Energy storage management system SOC estimation method based on offline identification of battery model parameters

Abstract

The invention relates to an energy storage management system SOC estimation method based on off-line identification of battery model parameters. The method comprises the following steps: S1, extracting current characteristics of a direct current side as input parameters of a dynamic test required for obtaining the battery model parameters in application scenes of peak regulation, frequency modulation and peak valley of an energy storage system; s2, dynamically testing the battery cell by adopting dynamic test cases with different current characteristics, and obtaining dynamic response test data of the input current and the output voltage of the battery cell; s3, identifying a parameter set of the battery model by adopting an offline identification method according to the dynamic response test data of the input current and the output voltage of the battery cell; s4, selecting battery model parameters according to system working modes, wherein the system working modes comprise peak regulation, frequency modulation and peak valley; and performing SOC estimation by using the selected battery model parameters. The battery model parameter set needed by the method is obtained through offline identification, an online identification method does not need to be used, the requirements for operation complex calculation and data storage are lowered, and the applicability is wider.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage batteries, and in particular to a SOC estimation method for an energy storage management system based on offline identification of battery model parameters. Background Art

[0002] Kalman filter has the advantages of anti-interference and high estimation accuracy, and it is widely used in battery management systems. Kalman filter uses battery model to construct state equation and uses recursive method to achieve optimal state estimation. Its estimation accuracy is highly dependent on the accuracy of battery model. In the peak regulation, frequency regulation and peak-valley usage scenarios of energy storage system, due to the strong nonlinear characteristics of the battery itself, it is not practical to use a single model parameter for state estimation. The method of real-time parameter estimation cannot guarantee the convergence of SOC estimation results. If the parameters are out of tune, it will cause the entire system to crash. Summary of the invention

[0003] The present invention aims to provide a SOC estimation method for an energy storage management system based on offline identification of battery model parameters.

[0004] The scheme of the present invention is as follows:

[0005] The SOC estimation method of the energy storage management system based on offline identification of battery model parameters includes the following steps:

[0006] S1, extracting the current characteristics of the DC side in the application scenarios of peak regulation, frequency regulation, and peak-valley of the energy storage system as input parameters for the dynamic test required to obtain the battery model parameters;

[0007] S2, dynamically testing the battery cell using dynamic test cases with different current characteristics to obtain dynamic response test data of the battery cell input current and output voltage;

[0008] S3, identifying a parameter set of a battery model using an offline identification method according to the dynamic response test data of the battery cell input current and output voltage;

[0009] S4, selecting battery model parameters according to the system operating mode, wherein the system operating modes include peak regulation, frequency regulation, and peak-valley regulation; and using the selected battery model parameters to perform SOC estimation.

[0010] The preferred implementation of the SOC estimation method of the energy storage management system based on offline identification of battery model parameters of the present invention is that the dynamic test in step S2 specifically includes the following contents:

[0011] S201, leaving it for 2 hours, charging with 1C constant current to the cut-off voltage, and then charging with constant voltage until the battery reaches a full charge state;

[0012] S202, leaving it for 2 hours, and continuously discharging the battery cell at different current rates to reduce the SOC from 100% to 5%; S203, leaving it for two hours, and discharging at 0.05C to the cut-off voltage;

[0013] S204, 1C constant current charging to the cut-off voltage, and then constant voltage charging until the battery reaches a full charge state; repeat steps S201 to S204 to perform tests for different use cases.

[0014] The preferred implementation method of the energy storage management system SOC estimation method based on offline identification of battery model parameters of the present invention is that in step S3, the battery model adopted refers to a first-order RC equivalent circuit model with hysteresis characteristics, and the parameters to be identified include the gain and deviation of the hysteresis voltage hyst, the internal resistance, the first-order RC circuit resistance, and the first-order RC circuit resistance.

[0015] A preferred implementation of the SOC estimation method of an energy storage management system based on offline identification of battery model parameters of the present invention is that, in step S3, the offline identification method adopts a least squares method.

[0016] The preferred implementation of the SOC estimation method of the energy storage management system based on offline identification of battery model parameters of the present invention is that the specific process of the least squares method is as follows:

[0017] S301, according to the equivalent circuit model, the battery terminal voltage is equal to the sum of the open circuit voltage and the hysteresis voltage minus the voltage divided by the first-order RC circuit and the ohmic internal resistance, that is:

[0018] v(t)=OCV(z(t))+hyst-R1i R1 (t)-R0i(t)

[0019] Furthermore, the discrete representation is:

[0020] v[k]=OCV(z[k])+M0s[k]+Mh[k]-R1i R1 [k]-R0i[k]

[0021] Wherein, M and M0 are the gain and deviation of the hysteresis voltage hyst, respectively, s[k] and h[k] are the sign value of the current direction and the normalized state value of the hysteresis voltage at time k;

[0022] S302, calculating the difference between the lower terminal voltage and the open circuit voltage at each moment, that is:

[0023]

[0024] S303, calculating the normalized state value of the hysteresis voltage, the sign value of the current direction, and the first-order RC circuit current at each moment, and constructing the following linear equation, which can be fitted by the least square method to obtain M, M0, R0, and R1;

[0025]

[0026] A preferred implementation of the SOC estimation method of the energy storage management system based on offline identification of battery model parameters of the present invention is that it also includes the following steps:

[0027] Before step S4, determine whether the communication with the energy management system is normal. If so, jump to S4; otherwise, jump to S5;

[0028] Step S5, using the battery model parameters at the previous moment to perform SOC estimation.

[0029] A preferred implementation of the SOC estimation method of the energy storage management system based on offline identification of battery model parameters of the present invention is that in step S4 or step S5, the Kalman filter algorithm is used to estimate the SOC of each battery cell.

[0030] A preferred implementation of the SOC estimation method of an energy storage management system based on offline identification of battery model parameters of the present invention is that the system operating mode is set by an energy management system.

[0031] The beneficial effect of the present invention is that the present invention intends to adjust the parameters of the battery model according to the real-time working mode of the energy storage battery through collaboration with the energy management system (EMS) for use in the Kalman filter to estimate the SOC. The battery model parameter set can be identified offline under different working conditions, thereby overcoming the limitations of the prior art and improving the adaptability of the algorithm in different scenarios.

[0032] Compared with the prior art, the present invention has the advantages of easy implementation, stable effect and simple architecture. The battery model parameter set required by the present invention is obtained through offline identification, without the need to use online identification methods, reducing the requirements for complex calculations and data storage of the battery management system. At the same time, the parameter set identified offline needs to be further verified by SOC estimation accuracy testing, that is, charging and discharging tests are carried out in the application scenarios of peak regulation, frequency regulation, and peak and valley of the energy storage system, recording the SOC changes of the battery cells, observing whether their state values ​​jump, and after standing still, comparing with the SOC corresponding to the open circuit voltage, so as to confirm the accuracy of the SOC estimation results. Ensure that the estimation algorithm is stable under different parameter inputs. Furthermore, the present invention can adjust parameters in real time according to the working mode of the energy storage system through collaboration with the energy management system (EMS), without the need to build a hierarchical architecture of cloud-edge collaboration for parameter adjustment, thereby reducing the complexity of collaborative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a flow chart of the SOC estimation method of the energy storage management system based on offline identification of battery model parameters.

[0034] Figure 2 This is a diagram of the energy storage system architecture involved in the present invention.

[0035] Figure 3 This is a first-order RC equivalent circuit model diagram involved in the present invention. DETAILED DESCRIPTION

[0036] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described below are only used to explain the present invention and will not limit the protection scope of the present invention.

[0037] The terms "first", "second" and the like in the specification, claims and embodiments of the present application are used to distinguish similar objects rather than to describe a specific order or sequential sequence.

[0038] The present invention is further described in detail below through preferred specific embodiments:

[0039] As attached Figure 1 As shown: The SOC estimation method of the energy storage management system based on offline identification of battery model parameters includes the following steps:

[0040] S1, respectively extracts the current characteristics of the DC side in the application scenarios of peak regulation, frequency regulation, and peak-valley regulation of the energy storage system as input parameters for the dynamic test required to obtain the battery model parameters; in different application scenarios, the current characteristics of the DC side are different, and the current characteristics refer to the statistical characteristics of the current and its duration at different rates of the battery during the charging and discharging process.

[0041] S2, using dynamic test cases with different current characteristics to dynamically test the battery cell, and obtain dynamic response test data of the battery cell input current and output voltage; the dynamic test in step S2 specifically includes the following contents:

[0042] S201, leaving it for 2 hours, charging with 1C constant current to the cut-off voltage, and then charging with constant voltage until the battery reaches a full charge state;

[0043] S202, leaving it for 2 hours, and continuously discharging the battery cell at different current rates to reduce the SOC from 100% to 5%; S203, leaving it for two hours, and discharging at 0.05C to the cut-off voltage;

[0044] S204, 1C constant current charging to the cut-off voltage, and then constant voltage charging until the battery reaches a full charge state; repeat steps S201 to S204 to perform tests for different use cases.

[0045] S3, based on the dynamic response test data of the cell input current and output voltage, an offline identification method is used to identify the parameter set of the battery model, and the battery model parameters identified under different working conditions are formed into a set and integrated into the energy storage battery management system for real-time parameter adjustment;

[0046] In step S3, the battery model used refers to a first-order RC equivalent circuit model with hysteresis characteristics, as shown in the attached Figure 3 As shown, the parameters to be identified include the gain and deviation of the hysteresis voltage hyst, the internal resistance, the first-order RC circuit resistance, and the first-order RC circuit resistance.

[0047] In step S3, the offline identification method adopts the least square method. The specific process of the least square method is as follows:

[0048] S301, according to the equivalent circuit model, the battery terminal voltage is equal to the sum of the open circuit voltage and the hysteresis voltage minus the voltage divided by the first-order RC circuit and the ohmic internal resistance, that is:

[0049] v(t)=OCV(z(t))+hyst-R1i R1 (t)-R0i(t)

[0050] Furthermore, the discrete representation is:

[0051] v[k]=OCV(z[k])+M0s[k]+Mh[k]-R1i R1 [k]-R0i[k]

[0052] Wherein, M and M0 are the gain and deviation of the hysteresis voltage hyst, respectively, s[k] and h[k] are the sign value of the current direction and the normalized state value of the hysteresis voltage at time k;

[0053] S302, calculating the difference between the lower terminal voltage and the open circuit voltage at each moment, that is:

[0054]

[0055] S303, calculating the normalized state value of the hysteresis voltage, the sign value of the current direction, and the first-order RC circuit current at each moment, and constructing the following linear equation, which can be fitted by the least square method to obtain M, M0, R0, and R1;

[0056]

[0057] S4, select battery model parameters according to the system working mode, the system working modes include peak regulation, frequency regulation, and peak-valley; use the selected battery model to estimate the SOC of each battery cell. The parameters of each battery cell are the same in the algorithm, but because the voltage change of each battery cell is inconsistent during the charging and discharging process of the battery, it will cause inconsistent SOC change, so it is necessary to estimate the SOC of each battery cell.

[0058] In this embodiment, the following steps are also included:

[0059] Before step S4, determine whether the communication with the energy management system is normal. If so, jump to S4; otherwise, jump to S5; Figure 2 As shown, the system operating mode is set by the energy management system (EMS) and can be sent to the battery management system through a communication protocol.

[0060] Step S5, use the battery model parameters of the previous moment to perform SOC estimation. It should be noted here that, generally in the case of abnormal communication, the energy storage battery management system will identify the communication failure and request the energy storage inverter to stop charging and discharging within a certain period of time, but this process requires response time. In order to reduce the impact on the SOC estimation algorithm, it is still necessary to use the battery model parameters used at the previous moment for estimation. The reason for the impact is that if the response time to stop charging and discharging is too long, there is still current in the system, and the wrong battery model parameters are used, the result of SOC estimation will be inaccurate.

[0061] In this embodiment, in step S4 or step S5, the Kalman filter algorithm is used to estimate the SOC of each battery cell.

[0062] The present invention extracts current characteristics according to various application scenarios of the energy storage system and uses them for parameter identification of the battery model to form multiple parameter sets, thereby solving the problems of poor adaptability of a single parameter and high requirements for a dynamic parameter identification algorithm.

[0063] By collaborating with the energy management system (EMS), parameters can be adjusted in real time according to the working mode of the energy storage system, avoiding the problem of complex dynamic parameter adjustment architecture of cloud-edge collaboration.

[0064] The preferred implementation modes of the present application are described in detail above in conjunction with the accompanying drawings. Typical known structures and common knowledge technologies in the preferred implementation modes are not described in detail here. Ordinary technicians in the relevant field can improve and implement the technical solutions of the present invention based on their own abilities under the inspiration given by the present implementation modes. Some typical known structures, known methods or common knowledge technologies should not become obstacles for ordinary technicians in the relevant field to implement the present application.

[0065] The scope of protection required by this application shall be based on the contents of its claims, and the contents recorded in the invention content, specific implementation methods and drawings of the specification shall be used to interpret the claims.

[0066] Within the technical concept of the present application, several modifications may be made to the specific implementation methods of the present application, and these modified specific implementation methods should also be regarded as within the protection scope of the present application.

Claims

1. A method for estimating SOC of an energy storage management system based on offline identification of battery model parameters, characterized in that: The following steps are involved: S1, extracting the current characteristics of the DC side in the application scenarios of peak regulation, frequency regulation, and peak-valley of the energy storage system as input parameters for the dynamic test required to obtain the battery model parameters; S2, dynamically testing the battery cell using dynamic test cases with different current characteristics to obtain dynamic response test data of the battery cell input current and output voltage; S3, identifying a parameter set of a battery model using an offline identification method according to the dynamic response test data of the battery cell input current and output voltage; S4, selecting battery model parameters according to the system operating mode, wherein the system operating modes include peak regulation, frequency regulation, and peak-valley regulation; and using the selected battery model parameters to perform SOC estimation.

2. The SOC estimation method of energy storage management system based on offline identification of battery model parameters according to claim 1 is characterized in that: The dynamic test in step S2 specifically includes the following contents: S201, leaving it for 2 hours, charging with 1C constant current to the cut-off voltage, and then charging with constant voltage until the battery reaches a full charge state; S202, leaving it for 2 hours, and continuously discharging the battery cell at different current rates to reduce the SOC from 100% to 5%; S203, leaving it for two hours, and discharging at 0.05C to the cut-off voltage; S204, 1C constant current charging to the cut-off voltage, and then constant voltage charging until the battery reaches a full charge state; repeat steps S201 to S204 to perform tests for different use cases.

3. The SOC estimation method of energy storage management system based on offline identification of battery model parameters according to claim 1 is characterized in that: In step S3, the battery model used refers to a first-order RC equivalent circuit model with hysteresis characteristics, and the parameters to be identified include the gain and deviation of the hysteresis voltage hyst, the internal resistance, the first-order RC circuit resistance, and the first-order RC circuit resistance.

4. The SOC estimation method of the energy storage management system based on offline identification of battery model parameters according to claim 3 is characterized in that: In step S3, the offline identification method adopts the least square method.

5. The SOC estimation method of energy storage management system based on offline identification of battery model parameters according to claim 4 is characterized in that: The specific process of least squares method is as follows: S301, according to the equivalent circuit model, the battery terminal voltage is equal to the sum of the open circuit voltage and the hysteresis voltage minus the voltage divided by the first-order RC circuit and the ohmic internal resistance, that is: v(t)=OCV(z(t))+hyst-R1i R1 (t)-R0i(t) Furthermore, the discrete representation is: v[k]=OCV(z[k])+M0s[k]+Mh[k]-R1i R1 [k]-R0i[k] Wherein, M and M0 are the gain and deviation of the hysteresis voltage hyst, respectively, s[k] and h[k] are the sign value of the current direction and the normalized state value of the hysteresis voltage at time k; S302, calculating the difference between the lower terminal voltage and the open circuit voltage at each moment, that is: S303, calculating the normalized state value of the hysteresis voltage, the sign value of the current direction, and the first-order RC circuit current at each moment, and constructing the following linear equation, which can be fitted by the least square method to obtain M, M0, R0, and R1; 6. The SOC estimation method of energy storage management system based on offline identification of battery model parameters according to claim 1 is characterized in that: The following steps are also included: Before step S4, determine whether the communication with the energy management system is normal. If so, jump to S4; otherwise, jump to S5; Step S5, using the battery model parameters at the previous moment to perform SOC estimation.

7. The SOC estimation method of energy storage management system based on offline identification of battery model parameters according to claim 6 is characterized in that: In step S4 or step S5, the Kalman filter algorithm is used to estimate the SOC of each battery cell.

8. The SOC estimation method of energy storage management system based on offline identification of battery model parameters according to claim 1 is characterized in that: The system operating mode is set by the energy management system.

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