CN110749827A - Intelligent battery SOC management system and method based on cloud platform - Google Patents

Abstract

The present disclosure discloses an intelligent battery SOC management system and method based on a cloud platform. The system includes: a BMS system, which is configured to collect operating parameters of battery modules and environmental parameters of current equipment, and upload them to a cloud server; The operating parameters of the battery module are used to calculate the SOC value of the battery module; the control instructions and new correction coefficients issued by the cloud server are received, and the calculated SOC value is corrected; the cloud server is configured to receive the battery collected by the BMS system. The operating parameters of the module and the environmental parameters of the current equipment are analyzed and processed, and the corresponding operating parameters and environmental parameter databases are established; according to the operating parameters, it is judged whether it is necessary to re-calibrate the battery SOC parameters, and if necessary, issue a control command to the BMS system, and re-adjust the correction coefficient according to the operating parameters of the battery module and its historical operating parameter information, obtain a new correction coefficient, and send it to the BMS system.

Description

Intelligent battery SOC management system and method based on cloud platform

technical field

The present disclosure relates to the technical field of power batteries, in particular to an intelligent battery SOC management system and method based on a cloud platform.

Background technique

With the rapid development of my country's economy, society and new energy industry, power batteries are widely used in electric buses, electric vehicles, and large-scale energy storage systems. The battery cells are used in series and parallel to form a battery module, but the performance of the battery cells in the module is different. It is necessary to use the BMS to improve the battery utilization rate and realize the dynamic monitoring of the battery. The accurate estimation of the battery SOC is the BMS function. the premise. However, the traditional BMS is limited by the limitation of operation speed and storage capacity, and the calculation accuracy of its SOC algorithm is not high. In addition, the existing BMS calibration is completed before leaving the factory, and it cannot be re-calibrated according to the environment used after leaving the factory, so that a reliable SOC value cannot be obtained in some special cases.

In the process of using the power battery, it is necessary to monitor the battery SOC and other parameters in real time. The calculation of state parameters such as SOC requires the help of the battery energy management system BMS. During the research and development process, the inventor found that the BMS of the battery module can only realize the simple calculation of data. The computing power of the processor itself is limited, and it is difficult to realize the calculation of complex high-precision prediction algorithms. Obviously, the BMS calculation accuracy is not high, and the actual situation of the battery module cannot be accurately grasped.

SUMMARY OF THE INVENTION

In order to overcome the above-mentioned shortcomings of the prior art, the present disclosure provides an intelligent battery SOC management system and method based on a cloud platform. With the help of the powerful computing power of the cloud platform, the SOC can be accurately calculated, and the SOC algorithm of the BMS data processing unit needs to be adjusted in real time, and the parameter correction of the data processing unit is carried out in time when necessary.

One aspect of the present disclosure provides a technical solution for an intelligent battery SOC management system based on a cloud platform:

An intelligent battery SOC management system based on a cloud platform, characterized by comprising:

BMS system, configured as:

1. When the communication with the cloud server is poor, collect the voltage, current and temperature operating state parameters of the battery module and the environmental parameters of the current device, and calculate the SOC value of the battery module according to the operating parameters of the battery module;

2. When the communication with the cloud server is in good condition, upload the collected data to the cloud server to form a battery module operation database, rely on the server's complex algorithms and high-performance computing capabilities to obtain high-precision SOC solutions, and rely on its own computing capabilities to solve the problem. The SOC is calculated synchronously, and the final calculated value is subject to the high-precision result of the server. The BMS system can judge whether the relevant parameters of the local SOC algorithm need to be corrected according to the deviation between the two, and send a correction algorithm request to the cloud server in a timely manner; , the BMS can also receive the control instructions and new correction coefficients issued by the cloud server, and perform parameter correction on its SOC algorithm;

Cloud server, configured as:

1. Receive the operating parameters of the battery module and the environmental parameter data of the current equipment collected by the BMS system, store the data in the corresponding historical status information database of the battery module, and use the powerful computing power of the cloud server to process the data based on complex algorithms. High-precision analysis and processing, calculate the SOC value of the battery module, and send it to the BMS system, and receive the SOC algorithm correction command sent by the BMS system to perform parameter correction when the result is seriously deviated from the calculation result of the BMS system;

2. The cloud server performs dynamic matching based on the data of the charging and discharging process, and combines the historical data of the battery modules in the cloud database to perform big data offline calculations on the battery attenuation, aging and health status, and determine whether the relevant parameters of the simplified SOC estimation method of the BMS need to be calculated. Correction. If necessary, a regulation command is issued to the BMS system, and the correction coefficient is re-adjusted according to the operating parameters of the battery module and its historical operating parameter information, a new correction coefficient is obtained, and sent to the BMS system.

Further, the cloud server includes:

The monitoring module is configured to obtain the data packets of the battery module operating parameters and the current equipment environmental parameters uploaded by the BMS system, parse and restore them, establish a database of corresponding operating parameters and environmental parameters, and send the data to the control module of the cloud server at the same time ;Compare the real-time operating parameters of the battery module with the historical operating parameter data of the battery module, and use the historical data of the battery module in the cloud database to perform big data offline calculations on battery attenuation, aging and health status, and determine whether it is necessary to The relevant parameters of the simplified SOC estimation method are corrected. If it is judged that correction is required, a battery parameter correction request is sent to the control module, and a BMS data processing unit parameter correction request is sent at the same time;

The control module is configured to send a control instruction to the BMS control unit after receiving the battery parameter correction request sent by the monitoring module, and at the same time send the received data to the data processing module and the parameter correction module; When the parameter correction request is received, the data received from the monitoring module is directly sent to the data processing module, and at the same time, the operating parameters of the battery module sent by the monitoring module are sent to the user terminal;

The data processing module is configured to receive the data sent by the control module, calculate the SOC status information of the battery module, and compare it with the historical operation data and control parameters of the battery pack and the battery pack of the same type in the historical database to obtain the optimal one. Charge-discharge curve control strategy and reliable operation mode, and send the calculation results of battery status information and charge-discharge management strategy to the BMS data processing unit;

The parameter correction module is configured to, after receiving the control instruction sent by the BMS control unit or the BMS data processing unit, calculate the operating parameters and environmental parameters of the battery module issued by the control module and combined with the historical operating parameter data of the battery module. The parameter correction coefficient of the BMS data processing unit is sent to the BMS system.

Further, the BMS system includes:

The BMS monitoring unit is configured to collect the operating parameters of the battery module and the environmental parameter data of the current equipment, and judge the communication status with the cloud server in real time. If the communication status with the cloud server is good, the collected data will be processed and analyzed. Packed and sent to the cloud server; at the same time, the collected operating parameters of the battery module and the environmental parameters of the current device are transmitted to the BMS control unit; if the communication with the cloud server is poor, the collected operating parameters of the battery module will be And the environmental parameters of the current equipment are transmitted to the BMS control unit;

The BMS control unit is configured to receive the control instructions issued by the control module, the operating parameters of the battery module and the environmental parameters of the current equipment collected by the BMS monitoring unit, and analyze and process the operating parameters of the battery module. The information judges whether the parameter correction of the BMS data processing unit can be performed. If it can be corrected, the control command is sent to the parameter correction module and the BMS data processing unit, and the command completion information is sent to the control module; if it is judged that the parameter correction cannot be performed, the battery The operating parameters and environmental parameters of the module and the analysis results are transmitted to the data processing unit of the BMS;

The BMS data processing unit is configured to receive the battery module SOC state parameters calculated by the data processing module when it communicates normally with the PTZ server, and simultaneously calculate the SOC based on its own computing power. If there is a serious deviation between the two results ( The data is outrageous), it is considered that the communication error or the local solution error is wrong, and the current result is abandoned and the next calculation data is waited. If the deviation between the two is within a threshold range, the high-precision result of the server shall prevail, and it is determined whether the relevant parameters of the local SOC algorithm need to be corrected. If the deviation between the two exceeds the threshold, a correction request will be issued, and the parameter correction module of the cloud server will send the correction coefficient of the SOC calculation algorithm of the BMS; when the calibration weight value issued by the parameter correction module is received, the SOC algorithm will be corrected, and Send the calibration completion command to the cloud server; when the PTZ server cannot communicate normally, an internal simplified algorithm is used to estimate the SOC value of the battery module, and the battery module is charged and discharged using the obtained battery status information.

Another aspect of the present disclosure provides a technical solution for a cloud platform-based intelligent battery SOC management method:

An intelligent battery SOC management method based on a cloud platform, the method includes the following steps:

Collect the operating parameters of the battery module in the current charge-discharge cycle and the environmental parameters of the current equipment, analyze and process the operating parameters of the battery module, and establish a database of corresponding operating parameters and environmental parameters;

The cloud server performs dynamic matching based on the data of the charging and discharging process, and combines the historical data of the battery modules in the cloud database to perform big data offline calculations on battery attenuation, aging and health status, and determine whether it is necessary to recalibrate the SOC algorithm of the BMS data processing unit;

If necessary, calculate the calibration weight value according to the collected battery module operating parameters and their historical operating parameter information;

The SOC algorithm of the BMS data processing unit is recalibrated according to the calibration weight value.

Through the above technical solutions, the beneficial effects of the present disclosure are:

(1) The intelligent battery SOC management system based on the cloud platform proposed in this disclosure can call and compare massive historical databases through the cloud server. More complex algorithms and faster operation speed can obtain high-precision SOC, and more efficient and reliable charging and discharging management strategies. With the help of 5G communication, real-time high-precision response can be achieved;

(2) The present disclosure monitors the running state of the battery pack in real time through the cloud platform, and can perform parameter correction on the BMS in time, and ensures the reliability of the SOC obtained when the BMS communication conditions are interrupted and in the independent working mode under different service lives and different working states of the battery. performance and the effectiveness of the BMS for energy management;

(3) The present disclosure uses the cloud computing platform to process a large amount of data of the battery module efficiently in real time, so as to achieve the high-precision requirements of the battery module SOC; it can realize the simplified processing of the data by the BMS, and realize the battery module under the requirement of ensuring the basic accuracy. Module SOC prediction;

(4) The same type of battery pack can also share data through the cloud platform, which increases the comparison samples for data analysis and improves reliability.

Description of drawings

The accompanying drawings, which constitute a part of the present disclosure, are used to provide further understanding of the present disclosure, and the exemplary embodiments of the present disclosure and their descriptions are used to explain the present application and do not constitute an improper limitation of the present disclosure.

1 is a structural diagram 1 of an intelligent battery SOC management system based on a cloud platform in Embodiment 1;

2 is a second structural diagram of an intelligent battery SOC management system based on a cloud platform in Embodiment 1;

FIG. 3 is a flowchart of an intelligent battery SOC management method based on a cloud platform in the second embodiment.

Detailed ways

The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.

Example 1

1 and 2 are structural diagrams of the cloud platform-based intelligent battery SOC management system involved in this embodiment. As shown in Figures 1 and 2, the intelligent battery SOC management system includes a cloud server, a BMS system and a client, wherein:

The cloud server includes a monitoring module, a control module, a data processing module, a parameter correction module and a wireless data transmission module, wherein:

The monitoring module is configured to obtain the data packets of the battery module operating parameters and the current equipment environmental parameters uploaded by the BMS system, parse and restore them, establish a database of corresponding operating parameters and environmental parameters, and send the data to the control module of the cloud server at the same time ;Compare the real-time operating parameters of the battery module with the historical operating parameter data of the battery module, and use the historical data of the battery module in the cloud database to perform big data offline calculations on battery attenuation, aging and health status, and determine whether it is necessary to The relevant parameters of the simplified SOC estimation method are corrected. If it is judged that correction is required, a battery parameter correction request is sent to the control module, and a BMS data processing unit parameter correction request is sent at the same time;

The control module is configured to receive a battery parameter correction request sent by the monitoring module, and in combination with the current battery module operating status information, send a control instruction to the BMS control unit through the wireless data transmission module of the cloud server, and the BMS control unit is based on the current operation of the battery. The status information judges whether parameter correction can be performed on the BMS data processing unit;

When the battery parameter correction request sent by the monitoring module is not received, the control module directly sends the battery operating parameters sent by the monitoring module to the data processing module, and at the same time, sends the battery operating parameters sent by the monitoring module to the user's mobile phone client (mobile APP) , which is convenient for users to monitor in real time, and finally save all the data to the corresponding database.

The data processing module is configured to receive the battery module current, voltage, temperature and other operating parameters sent by the control module, and calculate the SOC of the battery module using an accurate standard algorithm according to the battery module current, voltage, temperature and other operating parameters. At the same time, compare with the historical operation data and control parameters of the battery pack and the same type of battery pack in the cloud server to obtain the optimal charge-discharge curve control strategy and operation mode that can reliably avoid risks. The calculation results and charge and discharge management strategies of the battery are sent to the BMS data processing unit for display and control. At the same time, the parameters and calculation results of the battery module current, voltage, temperature, etc. are stored in the corresponding database.

The parameter correction module is configured to generate the current, voltage, temperature and other operating parameters of the battery module uploaded by the BMS control unit or the BMS data processing unit, combined with the historical operating parameter data of the battery module, to generate the data of the BMS data processing unit. Correction coefficient, send the obtained correction coefficient to the BMS data processing unit through the wireless data transmission module of the cloud server, and the BMS data processing unit corrects the SOC algorithm according to the new correction coefficient.

Specifically, the adjustment method of the correction coefficient is:

The parameter calibration module analyzes and processes the obtained battery module operating parameters and battery module historical data to obtain a calibrated SOC value;

Use the calibrated SOC value to analyze and predict the error of the SOC calculated by the BMS system, obtain a calibration weight value k, and send it to the BMS data processing unit to correct the SOC calculation algorithm of the BMS data processing unit.

The BMS system includes a BMS monitoring unit, a BMS control unit, and a BMS data processing unit, wherein:

The BMS monitoring unit is configured to collect the operating parameters of the battery module and the environmental parameters of the current device, the operating parameters of the battery module include operating parameters such as battery module current, voltage, temperature, and the environment of the current device. The parameters include ambient temperature, salt spray conditions, vibration parameters, etc. The collected operating parameters of the battery module and the environmental parameters of the current equipment are transmitted to the BMS wireless data transmission unit through the CAN bus, and the battery module is transmitted through the BMS wireless data transmission unit. The operating parameters of the battery module and the environmental parameters of the current equipment are processed and analyzed, packaged, and sent to the monitoring module of the cloud server; at the same time, the collected operating parameters of the battery module and the environmental parameters of the current equipment are transmitted to the BMS control unit.

The BMS control unit is configured to receive the control instruction issued by the control module, the operating parameters of the battery module and the environmental parameters of the current equipment collected by the BMS monitoring unit, and analyze and process the operating parameters of the battery module. The operation status information judges whether the parameter correction can be performed on the BMS data processing unit. If it can be corrected, the control instruction is sent to the parameter correction module and the BMS data processing unit, and the instruction completion information is sent to the control module; if it is judged that the parameter correction cannot be performed, then Transmit the operating parameters, environmental parameters and analysis results of the battery module to the data processing unit of the BMS;

When the communication is abnormal and the BMS system cannot perform data transmission with the cloud server, the BMS control unit can directly send the battery module operating parameters to the BMS data processing unit.

This embodiment corrects the parameters of the BMS data processing unit when necessary when the communication is good. When the communication is good, the SOC is calculated by the cloud platform, and the purpose of the correction is to enable the BMS to independently calculate the battery state when there is no communication condition. SOC.

The BMS data processing unit is configured to receive the SOC state parameter of the battery module calculated by the data processing module in the working mode of relying on the cloud platform, and simultaneously calculate the SOC by relying on its own computing power. If there is a serious deviation between the two results (The data is outrageous), it is considered that the communication error or the local solution error is wrong, and the current result is abandoned and the next calculation data is waited. If the deviation between the two is within a threshold range, the high-precision result of the server shall prevail, and it is determined whether the relevant parameters of the local SOC algorithm need to be corrected. If the deviation between the two exceeds the threshold, a correction request will be issued, and the parameter correction module of the cloud server will send the correction coefficient of the BMS SOC calculation algorithm; the final calculation result will be displayed, and the calculation result will be transmitted to the mobile client through the BMS wireless data transmission unit. (Mobile APP); Receive the calibration weight value issued by the parameter correction module, use the Bootloader to guide the BMS data processing unit to recalibrate according to the calibration weight value k issued by the parameter correction module, and pass the calibration completion instruction through the BMS wireless data transmission unit It is sent to the cloud server; in the independent working mode, it is configured to independently estimate the SOC by using the internal simplified algorithm according to the data transmitted by the control unit, and use the obtained battery status information to perform reliable charge and discharge management of the battery module.

In this embodiment, the wireless data transmission module and BMS wireless data transmission unit of the cloud server respectively use 5G communication modules, which can realize efficient data transmission between the battery module BMS and the cloud platform, and effectively ensure SOC estimation and charging and discharging. Manage the real-time nature of control strategies.

Embodiment 2

This embodiment provides a cloud platform-based intelligent battery SOC management method, which is implemented based on the structure diagram of the cloud platform-based intelligent battery SOC management system described in the first embodiment.

Referring to Figure 3, the cloud platform-based intelligent battery SOC management method includes the following steps:

Step 1: Collect the operating parameters of the battery module and the environmental parameters of the current equipment, analyze and process the operating parameters of the battery module, and establish a database of corresponding operating parameters and environmental parameters.

Specifically, the operating parameters of the battery module and the environmental parameters of the current equipment are collected through the BMS monitoring unit, where the operating parameters of the battery module include operating parameters such as battery module current, voltage, and temperature; the environmental parameters of the current equipment include: Ambient temperature, salt spray conditions, vibration parameters, etc.; the collected operating parameters of the battery module and the environmental parameters of the current equipment are processed and analyzed through the BMS wireless data transmission unit to process and analyze the operating parameters of the battery module and the environmental parameters of the current equipment Packaged and sent to the monitoring module of the cloud server.

The monitoring module obtains the TCP/IP packet of the battery module operating parameter data uploaded by the BMS monitoring unit, parses and restores it, establishes a database of corresponding operating parameters and environmental parameters, and monitors the operating parameters of the battery module and the use environment of the current equipment in real time. parameters, and write to the corresponding database.

Step 2: Determine whether the BMS data processing unit needs to be re-calibrated according to the operating parameters of the battery module, if so, go to Step 3; if not, return to Step 1.

Specifically, the monitoring module compares the real-time operating parameters of the battery module with the historical operating parameter data of the battery module, and combines the historical data of the battery module in the cloud database to perform big data offline calculations on battery decay, aging and health status. Determine whether the relevant parameters of the simplified SOC estimation method of the BMS need to be corrected, and send a parameter correction request to the control module.

Step 3: The control module sends a control instruction to the BMS control unit, and transmits the battery data of the current charge-discharge cycle to the data processing module and the parameter correction module.

Specifically, the control module receives the battery parameter correction request sent by the monitoring module, combines the current battery module operating state information, and sends a control instruction to the BMS control unit through the wireless data transmission module of the cloud server, and the BMS control unit is based on the current battery operating state information. It is judged whether parameter correction can be performed on the SOC value calculated by the BMS data processing module; if necessary, the BMS control unit sends a control instruction to the BMS data processing unit and the parameter correction module in the cloud.

Step 4: The parameter correction module generates a correction coefficient and sends it to the BMS data processing unit according to the battery module operating parameters and historical operating parameter information of the current charge-discharge cycle collected in step 3, and the BMS data processing unit adjusts the SOC algorithm according to the correction coefficient. Make corrections.

Further, the specific steps of the step 3 are:

(3-1) The control module sends the control instruction to the BMS control unit through the wireless data transmission module of the cloud server, and simultaneously transmits the battery operating parameter information of the current cycle to the parameter correction module;

(3-2) The BMS control unit receives the instruction sent by the cloud server and analyzes and processes it, and judges the change trend of the complexity and calculation accuracy of the SOC algorithm after the correction of the BMS data processing unit under the control instruction. If the SOC algorithm is found If the time complexity needs to be increased but it is approaching the minimum critical value of the computing speed, or the space complexity needs to be increased and the current is close to the maximum critical value of the available memory of the BMS hardware, the regulation will be aborted.

(3-3) The BMS control unit sends a control instruction to the parameter correction module and the BMS data processing unit, and sends the instruction completion information to the control module.

Further, the specific steps of the step 4 are:

(4-1) The parameter calibration module analyzes and processes the obtained battery operating state parameters and historical parameter data to obtain a calibrated SOC value;

(4-2) Using the calibrated SOC value to perform error analysis and estimation, obtain a calibration weight value k, and correct the SOC algorithm of the BMS data processing unit;

(4-3) The parameter correction module sends the calibration weight value to the BMS data processing unit through the wireless data transmission module, and the BMS data processing unit completes the calibration of the calculated SOC value according to the new calibration weight value k;

(4-4) The BMS data processing unit sends the calibration completion instruction to the cloud server through the BMS wireless data transmission unit.

From the above description, it can be seen that the above-mentioned embodiments achieve the following technical effects:

(1) The intelligent battery SOC management system based on the cloud platform proposed in this disclosure can call and compare massive historical databases through the cloud server. More complex algorithms and faster operation speed can obtain high-precision SOC, and more efficient and reliable charging and discharging management strategies. With the help of 5G communication, real-time high-precision response can be achieved;

(2) The present disclosure monitors the running state of the battery pack in real time through the cloud platform, and can perform parameter correction on the BMS in time, and ensures the reliability of the SOC obtained when the BMS communication is interrupted and in the independent working mode under different service lives and different working states of the battery. and the effectiveness of the BMS for energy management;

(3) The present disclosure uses the cloud computing platform to process a large amount of data of the battery module efficiently in real time, so as to achieve the high-precision requirements of the battery module SOC; it can realize the simplified processing of the data by the BMS, and realize the battery module under the requirement of ensuring the basic accuracy. Module SOC prediction;

(4) The same type of battery pack can also share data through the cloud platform, which increases the comparison samples for data analysis and improves reliability.

Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, they do not limit the protection scope of the present disclosure. Those skilled in the art should understand that on the basis of the technical solutions of the present disclosure, those skilled in the art do not need to pay creative efforts. Various modifications or variations that can be made are still within the protection scope of the present disclosure.

Claims (10)

1. The utility model provides an intelligent battery SOC management system based on cloud platform which characterized by includes:

a BMS system configured to:

when communication cannot be established with a cloud server, acquiring voltage, current and temperature running state parameters of the battery module and current equipment environment parameters, and calculating a battery module SOC value according to the running parameters of the battery module;

when communication is established with a cloud server, the collected data are uploaded to the cloud server to form a battery module operation database, an SOC solution result is obtained, meanwhile, SOC is synchronously calculated by depending on the SOC, a calculation value finally adopted is based on the result of the server, whether relevant parameters of a local SOC algorithm need to be corrected or not is judged according to the deviation of the calculation value and the result, and a correction algorithm request is sent to the cloud server at the right time; or receiving a regulation and control instruction and a new correction coefficient which are issued by the cloud server, and performing parameter correction on the SOC algorithm;

a cloud server configured to:

receiving operation parameters of the battery module and environmental parameter data of current equipment, which are acquired by the BMS, storing the data into a corresponding historical state information database of the battery module, analyzing and processing the data by using a cloud server, calculating an SOC value of the battery module, sending the SOC value to the BMS, and receiving an SOC algorithm correction instruction sent by the BMS to correct the parameters when serious deviation occurs between the result and a calculation result of the BMS;

dynamically matching the battery attenuation aging and health state according to the charging and discharging process data, performing big data offline calculation on the attenuation aging and health state of the battery by combining historical data of the battery module in the cloud database, and judging whether relevant parameters of a simplified SOC estimation method of the BMS need to be corrected or not; and if so, issuing a regulation and control instruction to the BMS, readjusting the correction coefficient according to the operation parameters of the battery module and the historical operation parameter information thereof to obtain a new correction coefficient, and issuing the new correction coefficient to the BMS.

2. The cloud platform-based intelligent battery SOC management system of claim 1, wherein the cloud server comprises:

the monitoring module is configured to acquire a data packet of battery module operation parameters and current equipment environment parameters uploaded by the BMS, analyze and restore the data packet, establish a corresponding operation parameter and environment parameter database, and send data to a regulation and control module of the cloud server; comparing the real-time operation parameters of the battery module with historical operation parameter data of the battery module, judging the operation state information of the battery module, and sending a battery parameter correction request to a regulation and control module and a BMS data processing unit parameter correction request simultaneously when the deviation of the operation state information of the battery module exceeds a set threshold value;

the regulation and control module is configured to send a regulation and control instruction to the BMS regulation and control unit after receiving the battery parameter correction request sent by the monitoring module, and simultaneously send the received data to the data processing module and the parameter correction module; when a parameter correction request sent by the monitoring module is not received, directly sending the data received from the monitoring module to the data processing module, and simultaneously sending the battery module operation parameters sent by the monitoring module to the user side;

the data processing module is configured to receive the data sent by the regulation and control module, calculate the SOC state information of the battery module, compare the SOC state information with historical operation data and control parameters of the battery pack and battery packs of the same type in a historical database to obtain an optimal charging and discharging curve control strategy and a reliable operation mode, and send the calculation result of the battery state information and a charging and discharging management strategy to the BMS data processing unit;

and the parameter correction module is configured to receive the regulation and control instruction sent by the BMS regulation and control unit, calculate the parameter correction coefficient of the BMS data processing unit according to the battery module operation parameter and the environmental parameter sent by the regulation and control module and by combining the historical operation parameter data of the battery module, and send the parameter correction coefficient to the BMS system.

3. The cloud platform-based intelligent battery SOC management system according to claim 2, wherein the cloud server further comprises a wireless data transmission module configured to enable data transmission between the cloud server and the BMS system.

4. The cloud platform-based intelligent battery SOC management system of claim 1, wherein the BMS system comprises:

the BMS monitoring unit is configured to collect the operation parameters of the battery module and the environmental parameter data of the current equipment, judge the communication condition with the cloud server in real time, and process, analyze and pack the collected data and send the data to the cloud server if the communication condition with the cloud server is good; simultaneously transmitting the collected operation parameters of the battery module and the current environmental parameters of the equipment to a BMS regulation and control unit; if the communication condition with the cloud server is poor, transmitting the acquired running parameters of the battery module and the current environmental parameters of the equipment to a BMS control unit;

the BMS control unit is configured to receive a control instruction sent by the control module, the operation parameters of the battery module and the current environmental parameters of the equipment, which are acquired by the BMS monitoring unit, analyze and process the operation parameters of the battery module, judge whether parameter correction needs to be carried out on the BMS data processing unit according to the current operation state information of the battery, and send the control instruction to the parameter correction module and the BMS data processing unit and send instruction completion information to the control module if the parameter correction needs to be carried out on the BMS data processing unit; if the judgment result does not need parameter correction, transmitting the operation parameters, the environmental parameters and the analysis result of the battery module to a data processing unit of the BMS;

the BMS data processing unit is configured to receive and display the SOC state parameters of the battery module calculated by the data processing module when the BMS data processing unit is communicated with the holder server; when a calibration weight value sent by the parameter correction module is received, correcting the SOC algorithm, and sending a calibration completion instruction to the cloud server; when the communication with the holder server is not carried out, the SOC value of the battery module is estimated, and the battery module is subjected to charge and discharge management by utilizing the obtained battery state information.

5. The intelligent cloud platform-based battery SOC management system according to claim 4, wherein the BMS system further comprises a BMS wireless data transmission unit configured to enable data transmission between the BMS system and a cloud server.

6. The cloud platform-based intelligent battery SOC management system of claim 4, wherein the BMS regulation unit is further configured to:

when the communication between the BMS and the cloud server is abnormal, the operation parameters of the battery module are directly sent to the BMS data processing unit;

the BMS data processing unit is further configured to:

processing the received battery module operation parameters, calculating and displaying SOC state parameters of the battery module; and meanwhile, transmitting the calculation result to the user side.

7. An intelligent battery SOC management method based on a cloud platform is characterized by comprising the following steps:

collecting the operation parameters of the battery module and the environment parameters of the current equipment in the current charge-discharge cycle period, analyzing and processing the operation parameters of the battery module, and establishing a corresponding operation parameter and environment parameter database;

judging whether the SOC algorithm of the BMS data processing unit needs to be re-corrected or not according to the operation parameters of the battery module;

if so, calculating a calibration weight value according to the collected battery module operation parameters and historical operation parameter information thereof;

and recalibrating the SOC algorithm of the BMS data processing unit according to the calibration weight value.

8. The intelligent battery SOC management method based on the cloud platform of claim 7, wherein the step of determining whether the measured SOC value needs to be re-corrected according to the operation parameters of the battery module comprises:

judging whether the running parameter deviation of the battery module exceeds a set threshold value or not;

when the running state information deviation of the battery module exceeds a set threshold value, the SOC parameter of the battery needs to be corrected again.

9. The intelligent cloud platform-based battery SOC management method according to claim 7, wherein the calculation of the calibration weight values comprises:

at the cloud server level:

the cloud server calculates, and under the online state of the network, the battery running state data is calculated and solved to obtain high-precision SOC state data, and the high-precision SOC state data is downloaded to the BMS for precise energy management, protection and control;

the cloud server dynamically matches the battery attenuation aging and health state according to the charging and discharging process data, performs big data offline calculation on the battery attenuation aging and health state according to the historical data of the battery module in the cloud database, and judges whether correction needs to be performed on relevant parameters of the simplified SOC estimation method of the BMS.

10. The intelligent cloud platform-based battery SOC management method according to claim 7, wherein the calculation of the calibration weight values comprises:

at the BMS level:

under the condition that the single machine is connected without a server, real-time calculation is carried out by an SOC estimation algorithm by depending on an embedded processor or a DSP coprocessor, and the energy management requirement of a power battery BMS system is met;

obtaining high-precision SOC solution by depending on a server under the online state of the server; meanwhile, the SOC is synchronously calculated by depending on the calculation capability of the SOC; when the energy management strategy control is carried out, if the deviation amount is larger than a set value, a communication error or a local solution error is considered, and the current result is abandoned to wait for the next calculation data; if the deviation of the two is within a threshold range, the calculation result of the server is taken as the standard, and whether the correction of the relevant parameters of the local SOC algorithm is required or not is judged; if the deviation of the two exceeds the threshold value, a correction request is sent out, and the server sends the correction parameters of the SOC calculation algorithm of the BMS.

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