A method for estimating energy storage battery SOE and SOP jointly

Abstract

The application discloses a kind of energy storage battery SOE and SOP joint estimation method, it uses the multi-step power prediction method based on constant power, and combined with extended Kalman filter establishes the joint estimator of battery SOE and SOP.This method can accurately and effectively estimate the SOE and SOP of battery in the implementation process, the error of terminal voltage and SOE is less than 3%, so that the prediction of SOP is more accurate and reliable.The method of the application has small amount of calculation, high robustness and accuracy, and can be applied to the estimation of SOE and SOP under complex working conditions.

Description

Technical Field

[0001] This invention belongs to the field of energy storage battery management technology, specifically a method for jointly estimating the state of energy (SOE) and state of power (SOP) of an energy storage battery. Background Technology

[0002] In practical applications of BMS, accurate SOE and SOP estimation are crucial. SOE represents the percentage of remaining energy in the battery relative to its maximum value. Compared to SOC, which only indicates the battery's capacity state and not its energy state, SOE estimation is a mainstream method based on various filters. Neural network-based methods also exist, but these require extensive training with large amounts of experimental data to improve accuracy, and their application is directly related to the training data, limiting their applicability to similar battery types. Regarding SOP estimation, the battery's "true" power capability is difficult to determine. Hybrid Pulse Power Characterization (HPPC) is a commonly used method, but it determines the battery's static peak power in a laboratory environment and is not suitable for estimating sustained peak power capability. Multi-parameter constrained dynamic peak power estimation is another popular approach, capable of reliable SOP estimation even under uncertain battery operating conditions.

[0003] Most current SOP estimation methods are based on power prediction using constant current, which does not align with constant power input / output scenarios. Both energy storage stations and electric vehicles experience significant constant power input / output demands during operation. For example, in real-world vehicle operation, if road conditions remain relatively stable and vehicle speed is steady, the vehicle's power demand is approximately constant, and the battery should maintain a constant power output. In this case, the vehicle management system's control strategy is influenced by the continuous peak power that the battery can provide in a constant power form.

[0004] Furthermore, there is a close relationship between SOE and SOP. Accurate SOE and SOP estimation is of great significance for the reliable operation of energy storage batteries. Therefore, there is an urgent need for an accurate joint estimation method of SOC and SOP. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a joint estimation method for SOE and SOP of energy storage batteries, so as to accurately and effectively estimate the SOE and SOP of energy storage batteries.

[0006] The technical solution adopted in this invention is: a joint estimation method for SOE and SOP of energy storage batteries, which includes:

[0007] Step 1: During the charging and discharging process of the energy storage battery, the terminal voltage U of the energy storage battery is sampled at intervals of sampling time Δt. tThe charging / discharging current I and the surface temperature T are sampled.

[0008] Step 2: Based on the Thevenin equivalent circuit model, and according to the battery terminal voltage U collected in real time in Step 1... t Based on the charging / discharging current I and surface temperature T, online parameter identification is completed, and the open-circuit voltage U is obtained. OC ohmic internal resistance R i Polarization internal resistance R D The four model parameters are: time constant τ;

[0009] Step 3: Using the obtained model parameters, estimate the real-time SOE of the current energy storage battery based on the extended Kalman filter algorithm;

[0010] Step 4: Based on the real-time SOE and model parameters of the energy storage battery, calculate the estimated value of the multi-constraint peak power capability of the energy storage battery.

[0011] Furthermore, the energy storage battery model in step two adopts the Thevenin equivalent circuit model, and its discrete equations are as follows:

[0012]

[0013] Among them, U OC，k+1 U is the open-circuit voltage at time k+1; t，k+1 U is the terminal voltage at time k+1; D，k+1 Let i be the polarization voltage at time k+1; L，k+1 R is the current at time k+1; i R is the internal resistance in ohms. D τ is the polarization resistance; τ is the time constant; Δt is the unit sampling time.

[0014] Furthermore, the model parameters are identified in real time using the least squares method with a forgetting factor to accurately describe the dynamic characteristics of the battery and reliably identify the battery model parameters.

[0015] Furthermore, the calculation equation for the real-time SOE of the energy storage battery is as follows:

[0016] z k =z k-1 -η i U t，k I L，k Δt / E a

[0017] Where z k Let η be the energy storage battery SOE at time k; i Energy efficiency of energy storage batteries; U t，k Let i be the terminal voltage at time k; L，k The current at time k is denoted by Δt; Δt is the unit sampling time; Ea This represents the maximum usable energy of the energy storage battery.

[0018] Furthermore, step three includes two steps: prior estimation-prediction and posterior estimation-correction. In prior estimation-prediction, the state value at time k is predicted using the state value at time k-1. In posterior estimation-correction, the state value at time k predicted by the model is corrected using the new measurement value at time k.

[0019] Furthermore, based on the model parameters, the system state-space equations of the energy storage battery are constructed as follows:

[0020]

[0021] in,

[0022]

[0023]

[0024]

[0025]

[0026] x k =[U D z] T Let u be the state vector of the energy storage battery system at time k; k Let ω be the input vector of the energy storage battery system at time k; k-1 The process noise of the energy storage battery system at time k-1; v k The measurement noise of the energy storage battery system at time k; y k This represents the output value of the energy storage battery system at time k.

[0027] Algorithm initialization: Set the initial values ​​of the state observer: x0, P0, Q0, R0;

[0028] Where x0 is the initial value of the input vector; P0 is the initial value of the error covariance; Q0 is the initial value of the process noise covariance; and R0 is the initial value of the measurement noise covariance.

[0029] Prior estimation-prediction, estimating the state vector and error covariance from (k-1) + Predicted (k) - ;

[0030] Prior state estimation:

[0031]

[0032] in, The prior estimate of the state vector at time k;

[0033] Prior error covariance estimation:

[0034]

[0035] in, P is the prior estimate of the error covariance at time k; k-1 Q represents the error covariance at time k-1; k-1 Let be the process noise covariance at time k-1.

[0036] Furthermore, the posterior estimation-correction uses the measurement y at time k. k Corrected prior estimates:

[0037] New Information Matrix:

[0038]

[0039] Among them, e k The error information at time k;

[0040] Kalman gain matrix calculation:

[0041]

[0042] Among them, R k-1 Let be the measurement noise covariance at time k-1;

[0043] System status correction:

[0044]

[0045] in, This is the state vector correction value at time k;

[0046] Error covariance correction:

[0047] Where I is the identity matrix; This represents the updated error covariance value at time k;

[0048] (k) + The output state vector at time k is the estimated value z of SOE at time k. k Terminal voltage estimate U t，k Then, it is utilized (k). + The output at time (k+1) is processed. - Prior state estimation at time 1.

[0049] Furthermore, in step four, the continuous peak charge and discharge power is first obtained using a multi-step power prediction method based on constant power. and The output system satisfies the following equations:

[0050]

[0051]

[0052] in, and These represent the continuous peak charge and discharge power based on constant power, respectively; τ is the time constant of the battery model; U OC，k+2Δt U is the open-circuit voltage at the termination time. OC，k+Δt U is the open-circuit voltage at time k+Δt; t，max and U t，min These are the design limits for the upper and lower cutoff voltages of the energy storage battery, respectively.

[0053] Based on these two equations, we obtain information about... and Two quadratic equations in one variable, solving the equations yields... and The solution is the one that falls within the battery design power limit, and that is the final solution.

[0054] Furthermore, in step four, besides the constraint of battery voltage, SOE is another necessary decision factor for predicting battery power. The battery's allowable operating current should meet the specified SOE operating range, and the maximum cutoff SOE limit value z should be taken. max and the minimum cutoff SOE limit value z min The maximum discharge power can be calculated using the following formula. and minimum charging power

[0055]

[0056] Among them, z k Let SOE be the energy storage battery at time k; E a Δt represents the maximum usable energy of the energy storage battery; Δt represents the unit sampling time.

[0057] Furthermore, in step four, the multi-constraint peak power capability estimate is calculated as follows:

[0058]

[0059] in, and These represent the multi-constraint peak discharge power and the multi-constraint peak charging power, respectively. max To design the battery's maximum discharge power, P min U is designed to maximize the charging power of the battery. t，k+2Δt The terminal voltage at the termination time; I maxTo design the maximum discharge current for the battery, I min Design the maximum charging current for the battery.

[0060] The beneficial effects of this invention are as follows: This invention proposes a multi-step power prediction method for constant power, and establishes a joint estimation method for battery SOE and SOP by combining extended Kalman filtering. It can accurately and effectively estimate the battery SOE and SOP, and has the advantages of low computational cost, high robustness and accuracy, and is suitable for the estimation of SOE and SOP under complex operating conditions. Attached Figure Description

[0061] Figure 1 This is a Thevenin equivalent circuit model diagram of the energy storage battery system in this embodiment of the invention;

[0062] Figure 2 This is a flowchart of the joint estimation algorithm for SOE and SOP of energy storage battery based on extended Kalman filtering in this invention;

[0063] Figure 3 This is a diagram showing the terminal voltage estimation results in an embodiment of the present invention, wherein: (a) comparison between estimated and measured values; (b) terminal voltage estimation error;

[0064] Figure 4 This is a diagram showing the SOE estimation results of an energy storage battery based on extended Kalman filtering in an embodiment of the present invention, wherein: (a) comparison between estimated value and reference value; (b) SOE estimation error;

[0065] Figure 5 The figure shows the peak power estimation results of the energy storage battery when the duration is 60s in the embodiment of the present invention, wherein: (a) peak discharge power estimation figure; (b) peak charging power estimation. Detailed Implementation

[0066] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] like Figure 1 As shown, this invention is a joint estimation method for SOE and SOP of an energy storage battery based on extended Kalman filtering, specifically including the following steps:

[0068] Step 1: During the charging and discharging process of the energy storage battery, the terminal voltage U of the energy storage battery is sampled at intervals of sampling time Δt. t The charging / discharging current I and the surface temperature T are sampled.

[0069] Step 2: Based on the Thevenin equivalent circuit model, and according to the battery terminal voltage U collected in real time in Step 1... t Based on the charging / discharging current I and surface temperature T, online parameter identification is completed, and the open-circuit voltage U is obtained. OC ohmic internal resistance R i Polarization internal resistance R D The four model parameters are: time constant τ;

[0070] Step 3: Using the obtained model parameters, estimate the real-time SOE of the current energy storage battery based on the extended Kalman filter algorithm;

[0071] Step 4: Based on the real-time SOE and model parameters of the energy storage battery, calculate the estimated value of the multi-constraint peak power capability of the energy storage battery.

[0072] Specifically, the energy storage battery model in step two adopts a first-order RC battery model, such as... Figure 1 As shown, its discrete equation is:

[0073]

[0074] Among them, U OC，k+1 U is the open-circuit voltage at time k+1; t，k+1 U is the terminal voltage at time k+1; D，k+1 Let i be the polarization voltage at time k+1; L，k+1 R is the current at time k+1; i R is the internal resistance in ohms. D τ is the polarization resistance; τ is the time constant; Δt is the unit sampling time.

[0075] Specifically, the calculation equation for the real-time SOE of the energy storage battery is as follows:

[0076] z k =z k-1 -η i U t，k I L，k Δt / E a

[0077] Where z k Let η be the energy storage battery SOE at time k; i Energy efficiency of energy storage batteries; U t，k Let i be the terminal voltage at time k; L，k The current at time k is denoted by Δt; Δt is the unit sampling time; E a This represents the maximum usable energy of the energy storage battery.

[0078] Specifically, step three includes two steps: prior estimation-prediction and posterior estimation-correction. In prior estimation-prediction, the state value at time k is predicted using the state value at time k-1. In posterior estimation-correction, the state value at time k predicted by the model is corrected using the new measurement value at time k.

[0079] Based on the model parameters, the system state-space equation of the energy storage battery is constructed as follows:

[0080]

[0081] in,

[0082]

[0083]

[0084]

[0085]

[0086] x k =[U D z] T Let u be the state vector of the energy storage battery system at time k; k Let ω be the input vector of the energy storage battery system at time k; k-1 The process noise of the energy storage battery system at time k-1; v k The measurement noise of the energy storage battery system at time k; y k This represents the output value of the energy storage battery system at time k.

[0087] Algorithm initialization: Set the initial values ​​of the state observer: x0, P0, Q0, R0;

[0088] Where x0 is the initial value of the input vector; P0 is the initial value of the error covariance; Q0 is the initial value of the process noise covariance; and R0 is the initial value of the measurement noise covariance.

[0089] Prior estimation-prediction, estimating the state vector and error covariance from (k-1) + Predicted (k) - ;

[0090] Prior state estimation:

[0091]

[0092] in, The prior estimate of the state vector at time k;

[0093] Prior error covariance estimation:

[0094]

[0095] in, P is the prior estimate of the error covariance at time k; k-1 Q represents the error covariance at time k-1; k-1 Let be the process noise covariance at time k-1.

[0096] Posterior estimation - correction, using the measurement y at time k k Corrected prior estimates:

[0097] New Information Matrix:

[0098]

[0099] Among them, e k The error information at time k;

[0100] Kalman gain matrix calculation:

[0101]

[0102] Among them, R k-1 Let be the measurement noise covariance at time k-1;

[0103] System status correction:

[0104]

[0105] in, This is the state vector correction value at time k;

[0106] Error covariance correction:

[0107]

[0108] Where I is the identity matrix; This represents the updated error covariance value at time k;

[0109] (k) + The output state vector at time k is the estimated value z of SOE at time k. k Terminal voltage estimate U t，k Then, it is utilized (k). + The output at time k is used to estimate the prior state at time (k+1).

[0110] In step four, the continuous peak charge and discharge power is first obtained using a multi-step power prediction method based on constant power. and The output system satisfies the following equations:

[0111]

[0112]

[0113]

[0114] in, and These represent the continuous peak charge and discharge power based on constant power, respectively; τ is the time constant of the battery model; U OC，k+2Δt U is the open-circuit voltage at the termination time. OC，k+Δt U is the open-circuit voltage at time k+Δt; t，max and U t，min These are the design limits for the upper and lower cutoff voltages of the energy storage battery, respectively.

[0115] Based on these two equations, we obtain information about... and Two quadratic equations in one variable, solving the equations yields... and The solution is the one that falls within the power limit of the battery design, and that is the final solution.

[0116] In step four, besides the battery voltage constraint, SOE is another necessary decision factor for predicting battery power. The battery's allowable operating current should meet the specified SOE operating range, and the maximum cutoff SOE limit value z should be taken. max and the minimum cutoff SOE limit value z min The maximum discharge power can be calculated using the following formula. and minimum charging power

[0117]

[0118] Where zk is the energy storage battery SOE at time k, E a Δt represents the maximum usable energy of the energy storage battery; Δt represents the unit sampling time.

[0119] In step four, the multi-constraint peak power capability estimate is calculated as follows:

[0120]

[0121] in, and These are the multi-constraint peak discharge power and the multi-constraint peak charging power, respectively; P max The maximum discharge power designed for the battery; P min Design the battery to its maximum charging power; U t，k+2Δt The terminal voltage at the termination time; I max The discharge current is the limit designed for the battery; I min Design the maximum charging current for the battery.

[0122] In one embodiment of the present invention, a lithium iron phosphate battery with a rated voltage of 3.2V and a rated capacity of 50Ah is used as the experimental object. The UDDS test is conducted to measure and record the terminal voltage, current and temperature data of the battery under different ambient temperatures in real time, with a recording period of 1 second.

[0123] Model parameters are identified using least squares with a forgetting factor. Figure 3 The figure shows the terminal voltage tracking situation, where (a) is a comparison between the estimated and measured terminal voltage values; and (b) is the terminal voltage estimation error. The simulation results show that the maximum terminal voltage error is less than 40mV.

[0124] Figure 4 The table shows the SOE (Self-Equation of Energy Storage Battery) estimated based on the Extended Kalman Filter (EKF) algorithm. (a) compares the estimated SOE value with the reference value; (b) shows the SOE estimation error. It can be seen that the maximum SOE error is less than 3%.

[0125] Figure 5 The results show the estimated peak power capability (SOP) of the energy storage battery over a duration of 60 seconds, where (a) is the estimated peak discharge power and (b) is the estimated peak charging power. In most cases, the battery's peak charging power is close to 800W, and the peak discharge power is close to 1500W. Notably, when the battery's SOE approaches 0.2, the peak discharge power rapidly decreases to zero, which helps avoid over-discharge. Finally, it can be concluded that accurate SOE estimation helps obtain a reliable battery SOP, ensuring the safe and reliable operation of the battery.

[0126] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A joint estimation method for SOE and SOP of an energy storage battery, characterized in that, Comprise: Step one, in the process of charging and discharging of the energy storage battery, the terminal voltage U of the energy storage battery, the charging and discharging current I and the surface temperature T are sampled at the interval of sampling time Δt. t ; Step two, based on Thevenin equivalent circuit model, according to the real-time collected battery terminal voltage U t , charge and discharge current I and surface temperature T, complete online parameter identification, obtain open circuit voltage U OC , ohmic internal resistance R i , polarization internal resistance R D And time constant τ Four model parameters; Step three, using the obtained model parameters, based on the extended Kalman filter algorithm to estimate the real-time SOE of the energy storage battery; Step four, based on the real-time SOE of the energy storage battery and the model parameters, the multi-constrained peak power capability estimate of the energy storage battery is calculated; In step four, the constant power based multi-step power prediction method is first used to obtain the continuous peak charge and discharge power and The output system satisfies the following equation: wherein, and are constant power-based continuous peak charge and discharge power, respectively; τ is the time constant of the battery model; U OC,k+2Δt is the open circuit voltage at the end of discharge; U OC,k+Δt is the open circuit voltage at the time k + Δt; U t,max and U t,min are the design limits for the upper and lower cut-off voltage of the energy storage battery, respectively. From these two equations, two monic quadratic equations are obtained for and Solving the equations gives the solutions for and where the solutions within the power range of the battery design limits are the final solutions. In step four, in addition to the constraint of the battery voltage, SOE is another necessary decision factor to predict the battery power, the allowed working current of the battery should meet the specified SOE working range, taking the maximum cut-off SOE limit value z max and the minimum cut-off SOE limit value z min The maximum discharge power is calculated by the following formula and the minimum charging power wherein z k is the SOE of the energy storage battery at time k; E a is the maximum available energy of the energy storage battery; and Δt is the unit sampling time. The multi-constrained peak power capability estimate is calculated by the following method: wherein, and Ppeakdischarge and Ppeakcharge are the multi-constraint peak discharge and charge power, respectively; P max Pdislim is the battery design limit discharge power; P min Pchrlim is the battery design limit charge power; U t,k+2Δt Uend is the terminal voltage at the end time; I max Idisl is the battery design limit discharge current; I min Ichl is the battery design limit charge current.

2. The method of claim 1, wherein, The energy storage battery model of the step two adopts Thevenin equivalent circuit model, and its discrete equation is: wherein U OC,k+1 is the open circuit voltage at time k+1; U t,k+1 is the terminal voltage at time k+1; U D,k+1 is the polarization voltage at time k+1; i L,k+1 is the current at time k+1; R i is the ohmic resistance; R D is the polarization resistance; τ is the time constant; and Δt is the unit sampling time.

3. The method of claim 1, wherein, The model parameters are identified in real time by least square method with forgetting factor.

4. The method of claim 1-3, wherein, The calculation equation of the real-time SOE of the energy storage battery is: z k = z k-1 - η i U t,k i L,k Δt / E a where z k is the SOE of the energy storage battery at time k; η i is the energy efficiency of the energy storage battery; U t,k is the terminal voltage at time k; i L,k is the current at time k; Δt is the unit sampling time; E a is the maximum available energy of the energy storage battery.

5. The method of claim 4, wherein, The step three includes two steps, i.e. priori estimation-prediction and posteriori estimation-correction; in the priori estimation-prediction, the state value at the k-1 time is used to predict the state value at the k time; in the posteriori estimation-correction, the new measurement value at the k time is used to correct the state value at the k time predicted by the model.

6. The method of claim 5, wherein, Based on the model parameters, the system state space equation of the energy storage battery is constructed as: Wherein, x k = [U D z] T is the state vector of the energy storage battery system at time k; u k is the input vector of the energy storage battery system at time k; ω k-1 is the process noise of the energy storage battery system at time k-1; v k is the measurement noise of the energy storage battery system at time k; y k is the output value of the energy storage battery system at time k; Algorithm initialization, setting the initial value of the state observer: x0, P0, Q0, R0; Wherein, x0 is the initial value of the input vector; P0 is the initial value of the error covariance; Q0 is the initial value of the process noise covariance; R0 is the initial value of the measurement noise covariance; Prior estimate-predict, state vector and error covariance estimate from (k-1) + Predict to (k) - ; Priori state estimation: wherein, is the state vector prior estimate at time k. Priori error covariance estimation: wherein, P is the error covariance prior estimate at time k; P k-1 Q is the process noise covariance at time k - 1. k-1 Q is the process noise covariance at time k - 1.

7. The method of claim 6, wherein, Posterior estimate - correction with the measurement value y at time k k Corrected prior estimate: Innovation matrix: where e k is the error innovation at time k; Kalman gain matrix calculation: wherein R k-1 is the measurement noise covariance at time k - 1. System state correction: wherein, is the state vector correction value at time k; Error covariance correction: where I is an identity matrix; is the error covariance update value at time k. (k) + The output state vector at time k is the estimate of the SOE at time k, z k and the terminal voltage estimate, U t,k , and then the output at time k is used to perform the prior state estimate at time k+1. + - time k+1.​

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