WO2023155220A1

WO2023155220A1 - Energy storage system sop optimization method and apparatus based on cloud data

Info

Publication number: WO2023155220A1
Application number: PCT/CN2022/077418
Authority: WO
Inventors: 张昉昀; 张清芳; 宋超
Original assignee: 福建时代星云科技有限公司
Priority date: 2022-02-16
Filing date: 2022-02-23
Publication date: 2023-08-24
Also published as: CN114629905B; CN114629905A

Abstract

An energy storage system SOP optimization method and apparatus based on cloud data. The method comprises: a battery management system collecting real-time data of each single cell in a battery pack in real time and uploading the real-time data to a cloud platform, wherein the real-time data comprises the present current, voltage, static voltage difference, temperature, SOC value and SOH value of the single cell; the battery management system calculating an SOP value of the single cell according to the real-time data; and on the basis of historical data of the single cell which is historically uploaded by the battery management system, the cloud platform determining whether the SOP value of the single cell needs to be subjected to optimization calculation. In the method, a battery management system collects real-time data such as the current, the voltage, the static voltage difference, the temperature, an SOC value and an SOH value of each single cell in a battery pack in real time, and calculates an SOP value of each single cell, and whether the SOP value needs to be subjected to optimization calculation is evaluated on the basis of historical data of the single cell, which is stored in a cloud, such that SOP differential calculation of each single cell is realized, thereby achieving the effect of accurately evaluating the aging differences between single cells of an energy storage system.

Description

A cloud-based data-based energy storage system SOP optimization method and device

technical field

The invention relates to the technical field of high-speed communication and cloud storage, in particular to a cloud data-based SOP optimization method and device for an energy storage system.

Background technique

Under the background of the rapid development of energy storage business, the stock of energy storage products will increase sharply, and subtle failures will be magnified in practical applications; in the entire energy storage system, battery packs are extremely important components. The quality requirements of the product are high, and the relevant logic algorithms in the battery management system BMS will also directly affect the service life and safety of the battery pack.

The SOP of the existing energy storage system is calculated based on the temperature, cell voltage and SOH collected in real time by the BMS, combined with the ex-factory SOP map table (factory SOP value record table) provided by the cell factory. The main defect is: on-board storage The onboard BMS data on the main control board of the energy system is limited, and it is impossible to accurately predict the aging difference caused by the individual differences of the batteries, which will lead to deviations in the calculation of SOP, potential overcharge and overdischarge failure conditions, and affect the life of the batteries. and other security risks.

Therefore, how to optimize the SOP differential calculation of each single cell has gradually become an urgent problem to be solved.

technical problem

The technical problem to be solved by the present invention is to provide an energy storage system SOP optimization method and device based on cloud data, which can optimize the SOP differential calculation of each battery cell.

technical solution

In order to solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a SOP optimization method for an energy storage system based on cloud data, comprising steps:

S1. The battery management system collects real-time data of each single battery cell in the battery pack in real time and uploads it to the cloud platform. The real-time data includes the current current, voltage, static pressure difference, temperature, and SOC value of the single battery cell and SOH value;

S2. The battery management system calculates the SOP value of the single cell according to the real-time data;

S3. The cloud platform judges whether to optimize the SOP value of the single cell based on the historical data of the single cell uploaded by the battery management system.

In order to solve the above technical problems, another technical solution adopted by the present invention is:

An energy storage system SOP optimization device based on cloud data, including a battery management system and a cloud platform;

The battery management system is used to collect real-time data of each single cell in the battery pack in real time, calculate the SOP value of the single cell according to the real-time data, and upload the real-time data to the cloud platform, the The real-time data includes the current current, voltage, static pressure difference, temperature, SOC value and SOH value of the single cell;

The cloud platform is used to determine whether to optimize the SOP value of the single battery based on the historical data of the single battery uploaded by the battery management system.

Beneficial effect

The beneficial effect of the present invention is that: the present invention provides an energy storage system SOP optimization method and device based on cloud data, and the battery management system of the energy storage system collects the current, voltage, static voltage Calculate the SOP value of each single cell based on real-time data such as difference, temperature, SOC value, and SOH value, and evaluate whether it is necessary to optimize the calculation of the SOP value based on the historical data of the single cell stored in the cloud, so as to realize the The SOP differential calculation of the battery can accurately evaluate the aging difference of each single battery in the energy storage system.

Description of drawings

FIG. 1 is an overall flow chart of an energy storage system SOP optimization method based on cloud data according to an embodiment of the present invention;

Fig. 2 is a schematic block diagram of an energy storage system in an energy storage system SOP optimization method based on cloud data according to an embodiment of the present invention;

FIG. 3 is a specific flow chart of an energy storage system SOP optimization method based on cloud data according to an embodiment of the present invention;

Fig. 4 is a SOP optimization calculation judgment process in a cloud data-based energy storage system SOP optimization method according to an embodiment of the present invention;

Fig. 5 is another SOP optimization calculation judgment process in another energy storage system SOP optimization method based on cloud data according to an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of an energy storage system SOP optimization device based on cloud data according to an embodiment of the present invention.

Label description:

1. An energy storage system SOP optimization device based on cloud data; 2. Battery management system; 3. Cloud platform.

Embodiments of the present invention

In order to describe the technical content, achieved goals and effects of the present invention in detail, the following descriptions will be made in conjunction with the embodiments and accompanying drawings.

Prior to this, the English abbreviations and professional terms involved in the present invention are described as follows:

BMS: Battery Management System, battery management system;

SOC: State Of Charge, state of charge;

SOH: State Of Health, battery health status;

SOP: State Of Power, charge and discharge power state;

MQTT: Message Queuing Telemetry Transport, Message Queuing Telemetry Transport Protocol;

TCP/IP: Transmission Control Protocol/Internet Protocol, Transmission Control Protocol/Internet Protocol.

Please refer to Figure 1 to Figure 4, a cloud data-based energy storage system SOP optimization method, including steps:

It can be seen from the above description that the beneficial effect of the present invention is that the battery management system of the energy storage system collects real-time data such as current, voltage, static pressure difference, temperature, SOC value and SOH value of each single cell in the battery pack in real time, Calculate the SOP value of each single cell, and evaluate whether it is necessary to optimize the calculation of the SOP value based on the historical data of the single cell stored in the cloud, so as to realize the differential calculation of the SOP of each single cell and achieve an accurate evaluation of energy storage The aging difference of each single cell in the system.

Further, the step S1 also includes the steps before:

S0. Preset an address space on the external memory of the main control board, which is used to store the factory SOP map table of each single cell and its remote modification flag, and establish the battery management system and the main The communication protocol between the control boards, the battery management system obtains the factory SOP through the communication protocol map table, and the external memory is a charged erasable programmable read-only memory.

From the above description, it can be seen that by dividing the address space on the external memory of the energy storage system to pre-store the factory SOP map table, it is convenient for the battery management system to directly query and calculate the SOP value according to the factory SOP map table, and at the same time set the remote modification flag. When performing SOP After optimizing the calculation, the cloud platform can modify the factory SOP by remotely modifying the flag bit The corresponding SOP value in the map table, update the factory SOP map table.

Further, the step S2 is specifically:

The battery management system queries the factory SOP according to the current temperature and SOC value of the single cell According to the charging and discharging power corresponding to the single cell in the map table, query the SOP attenuation coefficient corresponding to the single cell in the factory SOP map table according to the current SOH value of the single cell, and calculate the charging and discharging power The SOP value is obtained after multiplying by the SOP attenuation coefficient.

It can be seen from the above description that the SOP value (that is, the charge and discharge power) can be directly obtained from the SOC value/temperature gauge of the battery cell, but because the health state of the battery cell will be attenuated during use, that is, there is an SOP attenuation coefficient, so it is necessary to check it. The charging and discharging power obtained in the table is multiplied by the SOP attenuation coefficient to ensure the accuracy of the calculated SOP value.

Further, the step S3 is specifically:

S31. The cloud platform invokes the historical data of each single cell within a preset time period, and captures the static data within the preset time period;

S32. The cloud platform calculates the change rate of the static pressure difference of each single cell in each static time period according to the static time period corresponding to the static data. If the static pressure difference changes rate is greater than the calibration threshold, the cloud platform sends an SOP remote optimization calculation command to the battery management system, otherwise there is no need to perform SOP optimization calculation on the single battery cell, and the calibration threshold is each Monthly self-discharge rate.

From the above description, it can be seen that the difference of each single battery cell is judged by the change rate of static pressure difference, so as to decide whether to optimize the calculation of the SOP value of the battery cell calculated by the look-up table according to the difference, so as to ensure the accuracy of the battery cell. The accuracy of the SOP optimization assessment.

Further, the step S31 also includes:

The static judgment method of the static data is:

If the single cell is in a power-off state within half an hour and the current is less than 1A, then this time period is the resting time period.

It can be seen from the above description that the static data needs to be powered off for at least half an hour and the current is less than 1A to ensure the rationality of the static data and further ensure the accuracy of the SOP optimization evaluation of the battery.

Further, the step S3 is specifically:

S31. The battery management system uploads the calculated SOP value of the single cell to the cloud platform;

S32. The cloud platform calculates another SOP value based on the stored historical data, and compares it with the SOP value uploaded by the battery management system. If the difference between the two is greater than the 5% of the SOP value calculated by the cloud platform, the cloud platform sends an SOP remote optimization calculation instruction to the battery management system, and the battery management system will deliver the SOP The corresponding SOP value on the map table is modified to the SOP value calculated by the cloud platform, otherwise there is no need to perform SOP optimization calculation on the single cell.

It can be seen from the above description that due to the limited memory of the onboard battery management system, the historical data of the stored batteries is less, and the SOP can only be calculated by checking the value of the currently stored factory SOP map, and the SOP differential calculation for each battery cannot be achieved. Therefore, Through a large amount of historical data stored on the cloud platform, the SOP of each cell can also be calculated to obtain a more accurate SOP value. By comparing the SOP values calculated by the on-board battery management system and the cloud platform, according to the two sets of data The difference between them determines whether to optimize the SOP value calculated onboard, which further ensures the accuracy of the SOP optimization evaluation of the battery cell.

Further, after the step S3, it also includes the steps of:

S4. If the battery management system receives the SOP remote optimization calculation instruction sent by the cloud platform, the battery management system performs a second confirmation on the state of the single battery cell, specifically:

Judging whether the current static pressure difference of the single cell is greater than the static pressure difference corresponding to 8% SOC in the factory SOP map table, if so, perform the optimization calculation of the SOP value to obtain the optimized SOP value, otherwise directly set the SOP value in the step S2 The calculated SOP value is used as the optimized SOP value of the single cell;

If the battery management system does not receive the SOP remote optimization calculation instruction sent by the cloud platform, it directly uses the SOP value calculated in step S2 as the optimized SOP value of the single battery cell.

From the above description, it can be seen that through the second confirmation of the state of the battery cell, it is finally determined whether to optimize the calculation of the SOP value, to prevent false triggering of the SOP optimization calculation, and to further ensure the accuracy of the SOP optimization evaluation of the battery cell.

Further, in the step S4, it is judged whether the current static pressure difference of the single cell is greater than the factory SOP The static pressure difference corresponding to 8%SOC in the map table also includes:

If the judgment result cannot be obtained according to the factory SOP map table, then the static pressure difference change rate of the two groups is calculated according to the two sets of static data of the single cell. If the static pressure difference change rate of the two groups is When the difference exceeds the preset monthly self-discharge rate of the single cell, perform SOP optimization calculation to obtain the optimized SOP value; otherwise, directly use the SOP value calculated in step S2 as the single cell The optimized SOP value of the cell, the preset monthly self-discharge rate of the single cell is 2~5%.

From the above description, it can be seen that whether the current static pressure difference of the battery cell that cannot be obtained directly from the factory SOP map table is greater than the factory SOP The cell SOP calculation value corresponding to the static pressure difference corresponding to 8% SOC in the map table can be judged whether the SOP value needs to be optimized by comparing the difference between the two sets of static pressure difference change rates in another way. Calculated to perfect the secondary confirmation.

Further, performing SOP optimization calculation in the step S4 to obtain an optimized SOP value, specifically:

Carry out SOP limit power, charge and discharge recalibration or lower the charge cut-off voltage on the SOP value of the single cell to obtain an optimized SOP value;

The SOP power limit is to reduce the entire SOP value;

The recalibration of the charging and discharging is that the cloud platform recalculates based on the stored historical data to obtain a new SOP value, replaces the corresponding SOP value in the factory SOP map table, regenerates a new SOP map table and sends it to the battery management system;

The lowering of the charge cut-off voltage is to adjust the cut-off point of the full charge voltage of the single cell;

Also include after the step S4:

Marking the single cell for which the SOP value optimization calculation has been performed.

From the above description, it can be seen that the optimal calculation of the SOP value is realized through SOP power limit, charging and discharging recalibration, or lowering the charging cut-off voltage. At the same time, the cells that have been optimized for the SOP value calculation are marked to ensure that the follow-up can be traced accurately.

Please refer to Figure 5, an energy storage system SOP optimization device based on cloud data, including a battery management system and a cloud platform;

It can be seen from the above description that the beneficial effects of the present invention are: based on the same technical conception, combined with the above-mentioned SOP optimization method for an energy storage system based on cloud data, a SOP optimization device for an energy storage system based on cloud data is provided. The battery management system of the system collects real-time data such as current, voltage, static pressure difference, temperature, SOC value and SOH value of each single cell in the battery pack in real time, calculates the SOP value of each single cell, and based on the cloud storage Whether it is necessary to optimize the calculation of the SOP value for the historical data evaluation of the single cell, so as to realize the differential calculation of the SOP of each single cell, and accurately evaluate the aging difference of each single cell in the energy storage system.

The SOP optimization method and device of an energy storage system based on cloud data provided by the present invention are suitable for evaluating the aging difference of each battery cell in the energy storage system and realizing the differential calculation of the SOP of each battery cell. The following is carried out in conjunction with specific embodiments illustrate.

Please refer to Fig. 1, embodiment one of the present invention is:

An energy storage system SOP optimization method based on cloud data, as shown in Figure 1, includes steps:

S1. The battery management system collects the real-time data of each single cell in the battery pack in real time and uploads it to the cloud platform. The real-time data includes the current, voltage, static pressure difference, temperature, SOC value and SOH value of the single cell;

S3. The cloud platform judges whether it is necessary to optimize the calculation of the SOP value of the single cell based on the historical data of the single cell uploaded by the battery management system.

That is, in this embodiment, the battery management system of the energy storage system collects real-time data such as the current, voltage, static pressure difference, temperature, SOC value, and SOH value of each single cell in the battery pack in real time, and calculates the current and voltage of each single cell. Based on the SOP value of the cell, and based on the historical data of the single cell stored in the cloud, it is evaluated whether it is necessary to optimize the calculation of the SOP value, so as to realize the differential calculation of the SOP of each single cell, and accurately evaluate the individual cells of the energy storage system. Core aging differences.

Please refer to Fig. 2 to Fig. 4, embodiment two of the present invention is:

An energy storage system SOP optimization method based on cloud data, on the basis of the first embodiment above, the composition principle block diagram of the energy storage system adopted in this embodiment is shown in Figure 2, except for the above mentioned first embodiment The battery management system BMS and cloud platform also include a battery pack composed of individual cells, an energy management system EMS, a main control board, an AC/DC inverter PCS, a charging source and a load. Among them, the battery pack is the controlled object of this embodiment. In addition to the individual cells, there are also temperature sensors, current/voltage sensors, relays, and wiring harnesses set on each individual cell; the battery management system BMS According to the real-time data of the voltage, current, temperature and other real-time data collected by various sensors and other signal acquisition equipment installed on each single cell, the relevant state of the single cell, such as SOC value, SOH value, etc., will be estimated. And control each single cell to perform charging and discharging operations, and also transmit the collected real-time data to the cloud platform and the energy management system EMS through CAN or RS485 communication. The former is used to store various histories of the single cell in the cloud The latter is used to realize real-time monitoring of single cells; the main control board is the core controller set in the energy storage system, and it is not limited to this name. The main control board can receive data from each device in the energy storage system. Data, control and protect the entire energy storage system, transparently transmit the data sent by the battery management system to the cloud platform, and can receive instructions sent by the cloud platform and analyze them to the battery management system to realize the SOP of each single cell Optimal calculation; the AC-DC inverter PCS realizes the charging and discharging of the entire energy storage system through the mutual inversion of AC and DC; while the load and charging source can be simply understood as the electrical equipment that needs power from the energy storage system to work and the The daily power grid for charging the energy storage system; finally, the cloud platform includes the medium for storing data, data processing, and communication protocols for communicating with the energy management system and battery management system (including but not limited to MQTT, TCP/IP protocol etc.), to realize data transmission, including uploading and sending, through sending, OTA (Over-the-Air Technology, over-the-air technology) remote refresh of each device can be realized.

Based on the composition framework of the above-mentioned energy storage system, in combination with Fig. 3 and Fig. 4, in this embodiment, steps before step S1 include:

S0. Preset an address space on the external memory of the main control board, which is used to store the factory SOP map table of each single cell and its remote modification flag, and establish communication between the battery management system and the main control board Protocol, the battery management system obtains the factory SOP map table through the communication protocol, and the external memory is a live erasable programmable read-only memory.

That is to say, in this embodiment, at the initial stage of the development of the onboard battery management system, it is necessary to store the variables related to the SOP calculation in a specific location of the external erasable programmable read-only memory, and customize the onboard battery management system A dedicated communication protocol between the main control board and the main control board. For example, in this embodiment, the CAN bus is used for communication between the main control board and the battery management system BMS, then the CAN ID of the battery management system BMS is set to 0x100, and the CAN ID of the main control board is 0x200, then the BMS and the main control board Refer to Table 1 for the handshake protocol of multi-frame messages between boards:

Table 1:

The source code of the communication between the battery management system and the main control board is as follows:

CAN ID

0x200 22 01 01 //Request to update the configuration of the battery SOP module

0x100 01 22 01 //The first byte 01 means that the BMS can currently update the SOP configuration through logical judgment. If it is 00, it means that the condition is not met

CAN ID //Urgent above message

0x200 36 01 FF FF FF FF FF FF //Send the first packet configuration data of the battery SOP, and the BMS updates the specific storage location of the SOP module

0x100 01 36 01 //The first byte 01 means that the BMS can update the SOP configuration through logical judgment. If it is 00, it means that the condition is not satisfied

0x200 36 N FF FF FF FF FF FF //Send the Nth packet configuration data of the battery SOP, and the BMS updates the specific storage location of the SOP module

0x100 01 36 N //The first byte 01 means that the BMS can currently update the SOP configuration through logical judgment. If it is 00, it means that the condition is not met.

That is, by dividing the address space on the external memory of the energy storage system to pre-store the factory SOP map table, it is convenient for the battery management system to directly follow the factory SOP The map table queries and calculates the SOP value, and at the same time sets the remote modification flag bit. After the SOP optimization calculation is performed, the cloud platform can modify the corresponding SOP value in the factory SOP map table through the remote modification flag bit, and update the factory SOP map table.

In this embodiment, step S2 is specifically:

The battery management system queries the factory SOP according to the current temperature and SOC value of the single battery cell The charging and discharging power corresponding to the single cell in the map table, query the SOP attenuation coefficient corresponding to the single cell in the factory SOP map table according to the current SOH value of the single cell, and multiply the charging and discharging power by the SOP attenuation coefficient to get the SOP value. For example, in this embodiment, the following table 2 is the SOP form of a certain battery based on SOC value and temperature provided by the battery factory:

Table 2:

Table 3 is the relationship table between the SOP and SOH of a certain battery provided by the battery factory:

table 3:

That is to say, in this embodiment, the SOP value (charge and discharge power) can be directly obtained from the SOC value/temperature gauge of the battery cell, but because the health state of the battery cell will be attenuated during use, that is, there is an SOP attenuation coefficient, so it is necessary to The charge and discharge power obtained from the table lookup is multiplied by the SOP attenuation coefficient to ensure the accuracy of the calculated SOP value.

Wherein, in this embodiment, as shown in FIG. 4, step S3 is specifically:

S31. The cloud platform invokes the historical data of each single cell within a preset time period, and captures the static data within the preset time period, wherein the static judgment method of the static data is:

If the single cell is in the power-off state within half an hour and the current is less than 1A, this time period is the rest period.

S32. The cloud platform calculates the change rate of the static differential pressure of each single cell in each static period according to the static time period corresponding to the static data. If the static differential pressure change rate is greater than the calibration threshold, the cloud platform Send the SOP remote optimization calculation command to the battery management system, otherwise there is no need to perform SOP optimization calculation on the single cell, and the calibration threshold is the monthly self-discharge rate of the single cell.

For example, take time t1 and t2 as an example, assuming that t2-t1≥0.5h, then the period from t1 to t2 is a static time period, if the calibration threshold is set to A according to the monthly self-discharge rate, during the period from t1 to t2 , |(Ut2-Ut1)/(t2-t1)|≥A of a single cell, it is judged that the SOP value of the single cell needs to be optimized and calculated. In some embodiments, the self-discharge rate of the battery is 2-5% per month, and the calibration threshold can be set at 3%, for example.

That is, the difference of each single battery cell is judged by the change rate of the static pressure difference, so as to determine whether to optimize the SOP value of the battery cell calculated by looking up the table according to the difference, so as to ensure the optimal evaluation of the SOP of the battery cell At the same time, the static data needs to be powered off for at least half an hour and the current is less than 1A to ensure the rationality of the static data and further ensure the accuracy of the SOP optimization evaluation of the battery.

In addition, in this embodiment, step S3 also adopts another method, as shown in Figure 5:

S31. The battery management system BMS uploads the calculated SOP value of the single battery cell to the cloud platform.

S32. The cloud platform calculates another SOP value based on the stored historical data, and compares it with the SOP value uploaded by the battery management system. If the difference between the two is greater than 5% of the SOP value calculated by the cloud platform , the cloud platform sends SOP remote optimization calculation instructions to the battery management system, and the battery management system will deliver the SOP The corresponding SOP value on the map table is changed to the SOP value calculated in the cloud, otherwise there is no need to perform SOP value optimization calculation for the single cell.

Since in this embodiment, the memory of the onboard battery management system is limited, and the historical data of the stored battery cells is small, the SOP can only be calculated by checking the value of the currently stored factory SOP map, and the SOP differential calculation for each battery cell cannot be achieved. Therefore, through a large amount of historical data stored on the cloud platform, the SOP of each battery cell can also be calculated to obtain a more accurate SOP value. By comparing the SOP values calculated by the on-board battery management system and the cloud platform, according to the two groups The discrepancy between the data determines whether to optimize the SOP value calculated on-board, which further ensures the accuracy of the SOP optimization evaluation of the battery cell.

In this embodiment, after the step S3, the steps are also included:

S4. If the battery management system receives the SOP remote optimization calculation instruction sent by the cloud platform, the battery management system will perform a second confirmation on the status of the single battery cell, specifically:

Judging whether the current static pressure difference of the single cell is greater than the static pressure difference corresponding to 8% SOC in the factory SOP map table, if so, perform the optimization calculation of the SOP value to obtain the optimized SOP value, otherwise directly calculate the SOP value obtained in step S2 The value is used as the optimized SOP value of the single cell;

If the battery management system does not receive the SOP remote optimization calculation command sent by the cloud platform, it will directly use the SOP value calculated in step S2 as the optimized SOP value of the single battery cell.

That is, in this embodiment, when the battery management system receives the SOP remote optimization calculation instruction issued by the cloud platform, it can first set the factory SOP The data in the map table is backed up, and the single battery cell is confirmed twice through the static pressure difference to finally decide whether to optimize the calculation of the SOP value to prevent false triggering of the SOP optimization calculation and further ensure the accuracy of the SOP optimization evaluation of the battery cell.

In addition, in this embodiment, in the above step S4, it is judged whether the current static pressure difference of the single cell is greater than the factory SOP The static pressure difference corresponding to 8%SOC in the map table also includes:

If the judgment result cannot be obtained from the factory SOP map table, the two sets of static pressure difference change rates are calculated according to the two sets of static data of the single cell. If the difference between the two sets of static pressure differential change rates exceeds the preset When the monthly self-discharge rate of the single cell is calculated, the SOP optimization calculation is performed to obtain the optimized SOP value; otherwise, the SOP value calculated in step S2 is directly used as the optimized SOP value of the single cell, and the preset single cell The monthly self-discharge rate of the core is 2~5%.

That is, whether the current static pressure difference of the battery cell that cannot be obtained directly from the factory SOP map table is greater than the factory SOP The cell SOP calculation value corresponding to the static pressure difference corresponding to 8% SOC in the map table can be judged whether the SOP value needs to be optimized by comparing the difference between the two sets of static pressure difference change rates in another way. Calculated to perfect the secondary confirmation.

Wherein, the SOP optimization calculation is performed in the above step S4 to obtain an optimized SOP value, specifically:

Perform SOP power limit, charge and discharge recalibration or lower the charge cut-off voltage for the SOP value of the single cell to obtain the optimized SOP value.

Among them, the SOP limit power is to reduce the entire SOP value, such as setting a variable coefficient K that can be calibrated, the purpose is to prevent the battery from over-discharging and over-charging; re-calibration of charging and discharging is recalculated based on the stored historical data on the cloud platform to obtain a new SOP value, replace the corresponding SOP value in the factory SOP map table, regenerate a new SOP map table and send it to the battery management system, similar to the SOP power limit, the purpose is to prevent the battery from overcharging and over-discharging, and increase the battery life. Life and the effect of preventing thermal runaway; lowering the charge cut-off voltage is to adjust the cut-off point of the full charge voltage of the single cell, which can also effectively protect the cell, increase the life of the cell, and solve the problem of consistency of the cell.

In addition, after step S4, it also includes:

Mark the single cell for which the SOP value has been optimized and calculated.

That is to say, in this embodiment, the optimal calculation of the SOP value is realized through SOP power limit, charging and discharging recalibration, or lowering the charging cut-off voltage. At the same time, the single cells that have been optimized for the SOP value calculation are marked to ensure that the follow-up can be accurately traced. .

Please refer to Figure 6, the third embodiment of the present invention is;

On the basis of the first or second embodiment above, an energy storage system SOP optimization device 1 based on cloud data is provided, as shown in FIG. 6 , including a battery management system 2 and a cloud platform 3 .

Among them, the battery management system 2 is used to collect real-time data of each single cell in the battery pack in real time, calculate the SOP value of the single cell according to the real-time data, and upload the real-time data to the cloud platform 3. The real-time data includes the single cell The current current, voltage, static pressure difference, temperature, SOC value and SOH value of the cell; the cloud platform 3 is used to judge whether the SOP of the single cell is required based on the historical data of the single cell uploaded by the battery management system 2 Values are optimized.

That is, in this embodiment, in conjunction with the cloud data-based energy storage system SOP optimization method of the above-mentioned embodiment 1 or embodiment 2, an energy storage system SOP optimization device based on cloud data is provided, and the battery of the energy storage system The management system collects real-time data such as current, voltage, static pressure difference, temperature, SOC value, and SOH value of each single cell in the battery pack in real time, calculates the SOP value of each single cell, and calculates the SOP value of each single cell based on the single cell stored in the cloud. Whether it is necessary to optimize the calculation of the SOP value based on the historical data of the cell, so as to realize the differential calculation of the SOP of each single cell, and accurately evaluate the aging difference of each single cell in the energy storage system.

To sum up, the SOP optimization method of energy storage system based on cloud data provided by the present invention has the following beneficial effects:

1. Strengthen the safety wave protection of the battery cell, prevent overcharge and overdischarge due to individual differences in the battery cell through SOP optimization calculation, increase the life of the entire battery pack, and achieve the purpose of energy storage system safety protection;

2. It has dual protection mechanisms. The SOP calculation logic of the onboard battery management system has completed various failure mode evaluations and response measures. Adding the SOP remote optimization calculation of the cloud platform can further protect the battery cells and reduce the chance of loss of control. ;

3. During the remote SOP optimization evaluation screening process of the battery cells on the cloud platform, the battery cells with problems can be found in advance, so that customers can be notified in advance of the need for timely maintenance of the problem cells, so as to avoid the customer's maintenance after the problem occurs. Rights protection and complaints;

4. It has a certain degree of scalability, and the relevant configuration of the battery management system can be modified through the cloud platform in the future.

The above description is only an embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent transformations made by using the description of the present invention and the contents of the accompanying drawings, or directly or indirectly used in related technical fields, are all included in the same principle. Within the scope of patent protection of the present invention.

Claims

A kind of energy storage system SOP optimization method based on cloud data, it is characterized in that, comprising steps:

S1. The battery management system collects real-time data of each single battery cell in the battery pack in real time and uploads it to the cloud platform. The real-time data includes the current current, voltage, static pressure difference, temperature, and SOC value of the single battery cell and SOH value;

S2. The battery management system calculates the SOP value of the single cell according to the real-time data;

S3. The cloud platform judges whether to optimize the SOP value of the single cell based on the historical data of the single cell uploaded by the battery management system.
The SOP optimization method of an energy storage system based on cloud data according to claim 1, characterized in that, before the step S1, the steps further include:

S0. Preset an address space on the external memory of the main control board, which is used to store the factory SOP map table of each single cell and its remote modification flag, and establish the battery management system and the main The communication protocol between the control boards, the battery management system obtains the factory SOP map table through the communication protocol, and the external memory is a charged erasable programmable read-only memory.
A cloud data-based energy storage system SOP optimization method according to claim 2, wherein the step S2 is specifically:

The battery management system queries the charging and discharging power corresponding to the single cell in the factory SOP map table according to the current temperature and SOC value of the single cell, and queries the factory SOP according to the current SOH value of the single cell The SOP attenuation coefficient corresponding to the single cell in the map table, the SOP value is obtained by multiplying the charge and discharge power by the SOP attenuation coefficient.
The SOP optimization method of an energy storage system based on cloud data according to claim 1, wherein the step S3 is specifically:

S31. The cloud platform invokes the historical data of each single cell within a preset time period, and captures the static data within the preset time period;

S32. The cloud platform calculates the change rate of the static pressure difference of each single cell in each static time period according to the static time period corresponding to the static data. If the static pressure difference changes rate is greater than the calibration threshold, the cloud platform sends an SOP remote optimization calculation command to the battery management system, otherwise there is no need to perform SOP optimization calculation on the single battery cell, and the calibration threshold is each Monthly self-discharge rate.
The SOP optimization method of an energy storage system based on cloud data according to claim 4, wherein said step S31 further comprises:

The static judgment method of the static data is:

If the single cell is in a power-off state within half an hour and the current is less than 1A, then this time period is the resting time period.
The SOP optimization method of an energy storage system based on cloud data according to claim 1, wherein the step S3 is specifically:

S31. The battery management system uploads the calculated SOP value of the single cell to the cloud platform;

S32. The cloud platform calculates another SOP value based on the stored historical data, and compares it with the SOP value uploaded by the battery management system. If the difference between the two is greater than the 5% of the SOP value calculated by the cloud platform, the cloud platform sends an SOP remote optimization calculation instruction to the battery management system, and the battery management system modifies the corresponding SOP value on the factory SOP map table to the cloud platform The calculated SOP value, otherwise there is no need to perform SOP optimization calculation on the single cell.
A cloud-based data-based energy storage system SOP optimization method according to any one of claims 4 or 6, characterized in that, after the step S3, the steps further include:

S4. If the battery management system receives the SOP remote optimization calculation instruction sent by the cloud platform, the battery management system performs a second confirmation on the state of the single battery cell, specifically:

Judging whether the current static pressure difference of the single cell is greater than the static pressure difference corresponding to 8% SOC in the factory SOP map table, if so, perform the optimization calculation of the SOP value to obtain the optimized SOP value, otherwise directly set the SOP value in the step S2 The calculated SOP value is used as the optimized SOP value of the single cell;

If the battery management system does not receive the SOP remote optimization calculation instruction sent by the cloud platform, it directly uses the SOP value calculated in step S2 as the optimized SOP value of the single battery cell.
The SOP optimization method of an energy storage system based on cloud data according to claim 7, wherein in the step S4, it is judged whether the current static pressure difference of the single cell is greater than 8% in the factory SOP map table The static pressure difference corresponding to SOC also includes:

If the judgment result cannot be obtained according to the factory SOP map table, then the static pressure difference change rate of the two groups is calculated according to the two sets of static data of the single cell. If the static pressure difference change rate of the two groups is When the difference exceeds the preset monthly self-discharge rate of the single cell, perform SOP optimization calculation to obtain the optimized SOP value; otherwise, directly use the SOP value calculated in step S2 as the single cell The optimized SOP value of the cell, the preset monthly self-discharge rate of the single cell is 2~5%.
A method for optimizing SOP of an energy storage system based on cloud data according to claim 8, wherein the SOP optimization calculation is performed in the step S4 to obtain an optimized SOP value, specifically:

Carry out SOP limit power, charge and discharge recalibration or lower the charge cut-off voltage on the SOP value of the single cell to obtain an optimized SOP value;

The SOP power limit is to reduce the entire SOP value;

The recalibration of the charging and discharging is that the cloud platform recalculates based on the stored historical data to obtain a new SOP value, replaces the corresponding SOP value in the factory SOP map table, regenerates a new SOP map table and sends it to the battery management system;

The lowering of the charge cut-off voltage is to adjust the cut-off point of the full charge voltage of the single cell;

Also include after the step S4:

Marking the single cell for which the SOP value optimization calculation has been performed.
An energy storage system SOP optimization device based on cloud data, characterized in that it includes a battery management system and a cloud platform;

The battery management system is used to collect real-time data of each single cell in the battery pack in real time, calculate the SOP value of the single cell according to the real-time data, and upload the real-time data to the cloud platform, the The real-time data includes the current current, voltage, static pressure difference, temperature, SOC value and SOH value of the single cell;

The cloud platform is used to determine whether to optimize the SOP value of the single battery based on the historical data of the single battery uploaded by the battery management system.