Battery pack state information reconstruction method, computer equipment and medium

Abstract

The invention relates to the technical field of batteries, particularly provides a state information reconstruction method of a battery pack, computer equipment and a medium, and aims to reconstruct distorted and low-frequency battery state information into accurate and high-frequency battery state information. In order to achieve the purpose, the method provided by the invention comprises the following steps: acquiring low-frequency state information of each single battery cell, performing parameter identification on each single battery cell to obtain a target parameter of each single battery cell, and further processing the low-frequency state information of the single battery cell based on the target parameter for each single battery cell. And the high-frequency state information of the single battery cell is obtained. Based on the method, distorted and / or low-frequency battery state information can be reconstructed into accurate and high-frequency battery state information, so that the reliability of monitoring the battery pack can be effectively ensured.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery pack status information reconstruction method, computer equipment and medium. Background Art

[0002] At present, in the field of battery technology, the dynamic operating condition data of the battery system (including but not limited to the voltage and current of each single cell in the battery system) is uploaded to the cloud, and then analyzed and processed in the cloud to perform online detection and fault diagnosis of the battery system. However, in actual applications, although accurate and high-frequency dynamic operating condition data can be obtained through sampling of the battery management system (BMS), due to defects in communication technology and other reasons, the dynamic operating condition data received in the cloud may be distorted and low-frequency data. If these data are used for analysis and processing, misjudgment is likely to occur, and problems such as faults in the battery system cannot be discovered in a timely manner.

[0003] Accordingly, the art needs a new technical solution to solve the above problems. Summary of the invention

[0004] In order to overcome the above defects, the present invention is proposed to provide a battery pack state information reconstruction method, computer device and medium that solve or at least partially solve the technical problem of how to reconstruct distorted and low-frequency battery data into accurate and high-frequency battery data.

[0005] In a first aspect, a method for reconstructing state information of a battery pack is provided, wherein the battery pack includes a plurality of single cells, and the method includes:

[0006] Obtain low-frequency status information of each single battery cell;

[0007] Perform parameter identification on each single cell to obtain the target parameters of each single cell;

[0008] For each single battery cell, the low-frequency state information of the single battery cell is processed based on the target parameter to obtain the high-frequency state information of the single battery cell.

[0009] In a technical solution of the state information reconstruction method of the battery pack, the state information includes a single cell voltage, and the low-frequency state information of the single cell is processed based on the target parameter to obtain the high-frequency state information of the single cell, including:

[0010] According to the target parameter, a polarization voltage of an RC polarization link in an RC equivalent circuit model of the single cell is obtained;

[0011] Based on Kalman filter theory, the state equation and output observation equation of the monomer cell are established with the monomer current, monomer voltage and polarization voltage of the monomer cell as input, output and state quantity respectively;

[0012] Based on the state equation and the output observation equation, performing Kalman filtering on the cell voltage of the cell;

[0013] The high-frequency state information of the single cell is obtained according to the single cell voltage after Kalman filtering.

[0014] In a technical solution of the above-mentioned method for reconstructing the state information of the battery pack, the state information also includes the total current of the battery pack, and the single cell current of the single cell is obtained by:

[0015] The single cell current of the single cell is obtained according to the connection relationship of the single cell in the battery pack and the total current.

[0016] In a technical solution of the battery pack state information reconstruction method, the parameter identification of each single cell to obtain the target parameter of each single cell includes: identifying the target parameter of the RC equivalent circuit model of each single cell according to the single cell voltage and single cell current of each single cell, so as to obtain the polarization resistance and polarization capacitance of the RC polarization link in the RC equivalent circuit model of each single cell;

[0017] The step of obtaining the polarization voltage of the RC polarization link in the RC equivalent circuit model of the single cell according to the target parameter includes: obtaining the polarization voltage of the single cell according to the polarization resistance and polarization capacitance of the single cell.

[0018] In a technical solution of the above-mentioned method for reconstructing the state information of the battery pack, the method further includes establishing the output observation equation in the following manner:

[0019] Obtain the ohmic voltage of the ohmic internal resistance in the RC equivalent circuit model of each single cell;

[0020] Based on Kirchhoff's voltage law, and according to the ohmic voltage, polarization voltage and open circuit voltage of each single cell, a single cell voltage equation of each single cell is established;

[0021] According to the cell voltage equation of each cell, the output observation equation of each cell is established.

[0022] In a technical solution of the battery pack state information reconstruction method, the parameter identification of each single cell to obtain the target parameter of each single cell also includes: identifying the target parameter of the RC equivalent circuit model of each single cell according to the single cell voltage and single cell current of each single cell to obtain the ohmic internal resistance of each single cell;

[0023] The obtaining of the ohmic voltage of the ohmic internal resistance in the RC equivalent circuit model of each single cell includes: obtaining the ohmic voltage of each single cell according to the ohmic internal resistance and the single current of each single cell.

[0024] In a technical solution of the above-mentioned battery pack state information reconstruction method, the parameter identification of each single battery cell to obtain the target parameter of each single battery cell includes:

[0025] Taking the target parameters of each single cell as the optimization quantity, and according to the target parameters of each single cell and the single current, the polarization voltage equation of each single cell is established;

[0026] Based on Kirchhoff's voltage law and in accordance with the polarization voltage equation of each single cell and the single cell voltage of each single cell, a loop voltage equation of the RC equivalent circuit model of each single cell is established;

[0027] The target parameters of each single cell are optimized by using the least square method according to the loop voltage equation to obtain the optimal target parameters of each single cell.

[0028] In a technical solution of the above-mentioned method for reconstructing the state information of the battery pack, the parameter identification of each single cell to obtain the target parameter of each single cell also includes:

[0029] A particle swarm optimization algorithm is used to take the target parameter of each single cell as the optimization quantity and predict the first cell voltage of each single cell according to the target parameter of each single cell, and

[0030] With the first cell voltage and the second cell voltage of each cell satisfying a preset condition as a goal, the target parameters of each cell are optimized to obtain the optimal target parameters of each cell;

[0031] The second cell voltage is the cell voltage in the low-frequency state information.

[0032] In a technical solution of the above-mentioned method for reconstructing the state information of the battery pack, the preset condition at least includes that the root mean square error between the first cell voltage and the second cell voltage is less than a preset error value.

[0033] In a technical solution of the above-mentioned battery pack status information reconstruction method, after processing the low-frequency status information of each single battery cell based on the target parameter to obtain the high-frequency status information of the single battery cell, the method further includes: obtaining the high-frequency status information of the battery pack based on the high-frequency status information of each single battery cell.

[0034] In a second aspect, a computer device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the method described in any one of the technical solutions of the above-mentioned battery pack state information reconstruction method.

[0035] In one technical solution of the above-mentioned computer device, the computer device is a cloud server or a local computer.

[0036] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute the method described in any one of the technical solutions of the above-mentioned battery pack state information reconstruction method.

[0037] Solution 1. A method for reconstructing state information of a battery pack, wherein the battery pack includes a plurality of single cells, and the method includes:

[0038] Obtain low-frequency status information of each single cell;

[0039] Perform parameter identification on each single cell to obtain the target parameters of each single cell;

[0040] For each single battery cell, the low-frequency state information of the single battery cell is processed based on the target parameter to obtain the high-frequency state information of the single battery cell.

[0041] Solution 2. The method according to Solution 1 is characterized in that the state information includes a single cell voltage, and the low-frequency state information of the single cell is processed based on the target parameter to obtain the high-frequency state information of the single cell, including:

[0042] According to the target parameter, a polarization voltage of an RC polarization link in an RC equivalent circuit model of the single cell is obtained;

[0043] Based on Kalman filter theory, the state equation and output observation equation of the monomer cell are established with the monomer current, monomer voltage and polarization voltage of the monomer cell as input, output and state quantity respectively;

[0044] Based on the state equation and the output observation equation, performing Kalman filtering on the cell voltage of the cell;

[0045] The high-frequency state information of the single cell is obtained according to the single cell voltage after Kalman filtering.

[0046] Solution 3. The method according to Solution 2 is characterized in that the status information also includes the total current of the battery pack, and the single cell current of the single cell is obtained by the following method:

[0047] The single cell current of the single cell is obtained according to the connection relationship of the single cell in the battery pack and the total current.

[0048] Scheme 4. The method according to Scheme 2, characterized in that:

[0049] The parameter identification of each single cell is performed to obtain the target parameter of each single cell, including: according to the single cell voltage and single cell current of each single cell, the target parameter of the RC equivalent circuit model of each single cell is identified to obtain the polarization resistance and polarization capacitance of the RC polarization link in the RC equivalent circuit model of each single cell;

[0050] The step of obtaining the polarization voltage of the RC polarization link in the RC equivalent circuit model of the single cell according to the target parameter includes: obtaining the polarization voltage of the single cell according to the polarization resistance and polarization capacitance of the single cell.

[0051] Solution 5. The method according to Solution 2 is characterized in that the method further comprises establishing the output observation equation in the following manner:

[0052] Obtain the ohmic voltage of the ohmic internal resistance in the RC equivalent circuit model of each single cell;

[0053] Based on Kirchhoff's voltage law, and according to the ohmic voltage, polarization voltage and open circuit voltage of each single cell, a single cell voltage equation of each single cell is established;

[0054] According to the cell voltage equation of each cell, the output observation equation of each cell is established.

[0055] Solution 6. The method according to Solution 5, characterized in that:

[0056] The parameter identification of each single cell to obtain the target parameter of each single cell also includes: identifying the target parameter of the RC equivalent circuit model of each single cell according to the single cell voltage and single cell current of each single cell to obtain the ohmic internal resistance of each single cell;

[0057] The obtaining of the ohmic voltage of the ohmic internal resistance in the RC equivalent circuit model of each single cell includes: obtaining the ohmic voltage of each single cell according to the ohmic internal resistance and the single current of each single cell.

[0058] Solution 7. The method according to Solution 1 is characterized in that the step of performing parameter identification on each battery cell to obtain target parameters of each battery cell comprises:

[0059] Taking the target parameters of each single cell as the optimization quantity, and according to the target parameters of each single cell and the single current, the polarization voltage equation of each single cell is established;

[0060] Based on Kirchhoff's voltage law and in accordance with the polarization voltage equation of each single cell and the single cell voltage of each single cell, a loop voltage equation of the RC equivalent circuit model of each single cell is established;

[0061] The target parameters of each single cell are optimized by using the least square method according to the loop voltage equation to obtain the optimal target parameters of each single cell.

[0062] Solution 8. The method according to Solution 1 is characterized in that the step of performing parameter identification on each single cell to obtain target parameters of each single cell further comprises:

[0063] A particle swarm optimization algorithm is used to take the target parameter of each single cell as the optimization quantity and predict the first cell voltage of each single cell according to the target parameter of each single cell, and

[0064] With the first cell voltage and the second cell voltage of each cell satisfying a preset condition as a goal, the target parameters of each cell are optimized to obtain the optimal target parameters of each cell;

[0065] The second cell voltage is the cell voltage in the low-frequency state information.

[0066] Solution 9. The method according to Solution 8, characterized in that:

[0067] The preset condition at least includes that a root mean square error between the first cell voltage and the second cell voltage is less than a preset error value.

[0068] Solution 10. The method according to Solution 1 is characterized in that, after processing the low-frequency state information of each single cell based on the target parameter to obtain the high-frequency state information of the single cell, the method further comprises:

[0069] The high frequency status information of the battery pack is obtained according to the high frequency status information of each single battery cell.

[0070] Scheme 11. A computer device comprising a processor and a storage device, wherein the storage device is suitable for storing multiple program codes, and is characterized in that the program codes are suitable for being loaded and run by the processor to execute the battery pack state information reconstruction method described in any one of Schemes 1 to 10.

[0071] Solution 12. The computer device according to Solution 11 is characterized in that the computer device is a cloud server or a local computer.

[0072] Solution 13. A computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the state information reconstruction method of a battery pack described in any one of Solutions 1 to 10.

[0073] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0074] In a technical solution for implementing the battery pack status information reconstruction method provided by the present invention, the battery pack may include multiple single cells. The method can obtain the low-frequency status information of each single cell, perform parameter identification on each single cell to obtain the target parameter of each single cell, and then process the low-frequency status information of each single cell based on the target parameter to obtain the high-frequency status information of the single cell. Based on the above method, the distorted and / or low-frequency battery pack status information can be reconstructed into accurate and high-frequency battery pack status information, thereby effectively ensuring the reliability of monitoring the battery pack.

[0075] In another technical solution of the state information reconstruction method of the battery pack provided by the present invention, the single cell current, single cell voltage and polarization voltage (polarization voltage of the RC polarization link in the RC equivalent circuit model) of each single cell can be used as input, output and state quantity respectively, and the state equation and output observation equation of each single cell are established, and then based on the state equation and the output observation equation, the single cell voltage of each single cell is Kalman filtered. Based on the above method, the RC equivalent circuit model can be used to conveniently and accurately complete Kalman filtering, thereby improving the efficiency and accuracy of the reconstruction of the state information of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. Among them:

[0077] Figure 1 is a schematic flow chart of main steps of a method for reconstructing state information of a battery pack according to an embodiment of the present invention;

[0078] Figure 2 is a schematic flow chart of main steps of a method for performing Kalman filtering on a cell voltage according to an embodiment of the present invention;

[0079] Figure 3 It is a schematic diagram of the first-order RC equivalent circuit model;

[0080] Figure 4 This is a flow chart of Kalman filtering;

[0081] Figure 5 This is a schematic flow chart of the main steps of a method for obtaining optimal target parameters using the least squares method according to an embodiment of the present invention;

[0082] Figure 6 is a flow chart of main steps of a method for obtaining optimal target parameters using a particle swarm optimization algorithm according to an embodiment of the present invention;

[0083] Figure 7 It is a flow chart of particle swarm optimization algorithm;

[0084] Figure 8 is a schematic diagram of a cell voltage and its error value in low-frequency state information according to an embodiment of the present invention;

[0085] Fig. 9 is a schematic diagram of a cell voltage and its error value in high frequency state information according to an embodiment of the present invention;

[0086] Fig.10 It is a main structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0087] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0088] In the description of the present invention, "processor" may include hardware, software or a combination of the two. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B or A and B.

[0089] The relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, based on the reasonable purposes of business scenarios, to process the personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with the user's authorization.

[0090] The user personal information processed by the applicant will vary depending on the specific product / service scenario, and shall be based on the specific scenario in which the user uses the product / service, which may involve the user's account information, device information, driving information, vehicle information or other related information. The applicant will treat the user's personal information and its processing with a high degree of diligence.

[0091] The Applicant attaches great importance to the security of user personal information and has adopted reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0092] An embodiment of a method for reconstructing battery pack status information is described below.

[0093] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for reconstructing battery pack status information according to an embodiment of the present invention. The battery pack may include a plurality of single cells. Figure 1 As shown, the battery pack status information reconstruction method in the embodiment of the present invention mainly includes the following steps S101 to S103.

[0094] Step S101: obtaining low-frequency status information of each single battery cell.

[0095] The cell voltage is the terminal voltage of a single cell.

[0096] Step S102: performing parameter identification on each battery cell to obtain target parameters of each battery cell.

[0097] The target parameter is a parameter of a single cell that is essential for processing the low-frequency state information of the single cell into high-frequency state information. Different types of state information may need to identify different target parameters, and the corresponding target parameters may be obtained according to the actual state information type. For example, in some embodiments, the state information includes a single cell voltage. If the low-frequency single cell voltage needs to be processed into a high-frequency single cell voltage, the target parameters that need to be identified may include the polarization resistance and polarization capacitance of the single cell.

[0098] Step S103: for each single battery cell, the low-frequency state information of the single battery cell is processed based on the target parameter to obtain the high-frequency state information of the single battery cell.

[0099] After obtaining the target parameters of each battery cell, filtering can be used to convert the low-frequency state information of the battery cell into high-frequency state information based on the target parameters. For example, in some embodiments, Kalman filtering (KF) can be used to obtain the high-frequency state information.

[0100] In an embodiment of the present invention, after obtaining the high-frequency status information of each single battery cell, the high-frequency status information of the battery pack can be obtained based on the high-frequency status information. In some embodiments, the cell voltage dynamic curve of each single battery cell can be obtained based on the high-frequency status information of each single battery cell, and then the high-frequency status information of the battery pack can be obtained based on the cell voltage dynamic curve. The cell voltage dynamic curve is a curve of the cell voltage changing with time. The cell voltage curve can be used to obtain the high-frequency status information curve of the application carrier of the battery pack such as a vehicle, and then the state of the application carrier such as the vehicle can be estimated, etc. Those skilled in the art can flexibly set the application scenario of the cell voltage dynamic curve according to actual needs, and the embodiment of the present invention does not make specific limitations.

[0101] Based on the methods described in steps S101 to S103 above, distorted and / or low-frequency state information can be reconstructed into accurate and high-frequency state information.

[0102] The above step S102 is further explained below.

[0103] In some implementations of the above step S102, Figure 2 The following steps S1021 to S1024 are shown to obtain high frequency status information of each single battery cell.

[0104] Step S1021: according to the target parameters, the polarization voltage of the RC polarization link in the RC equivalent circuit model of the single cell is obtained.

[0105] See attached Figure 3 , Figure 3 The structure of the first-order RC equivalent circuit model is shown as an example. Figure 3 As shown, OCV represents the open circuit voltage of a single cell, R0 represents the ohmic internal resistance, and V0 represents the voltage drop of the ohmic internal resistance, i.e., the ohmic voltage. p Represents polarization resistance, C P Represents polarized capacitance, R p With C P Form an RC polarization link, V P It represents the polarization voltage of this polarization link. V represents the single cell voltage, that is, the terminal voltage of the single cell.

[0106] It should be noted that those skilled in the art can flexibly select a certain order of RC equivalent circuit model according to actual needs. For example, in some embodiments, any one of the 1st-20th order RC equivalent circuit models can be selected.

[0107] Step S1022: Based on the Kalman filter theory, the single cell current, single cell voltage and polarization voltage of the single cell are used as input, output and state quantity respectively, to establish the state equation and output observation equation of the single cell.

[0108] In the embodiment of the present invention, if the status information includes the single cell current, it can be directly obtained; if the status information does not include the single cell current but includes the total current of the battery pack, the single cell current of each single cell is obtained using the total current according to the connection relationship of each single cell in the battery system. In some embodiments, the single cells in the battery pack are connected in parallel. At this time, the single cell current of each single cell can be obtained according to the ratio of the total current to the total number of single cells in the battery pack. In other words, the total current is evenly divided according to the number of single cells, and the single cell current of each single cell is the current value after the even division.

[0109] The state equation and output observation equation are shown in equations (1) and (2) respectively.

[0110]

[0111] The meanings of the parameters in formulas (1) and (2) are as follows:

[0112] A sampling segment of a low-frequency state information is composed of multiple sampling points, and k represents the sequence number of each sampling point in the above sampling segment.

[0113] x k represents the optimization amount, that is, the polarization voltage corresponding to the kth sampling point. k Represents the output, that is, the single cell voltage corresponding to the kth sampling point. k represents the input quantity, that is, the monomer current corresponding to the kth sampling point. k and v k A represents the input noise and output noise corresponding to the kth sampling point. k , B k , C k , D k They all represent the coefficients corresponding to the kth sampling point.

[0114] See attached Figure 4 , Figure 4 The process of Kalman filtering is shown as an example. Figure 4 As shown, the input quantity u k With coefficient B k Multiply to get Bk *u k , the optimization value x corresponding to the kth sampling point k With coefficient A k Multiply to get A k *x k , A k *u k , B k *u k With input noise w k Add together to get the optimization value x corresponding to the k+1th sampling point k+1 .x k+1 After processing by the unit lag operator, we get x k This process corresponds to the state equation. Input quantity u k With coefficient D k Multiply to get D k *u k , optimization amount x k With coefficient C k Multiply to get C k *x k , C k *x k , D k *u k With output noise v k Add together to get the output y k . This process corresponds to the output observation equation.

[0115] Step S1023: Based on the state equation and the output observation equation, Kalman filtering is performed on the cell voltage of the single cell.

[0116] In this step, a conventional Kalman filtering method may be used to perform Kalman filtering on the cell voltage based on the state equation and the output observation equation obtained in step S1022.

[0117] Step S1024: Obtain high-frequency state information of the single cell according to the single cell voltage after Kalman filtering. In this step, the single cell voltage obtained after filtering is used as the reconstructed single cell voltage. The embodiment of the present invention does not elaborate on the specific process of Kalman filtering.

[0118] In the method described in the above steps S1021 to S1024, a more accurate cell voltage can be obtained through Kalman filtering, thereby eliminating the distortion problem of the cell voltage. When the cell voltage is distorted, its voltage curve that changes with time will be sawtooth-shaped. Based on this, it can be determined that the distorted cell voltage is low-frequency data, and after Kalman filtering, the distorted cell voltage can be reconstructed into high-frequency data, and the voltage curve is no longer sawtooth-shaped, thereby obtaining high-frequency state information. At the same time, based on the above method, the RC equivalent circuit model can be used to conveniently and accurately complete the Kalman filtering, thereby improving the efficiency and accuracy of the battery pack state information reconstruction.

[0119] The above steps S1021 and S1022 are further explained below.

[0120] (i) Explain step S1021.

[0121] In some implementations of the above step S1021, the polarization voltage can be obtained through the following steps 11 to 12.

[0122] Step 11: According to the cell voltage and the cell current of each cell, identify the target parameters of the RC equivalent circuit model of each cell to obtain the target parameters of each cell. The target parameters include but are not limited to the ohmic internal resistance, polarization resistance and polarization capacitance of the RC equivalent circuit model. Figure 3 As shown, the target parameters may include R0, R p , C P .

[0123] Step 12: Obtain the polarization voltage of each cell according to the polarization resistance and polarization capacitance of the RC polarization link in the target parameters of each cell. In this step, the voltage calculation method of the conventional RC parallel circuit can be used to calculate the voltage of the RC polarization link formed by the parallel polarization resistance and polarization capacitance. This voltage is the polarization voltage.

[0124] In the method described in the above steps 11 to 12, the target parameters of the RC equivalent circuit model are identified using low-frequency state information, which can ensure the accuracy of the target parameters, thereby improving the accuracy of the polarization voltage, which is conducive to reliably completing the Kalman filter.

[0125] The method for obtaining the target parameters in the above step 11 is described below.

[0126] In some implementations of step 11 above, Figure 5 The following steps S201 to S203 shown in the figure are used to obtain target parameters.

[0127] Step S201: Taking the target parameter of each single cell as the optimization quantity, and establishing the polarization voltage equation of each single cell according to the target parameter of each single cell and the single cell current.

[0128] The polarization voltage equation can be shown as follows (3).

[0129]

[0130] The meanings of the parameters in formula (3) are as follows:

[0131] A sampling segment of a low-frequency state information is composed of multiple sampling points, and k represents the sequence number of each sampling point in the above sampling segment.

[0132] V P,k Represents the polarization voltage corresponding to the kth sampling point, which is the polarization resistance R p and polarization capacitance C p The voltage drop V on the polarization link is formed P . V P,k-1 With V P,k The meaning is similar. k-1 represents the monomer current corresponding to the k-1th sampling point. T represents the time interval between two sampling points.

[0133] Step S202: Based on Kirchhoff's voltage law and according to the polarization voltage equation of each single cell and the cell voltage of each single cell, a loop voltage equation of the RC equivalent circuit model of each single cell is established.

[0134] Without considering the ohmic internal resistance in the RC equivalent circuit model, the loop voltage equation shown in equation (4) can be constructed, while considering the ohmic internal resistance in the RC equivalent circuit model, the loop voltage equation shown in equation (5) can be constructed.

[0135] V k -OCV k =V P,k (4)

[0136] V k -OCV k =V 0,k +V P,k (5)

[0137] Among them, V 0,k The calculation formula of is shown in formula (6).

[0138] V 0,k =I k *R0 (6)

[0139] The meanings of the parameters in formulas (4) to (6) are as follows:

[0140] V k Indicates the monomer voltage corresponding to the kth sampling point, OCV k represents the open circuit voltage corresponding to the kth sampling point, V 0,k Represents the ohmic voltage corresponding to the kth sampling point.

[0141] Furthermore, in this embodiment, taking into account the ohmic internal resistance in the RC equivalent circuit model, the above formulas (3), (5) to (6) can be simplified to the form of matrix product as shown in the following formula (7), in which the system error is omitted.

[0142] D k =[D k-1 ,I k ,I k-1 ]*[c1;c2;c3] (7)

[0143] Further arrangement of formula (7) can obtain the matrix form shown in the following formula (8).

[0144]

[0145] D in formulas (7) to (8) k The calculation formulas for c1, c2, and c3 are shown in equations (9) to (12) below, respectively.

[0146] D k =V k -OCV k (9)

[0147]

[0148] c2=R0 (11)

[0149]

[0150] The meanings of the parameters in formulas (7) to (12) are the same as those of the relevant parameters mentioned in the previous formulas and will not be repeated here.

[0151] Step S203: using the least square method and optimizing the target parameters of each single cell according to the loop voltage equation to obtain the optimal target parameters of each single cell.

[0152] Taking the formula (8) in the above step S202 as an example, the left side of the equation can be used as the observed value of D (D = V-OCV), and the right side of the equation can be used as the predicted value of D obtained according to the target parameter. With the purpose of minimizing the residual square sum of the above observed value and the predicted value, the least square method is used to solve the coefficients c1, c2, c3, and then the target parameter can be obtained based on c1, c2, c3. The target parameter at this time is the optimal target parameter. In this embodiment, the conventional least square method can be used to solve the above system, and the embodiment of the present invention does not elaborate on the specific process of the least square method.

[0153] Based on the method described in steps S201 to S203 above, the optimal target parameters of the RC equivalent circuit model can be quickly and accurately obtained using the least squares method.

[0154] In some implementations of the above step 11, Figure 6 The following steps S301 to S302 shown in the figure are to obtain target parameters.

[0155] Step S301: Using a particle swarm optimization algorithm, taking the target parameter of each single cell as the optimization amount and predicting the first cell voltage of each single cell according to the target parameter of each single cell. In order to distinguish it from the predicted first cell voltage, in this embodiment, the cell voltage of the single cell in the low-frequency state information is described as the second cell voltage.

[0156] Step S302: With the first cell voltage and the second cell voltage of each cell satisfying the preset conditions as the goal, the target parameters of each cell are optimized to obtain the optimal target parameters of each cell. Among them, the preset conditions include at least the root mean square error between the first cell voltage and the second cell voltage being less than the preset error value, and may also include the number of iterations executed by the particle swarm optimization algorithm reaching the preset number threshold. The smaller the root mean square error, the smaller the deviation between the first and second cell voltages, the higher the prediction accuracy of the first cell voltage, and the better the target parameter used as the optimization amount at this time. When the root mean square error between the first and second cell voltages is less than the preset error value, it indicates that the target parameter at this time has reached the optimal value, and the particle swarm optimization can be stopped. Based on the root mean square error between the first and second cell voltages, the prediction accuracy of the first cell voltage can be accurately measured, and then the optimal target parameters of the cell can be reliably obtained, thereby improving the accuracy of the optimal target parameters. Those skilled in the art can flexibly set the specific values ​​of the preset error value and the number threshold according to actual needs, and the embodiment of the present invention is not specifically limited.

[0157] See attached Figure 7 , Figure 7 The process of particle swarm optimization algorithm is shown as an example. Figure 7As shown, in this embodiment, the optimal target parameters can be obtained through the following steps S401 to S404. Step S401: Initialize the parameters in the particle swarm optimization algorithm. Step S402: Calculate the evaluation function of each particle. A particle represents a set of target parameters, and a set of target parameters includes the resistance value of a polarization resistor, the capacitance value of a polarization capacitor, and the resistance value of an ohmic internal resistor. The evaluation function is a function used to calculate the root mean square error between the first monomer voltage and the second monomer voltage. Step S403: Update the position and speed of each particle. The position represents the parameter that changes each time the particle is updated, and the speed represents the increment when the parameter is updated. Step S404: Determine whether the preset conditions are met. If the calculation result of the evaluation function is less than the preset error value, it is determined that the preset conditions are met; if the number of iterative updates of the particle has reached the preset number threshold, it can also be determined that the preset conditions are met. Otherwise, it can be determined that the preset condition is not met. At this time, the process can go to step S402, calculate the evaluation function again according to the position and velocity of the particle updated in step S403, and continue to execute subsequent steps S403 and S404 until the preset condition is met.

[0158] Based on the method described in the above steps S301 to S302, the particle swarm optimization algorithm can be used to quickly and accurately obtain the optimal target parameters of the RC equivalent circuit model.

[0159] (ii) Explain step S1022.

[0160] In some implementations of the above step S1022, the output observation equation may be established through the following steps 21 to 23.

[0161] Step 21: Obtain the ohmic voltage of the ohmic internal resistance in the RC equivalent circuit model of each single cell. Figure 3 As shown, the ohmic voltage is the voltage drop across the ohmic internal resistance R0.

[0162] Step 22: Based on Kirchhoff's voltage law and according to the ohmic voltage, polarization voltage and open circuit voltage of each single cell, a single cell voltage equation of each single cell is established.

[0163] By using the formulas (3), (5) to (12) in the aforementioned steps S201 to S202, a polarization voltage equation as shown in the following formula (13) can be established.

[0164]

[0165] The meanings of the parameters in formula (13) are as follows:

[0166] A sampling segment of low-frequency state information is composed of multiple sampling points, k represents the sequence number of each sampling point in the above sampling segment, n represents the total number of single cells in the battery pack, and p represents the sequence number of the single cell.

[0167] Indicates the polarization voltage corresponding to the first single cell in the battery pack. Indicates the polarization voltage corresponding to the kth sampling point Indicates the polarization voltage corresponding to the k+1th sampling point The meaning and similar.

[0168] R1 and C1 represent the polarization resistance and polarization capacitance of the first single cell in the battery pack, respectively. R2…R n The meaning is similar to R1, C2…C n The meaning is similar to C1.

[0169] It also indicates the polarization capacitance corresponding to the first single cell in the battery pack. The meaning and In some embodiments, Replace with C1…C n .

[0170] I represents a matrix with n rows and 1 column, and each element of the matrix represents the single current of each single cell. In this embodiment, each single cell in the battery pack is connected in parallel, so the single current of each single cell is equal, which is I.

[0171] Furthermore, according to the polarization voltage equation shown in formula (13), the ohmic voltage and open circuit voltage of the single cell, the single cell voltage equation shown in formula (14) can be obtained.

[0172]

[0173] in, I and OCV represent the polarization voltage, ohmic internal resistance, monomer current and open circuit voltage of the i-th single cell, respectively. Indicates ohm voltage.

[0174] Step 23: According to the cell voltage equation of each cell, establish the output observation equation of each cell.

[0175] First, using the cell voltage equation shown in the above formula (14), the output voltage equation shown in the following formula (15) can be established.

[0176]

[0177] The meanings of the parameters in formula (15) are as follows:

[0178] U sys represents the output voltage of the battery pack, OCV represents a matrix of n rows and 1 column, and each element of the matrix represents the open circuit voltage of each single cell. In this embodiment, the single cells in the battery pack are connected in parallel, and the open circuit voltages of the single cells are equal, which are all OCV. The meanings of the remaining parameters refer to the meanings of the relevant parameters in the above formula (14), which will not be repeated here.

[0179] Then, U sys As y k , substituting it into formula (2), we can get the output observation equation.

[0180] Based on the method described in steps 21 to 23 above, an output voltage equation including polarization resistance, polarization capacitance and ohmic internal resistance can be constructed, and then the output voltage equation is used to construct an output observation equation including the above target parameters. In this way, when using the output observation equation for Kalman filtering, the actual target parameters of each single cell can be taken into account, thereby ensuring targeted and accurate filtering of the cell voltage of each single cell.

[0181] In some implementations of the above step 21, the ohmic voltage can be obtained by following steps 211 to 212.

[0182] Step 211: According to the cell voltage and the cell current of each cell, the target parameters of the RC equivalent circuit model of each cell are identified to obtain the target parameters of each cell. The target parameters include but are not limited to the ohmic internal resistance, polarization resistance and polarization capacitance of the RC equivalent circuit model. Figure 3 As shown, the target parameters may include R0, R p , C P The method for identifying the target parameters in this step is the same as the method used in step 11 of the aforementioned step S1022, and will not be repeated here.

[0183] Step 212: Obtain the ohmic voltage of each single cell according to the ohmic internal resistance in the target parameter of each single cell and the single current of each single cell. Specifically, the product of the single current and the ohmic internal resistance can be used as the ohmic voltage.

[0184] In the method described in the above steps 211 to 212, the target parameters of the RC equivalent circuit model are identified using the dynamic operating condition data to be reconstructed, which can ensure the accuracy of the target parameters, thereby improving the accuracy of the ohmic voltage, which is conducive to the reliability of Kalman filtering.

[0185] The following is combined with Figure 8 To Attachment Fig. 9, the effect of the battery pack status information reconstruction method provided by the present invention is described. Among them, Figure 8 The single cell voltage and its error value in the low-frequency status information of the battery pack are shown as an example. Fig. 9 The single cell voltage and its error value in the high frequency state information reconstructed by the method provided by the present invention are exemplarily shown.

[0186] like Figure 8 As shown, the curve marked as "original voltage" on the left is the curve of the change of the single cell voltage in the low-frequency status information over time. The curve is jagged, and it can be determined that it is low-frequency data. The curve marked as "original error value" on the right is the curve of the change of the error between the single cell voltage in the low-frequency status information and the real single cell voltage of the battery pack over time. The error range is roughly between ±30mV, and the error is relatively large. The real single cell voltage of the battery pack is the high-frequency single cell voltage acquired and output by the battery management system. The single cell voltage in the low-frequency status information of the battery pack is the low-frequency single cell voltage formed after the single cell voltage output by the above-mentioned battery management system is processed by network transmission, etc. The single cell voltage in the high-frequency status information of the battery pack is the high-frequency single cell voltage reconstructed based on the low-frequency status information of the battery pack using the status information reconstruction method provided by the present invention.

[0187] like Fig. 9 As shown, the curve on the left marked as "cloud-based single-cell voltage first-order target parameter identification + Kalman filtering" is a curve of single-cell voltage variation over time in high-frequency dynamic information after reconstruction using the method provided by the present invention. This curve eliminates Figure 8 The sawtooth in Figure 8 The curve on the right marked as "KF filter error value" is a curve showing the error between the single cell voltage in the high-frequency dynamic information reconstructed by the method provided by the present invention and the actual single cell voltage of the aforementioned battery pack over time. The error range is roughly between ±10mV. Figure 8 The error is greatly reduced.

[0188] Through the above analysis, it can be determined that the method provided by the present invention can reconstruct low-frequency and distorted low-frequency state information into high-frequency and accurate high-frequency state information.

[0189] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present invention, different steps do not have to be executed in such an order, they can be executed simultaneously (in parallel) or in other orders. These adjusted schemes are equivalent to the technical schemes described in the present invention, and therefore will also fall within the scope of protection of the present invention.

[0190] It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0191] Furthermore, the present invention also provides a computer device.

[0192] See attached Fig.10 , Fig.10 1 is a schematic diagram of the main structure of a computer device embodiment according to the present invention. Fig.10 As shown, the computer device in the embodiment of the present invention mainly includes a storage device and a processor. The storage device can be configured to store a program for executing the battery pack state information reconstruction method of the above method embodiment, and the processor can be configured to execute the program in the storage device, which includes but is not limited to the program for executing the battery pack state information reconstruction method of the above method embodiment. For ease of explanation, only the part related to the embodiment of the present invention is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention.

[0193] In an embodiment of the present invention, the computer device may be a control device device formed by various electronic devices. In some possible implementations, the computer device may be a cloud server that can at least provide cloud computing (Cloud Computing) to provide cloud services. Based on this, low-frequency state information reconstruction and fault monitoring and other processing can be performed on a large number of battery packs at the same time, and the reliability of low-frequency state information reconstruction can be effectively guaranteed based on the high computing performance of the cloud server. In an example of an application scenario, the application carrier of the battery pack is a vehicle (such as an electric vehicle or an electric-oil hybrid vehicle, etc.), and the vehicle collects the dynamic working condition data of the power battery through its own configured battery management system, and uploads these dynamic working condition data to the above-mentioned cloud server. The cloud server reconstructs the received dynamic working condition data that may be distorted and / or low-frequency into accurate and high-frequency dynamic working condition data by executing the state information reconstruction method provided by the present invention, and then monitors whether the power battery has a fault according to these dynamic working condition data, and performs fault alarm and other processing. In addition, in some other possible implementations, the computer device may also be a local computer. The application scenario example of the local computer is similar to the application scenario of the aforementioned cloud server, and will not be repeated here.

[0194] In some possible implementations, the computer device may include multiple storage devices and multiple processors. The program for executing the battery pack state information reconstruction method of the above method embodiment may be divided into multiple subprograms, each of which may be loaded and run by a processor to execute different steps of the battery pack state information reconstruction method of the above method embodiment. Specifically, each subprogram may be stored in different storage devices, and each processor may be configured to execute programs in one or more storage devices to jointly implement the battery pack state information reconstruction method of the above method embodiment, that is, each processor executes different steps of the battery pack state information reconstruction method of the above method embodiment to jointly implement the battery pack state information reconstruction method of the above method embodiment.

[0195] The above-mentioned multiple processors may be processors deployed on the same device. For example, the above-mentioned computer device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors may also be processors deployed on different devices. For example, the above-mentioned computer device may be a server cluster, and the above-mentioned multiple processors may be processors on different servers in the server cluster.

[0196] Furthermore, the present invention also provides a computer-readable storage medium.

[0197] In an embodiment of a computer-readable storage medium according to the present invention, the computer-readable storage medium may be configured to store a program for executing the battery pack state information reconstruction method of the above method embodiment, and the program may be loaded and run by a processor to implement the above battery pack state information reconstruction method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium may be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0198] So far, the technical solution of the present invention has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for reconstructing state information of a battery pack, characterized in that: The battery pack includes a plurality of single cells, and the method includes: Obtain low-frequency status information of each single battery cell; Perform parameter identification on each single cell to obtain the target parameters of each single cell; For each single battery cell, the low-frequency state information of the single battery cell is processed based on the target parameter to obtain the high-frequency state information of the single battery cell.

2. The method according to claim 1, characterized in that The state information includes a single cell voltage, and the low-frequency state information of the single cell is processed based on the target parameter to obtain the high-frequency state information of the single cell, including: According to the target parameter, a polarization voltage of an RC polarization link in an RC equivalent circuit model of the single cell is obtained; Based on Kalman filter theory, the state equation and output observation equation of the monomer cell are established with the monomer current, monomer voltage and polarization voltage of the monomer cell as input, output and state quantity respectively; Based on the state equation and the output observation equation, performing Kalman filtering on the cell voltage of the cell; The high-frequency state information of the single cell is obtained according to the single cell voltage after Kalman filtering.

3. The method according to claim 2, characterized in that The state information also includes the total current of the battery pack, and the single current of the single battery cell is obtained by: The single cell current of the single cell is obtained according to the connection relationship of the single cell in the battery pack and the total current.

4. The method according to claim 2, characterized in that: The parameter identification of each single cell is performed to obtain the target parameter of each single cell, including: according to the single cell voltage and single cell current of each single cell, the target parameter of the RC equivalent circuit model of each single cell is identified to obtain the polarization resistance and polarization capacitance of the RC polarization link in the RC equivalent circuit model of each single cell; The step of obtaining the polarization voltage of the RC polarization link in the RC equivalent circuit model of the single cell according to the target parameter includes: obtaining the polarization voltage of the single cell according to the polarization resistance and polarization capacitance of the single cell.

5. The method according to claim 2, characterized in that: The method further comprises establishing the output observation equation by: Obtain the ohmic voltage of the ohmic internal resistance in the RC equivalent circuit model of each single cell; Based on Kirchhoff's voltage law, and according to the ohmic voltage, polarization voltage and open circuit voltage of each single cell, a single cell voltage equation of each single cell is established; According to the cell voltage equation of each cell, the output observation equation of each cell is established.

6. The method according to claim 5, characterized in that The parameter identification of each single cell to obtain the target parameter of each single cell also includes: identifying the target parameter of the RC equivalent circuit model of each single cell according to the single cell voltage and single cell current of each single cell to obtain the ohmic internal resistance of each single cell; The obtaining of the ohmic voltage of the ohmic internal resistance in the RC equivalent circuit model of each single cell includes: obtaining the ohmic voltage of each single cell according to the ohmic internal resistance and the single current of each single cell.

7. The method according to claim 1, characterized in that The step of performing parameter identification on each single cell to obtain target parameters of each single cell includes: Taking the target parameters of each single cell as the optimization quantity, and according to the target parameters of each single cell and the single current, the polarization voltage equation of each single cell is established; Based on Kirchhoff's voltage law and according to the polarization voltage equation of each single cell and the single cell voltage of each single cell, a loop voltage equation of the RC equivalent circuit model of each single cell is established; The target parameters of each single cell are optimized by using the least square method according to the loop voltage equation to obtain the optimal target parameters of each single cell.

8. The method according to claim 1, characterized in that The performing parameter identification on each single cell to obtain target parameters of each single cell also includes: A particle swarm optimization algorithm is used to optimize the target parameters of each single cell and predict the first cell voltage of each single cell according to the target parameters of each single cell, and With the first cell voltage and the second cell voltage of each cell satisfying a preset condition as a goal, the target parameters of each cell are optimized to obtain the optimal target parameters of each cell; The second cell voltage is the cell voltage in the low-frequency state information.

9. The method according to claim 8, characterized in that The preset condition at least includes that a root mean square error between the first cell voltage and the second cell voltage is less than a preset error value.

10. The method according to claim 1, characterized in that After processing the low-frequency state information of each single cell based on the target parameter to obtain the high-frequency state information of the single cell, the method further includes: The high frequency status information of the battery pack is obtained according to the high frequency status information of each single battery cell.

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