CN110749827B - Intelligent battery SOC management system and method based on cloud platform - Google Patents

Intelligent battery SOC management system and method based on cloud platform Download PDF

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CN110749827B
CN110749827B CN201911214263.1A CN201911214263A CN110749827B CN 110749827 B CN110749827 B CN 110749827B CN 201911214263 A CN201911214263 A CN 201911214263A CN 110749827 B CN110749827 B CN 110749827B
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bms
soc
battery
battery module
module
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CN110749827A (en
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兰欣
李祥瑞
谢国芳
唐清林
纪少波
程勇
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Yuanshan Jinan Electronic Technology Co ltd
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Shandong University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/371—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with remote indication, e.g. on external chargers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements

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  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The utility model discloses an intelligent battery SOC management system and method based on cloud platform, the system includes: the BMS system is configured to collect the operation parameters of the battery module and the environmental parameters of the current equipment and upload the operation parameters and the environmental parameters to the cloud server; calculating the SOC value of the battery module according to the operation parameters of the battery module; receiving a regulation and control instruction and a new correction coefficient issued by the cloud server, and correcting the calculated SOC value; the cloud server is configured to receive the operation parameters of the battery module and the environmental parameters of the current equipment, which are acquired by the BMS system, analyze and process the operation parameters and the environmental parameters, and establish a corresponding operation parameter and environmental parameter database; and judging whether the battery SOC parameters need to be re-corrected according to the operation parameters, if so, issuing a regulation and control instruction to the BMS, re-adjusting the correction coefficients according to the operation parameters of the battery module and historical operation parameter information thereof to obtain new correction coefficients, and issuing the new correction coefficients to the BMS.

Description

Intelligent battery SOC management system and method based on cloud platform
Technical Field
The 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
With the rapid development of economic society and new energy industry in China, the power battery is widely applied to electric buses, electric automobiles, large-scale energy storage systems and the like. Battery monomer constitutes battery module through cluster, parallelly connected mode and reuses, but battery monomer performance differs in the module, need improve battery utilization rate and realize the dynamic monitoring of battery with the help of BMS, and accurate estimation battery SOC then is the prerequisite that BMS realized the function. The conventional BMS is limited by the operation speed and the storage capacity, and the calculation accuracy of the SOC algorithm is not high. And the existing BMS correction is finished before the factory leaves, and cannot be re-calibrated according to the used environment after the factory leaves, so that a reliable SOC value cannot be obtained under some special conditions.
In the using process of the power battery, the state parameters such as the SOC of the battery need to be monitored in real time. The calculation of state parameters such as SOC need be with the help of battery energy management system BMS, and the inventor finds at the research and development in-process, and the BMS of battery module can only realize the simple and convenient calculation of data, if only obtain battery SOC according to BMS, because receive BMS hardware embedded processor self computing power restriction, be difficult to realize the calculation of complicated high accuracy prediction algorithm, obviously can appear the not high situation of BMS computational accuracy, can't grasp battery module actual conditions accurately.
Disclosure of Invention
In order to overcome the defects of the prior art, the intelligent battery SOC management system and method based on the cloud platform are provided in the disclosure, when the communication condition is good, the system can accurately calculate the SOC according to the running environment of a battery module and by means of the powerful computing capability of the cloud platform by combining the existing historical data, judge whether the SOC algorithm of the BMS data processing unit needs to be adjusted in real time, and timely correct the parameters of the data processing unit when needed.
The technical scheme of the intelligent battery SOC management system based on the cloud platform provided by the disclosure in one aspect is as follows:
the utility model provides an intelligent battery SOC management system based on cloud platform which characterized by includes:
a BMS system configured to:
1. when the communication condition with the cloud server is poor, 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;
2. when the communication condition with the cloud server is good, the acquired data are uploaded to the cloud server to form a battery module operation database, high-precision SOC solution is obtained by depending on a complex algorithm and high-performance computing capability of the server, meanwhile, SOC is synchronously computed by depending on the computing capability of the BMS, a finally adopted computed value is based on a high-precision result of the server, and the BMS can judge whether relevant parameters of a local SOC algorithm need to be corrected or not according to the deviation of the two, and sends a correction algorithm request to the cloud server in due time; in addition, the BMS can also receive a regulation and control instruction and a new correction coefficient which are automatically issued by the cloud server, and carry out parameter correction on the SOC algorithm;
a cloud server configured to:
1. receiving operation parameters of the battery module and environmental parameter data of current equipment, which are acquired by the BMS system, storing the data into a corresponding battery module historical state information database, performing high-precision analysis processing on the data based on a complex algorithm by using the powerful computing capability of the cloud server, calculating an SOC value of the battery module, issuing the SOC value to the BMS system, and receiving an SOC algorithm correction instruction sent by the BMS system to perform parameter correction when serious deviation occurs between the result and a calculation result of the BMS system;
2. 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. 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.
Further, the cloud server includes:
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, performing big data offline calculation on the attenuation aging and health state of the battery by combining the historical data of the battery module in the cloud database, judging whether relevant parameters of a simplified SOC estimation method of the BMS need to be corrected or not, if so, sending a battery parameter correction request to a regulation and control module, and simultaneously sending a parameter correction request of a BMS data processing unit;
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 or the BMS data processing unit, calculate the parameter correction coefficient of the BMS data processing unit according to the battery module running parameters and the environmental parameters sent by the regulation and control module and by combining the historical running parameter data of the battery module, and send the parameter correction coefficient to the BMS system.
Further, the BMS system includes:
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 can be performed 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 can be performed; if the battery module is judged to be incapable of 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;
and the BMS data processing unit is configured to receive the SOC state parameters of the battery module calculated by the data processing module when the BMS data processing unit is in normal communication with the holder server, synchronously calculate the SOC according to the calculation capability of the BMS data processing unit, consider that the communication error or the local solution error occurs if the two results have serious deviation (data dispersion spectrum), and abandon the result to wait for the next calculation data. And if the deviation of the two is within a threshold range, judging whether the relevant parameters of the local SOC algorithm need to be corrected or not according to the high-precision result of the server. If the deviation of the two exceeds the threshold value, a correction request is sent out, and a parameter correction module of the cloud server sends a correction coefficient of the SOC calculation algorithm of the BMS; 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 cradle head server cannot normally communicate, an internal simplified algorithm is adopted to estimate the SOC value of the battery module, and the obtained battery state information is used for carrying out charge and discharge management on the battery module.
The technical scheme of the intelligent battery SOC management method based on the cloud platform provided by the other aspect of the disclosure is as follows:
an intelligent battery SOC management method based on a cloud platform comprises 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;
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 by combining historical data of the battery module in the cloud database, and judges whether the SOC algorithm of the BMS data processing unit needs to be corrected again;
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.
Through above-mentioned technical scheme, this disclosed beneficial effect is:
(1) according to the intelligent battery SOC management system based on the cloud platform, the comparison mass historical database can be called through the cloud server, the limitation of the operational capability of a traditional BMS data processing unit is broken through, the calculation speed and accuracy of the SOC are improved, the high-accuracy SOC is obtained through a more complex algorithm and a faster calculation speed, and a more efficient and more reliable charging and discharging management strategy is adopted, so that real-time high-accuracy response can be realized by means of 5G communication;
(2) according to the method and the system, the running state of the battery pack is monitored in real time through the cloud platform, parameter correction can be performed on the BMS in time, and the reliability of the SOC obtained when the BMS is in an independent working mode due to interruption of communication conditions of the BMS and the effectiveness of energy management performed by the BMS are guaranteed under different service lives and different working states of the battery;
(3) according to the method, a cloud computing platform is used for carrying out real-time efficient processing on a large amount of data of the battery module, and the high-precision requirement of the SOC of the battery module is met; the simplified processing of the BMS on the data can be realized, and the SOC prediction of the battery module can be realized under the condition of ensuring the basic precision requirement;
(4) the battery packs of the same model can also share data through the cloud platform, so that comparison samples of data analysis are increased, and reliability is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the disclosure and together with the description serve to explain the application and not to limit the disclosure.
FIG. 1 is a first block diagram of an intelligent battery SOC management system based on a cloud platform according to an embodiment;
FIG. 2 is a block diagram of an intelligent battery SOC management system based on a cloud platform according to an embodiment II;
fig. 3 is a flowchart of an intelligent battery SOC management method based on a cloud platform according to an embodiment two.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Fig. 1 and 2 are structural diagrams of an intelligent battery SOC management system based on a cloud platform according to the present embodiment. As shown in fig. 1 and 2, the intelligent battery SOC management system includes a cloud server, a BMS system, and a client, wherein:
the cloud server comprises a monitoring module, a regulation and control module, a data processing module, a parameter correction module and a wireless data transmission module, wherein:
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, performing big data offline calculation on the attenuation aging and health state of the battery by combining the historical data of the battery module in the cloud database, judging whether relevant parameters of a simplified SOC estimation method of the BMS need to be corrected or not, if so, sending a battery parameter correction request to a regulation and control module, and simultaneously sending a parameter correction request of a BMS data processing unit;
the regulation and control module is configured to receive a battery parameter correction request sent by the monitoring module, and send a regulation and control instruction to the BMS regulation and control unit through the wireless data transmission module of the cloud server in combination with the current battery module running state information, and the BMS regulation and control unit judges whether parameter correction can be performed on the BMS data processing unit according to the current battery running state information;
when a battery parameter correction request sent by the monitoring module is not received, the regulation and control module directly sends the battery operation parameters sent by the monitoring module to the data processing module, and meanwhile, the battery operation parameters sent by the monitoring module are sent to a user mobile phone client (mobile APP), so that the real-time monitoring of a user is facilitated, and finally all data are stored in a corresponding database.
The data processing module is configured to receive the battery module current, voltage, temperature and other operation parameters sent by the regulation and control module, calculate the state information of the battery module such as SOC by adopting an accurate standard algorithm according to the battery module current, voltage, temperature and other operation parameters, compare the state information with historical operation data and control parameters of the battery pack and the battery packs of the same model in the cloud server to obtain an optimal charging and discharging curve control strategy and an operation mode capable of reliably avoiding risks, send the calculation result of the battery state information and the charging and discharging management strategy to the BMS data processing unit for displaying and controlling, and store the battery module current, voltage, temperature and other parameters and the calculation result in a corresponding database.
The parameter correction module is configured to generate correction coefficients of the BMS data processing unit according to operation parameters such as current, voltage and temperature of the battery module uploaded by the BMS regulation and control unit or the BMS data processing unit and by combining historical operation parameter data of the battery module, the obtained correction coefficients are sent 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 coefficients.
Specifically, the method for adjusting the correction coefficient includes:
the parameter correction module analyzes and processes the obtained battery module operation parameters and battery module historical data to obtain a calibration SOC value;
and carrying out error analysis and pre-estimation on the SOC calculated by the BMS system by using the calibration SOC value to obtain a calibration weight value k, and sending the calibration weight value k to the BMS data processing unit to correct the SOC calculation algorithm of the BMS data processing unit.
The BMS system comprises a BMS monitoring unit, a BMS regulating and controlling unit and a BMS data processing unit, wherein:
the BMS monitoring unit is configured to collect operation parameters of the battery module and environment parameters of current equipment, the operation parameters of the battery module comprise operation parameters such as current, voltage and temperature of the battery module, the environment parameters of the current equipment comprise environment temperature, salt spray conditions, vibration parameters and the like, the collected operation parameters of the battery module and the environment parameters of the current equipment are transmitted to the BMS wireless data transmission unit through the CAN bus, the operation parameters of the battery module and the environment parameters of the current equipment are processed, analyzed and then packaged and transmitted to the monitoring module of the cloud server through the BMS wireless data transmission unit; and simultaneously transmitting the collected operation parameters of the battery module and the current environmental parameters of the equipment to the 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 can be performed 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 correction can be performed; if the battery module is judged to be incapable of 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;
when the communication is unusual, when BMS system can't carry out data transmission with high in the clouds server, BMS regulation and control unit can directly send battery module operating parameter to BMS data processing unit.
The present embodiment performs parameter correction on the BMS data processing unit when necessary in a good communication condition in which the SOC is calculated by the cloud platform, and the purpose of the correction is to enable the BMS to independently calculate the SOC more suitable for the battery state when there is no communication condition.
The BMS data processing unit is configured in a cloud platform-dependent working mode, receives SOC state parameters of the battery module calculated by the data processing module, synchronously calculates SOC according to self calculation capacity, considers that communication errors or local solution errors occur if serious deviation (data off-spectrum) occurs in two results, and gives up the result to wait for next calculation data. And if the deviation of the two is within a threshold range, judging whether the relevant parameters of the local SOC algorithm need to be corrected or not according to the high-precision result of the server. If the deviation of the two exceeds the threshold value, a correction request is sent out, and a parameter correction module of the cloud server sends a correction coefficient of the SOC calculation algorithm of the BMS; displaying the final calculation result, and simultaneously transmitting the calculation result to a mobile phone client (mobile APP) through a BMS wireless data transmission unit; receiving a calibration weight value sent by a parameter correction module, utilizing a Bootloader to guide a BMS data processing unit to recalibrate according to a calibration weight value k sent by the parameter correction module, and sending a calibration completion instruction to a cloud server through a BMS wireless data transmission unit; and in the independent working mode, the SOC is independently estimated by adopting an internal simplified algorithm according to the data transmitted by the regulating and controlling unit, and the battery module is reliably charged and discharged by utilizing the obtained battery state information.
In this embodiment, the wireless data transmission module and the BMS wireless data transmission unit of the cloud server respectively adopt a 5G communication module, so that efficient data transmission between the battery module BMS and the cloud platform can be realized, and the instantaneity of SOC estimation and charge-discharge management control strategy can be effectively ensured.
Example two
The embodiment provides an intelligent battery SOC management method based on a cloud platform, which is implemented based on the structure diagram of the intelligent battery SOC management system based on the cloud platform in the first embodiment.
Referring to fig. 3, the intelligent battery SOC management method based on the cloud platform includes the following steps:
step 1: collecting the operation parameters of the battery module and the environmental parameters of the current equipment, analyzing and processing the operation parameters of the battery module, and establishing a corresponding operation parameter and environmental parameter database.
Specifically, the operating parameters of the battery module and the current environmental parameters of the equipment are collected through a BMS monitoring unit, wherein the operating parameters of the battery module comprise operating parameters such as current, voltage and temperature of the battery module; the environmental parameters of the current equipment comprise environmental temperature, salt spray conditions, vibration parameters and the like; the collected operation parameters of the battery module and the environment parameters of the current equipment are processed, analyzed and then packaged through the BMS wireless data transmission unit, and the operation parameters of the battery module and the environment parameters of the current equipment are sent to the monitoring module of the cloud server.
The monitoring module acquires a TCP/IP packet of the battery module operation parameter data uploaded by the BMS monitoring unit, analyzes and restores the TCP/IP packet, establishes a corresponding operation parameter and environment parameter database, monitors the operation parameters of the battery module and the current equipment use environment parameters in real time, and writes the operation parameters and the current equipment use environment parameters into the corresponding database.
Step 2: and (3) judging whether the BMS data processing unit needs to be re-corrected or not according to the operation parameters of the battery module, if so, entering the step 3, and if not, returning to the step 1.
Specifically, real-time operation parameters of the battery module are compared with historical operation parameter data of the battery module through the monitoring module, big data offline calculation is carried out on attenuation aging and health states of the battery by combining the historical data of the battery module in the cloud database, whether relevant parameters of the simplified SOC estimation method of the BMS need to be corrected or not is judged, and a parameter correction request is sent to the regulation and control module.
And step 3: the regulation and control module sends a regulation and control instruction to the BMS regulation and control unit, and transmits the battery data of the current charge-discharge cycle period to the data processing module and the parameter correction module.
Specifically, the regulation and control module sends a regulation and control instruction to the BMS regulation and control unit through the wireless data transmission module of the cloud server by receiving a battery parameter correction request sent by the monitoring module and combining the current running state information of the battery module, and the BMS regulation and control unit judges whether parameter correction can be carried out on the SOC value calculated by the BMS data processing module according to the current running state information of the battery; and if so, the BMS regulation and control unit sends regulation and control instructions to the BMS data processing unit and the cloud parameter correction module.
And 4, step 4: and (3) generating a correction coefficient by the parameter correction module according to the battery module operation parameters and the historical operation parameter information of the current charge-discharge cycle period collected in the step (3) and sending the correction coefficient to the BMS data processing unit, and correcting the SOC algorithm by the BMS data processing unit according to the correction coefficient.
Further, the specific steps of step 3 are:
(3-1) the regulation and control module sends a regulation and control instruction to the BMS regulation and control unit through a wireless data transmission module of the cloud server, and meanwhile, battery operation parameter information of the current cycle period is transmitted to the parameter correction module;
(3-2) the BMS control unit receives the command sent by the cloud server and analyzes and processes the command, the change trend of the SOC algorithm complexity and the calculation precision after the BMS data processing unit corrects the command is judged, if the SOC algorithm time complexity needs to be increased but is close to the minimum critical value of the calculation speed currently or the space complexity needs to be increased but is close to the maximum critical value of the available memory of the BMS hardware currently, the control is stopped, and if the situation does not exist, the follow-up operation is continued;
and (3-3) the BMS regulation and control unit sends a regulation and control instruction to the parameter correction module and the BMS data processing unit and sends instruction completion information to the regulation and control module.
Further, the specific steps of step 4 are as follows:
(4-1) analyzing and processing the obtained battery running state parameters and historical parameter data by the parameter correction module to obtain a calibration SOC value;
(4-2) carrying out error analysis and pre-estimation by using the calibration SOC value to obtain a calibration weight value k, and correcting the SOC algorithm of the BMS data processing unit;
(4-3) the parameter correction module issues the calibration weight value to the BMS data processing unit through the wireless data transmission module, and the BMS data processing unit finishes the calibration of the calculated SOC value according to the new calibration weight value k;
and (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-described embodiments achieve the following technical effects:
(1) according to the intelligent battery SOC management system based on the cloud platform, the comparison mass historical database can be called through the cloud server, the limitation of the operational capability of a traditional BMS data processing unit is broken through, the calculation speed and accuracy of the SOC are improved, the high-accuracy SOC is obtained through a more complex algorithm and a faster calculation speed, and a more efficient and more reliable charging and discharging management strategy is adopted, so that real-time high-accuracy response can be realized by means of 5G communication;
(2) according to the method and the system, the running state of the battery pack is monitored in real time through the cloud platform, parameter correction can be performed on the BMS in time, and the reliability of the SOC obtained when the BMS is in an independent working mode due to communication interruption and the effectiveness of the BMS in energy management are guaranteed under different service lives and different working states of the battery;
(3) according to the method, a cloud computing platform is used for carrying out real-time efficient processing on a large amount of data of the battery module, and the high-precision requirement of the SOC of the battery module is met; the simplified processing of the BMS on the data can be realized, and the SOC prediction of the battery module can be realized under the condition of ensuring the basic precision requirement;
(4) the battery packs of the same model can also share data through the cloud platform, so that comparison samples of data analysis are increased, and reliability is improved.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and 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 running 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 request 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; 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;
the BMS system includes:
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 data processing unit is configured to receive the SOC state parameters of the battery module calculated by the data processing module when the BMS data processing unit is in normal communication with the cloud server, synchronously calculate the SOC according to the calculation capability of the BMS data processing unit, and if the two results have serious deviation, the BMS data processing unit considers that the communication error or the local solution error occurs and abandons the current result to wait for the next calculation data; if the deviation of the two is within a threshold range, the high-precision result of the server is taken as the standard, and whether the relevant parameters of the local SOC algorithm need to be corrected or not is judged; if the deviation of the two exceeds the threshold value, sending a correction request, and enabling a parameter correction module of the cloud server to send a correction coefficient of the SOC calculation algorithm of the BMS; 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 cloud server cannot be carried out normally, the SOC value of the battery module is estimated by adopting an internal simplified algorithm, and the battery module is subjected to charge and discharge management by utilizing the obtained battery state information.
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 simultaneously send data to the 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 module and battery modules 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 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 cloud 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; and when the communication with the cloud server is not performed, estimating the SOC value of the battery module, and performing charge and discharge management on the battery module by using the obtained battery state information.
5. The cloud platform-based intelligent 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 the 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;
recalibrating the SOC algorithm of the BMS data processing unit according to the calibration weight value;
the BMS data processing unit is configured to receive the SOC state parameters of the battery module calculated by the data processing module when the BMS data processing unit is in normal communication with the cloud server, synchronously calculate the SOC by depending on the calculation capability of the BMS data processing unit, and if the two results have serious deviation, the BMS data processing unit considers that the communication error or the local solution error occurs and abandons the result to wait for the next calculation data; if the deviation of the two is within a threshold range, the high-precision result of the server is taken as the standard, and whether the relevant parameters of the local SOC algorithm need to be corrected or not is judged; if the deviation of the two exceeds the threshold value, sending a correction request, and enabling a parameter correction module of the cloud server to send a correction coefficient of the SOC calculation algorithm of the BMS; 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 cloud server cannot be carried out normally, the SOC value of the battery module is estimated by adopting an internal simplified algorithm, and the battery module is subjected to charge and discharge management by utilizing the obtained battery state information.
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:
in the cloud server layer:
the cloud server calculates, and under the online state of a 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 relevant parameters of the simplified SOC estimation method of the BMS need to be corrected or not.
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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