Battery soc management system and method based on metaverse and power battery

Abstract

The application relates to a battery SOC management system, method and power battery based on a meta universe. The system is composed of a battery BMS, a collection system, a meta universe BMS, an exceeding threshold module, a threshold meeting module, a parameter checking module, a correction algorithm module, a correction exit module, a mapping true value module, a meta universe general brain and an alarm system. The battery BMS is arranged in a battery pack and can independently and autonomously calculate the SOC value in the battery pack. The battery BMS can map the calculation parameters in the battery BMS to the meta universe BMS in real time through the calculation collection system. The meta universe BMS is arranged in a data cloud, and the meta universe BMS 3 estimates the SOC value through the parameters transmitted by the collection system. The meta universe general brain is arranged in a remote cloud and can remotely receive all SOC estimation conditions to store and record information. The meta universe general brain can call the previous SOC estimation conditions to assist in calculation and judgment during the estimation process of the meta universe BMS 3.

Description

Technical Field

[0001] This invention belongs to the field of power battery technology, specifically relating to a battery SOC management system, method, power battery, and electric vehicle based on the metaverse. Background Technology

[0002] Power batteries are a key component of new energy electric vehicles, and battery SOC estimation is a core technology of power battery management systems. Currently, there are two major problems with power battery SOC management: 1. Poor estimation accuracy; 2. Errors are prone to accumulate, seriously affecting battery safety. Summary of the Invention

[0003] The purpose of this invention is to provide a battery SOC management system based on the metaverse, and also to provide a power battery and electric vehicle to solve the problems of poor battery SOC estimation accuracy, easy accumulation of SOC error, and inability to be remotely controlled in the prior art.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A battery SOC management system based on metaverse consists of a battery BMS1, a data acquisition system2, a metaverse BMS3, an over-threshold module4, a threshold compliance module5, a parameter verification module6, a correction algorithm module7, a correction exit module8, a mapping truth module9, a metaverse central brain10, and an alarm system11.

[0006] The battery BMS1 is connected to the metaverse brain 10 via the acquisition system 2, the metaverse BMS3, the threshold exceedance module 4, the parameter verification module 6, the correction algorithm module 7, and the mapping truth module 9; the metaverse BMS3 is connected to the metaverse brain 10 via the threshold meetance module 5 and the correction exit module 8; the metaverse BMS3 is also connected to the battery BMS1 via the alarm system 11; the correction exit module 8 and the mapping truth module 9 are respectively connected to the battery BMS1; the alarm system 11 is connected to the metaverse brain 10.

[0007] The battery BMS1 can map its internal calculation parameters to the metaverse BMS3 in real time through the calculation acquisition system 2; the metaverse BMS3 includes a simulation system and a SOC database, and can perform SOC numerical estimation through the parameters transmitted by the acquisition system 2; the metaverse brain 10 can retrieve previous SOC estimation information for auxiliary calculation and judgment during the estimation process of the metaverse BMS3.

[0008] Furthermore, the battery BMS1 is arranged inside the battery pack and can independently calculate the internal SOC value.

[0009] Furthermore, a separate SOC estimation algorithm is employed, including but not limited to Kalman filtering estimation, open-circuit voltage method, and hybrid algorithm.

[0010] Furthermore, the Metaverse BMS3 is deployed in the data cloud. The Metaverse BMS3 performs SOC numerical estimation based on the parameters transmitted by the acquisition system 2, and the estimation method is consistent with that of the Battery BMS1.

[0011] Furthermore, the layout is exactly the same as the battery BMS1 function, arranged one-to-one.

[0012] Furthermore, the metaverse total brain 10 is deployed in a remote cloud, capable of remotely receiving all SOC estimation information and storing and recording it.

[0013] Furthermore, the simulation system can take the form of, but is not limited to, numerical models, large-scale games, and simulation models. The SOC database receives SOC computational fault case parameter updates from the metaverse brain 10.

[0014] A battery SOC management method based on the metaverse includes the following steps:

[0015] A. Define the SOC value estimated by battery BMS1 as S1, and the SOC value estimated by metaverse BMS3 as S2.

[0016] B. During actual vehicle operation, the Metaverse BMS3 performs SOC value correction every 90 seconds, and makes judgments and executes based on the corrected values. The judgment and execution process takes ≤300ms.

[0017] C. If |S1-S2|≤1%, then enter the threshold 5 module, exit the 8 module after correction, send a command to the battery BMS1, do not perform SOC management control, the battery adopts the SOC value estimated by the battery BMS1, and enter the next 90s interval correction.

[0018] D. If 1% < |S1-S2| ≤ 5%, then proceed to the threshold exceedance module 4, and then to the parameter verification module 6: verify whether all parameters mapped by the acquisition system 2 have errors. If so, perform a new SOC algorithm estimation in the metaverse BMS3 and continue with a new round of judgment; otherwise, perform SOC correction in the correction algorithm module 7: define the corrected SOC as SJ. SJ = (√(S1*S2) + (S1+S2) / 2) / 2*exp(cos(A))*tan(B)*ln(e-sinC), where A is the SOC estimation compensation coefficient, generally taken as 88°≤A≤91°; B is the open-circuit voltage safety factor, generally taken as 43°≤B≤46°; C is the integral compensation coefficient, generally taken as 0°≤A≤3°. After calculating SJ, map and assign it to the battery BMS1. The battery BMS1 uses the new SOC value SJ as the basis for the next 90s estimation.

[0019] E. If 5% < |S1-S2|, then enter alarm system module 11. The SOC estimate indicates a major fault, and feedback is sent to battery BMS1 as a secondary high-level fault.

[0020] A power battery, comprising the battery SOC management system based on the metaverse as described in claim 1.

[0021] A vehicle comprising a power battery as described in claim 7.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] This invention is based on a metaverse-based battery SOC management system, which solves the problems of poor battery SOC estimation accuracy, easy accumulation of SOC error, and inability to be remotely controlled in the prior art. It features high calculation accuracy and reliable control. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A diagram of the battery SOC management system based on the metaverse.

[0026] In the diagram: 1. Battery Management System (BMS) 2. Acquisition System 3. Metaverse BMS 4. Threshold Exceeded Module 5. Threshold Met Module 6. Parameter Verification Module 7. Correction Algorithm Module 8. Correction Exit Module 9. Mapping Truth Value Module 10. Metaverse Overall Brain 11. Alarm System Detailed Implementation

[0027] The present invention will be further described below with reference to embodiments:

[0028] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] like Figure 1 As shown, the present invention is a battery SOC management system based on the metaverse, which consists of a battery BMS1, a data acquisition system 2, a metaverse BMS3, an over-threshold module 4, a threshold compliance module 5, a parameter verification module 6, a correction algorithm module 7, a correction exit module 8, a mapping truth module 9, a metaverse central brain 10, and an alarm system 11.

[0031] The battery BMS1 is connected to the metaverse central brain 10 via the acquisition system 2, the metaverse BMS3, the threshold exceedance module 4, the parameter verification module 6, the correction algorithm module 7, and the mapping truth module 9. The metaverse BMS3 is connected to the metaverse central brain 10 via the threshold compliance module 5 and the correction exit module 8. The metaverse BMS3 is also connected to the battery BMS1 via the alarm system 11. The correction exit module 8 and the mapping truth module 9 are respectively connected to the battery BMS1. The alarm system 11 is connected to the metaverse central brain 10. The metaverse central brain 10 is connected to the SOC database. Figure 1 In the diagram, the components mentioned above are connected for communication, and the arrows point in the direction of receiving signals.

[0032] The battery BMS1 is a real battery BMS system, located inside the battery pack, and employs a separate SOC estimation algorithm. This algorithm includes, but is not limited to, Kalman filtering, open-circuit voltage estimation, and hybrid algorithm. The battery BMS1 can independently calculate its internal SOC value.

[0033] The battery BMS1 can map its internal calculation parameters to the metaverse BMS3 in real time through the calculation acquisition system 2.

[0034] The Metaverse BMS3 is deployed in the data cloud, with the same deployment method and function as the Battery BMS1, arranged in a one-to-one manner. The Metaverse BMS3 performs SOC value estimation based on the parameters transmitted by the Acquisition System 2, using the same estimation method as the Battery BMS1.

[0035] The Metaverse BMS3 includes a simulation system and a SOC database. The simulation system includes, but is not limited to, numerical models, large-scale games, and simulation models. The SOC database receives SOC computational fault case parameter updates from the Metaverse Total Brain 10.

[0036] During actual vehicle operation, the Metaverse BMS3 performs SOC value correction every 90 seconds, and makes judgments and executes based on the corrected values. The judgment and execution process takes ≤300ms.

[0037] Define the SOC value estimated by battery BMS1 as S1, and the SOC value estimated by metaverse BMS3 as S2;

[0038] If |S1-S2|≤1%, then enter the threshold 5 module, exit the 8 module after correction, feed back the instruction to the battery BMS1, do not perform SOC management control, the battery adopts the SOC value estimated by the battery BMS1, and enter the next 90s interval correction.

[0039] If 1% < |S1-S2| ≤ 5%, then proceed to the threshold exceedance module 4, and then to the parameter verification module 6: verify whether all parameters mapped by the acquisition system 2 have errors. If so, perform a new SOC algorithm estimation in the metaverse BMS3 and continue with a new round of judgment; otherwise, perform SOC correction in the correction algorithm module 7: define the corrected SOC as SJ. SJ = (√(S1*S2) + (S1+S2) / 2) / 2*exp(cos(A))*tan(B)*ln(e-sinC), where A is the SOC estimation compensation coefficient, generally taken as 88°≤A≤91°; B is the open-circuit voltage safety factor, generally taken as 43°≤B≤46°; C is the integral compensation coefficient, generally taken as 0°≤A≤3°. After calculating SJ, it is mapped and assigned to the battery BMS1. The battery BMS1 uses the new SOC value SJ as the basis for the next 90s estimation.

[0040] If 5% < |S1-S2|, then the alarm system module 11 is activated. The SOC estimate indicates a major fault, and the fault is reported to the battery BMS1 as a secondary high-level fault.

[0041] The Metaverse Brain 10 is deployed in a remote cloud and can remotely receive all SOC estimation information for storage and recording. During the estimation process of Metaverse BMS3, the Metaverse Brain 10 can retrieve previous SOC estimation information for auxiliary calculation and judgment.

[0042] The present invention also provides a power battery, including the aforementioned battery SOC management system based on a metaverse.

[0043] The present invention also provides a vehicle including the aforementioned power battery.

[0044] Example 1

[0045] A battery SOC management system based on a metaverse comprises a battery BMS1, a data acquisition system 2, a metaverse BMS3, an over-threshold module 4, a threshold compliance module 5, a parameter verification module 6, a correction algorithm module 7, a correction exit module 8, a truth mapping module 9, a metaverse central brain 10, and an alarm system 11. The battery BMS1 is connected to the metaverse central brain 10 via the data acquisition system 2, metaverse BMS3, over-threshold module 4, parameter verification module 6, correction algorithm module 7, and truth mapping module 9. The metaverse BMS3 is connected to the metaverse central brain 10 via the threshold compliance module 5 and the correction exit module 8. The metaverse BMS3 is also connected to the battery BMS1 via the alarm system 11. The correction exit module 8 and the truth mapping module 9 are respectively connected to the battery BMS1. The alarm system 11 is connected to the metaverse central brain 10. The metaverse central brain 10 is connected to an SOC database. Figure 1 In the diagram, the components mentioned above are connected for communication, and the arrows point in the direction of receiving signals.

[0046] The battery BMS1 is a real battery BMS system, located inside the battery pack. It employs a separate SOC estimation algorithm, specifically the Kalman filter estimation method, and can independently calculate its internal SOC value. The battery BMS1 can map its internal calculation parameters to the metaverse BMS3 in real time via the calculation acquisition system 2. The metaverse BMS3 is deployed in the data cloud, with a configuration and function identical to the battery BMS1, arranged one-to-one. The metaverse BMS3 performs SOC value estimation using parameters transmitted from the acquisition system 2, employing the same estimation method as the battery BMS1. The metaverse BMS3 includes a simulation system and an SOC database. The simulation system is a numerical model, and the SOC database receives SOC calculation fault case parameter updates from the metaverse central brain 10. The metaverse central brain 10 is deployed in a remote cloud and can remotely receive and store all SOC estimation information. During the estimation process of the metaverse BMS3, the metaverse central brain 10 can retrieve previous SOC estimation information for auxiliary calculation and judgment.

[0047] A battery SOC management method based on the metaverse includes the following steps:

[0048] Define the SOC value estimated by battery BMS1 as S1, and the SOC value estimated by metaverse BMS3 as S2;

[0049] During actual vehicle operation, the Metaverse BMS3 performs SOC value correction every 90 seconds, and makes judgments and executes based on the corrected values. The judgment and execution process takes ≤300ms.

[0050] If |S1-S2|≤1%, then enter the threshold 5 module, exit the 8 module after correction, feed back the instruction to the battery BMS1, do not perform SOC management control, the battery adopts the SOC value estimated by the battery BMS1, and enter the next 90s interval correction.

[0051] If 1% < |S1-S2| ≤ 5%, then proceed to the threshold exceedance module 4, and then to the parameter verification module 6: verify whether all parameters mapped by the acquisition system 2 have errors. If so, perform a new SOC algorithm estimation in the metaverse BMS3 and continue with a new round of judgment; otherwise, perform SOC correction in the correction algorithm module 7: define the corrected SOC as SJ. SJ = (√(S1*S2) + (S1+S2) / 2) / 2*exp(cos(A))*tan(B)*ln(e-sinC), where A is the SOC estimation compensation coefficient, generally taken as 88°≤A≤91°; B is the open-circuit voltage safety factor, generally taken as 43°≤B≤46°; C is the integral compensation coefficient, generally taken as 0°≤A≤3°. After calculating SJ, it is mapped and assigned to the battery BMS1. The battery BMS1 uses the new SOC value SJ as the basis for the next 90s estimation.

[0052] If 5% < |S1-S2|, then the alarm system module 11 is activated. The SOC estimate indicates a major fault, and the fault is reported to the battery BMS1 as a secondary high-level fault.

[0053] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A metaverse-based battery SOC management system, characterized by: The battery BMS (1), the acquisition system (2), the metaverse BMS (3), the threshold exceeding module (4), the threshold meeting module (5), the parameter checking module (6), the correction algorithm module (7), the correction exit module (8), the mapping true value module (9), the metaverse general brain (10) and the alarm system (11) are connected. The battery BMS (1) is connected with the metaverse general brain (10) through the acquisition system (2), the metaverse BMS (3), the threshold exceeding module (4), the parameter checking module (6), the correction algorithm module (7) and the mapping true value module (9); the metaverse BMS (3) is connected with the metaverse general brain (10) through the threshold meeting module (5) and the correction exit module (8); the metaverse BMS (3) is also connected with the battery BMS (1) through the alarm system (11); the correction exit module (8) and the mapping true value module (9) are connected with the battery BMS (1) respectively; the alarm system (11) is connected with the metaverse general brain (10). The battery BMS (1) can map the calculation parameters in the battery BMS (1) to the metaverse BMS (3) in real time through the acquisition system (2); the metaverse BMS (3) includes a simulation system and an SOC database, and can perform SOC value estimation through the parameters transmitted by the acquisition system (2). The metaverse general brain (10) can call the previous SOC estimation situation to assist in calculation and judgment during the estimation process of the metaverse BMS (3).

2. The battery SOC management system based on the metaverse according to claim 1, wherein: The battery BMS (1) is arranged in the battery pack and can independently and autonomously calculate the internal SOC value.

3. The metaverse-based battery SOC management system of claim 2, wherein: A separate SOC estimation algorithm is used.

4. The metaverse-based battery SOC management system of claim 1, wherein: The metaverse BMS (3) is arranged in the data cloud, and the metaverse BMS (3) performs SOC value estimation through the parameters transmitted by the acquisition system (2), and the estimation method is consistent with that of the battery BMS (1).

5. The metaverse-based battery SOC management system of claim 4, wherein: The arrangement form is exactly the same as the function of the battery BMS (1), and the arrangement is one-to-one.

6. The metaverse-based battery SOC management system of claim 1, wherein: The metaverse general brain (10) is arranged in the remote cloud and can remotely receive all SOC estimation situations to store and record information.

7. A meta-universe-based battery SOC management method based on the meta-universe-based battery SOC management system of claim 1, characterized by, The method comprises the following steps: A, define the SOC value estimated by the battery BMS (1) as S1, and the SOC value estimated by the metaverse BMS (3) as S2; B, the metaverse BMS (3) performs SOC value correction every 90s during the actual operation of the vehicle, and the corrected value is used for judgment and execution, and the judgment and execution process is ≤300ms; C, if |S1-S2|≤1%, the threshold meeting module (5) is entered, the correction exit module (8) is passed, the instruction is fed back to the battery BMS (1), the SOC management control is not performed, the battery uses the SOC value estimated by the battery BMS (1), and the next 90s interval correction is entered. D, if 1% < |S1-S2| ≤ 5%, enter the threshold exceeding module (4), enter the parameter checking module (6): check whether all the parameters mapped by the acquisition system (2) are error, if yes, perform new SOC algorithm estimation in the meta-universe BMS (3), continue to perform a new round of judgment; if no, perform SOC correction in the correction algorithm module (7): define the corrected SOC as SJ; SJ = (√(S1*S2)+(S1+S2) / 2) / 2*exp(cos(A))*tan(B)*ln(e-sinC), wherein A is a SOC estimation compensation coefficient, 88° ≤ A ≤ 91°; B is an open-circuit voltage safety coefficient, 43° ≤ B ≤ 46°; C is an integral compensation coefficient, 0° ≤ A ≤ 3°; after SJ is calculated, it is mapped and assigned to the battery BMS (1), and the battery BMS (1) uses the new SOC value SJ to perform estimation for the next 90s; E, if 5% < |S1-S2|, enter the alarm system (11) module, SOC estimation has a major fault, and feedback to the battery BMS (1) according to the next high-level fault.

8. A power cell, characterized by: A battery SOC management system based on a meta-universe, including the battery SOC management system according to claim 1.

9. A vehicle characterized by: A power battery, including the power battery according to claim 8.

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