Self-learning dynamic SOC calibration method and device based on energy storage application scene

Abstract

The embodiment of the invention provides a self-learning dynamic SOC calibration method and device based on an energy storage application scenario, and the method comprises the steps: judging the operation state of an energy storage system, and when the operation state of the energy storage system is judged to be a charging / discharging state, carrying out the self-learning dynamic SOC calibration of the energy storage system; if yes, further judging whether the energy storage system reaches an SOC dynamic calibration condition or a self-learning updating SOC dynamic calibration database updating condition; if it is judged that the energy storage system reaches the SOC dynamic calibration condition, dynamic calibration processing is carried out on the SOC value of the energy storage system; and if it is judged that the energy storage system reaches the SOC dynamic calibration database self-learning updating condition, carrying out self-learning updating processing on the SOC dynamic calibration database of the energy storage system.

Description

Technical Field

[0001] This application relates to the field of battery SOC calibration technology, and in particular to a self-learning dynamic calibration SOC method and device based on energy storage application scenarios. Background Technology

[0002] State of Charge (SOC) is a crucial component of battery management systems, serving as a key indicator of remaining battery capacity. For users, SOC deviation directly impacts the user experience. Many factors influence SOC accuracy, including the sampling accuracy of the current sensor, the ambient temperature, and the effectiveness of the thermal management system.

[0003] Based on the current characteristics of lithium iron phosphate batteries, most manufacturers calculate SOC using a method based on ampere-hour integration, end-charge / discharge SOC calibration, and static SOC-OCV calibration. However, ampere-hour integration itself accumulates errors, which increase over time. Furthermore, in calculating the SOC of energy storage systems, in some complex application scenarios, the average cycle for triggering full charge / discharge cycles is long, meaning the battery may not reach the end-charge / discharge calibration conditions for an extended period. This leads to increasingly larger accumulated errors from ampere-hour integration, and even SOC jumps at the end of charge / discharge. Secondly, in energy storage projects used in frequency regulation scenarios, the system is in a long-term operating state with short periods of inactivity, resulting in fewer opportunities to trigger static SOC-OCV calibration, ultimately preventing the system from performing inactive SOC calibration. Finally, as battery life degrades, battery characteristics and capacity change, gradually increasing the deviation between the system's SOC calibration database and the actual operating data. Even if the system triggers SOC calibration conditions, new calibration deviations may occur, and even if the system's SOC is accurate, calibration may introduce new errors. In summary, the above situations, along with the problem that the displayed SOC differs significantly from the actual SOC during system operation, have resulted in a poor user experience. Summary of the Invention

[0004] In view of this, embodiments of this application provide a self-learning dynamic calibration SOC method based on energy storage application scenarios. One or more embodiments of this application also relate to a self-learning dynamic calibration SOC device, a computing device, a computer-readable storage medium, and a computer program based on energy storage application scenarios, to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this application, a self-learning dynamic calibration SOC method based on energy storage application scenarios is provided, including:

[0006] The operating status of the energy storage system is determined, and when the operating status of the energy storage system is determined to be charging / discharging, it is further determined whether the energy storage system has reached the SOC dynamic calibration condition or the self-learning update SOC dynamic calibration database update condition.

[0007] If it is determined that the energy storage system has reached the SOC dynamic calibration condition, the SOC value of the energy storage system shall be dynamically calibrated.

[0008] If it is determined that the energy storage system has reached the conditions for self-learning and updating the SOC dynamic calibration database, the SOC dynamic calibration database of the energy storage system will be updated through self-learning.

[0009] Preferably, it further includes:

[0010] Construct and maintain a dynamic SOC calibration database for the energy storage system, which includes ambient temperature information, charge / discharge current rate information, maximum / minimum cell voltage information, and charge / discharge SOC calibration values.

[0011] Preferably, the SOC dynamic calibration conditions include:

[0012] The current ambient temperature change is within the preset temperature range;

[0013] The current change is within the preset charging current multiplier range or the preset discharging current multiplier range;

[0014] The current ambient temperature change is within the preset temperature range and the current current change is within the preset charging current rate range or the preset discharging current rate range, and the stable duration exceeds the preset duration.

[0015] During charging, the maximum single-cell voltage in the energy storage system reaches the highest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database; or during discharging, the minimum single-cell voltage in the energy storage system reaches the lowest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database.

[0016] Preferably, the dynamic calibration process for the SOC value of the energy storage system includes:

[0017] Obtain the current charging SOC value of the energy storage system calculated using the ampere-hour integration method; or obtain the current discharging SOC value of the energy storage system calculated using the ampere-hour integration method.

[0018] The calculated current charging SOC value is modified to the charging SOC calibration value corresponding to the highest single-cell voltage information in the SOC dynamic calibration database; or the calculated current discharging SOC value is modified to the discharging SOC calibration value corresponding to the lowest single-cell voltage information in the SOC dynamic calibration database.

[0019] Preferably, the self-learning update conditions for the SOC dynamic calibration database include:

[0020] During charging, the maximum single-cell voltage in the energy storage system is higher than the preset full-charge standard voltage; or during discharging, the minimum single-cell voltage in the energy storage system is lower than the preset empty-out standard voltage; the full-charge standard voltage is greater than the empty-out standard voltage.

[0021] The current ambient temperature change is within the preset temperature range;

[0022] The current change is within the preset charging current multiplier range or the preset discharging current multiplier range;

[0023] The current ambient temperature change is within a preset temperature range and the current current change is within a preset charging current rate range or a preset discharging current rate range. Furthermore, during charging, the maximum single-cell voltage in the energy storage system is higher than the preset full charge standard voltage, or during discharging, the minimum single-cell voltage in the energy storage system is lower than the preset discharge standard voltage and then remains stable.

[0024] During charging, the maximum single-cell voltage in the energy storage system reaches the highest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database; or during discharging, the minimum single-cell voltage in the energy storage system reaches the lowest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database.

[0025] Preferably, the self-learning update process for the SOC dynamic calibration database of the energy storage system includes:

[0026] Based on the actual operating data of the energy storage system, calculate the current charging SOC value of the energy storage system during charging or the current discharging SOC value of the energy storage system during discharging;

[0027] Modify the charging SOC calibration value corresponding to the highest single-cell voltage information in the SOC dynamic calibration database to the charging SOC value; or modify the discharging SOC calibration value corresponding to the lowest single-cell voltage information in the SOC dynamic calibration database to the discharging SOC value.

[0028] According to a second aspect of the embodiments of this application, a self-learning dynamic calibration SOC device based on an energy storage application scenario is provided, comprising:

[0029] The judgment module is configured to judge the operating status of the energy storage system, and when the operating status of the energy storage system is judged to be charging / discharging, it further judges whether the energy storage system has reached the SOC dynamic calibration condition or the self-learning update SOC dynamic calibration database update condition.

[0030] The first calibration module is configured to perform dynamic calibration processing on the SOC value of the energy storage system when it is determined that the energy storage system has reached the SOC dynamic calibration condition.

[0031] The second calibration module is configured to perform a self-learning update process on the SOC dynamic calibration database of the energy storage system when it is determined that the energy storage system has reached the self-learning update condition for the SOC dynamic calibration database.

[0032] Preferably, it also includes a building module configured to build and maintain a dynamic SOC calibration database containing ambient temperature information, charge / discharge current rate information, maximum / minimum cell voltage information, and charge / discharge SOC calibration values ​​in the energy storage system.

[0033] According to a third aspect of the embodiments of this application, a computing device is provided, comprising:

[0034] Memory and processor;

[0035] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement any of the steps of the self-learning dynamic calibration SOC method based on energy storage application scenarios.

[0036] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, which stores computer-executable instructions that, when executed by a processor, implement the steps of any one of the self-learning dynamic calibration SOC methods based on energy storage application scenarios.

[0037] According to a fifth aspect of the embodiments of this application, a computer program is provided, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described self-learning dynamic calibration SOC method based on energy storage application scenarios.

[0038] The self-learning dynamic calibration SOC method and apparatus based on energy storage application scenarios provided in this application embodiment determine the operating state of the energy storage system. When the operating state of the energy storage system is determined to be charging / discharging, it further determines whether the energy storage system has reached the SOC dynamic calibration condition or the self-learning update condition of the SOC dynamic calibration database. If the energy storage system has reached the SOC dynamic calibration condition, the SOC value of the energy storage system is dynamically calibrated. If the energy storage system has reached the self-learning update condition of the SOC dynamic calibration database, the SOC dynamic calibration database of the energy storage system is updated through self-learning. This application's embodiments can increase the likelihood of SOC calibration by adding multiple SOC calibration points at the charging / discharging ends of the system in complex operating scenarios where the system cannot reach full charge / discharge calibration conditions for extended periods. This allows for timely SOC calibration and prevents large cumulative errors caused by prolonged lack of calibration. In energy storage projects applied in frequency regulation scenarios, the system is in a long-term operating state, and the opportunity to trigger static SOC-OCV calibration is relatively low. However, the self-learning dynamic calibration SOC algorithm can still perform SOC calibration while the system is in operation. As battery life degrades, battery characteristics and capacity change, and the deviation between the system's SOC calibration database and the actual operating data gradually increases. Even if the SOC calibration conditions are met, the calibrated SOC value will be inaccurate. Even if the system's SOC is accurate, calibration may introduce new errors. The self-learning dynamic calibration SOC algorithm can automatically update the system's dynamic SOC calibration database based on the actual operating state of the battery within a certain period. Attached Figure Description

[0039] Figure 1 This is a flowchart of a self-learning dynamic calibration SOC method based on an energy storage application scenario, provided in one embodiment of this application;

[0040] Figure 2 This is a flowchart of a dynamic calibration process provided in one embodiment of this application;

[0041] Figure 3 This is a flowchart of a self-learning and dynamic calibration database update provided in one embodiment of this application;

[0042] Figure 4 This is a schematic diagram of a self-learning dynamic calibration SOC device based on an energy storage application scenario, provided in one embodiment of this application;

[0043] Figure 5 This is a structural block diagram of a computing device provided in one embodiment of this application. Detailed Implementation

[0044] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0045] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.

[0046] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0047] This application provides a self-learning dynamic calibration SOC method based on energy storage application scenarios. This application also relates to a self-learning dynamic calibration SOC device based on energy storage application scenarios, a computing device, a computer-readable storage medium, and a computer program, which will be described in detail in the following embodiments.

[0048] Figure 1 A flowchart of a self-learning dynamic calibration SOC method based on an energy storage application scenario, according to an embodiment of this application, is shown, which specifically includes the following steps.

[0049] Step S101: Determine the operating status of the energy storage system, and when the operating status of the energy storage system is determined to be charging / discharging, further determine whether the energy storage system has reached the SOC dynamic calibration condition or the self-learning update SOC dynamic calibration database update condition.

[0050] In one optional implementation, the SOC dynamic calibration conditions include:

[0051] The current ambient temperature change is within the preset temperature range;

[0052] The current change is within the preset charging current multiplier range or the preset discharging current multiplier range;

[0053] The current ambient temperature change is within the preset temperature range and the current current change is within the preset charging current rate range or the preset discharging current rate range, and the stable duration exceeds the preset duration.

[0054] During charging, the maximum single-cell voltage in the energy storage system reaches the highest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database; or during discharging, the minimum single-cell voltage in the energy storage system reaches the lowest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database.

[0055] In one optional implementation, the self-learning update conditions for the SOC dynamic calibration database include:

[0056] During charging, the maximum single-cell voltage in the energy storage system is higher than the preset full-charge standard voltage; or during discharging, the minimum single-cell voltage in the energy storage system is lower than the preset empty-out standard voltage; the full-charge standard voltage is greater than the empty-out standard voltage.

[0057] The current ambient temperature change is within the preset temperature range;

[0058] The current change is within the preset charging current multiplier range or the preset discharging current multiplier range;

[0059] The current ambient temperature change is within a preset temperature range and the current current change is within a preset charging current rate range or a preset discharging current rate range. Furthermore, during charging, the maximum single-cell voltage in the energy storage system is higher than the preset full charge standard voltage, or during discharging, the minimum single-cell voltage in the energy storage system is lower than the preset discharge standard voltage and then remains stable.

[0060] During charging, the maximum single-cell voltage in the energy storage system reaches the highest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database; or during discharging, the minimum single-cell voltage in the energy storage system reaches the lowest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database.

[0061] Step S102: If it is determined that the energy storage system has reached the SOC dynamic calibration condition, the SOC value of the energy storage system is dynamically calibrated.

[0062] In one optional implementation, the dynamic calibration process for the SOC value of the energy storage system includes:

[0063] Obtain the current charging SOC value of the energy storage system calculated using the ampere-hour integration method; or obtain the current discharging SOC value of the energy storage system calculated using the ampere-hour integration method.

[0064] The calculated current charging SOC value is modified to the charging SOC calibration value corresponding to the highest single-cell voltage information in the SOC dynamic calibration database; or the calculated current discharging SOC value is modified to the discharging SOC calibration value corresponding to the lowest single-cell voltage information in the SOC dynamic calibration database.

[0065] Step S103: If it is determined that the energy storage system has reached the self-learning update condition for the SOC dynamic calibration database, perform self-learning update processing on the SOC dynamic calibration database of the energy storage system.

[0066] In one optional implementation, the self-learning update process for the SOC dynamic calibration database of the energy storage system includes:

[0067] Based on the actual operating data of the energy storage system, calculate the current charging SOC value of the energy storage system during charging or the current discharging SOC value of the energy storage system during discharging;

[0068] Modify the charging SOC calibration value corresponding to the highest single-cell voltage information in the SOC dynamic calibration database to the charging SOC value; or modify the discharging SOC calibration value corresponding to the lowest single-cell voltage information in the SOC dynamic calibration database to the discharging SOC value.

[0069] In an optional implementation, this application further includes: constructing and maintaining a dynamic SOC calibration database that includes ambient temperature information, charge / discharge current rate information, maximum / minimum cell voltage information, and charge / discharge SOC calibration values ​​in the energy storage system.

[0070] The self-learning dynamic calibration SOC scheme for energy storage applications provided in this application is specifically designed for energy storage systems. Firstly, the SOC calibration adopts a scheme that adds calibration points at the end of charging and discharging. The charging end calibration points are SOC at 70%, 80%, and 90%; the discharging end calibration points are SOC at 10%, 20%, and 30%. This significantly increases the system's calibration opportunities, reduces the increase in accumulated system errors, and allows for timely correction. Secondly, a dynamic SOC calibration method is added. Under a scenario of continuous charging and discharging at a certain ambient temperature and charge / discharge current rate, the system can still perform SOC calibration when the conditions for dynamic calibration are met. Finally, during system operation, the system will gradually update the database required for dynamic calibration based on the actual operating data at regular intervals. That is, the dynamic calibration database required by the system can self-learn based on the actual operating data. This avoids additional calibration deviations caused by battery life degradation and changes in battery characteristics due to prolonged operation.

[0071] Furthermore, the calibration points for the charging and discharging ends in this application refer to the dynamic calibration database set at the factory, namely the lowest single-cell voltage value corresponding to 10%, 20%, and 30%; or the highest single-cell voltage value corresponding to 70%, 80%, and 90%. When updating subsequently, the corresponding SOC value will be updated according to the lowest single-cell voltage value / the highest single-cell voltage value after the storage conditions are met.

[0072] Figure 2 The following is a flowchart of a dynamic calibration process according to an embodiment of this application, which includes the following steps: First, the operating state of the system is determined. If the system is currently in a static state, both the charging and discharging calibration flags are set to 0, and the duration of the charging and discharging current is cleared to zero, meaning that the system does not perform dynamic calibration in the static state. If the system is in a charging state, the discharging dynamic calibration flag is first set to 0, and then the ambient temperature is determined to be within the range of 25±5℃. If it is within this range, the ambient temperature fluctuation range is considered small. Next, the charging current is determined to be within the range of 0.5C±20A. If it is within this range, the charging current is considered stable. Finally, the duration under this environment is determined to be more than 5 minutes. If it is longer than 5 minutes under this environment, the prerequisite for dynamic calibration is met. Based on this, the highest single-cell voltage is determined to be the calibration value in the dynamic calibration database. If the calibration value is reached, the corresponding voltage calibration flag is determined to be 0. If the voltage calibration flag is 0, the voltage calibration value is considered not yet calibrated, and dynamic calibration can be performed. At the same time, the voltage calibration flag is set to 1 to prevent secondary calibration under the same state. If the system is in a discharging state, the logic is similar to that in the charging state.

[0073] It should be noted that when the system is in charging or discharging state, each dynamic calibration point is calibrated only once, i.e., the voltage calibration flag is set to 1. If the charging or discharging state changes, the corresponding voltage calibration flag is set to 0. The purpose is to prevent the same dynamic calibration point from being calibrated multiple times.

[0074] The dynamic calibration database tables are shown in Table 1 and Table 2 below.

[0075] Table 1: Discharge Dynamic Calibration Database Table

[0076] Serial Number Temperature / °C Current / C Minimum single-cell voltage / mV SOC / % 1 25 0.25 2881 11 2 25 0.25 2981 20 3 25 0.25 3081 30 4 25 0.5 2881 10 5 25 0.5 2981 20 6 25 0.5 3081 30

[0077] Table 2: Charging Dynamic Calibration Database Table

[0078]

[0079] During the operation of the energy storage BMS system, the SOC dynamic calibration process is as follows: Figure 1 As shown:

[0080] First, determine the system's operating status, namely charging, discharging, and resting states;

[0081] Then, determine the conditions required for the system to achieve SOC dynamic calibration:

[0082] The current ambient temperature variation is within the range of 25±5℃;

[0083] The current variation is currently within the range of 0.5C ± 20A;

[0084] The current ambient temperature and current have remained stable for more than 5 minutes.

[0085] The highest / lowest cell voltage reaches the charge / discharge voltage calibration value;

[0086] Based on the SOC dynamic calibration database stored in the system, the SOC value is matched with the current maximum / minimum single-cell voltage, and then the system SOC value is calibrated.

[0087] Finally, the dynamically calibrated SOC is stored in the system's real SOC, and then a smoothing algorithm is used to gradually bring the displayed SOC closer to the actual SOC.

[0088] Figure 3The flowchart shown is for the self-learning dynamic calibration database update process. First, it checks if a discharge calibration has been achieved. If so, the discharge calibration flag is set to 1; otherwise, it is set to 0. Next, it checks if the ambient temperature is within the range of 25±5℃. If so, the temperature stability flag is set to 1; otherwise, it is set to 0. Then, it checks if the charging current is within the range of 0.5C±20A. If so, it checks if the charging current stability duration exceeds 5 minutes. If so, the current stability flag is set to 1; otherwise, it is set to 0. Finally, it checks if the discharge calibration flag, temperature stability flag, and current stability flag are all set to 1, and if the minimum single-cell voltage has reached the discharge voltage calibration value. If all conditions are met, the corresponding SOC value for the voltage calibration is stored. The self-learning dynamic calibration database update logic when the system is in a discharge state and has reached full charge calibration is similar.

[0089] During the operation of an energy storage BMS system, a self-learning process for updating the dynamic calibration database is implemented, such as... Figure 3 As shown:

[0090] First, determine whether the vent calibration / full charge calibration has been achieved. If the vent calibration / full charge calibration has been achieved, set the vent calibration / full charge calibration flag to 1; otherwise, set the vent calibration / full charge calibration flag to 0. Wherein, vent standard: the lowest cell voltage in the system is lower than a preset first value, such as 2800mV (TBD); full charge standard: the highest cell voltage in the system is higher than a preset second value, such as 3600mV (TBD); the values ​​are set according to different depths of discharge.

[0091] Then, determine whether the ambient temperature change is within the range of 25±5℃ and whether the temperature stability duration is within 5 minutes. If it is within this range, set the temperature stability flag to 1; otherwise, set the temperature stability flag to 0.

[0092] Secondly, determine whether the current is within the range of 0.5C±20A and whether the current stabilization time exceeds 5 minutes. If it is within this range, set the current stabilization flag to 1; otherwise, set the current stabilization flag to 0.

[0093] Finally, determine whether the lowest / highest single-cell voltage has reached the corresponding voltage calibration value, and determine whether the discharge calibration flag bit is 1, the temperature stability flag bit is 1, and the current stability flag bit is 1. If all the above conditions are met, then store the SOC value corresponding to the voltage calibration value.

[0094] Specifically, when updating the database, the SOC value calculated for the corresponding voltage will be stored after the corresponding storage conditions are met; the voltage calibration value is a fixed voltage value in the database; the SOC value corresponding to the voltage calibration value here refers to the SOC value calculated by the system after the storage conditions are met.

[0095] This application also provides an embodiment of a self-learning dynamic calibration SOC device based on energy storage application scenarios. Figure 4 This illustration shows a schematic diagram of a self-learning dynamic calibration SOC device for energy storage applications, according to an embodiment of this application. Figure 4 As shown, the device includes:

[0096] The judgment module is configured to judge the operating status of the energy storage system, and when the operating status of the energy storage system is judged to be charging / discharging, it further judges whether the energy storage system has reached the SOC dynamic calibration condition or the self-learning update SOC dynamic calibration database update condition.

[0097] The first calibration module is configured to perform dynamic calibration processing on the SOC value of the energy storage system when it is determined that the energy storage system has reached the SOC dynamic calibration condition.

[0098] The second calibration module is configured to perform a self-learning update process on the SOC dynamic calibration database of the energy storage system when it is determined that the energy storage system has reached the self-learning update condition for the SOC dynamic calibration database.

[0099] This application also includes a building module configured to build and maintain a dynamic SOC calibration database containing ambient temperature information, charge / discharge current rate information, maximum / minimum cell voltage information, and charge / discharge SOC calibration values ​​in the energy storage system.

[0100] The above is an illustrative scheme of a self-learning dynamic calibration SOC device based on an energy storage application scenario in this embodiment. It should be noted that the technical solution of this self-learning dynamic calibration SOC device based on an energy storage application scenario and the technical solution of the self-learning dynamic calibration SOC method based on an energy storage application scenario described above belong to the same concept. Details not described in detail in the technical solution of the self-learning dynamic calibration SOC device based on an energy storage application scenario can be found in the description of the technical solution of the self-learning dynamic calibration SOC method based on an energy storage application scenario described above.

[0101] Figure 5 A structural block diagram of a computing device 500 according to an embodiment of this application is shown. The components of the computing device 500 include, but are not limited to, a memory 510 and a processor 520. The processor 520 is connected to the memory 510 via a bus 530, and a database 550 is used to store data.

[0102] The computing device 500 also includes an access device 540, which enables the computing device 500 to communicate via one or more networks 560. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Wi-MAX interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0103] In one embodiment of this application, the aforementioned components of the computing device 500 and Figure 5 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 5 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0104] The computing device 500 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices; or stationary computing devices such as desktop computers or PCs. The computing device 500 can also be a mobile or stationary server.

[0105] The processor 520 is used to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the self-learning dynamic calibration SOC method based on the energy storage application scenario described above.

[0106] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device belongs to the same concept as the technical solution of the self-learning dynamic calibration SOC method based on energy storage application scenarios described above. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the self-learning dynamic calibration SOC method based on energy storage application scenarios described above.

[0107] An embodiment of this application also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described self-learning dynamic calibration SOC method based on energy storage application scenarios.

[0108] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the self-learning dynamic calibration SOC method based on energy storage application scenarios described above. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the self-learning dynamic calibration SOC method based on energy storage application scenarios described above.

[0109] An embodiment of this application also provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described self-learning dynamic calibration SOC method based on energy storage application scenarios.

[0110] The above is an illustrative scheme of a computer program according to this embodiment. It should be noted that the technical solution of this computer program belongs to the same concept as the above-described self-learning dynamic calibration SOC method based on energy storage application scenarios. For details not described in detail in the technical solution of the computer program, please refer to the description of the above-described self-learning dynamic calibration SOC method based on energy storage application scenarios.

[0111] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted 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 media may not include electrical carrier signals and telecommunication signals.

[0113] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this application.

[0114] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0115] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of the embodiments of this application, thereby enabling those skilled in the art to better understand and utilize this application. This application is limited only by the claims and their full scope and equivalents.

Claims

1. A self-learning dynamic calibration SOC method based on energy storage application scenarios, characterized in that, include: The operating status of the energy storage system is determined, and when the operating status of the energy storage system is determined to be charging / discharging, it is further determined whether the energy storage system has reached the SOC dynamic calibration condition or the self-learning update SOC dynamic calibration database update condition. If it is determined that the energy storage system has reached the SOC dynamic calibration condition, the SOC value of the energy storage system shall be dynamically calibrated. If it is determined that the energy storage system has reached the conditions for self-learning and updating the SOC dynamic calibration database, the SOC dynamic calibration database of the energy storage system will be updated through self-learning.

2. The method according to claim 1, characterized in that, Also includes: Construct and maintain a dynamic SOC calibration database for the energy storage system, which includes ambient temperature information, charge / discharge current rate information, maximum / minimum cell voltage information, and charge / discharge SOC calibration values.

3. The method according to claim 2, characterized in that, The SOC dynamic calibration conditions include: The current ambient temperature change is within the preset temperature range; The current change is within the preset charging current multiplier range or the preset discharging current multiplier range; The current ambient temperature change is within the preset temperature range and the current current change is within the preset charging current rate range or the preset discharging current rate range, and the stable duration exceeds the preset duration. During charging, the maximum single-cell voltage in the energy storage system reaches the highest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database; or during discharging, the minimum single-cell voltage in the energy storage system reaches the lowest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database.

4. The method according to claim 3, characterized in that, The dynamic calibration process for the SOC value of the energy storage system includes: Obtain the current charging SOC value of the energy storage system calculated using the ampere-hour integration method; or obtain the current discharging SOC value of the energy storage system calculated using the ampere-hour integration method. The calculated current charging SOC value is modified to the charging SOC calibration value corresponding to the highest single-cell voltage information in the SOC dynamic calibration database; or the calculated current discharging SOC value is modified to the discharging SOC calibration value corresponding to the lowest single-cell voltage information in the SOC dynamic calibration database.

5. The method according to claim 2, characterized in that, The self-learning update conditions for the SOC dynamic calibration database include: During charging, the maximum single-cell voltage in the energy storage system is higher than the preset full-charge standard voltage; or during discharging, the minimum single-cell voltage in the energy storage system is lower than the preset empty-out standard voltage; the full-charge standard voltage is greater than the empty-out standard voltage. The current ambient temperature change is within the preset temperature range; The current change is within the preset charging current multiplier range or the preset discharging current multiplier range; The current ambient temperature change is within a preset temperature range and the current current change is within a preset charging current rate range or a preset discharging current rate range. Furthermore, during charging, the maximum single-cell voltage in the energy storage system is higher than the preset full charge standard voltage, or during discharging, the minimum single-cell voltage in the energy storage system is lower than the preset discharge standard voltage and then remains stable. During charging, the maximum single-cell voltage in the energy storage system reaches the highest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database; or during discharging, the minimum single-cell voltage in the energy storage system reaches the lowest single-cell voltage corresponding to the current ambient temperature change and the current current change in the SOC dynamic calibration database.

6. The method according to claim 5, characterized in that, The self-learning update process for the SOC dynamic calibration database of the energy storage system includes: Based on the actual operating data of the energy storage system, calculate the current charging SOC value of the energy storage system during charging or the current discharging SOC value of the energy storage system during discharging; Modify the charging SOC calibration value corresponding to the highest single-cell voltage information in the SOC dynamic calibration database to the charging SOC value; or modify the discharging SOC calibration value corresponding to the lowest single-cell voltage information in the SOC dynamic calibration database to the discharging SOC value.

7. A self-learning dynamic calibration SOC device based on energy storage application scenarios, characterized in that, include: The judgment module is configured to judge the operating status of the energy storage system, and when the operating status of the energy storage system is judged to be charging / discharging, it further judges whether the energy storage system has reached the SOC dynamic calibration condition or the self-learning update SOC dynamic calibration database update condition. The first calibration module is configured to perform dynamic calibration processing on the SOC value of the energy storage system when it is determined that the energy storage system has reached the SOC dynamic calibration condition. The second calibration module is configured to perform a self-learning update process on the SOC dynamic calibration database of the energy storage system when it is determined that the energy storage system has reached the self-learning update condition for the SOC dynamic calibration database.

8. The apparatus according to claim 7, characterized in that, It also includes a building module configured to build and maintain a dynamic SOC calibration database containing ambient temperature information, charge / discharge current rate information, maximum / minimum cell voltage information, and charge / discharge SOC calibration values ​​in the energy storage system.

9. A computing device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the self-learning dynamic calibration SOC method based on any one of claims 1 to 6 in the energy storage application scenario.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the self-learning dynamic calibration SOC method based on any one of claims 1 to 6 in an energy storage application scenario.

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