b as the system measurement variable y _k and system input variable respectively , according to the equivalent circuit model to establish the battery space state equation as

In the formula, U _bs and U _bl are the terminal voltages of two RC parallel circuits, τ ₁ and τ ₂ are time constants, ω _k is the system noise, Δt is the sampling period, and k is a natural number greater than 1.

2) According to Kirchhoff's voltage law, combined with the battery equivalent circuit model, the battery output measurement equation can be obtained as: [U _b,k ]=U _b0,k -R _k I _b,k -U _bs,k -U _bl,k +υ _k =g _k (x _k )+υ _k =y _k , where υ _k is the system measurement noise, and k is a natural number greater than 1.

4. Use the noise estimator (3) to obtain the noise estimate at time k (4)

Use the noise estimator to combine the system noise estimation information at the previous moment to obtain the noise estimate at time k (4), namely

5. The noise estimate at time k (4) As the statistical information values (q _k , Q _k , r _k , R _k ) of the unscented Kalman filter method UKF(6), namely

Taking the battery state of charge SOC in the battery space state equation (5) and the terminal voltage of two RC parallel circuits as the state variables of the unscented Kalman filter method UKF (6), that is

The input state space equation and the output voltage state space equation of the battery system space state equation (5) are respectively used as the nonlinear state equation f(·) and measurement equation g(·) of the unscented Kalman filter method UKF(6), namely

g _k (x _k )=U _0,k -R _k I _b,k -U _bs,k -U _bl,k .

6. Use the unscented Kalman filter method UKF (6) to estimate the battery SOC.

1) Initialize the state variable x mean E() and noise information:

2) Calculate the sampling points x _i,k and the corresponding weight ω: In the formula, λ=α ² (n+h)-n, n=3, the value of α is 1, the value of β is 2, and the value of h is 0;

3) Time update of state estimation and mean square error: state estimation time is updated as

The mean square error time is updated as The system output time is updated as

4) Calculate the gain matrix:

5) The measurement update of state estimation and mean square error: the state estimation measurement is updated as The mean squared error measure is updated to

At the same time, the state variables are estimated The output of the first element of is the estimated value of SOC _b,k of the battery system at time k.

7. After adding the compensation value ΔSOC _b,k obtained by the parameter corrector (2) at time k to the SOC _b,k output by the battery system equivalent circuit model (1) at time k, it is used as the equivalent value of the battery system at time k The new input quantity SOC _r,k of the circuit model, so as to update the performance parameters of the battery system, and then obtain the equivalent circuit model of the battery system at k+1 time, and output the state of charge SOC _b,k+1 of the battery system at k+1 time, Through such a cycle, an accurate estimation value of the state of charge of the parallel battery system is obtained.

Finally, it should be noted that the combination of the above embodiments only illustrates the technical solution of the present invention rather than limiting it. Those of ordinary skill in the art should understand that those skilled in the art can modify or equivalently replace the specific embodiments of the present invention, but these modifications or changes are within the protection scope of the pending claims.

Claims (4)

1. A method for estimating the state of charge of a parallel battery system, said method comprising the following steps: Step (1): Determine the equivalent circuit model of the parallel battery system based on the battery cell model, the characteristics of the parallel circuit and the SOC of the battery system; Step (2): Detect the currents I1~IN of N branches in the battery system, and obtain the predicted output current of the battery system from the equivalent circuit model of the battery system, use the two currents as input quantities, and obtain the output current of the battery system through the parameter corrector State of charge compensation value ΔSOC _b ; Step (3): Use the noise estimator to obtain the estimated value of the system noise, and use the battery system model combined with the definition of the state of charge to establish the battery space state equation; Step (4): Use the system noise estimate to replace the noise statistics in the unscented Kalman filter method UKF, and use the battery state of charge in the battery space state equation and the terminal voltage of two RC parallel circuits as the unscented Kalman filter The state variables of the UKF method, the input state space equation and the output voltage state space equation of the battery space state equation are respectively used as the nonlinear state equation f(·) and the measurement equation g(·) of the unscented Kalman filter method UKF, and the battery The predicted value of the system terminal voltage and the detected value of the battery system terminal voltage are used as the input of the UKF filter gain of the unscented Kalman filter method; Step (5): Superimpose the battery system state of charge estimated value SOC _b output by the unscented Kalman filter method UKF and the state of charge compensation value ΔSOC _b to obtain the modified battery system state of charge SOC _r ; Step (6): Utilize SOC _r to update the battery system model, and obtain the estimated value of the state of the battery system at the next moment, and update it cyclically to obtain an accurate estimated value of the state of charge of the parallel battery system; The design of the parameter corrector is as follows: the SOC corrector is composed of N PID regulators and a weighting device, and the two inputs of each PID regulator are the i-th branch battery string output current I _i and the battery model output 1/N of the total current I _b ; get the SOC compensation value ΔSOC _i of N branch batteries after passing through each PID regulator, namely In the formula, k _P is a proportional constant, k _I is an integral constant, k _D is a differential constant, s is an integral factor, and i is a natural number greater than 1; after passing through the weighter, the battery system SOC compensation value ΔSOC _b is obtained, namely In the formula, k _i is the weighting coefficient; The establishment of the battery space state equation is as follows: 1), take the battery SOC _b and the terminal voltage of the two RCs as the system state variable x _k , take U _b and I _b as the system measurement variable y _k and the system input variable respectively, and establish the battery space state equation according to the equivalent circuit model for In the formula, U _bs and U _bl are the terminal voltages of two RC parallel circuits, τ ₁ and τ ₂ are time constants, ω _k is the system noise, Δt is the sampling period, and k is a natural number greater than 1; 2) According to Kirchhoff's voltage law, combined with the battery equivalent circuit model, the battery output measurement equation is obtained as: [U _b,k ]=U _b0,k -R _k I _b,k -U _bs,k - U _bl,k +υ _k =g _k (x _k )+υ _k =y _k , where υ _k is the system measurement noise, and k is a natural number greater than 1. 2. The method for estimating the state of charge of a parallel battery system according to claim 1, wherein the Unscented Kalman Filter method UKF comprises the following steps: 1) Initialize x, mean E, variance and noise statistics: 2) Calculate sampling points x _i,k and corresponding weight ω _i : In the formula, λ=α ² (n+h)-n, n is the dimension of the state variable; ω ^m and ω ^c represent the weight of the variance and the mean value respectively, and the operator It is the Cholesky decomposition of a symmetric matrix, and α, β, and h are all constants; 3) Time update of state estimation and mean square error: state estimation time is updated as In the formula, q _k is the noise mean value of the state equation; the mean square error time is updated as Q _k is the noise variance of the state equation; the system output time is updated as In the formula, g _k-1 ( ) is the measurement equation, r _k is the noise mean value of the measurement equation; 4) Calculate the gain matrix: In the formula, P _y,k is the autocovariance, P _xy,k is the autocovariance, R _k is the state equation noise variance; 5) The measurement update of state estimation and mean square error: the state estimation measurement is updated as The mean squared error measure is updated to 3. A method for estimating the state of charge of a parallel battery system according to claim 2, wherein the noise estimator is: In the formula, diag( ) is a diagonal matrix, Respectively represent the estimated value of the state equation noise mean, the state equation noise variance estimate, the measurement equation noise mean value, and the measurement equation noise variance estimate at time k, that is, the system noise estimate at time k. 4 . The method for estimating the state of charge of a parallel battery system according to claim 1 , wherein the battery type is one of a lithium-ion battery or a lead-acid battery.