Battery OCV estimation method, device and storage medium with voltage compensation

Abstract

The present invention belongs to the technical field of batteries in the new energy industry, and specifically relates to a battery OCV estimation method, device and storage medium with voltage compensation, the method comprising performing an offline HPPC test on a battery cell to obtain voltage and current data; using the HPPC test data to identify battery parameters and obtain battery model parameters; determining a compensation voltage value based on the identified battery model parameters in combination with the HPPC test data; using the voltage and current of the battery cell when it is actually running online to obtain a first estimate of the OCV of the battery cell; combining the first estimate of the OCV and the compensation voltage value to obtain a second estimate of the OCV. The present invention can perform voltage compensation on the OCV obtained by real-time estimation, eliminate inherent errors, and effectively improve the accuracy of OCV estimation.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries in the new energy industry, including energy storage battery systems, new energy vehicle battery systems and other devices and equipment using lithium-ion batteries, and specifically relates to a battery OCV estimation method, device and storage medium with voltage compensation. Background Art

[0002] As an important performance parameter indicator of lithium-ion batteries, battery open circuit voltage (OCV) is widely used for battery status assessment. For example, in the field of SOC (battery state of charge, i.e. State of Charge) and SOH (battery state of health, State of Health) estimation, OCV is required as a necessary input parameter.

[0003] In the prior art, there are two ways to perform OCV. One is offline measurement, which requires long-term static state, is cumbersome to operate, and takes too long. The other is to use real-time voltage and current data for online identification, which can calculate OCV in real time. This method has the disadvantages of fluctuations in estimation results and inability to effectively eliminate errors. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a battery OCV estimation method with voltage compensation, which can perform voltage compensation on the OCV estimated in real time, eliminate inherent errors, and effectively improve the accuracy of OCV estimation.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] A battery OCV estimation method with voltage compensation comprises the following steps:

[0007] Performing an offline HPPC test on the battery unit to obtain HPPC test data, wherein the HPPC test data includes voltage and current data;

[0008] Using the HPPC test data, battery parameter identification is performed to obtain battery model parameters;

[0009] Determine the compensation voltage value according to the battery model parameters and the HPPC test data;

[0010] Using the voltage and current of the battery cell when it is actually online, a first estimated value of the OCV of the battery cell is obtained;

[0011] The first estimated value of OCV and the compensation voltage value are combined to obtain a second estimated value of OCV.

[0012] Furthermore, the voltage and current data in the HPPC test data include static data after discharging at different SOC preset values, and pulse data in the HPPC.

[0013] Furthermore, the specific steps of obtaining the battery model parameters include:

[0014] By performing battery parameter identification on each SOC preset value, a sequence of different SOC preset values ​​and corresponding battery parameters is obtained.

[0015] Furthermore, the battery model is an equivalent circuit model, and the parameters of the battery model include but are not limited to: battery cell OCV, ohmic internal resistance , Polarization internal resistance , polarized capacitor and time constant τ.

[0016] Furthermore, the specific steps of determining the compensation voltage value include:

[0017] Determine the polarization voltage of the battery cell according to the time constant τ in the battery model parameters and the static data at each SOC The time that is less than the set threshold , and static data The voltage value corresponding to the moment ;

[0018] The sequence relationship with SOC is:

[0019] ;

[0020] In the formula, is the nth SOC preset value, n is the number of SOC preset values, The time corresponding to the nth SOC preset value ;

[0021] The sequence relationship with SOC is:

[0022] ;

[0023] In the formula, is the nth SOC preset value, n is the number of SOC preset values, The voltage corresponding to the nth SOC preset value ;

[0024] From the static data at each SOC, determine the time corresponding to the last static moment as well as The voltage value corresponding to the time ;

[0025] The sequence relationship with SOC is:

[0026] ;

[0027] In the formula, is the nth SOC preset value, n is the number of SOC preset values, The time corresponding to the nth SOC preset value ;

[0028] The sequence relationship with SOC is:

[0029] ;

[0030] In the formula, is the nth SOC preset value, n is the number of SOC preset values, The voltage corresponding to the nth SOC preset value ;

[0031] At each SOC, the compensation voltage value That is:

[0032] ;

[0033] The sequence relationship with SOC is:

[0034] ;

[0035] In the formula, is the nth SOC preset value, n is the number of SOC preset values, The voltage corresponding to the nth SOC preset value .

[0036] Furthermore, the first estimated value of the OCV of the battery cell is obtained by using the voltage and current of the battery cell when it is actually running online, including:

[0037] The real-time online voltage and current are used in combination with an online identification algorithm to estimate the OCV of the battery cell in real time, and the OCV value is obtained as a first estimated OCV value OCV1 of the battery cell.

[0038] Further, according to the first estimated OCV value OCV1 and the compensation voltage value , and obtain the second estimated value of OCV OCV2, namely:

[0039] .

[0040] A computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a battery OCV estimation method with voltage compensation.

[0041] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a battery OCV estimation method with voltage compensation.

[0042] In summary, compared with the prior art, the above technical solution has the following beneficial effects:

[0043] The present invention provides a battery OCV estimation method with voltage compensation. By performing voltage compensation using an online estimated OCV value, the accuracy of OCV estimation can be improved, and a basis for battery system state estimation can be provided. The OCV estimated in real time can be voltage compensated to eliminate inherent errors, thereby effectively improving the accuracy of OCV estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of an OCV estimation method with voltage compensation according to an embodiment of the present invention;

[0045] Figure 2 is a HPPC test curve diagram of an embodiment of the present invention;

[0046] Figure 3 is an equivalent circuit model diagram of an embodiment of the present invention;

[0047] Figure 4 is an OCV-SOC relationship diagram of an embodiment of the present invention;

[0048] Figure 5 is a relationship diagram of OCV-time constant τ of an embodiment of the present invention;

[0049] Figure 6 is a pulse test curve diagram of a battery module according to an embodiment of the present invention;

[0050] Figure 7 for Figure 6 A partial enlarged view of the

[0051] Figure 8 The embodiment of the present invention - Relationship diagram;

[0052] Fig. 9 2 is a comparison chart of OCV estimation results according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The principles and features of the present invention are described below in conjunction with all the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0054] The embodiment of the present invention discloses an OCV estimation method with voltage compensation.

[0055] Reference Figure 1-Figure 9 , an OCV estimation method with voltage compensation, comprising the following steps:

[0056] S1: Perform an offline hybrid pulse power test on the battery cell to obtain voltage and current data.

[0057] Specifically, in this embodiment, a 1P16S battery module is used as a test object to perform a HPPC test (hybrid pulse power test). The current and voltage curves obtained are as follows: Figure 2 shown.

[0058] The preset SOC value is:

[0059] ,

[0060] When the battery module SOC value is adjusted to the preset value, the battery module is left to stand for 3 hours to allow the electrochemical reaction inside the battery cell to regain balance.

[0061] S2: Use HPPC test data to identify battery parameters and obtain battery model parameters.

[0062] Specifically, in this embodiment, a battery equivalent circuit model is established, such as Figure 3 As shown in the figure, the battery performance parameters included in the model include battery open circuit voltage OCV, battery ohmic internal resistance , battery polarization internal resistance , battery polarization capacitance , time constant τ( )

[0063] Using HPPC test data, extract the battery OCV results, such as Figure 4 As shown, the time constant τ is obtained, as Figure 5 shown.

[0064] The relationship between OCV and time constant τ as SOC changes is shown in Table 1.

[0065] Table 1 OCV and time constant τ

[0066]

[0067] S3: Determine the compensation voltage value based on the identified battery model parameters and HPPC data.

[0068] Specifically, the polarization voltage of the battery cell is determined based on the time constant τ in the battery model parameters and the static data at each SOC. The time that is less than the set threshold , and static data The voltage value corresponding to the moment .

[0069] When the current of the battery module disappears, the current is 0, and the battery enters zero input response. At this time, the polarization voltage formula of the battery is as follows:

[0070] ;

[0071] In the formula, is the polarization voltage at time t, e is a natural constant, is the polarization voltage at the initial moment;

[0072] The terminal voltage formula of the equivalent circuit model is:

[0073] ;

[0074] In the formula, is the battery terminal voltage at time t, is the OCV of the battery;

[0075] When t=3*τ, the battery polarization voltage is:

[0076] ;

[0077] At this time, the battery polarization voltage has been reduced to 0.05 of the original value. In engineering, it is believed that the battery has entered a steady state. The time that is less than the set threshold , which is 3*τ, in the static data The voltage value corresponding to the moment is ,but and As shown in Table 2.

[0078] Table 2 and

[0079]

[0080] From the static data at each SOC, determine the time corresponding to the last static moment as well as The voltage value corresponding to the time In this embodiment, the battery is left at rest for 3 hours at each SOC. =10800s, corresponding voltage As shown in Table 3.

[0081] Table 3

[0082]

[0083] At each SOC, the compensation voltage value That is:

[0084] - ;

[0085] The compensation voltage value The results are shown in Table 4 and Figure 4 shown.

[0086] Table 4

[0087]

[0088] S4: Obtain a first estimated value of the OCV of the battery cell by using the voltage and current of the battery cell when it is actually operating online.

[0089] Specifically, a lithium iron phosphate battery system with 16 cells connected in series is selected and charged using a pulse current. The total voltage of the battery system is as follows: Figure 6 and Figure 7 shown.

[0090] In practical applications, recursive least squares (RLS) is a commonly used online parameter identification method, which seeks the optimal estimation of parameters based on the principle of minimizing the sum of squares of errors between measured data and predicted data. The Forgetting Factor Recursive Least Square (FFRLS) method with forgetting factor is used. The forgetting factor is introduced in this method to increase the weight of new data and reduce the influence of historical data, so that the identification results can better track the real-time changes of parameters.

[0091] Consider the model:

[0092] (1)

[0093] Where: is the observation matrix at time k, is white noise with zero mean, is the control matrix at time k; , ,……, is the observation matrix coefficient, , ,……, is the coefficient of the control matrix. Let:

[0094] (2)

[0095] (3)

[0096] Then equation (1) can be written as equation (4).

[0097] (4)

[0098] The conventional recursive least squares method is as follows:

[0099] (5)

[0100] Where: is the parameter to be estimated, is the covariance matrix at the kth moment; is the input matrix; is the identity matrix; is the gain matrix; is the covariance matrix at the k+1th moment.

[0101] Considering the phenomenon of data saturation, it is necessary to weight the original old data in the recursive process to reduce the influence of the old data on the new data and enhance the influence of the new data. The modified algorithm is called the recursive least squares method with forgetting factor, as shown in formula (6).

[0102] (6)

[0103] Where: It is called the forgetting factor. Represents different forgetting speeds for old data. =1: no forgetting; =0: All forgotten.

[0104] The present invention adopts a first-order RC model, such as Figure 3 As shown. , ohmic internal resistance , polarization internal resistance and time constant All of them are used as parameters to be estimated. Referring to the circuit structure diagram of the first-order RC model, based on the basic circuit principle, the calculation formula of the terminal voltage in the frequency domain can be written as shown in formula (7).

[0105] (7)

[0106] in, is the battery system open circuit voltage (OCV), Polarization internal resistance, Polarized capacitors, is the current passing through the cell, is the ohmic internal resistance, is the battery terminal voltage, and s represents the complex frequency domain.

[0107] make , we get equations (8) and (9).

[0108] (8)

[0109] (9)

[0110] make:

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] in, is the time interval; is the state value of the observed physical quantity at time k; is the state value of the observed physical quantity at time k-1; s represents the complex frequency domain; a, b, c are intermediate variables introduced in the calculation process. In order to simplify the calculation formula, they have no actual physical meaning and are only temporary variables.

[0116] make , , .

[0117] After bipolar transformation and simplification, we get (10).

[0118] (10)

[0119] in, is the current passing through the battery system at the kth moment. k1, k2, and k3 are intermediate variables introduced in the intermediate calculation process. In order to simplify the calculation formula, they have no actual physical meaning and are only temporary variables.

[0120] (11)

[0121] (12)

[0122] (13)

[0123] It is calculated that:

[0124] (14)

[0125] (15)

[0126] (16)

[0127] (17)

[0128] Thus, it can be achieved The OCV obtained is the first estimated OCV value OCV1.

[0129] S5: Based on the first estimated OCV value OCV1 and the compensation voltage value , and obtain the second estimated value of OCV, OCV2, as follows:

[0130] .

[0131] The results of the true OCV value, the first estimated OCV value, and the second estimated OCV value are as follows: Figure 8 shown.

[0132] It can be seen that after voltage compensation, the second estimated value of OCV is more consistent with the true value of OCV, and the estimation effect is better.

[0133] A computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a battery OCV estimation method with voltage compensation.

[0134] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a battery OCV estimation method with voltage compensation.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A battery OCV estimation method with voltage compensation, characterized in that: The following steps are involved: Performing an offline HPPC test on the battery unit to obtain HPPC test data, wherein the HPPC test data includes voltage and current data; Using the HPPC test data, battery parameter identification is performed to obtain battery model parameters; Determine the compensation voltage value according to the battery model parameters and the HPPC test data; Using the voltage and current of the battery cell when it is actually online, a first estimated value of the OCV of the battery cell is obtained; The first estimated value of OCV and the compensation voltage value are combined to obtain a second estimated value of OCV; the voltage and current data in the HPPC test data include static data after discharge at different SOC preset values, and pulse data in the HPPC; The battery model is an equivalent circuit model. The parameters of the battery model include the battery cell OCV, ohmic internal resistance , Polarization internal resistance , polarized capacitor and time constant τ; The specific steps of determining the compensation voltage value include: Determine the polarization voltage of the battery cell according to the time constant τ in the battery model parameters and the static data at each SOC The time that is less than the set threshold , and static data The voltage value corresponding to the moment ; The sequence relationship with SOC is: ； In the formula, is the nth SOC preset value, n is the number of SOC preset values, The time corresponding to the nth SOC preset value ; The sequence relationship with SOC is: ； In the formula, is the nth SOC preset value, n is the number of SOC preset values, The voltage corresponding to the nth SOC preset value ; From the static data at each SOC, determine the time corresponding to the last static moment as well as The voltage value corresponding to the time ; The sequence relationship with SOC is: ； In the formula, is the nth SOC preset value, n is the number of SOC preset values, The time corresponding to the nth SOC preset value ; The sequence relationship with SOC is: ； In the formula, is the nth SOC preset value, n is the number of SOC preset values, The voltage corresponding to the nth SOC preset value ; At each SOC, the compensation voltage value That is: ； The sequence relationship with SOC is: ； In the formula, is the nth SOC preset value, n is the number of SOC preset values, The voltage corresponding to the nth SOC preset value ; When the current of the battery module disappears, the current is 0, and the battery enters zero input response. At this time, the polarization voltage formula of the battery is as follows: ； In the formula, is the polarization voltage at time t, e is a natural constant, is the polarization voltage at the initial moment; The terminal voltage formula of the equivalent circuit model is: ； In the formula, is the battery terminal voltage at time t, is the OCV of the battery; When t=3*τ, the battery polarization voltage is: ； At this time, the battery polarization voltage has been reduced to 0.05 of the original value. In engineering, it is believed that the battery has entered a steady state. The time that is less than the set threshold , which is 3*τ, in the static data The voltage value corresponding to the moment is .

2. The method according to claim 1, characterized in that The specific steps of obtaining the battery model parameters include: By performing battery parameter identification on each SOC preset value, a sequence of different SOC preset values ​​and corresponding battery parameters is obtained.

3. The method according to claim 1, characterized in that Using the voltage and current of the battery cell when it is actually online, a first estimated value of the OCV of the battery cell is obtained, including: The real-time online voltage and current are used in combination with an online identification algorithm to estimate the OCV of the battery cell in real time, and the OCV value is obtained as a first estimated OCV value OCV1 of the battery cell.

4. The method according to claim 3, characterized in that According to the first estimated OCV value OCV1 and the compensation voltage value , and obtain the second estimated value of OCV OCV2, namely: 。 5. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a battery OCV estimation method with voltage compensation as described in any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a battery OCV estimation method with voltage compensation according to any one of claims 1 to 4 is implemented.

Images