WO2021000905A1 - 电池状态监测方法、边缘处理器、系统及存储介质 - Google Patents

电池状态监测方法、边缘处理器、系统及存储介质 Download PDF

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WO2021000905A1
WO2021000905A1 PCT/CN2020/099868 CN2020099868W WO2021000905A1 WO 2021000905 A1 WO2021000905 A1 WO 2021000905A1 CN 2020099868 W CN2020099868 W CN 2020099868W WO 2021000905 A1 WO2021000905 A1 WO 2021000905A1
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battery
parameters
data
model
state
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PCT/CN2020/099868
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English (en)
French (fr)
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王宏伟
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华人运通(江苏)技术有限公司
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • 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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Definitions

  • the invention relates to the technical field of power batteries, in particular to a battery state monitoring method, an edge processor, a system and a storage medium.
  • Power batteries are an important part of electric vehicles, especially pure electric vehicles, which are the only power to drive vehicles.
  • the battery state of charge data (SOC, State of charge) is one of the important parameters that characterize the battery state.
  • SOC State of charge
  • the state of charge data can be used to estimate the range of the vehicle to prevent the vehicle from breaking down or over-discharging the battery during driving. The battery itself is damaged; therefore, accurate calculation of the state of charge data is an important guarantee for battery safety.
  • the accuracy of the battery algorithm depends on the accuracy of the battery model.
  • the battery The parameters of the model no longer match the battery cell, and the external characteristics of the battery model are quite different from the battery cell characteristics; on the other hand, due to the limited computing resources of the battery management system (BMS, Battery management system), it is difficult for the battery management system to use the battery
  • BMS Battery management system
  • the real-time operating data of the battery is used to correct the parameters of the battery model; therefore, in the case of battery aging or extreme environments, the accuracy of the battery model is reduced, resulting in excessive calculation errors of the state of charge data, and it is difficult to accurately monitor the working status of the battery.
  • the purpose of the present invention is to provide a battery state monitoring method, edge processor, system and storage medium, which can effectively avoid the deterioration of the state of charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environment The calculation error is too large to accurately monitor the working status of the battery.
  • an embodiment of the present invention provides a battery state monitoring method, including:
  • the corrected parameter is returned to the battery management system, so that the battery management system verifies the corrected parameter again according to the real-time operating data, and after the verification is passed again , Updating the corrected parameters to the battery model, and monitoring the state of the battery by applying the updated battery model.
  • the real-time operating data includes the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery status data includes the state of charge data and health status data of the battery;
  • the correcting the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters specifically includes:
  • the parameters of the battery model are selected based on the current temperature of the battery cell, the state of charge data, and the state of health data; wherein, the parameters of the battery model include DC resistance, short-term resistance, long-term resistance, and short-term resistance. Time capacitance and long time capacitance;
  • the first equivalent circuit model is:
  • R0 DC resistance;
  • R1 is short-term resistance;
  • R2 long-term resistance;
  • C1 is short-term capacitance;
  • C2 long-term capacitance;
  • Cap is Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the least square method is used to modify the parameters of the first equivalent circuit model, that is, the parameters of the battery model are modified to obtain the modified parameters.
  • the verification of the modified parameters according to the real-time operating data specifically includes:
  • the battery management system re-verifies the corrected parameters according to the real-time operating data, which specifically includes:
  • the battery management system detects the resting time of the battery
  • the battery management system When the resting time of the battery is greater than a preset time threshold, the battery management system establishes a second equivalent circuit model according to the corrected parameter;
  • an embodiment of the present invention also provides a battery status monitoring method, including:
  • the real-time operating data and the battery status data are sent to the edge processor, so that the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery status data to obtain the corrected And verify the corrected parameters according to the real-time operating data;
  • the updated battery model is applied to monitor the state of the battery.
  • the real-time operating data includes the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery status data includes the state of charge data and health status data of the battery;
  • the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters, which specifically includes:
  • the edge processor selects the parameters of the battery model based on the current temperature of the battery cell, the state of charge data and the state of health data; wherein, the parameters of the battery model include DC resistance, short-term resistance, Long-term resistance, short-term capacitance and long-term capacitance;
  • the first equivalent circuit model is:
  • R0 DC resistance;
  • R1 is short-term resistance;
  • R2 long-term resistance;
  • C1 is short-term capacitance;
  • C2 long-term capacitance;
  • Cap is Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the least square method is used to modify the parameters of the first equivalent circuit model, that is, the parameters of the battery model are modified to obtain the modified parameters.
  • the edge processor verifies the modified parameters according to the real-time operating data, which specifically includes:
  • the edge processor applies the current current of the battery to the modified first equivalent circuit model, and calculates the cell voltage
  • the re-verification of the corrected parameters according to the real-time operating data specifically includes:
  • the ampere-hour integration method is used to calculate the state of charge data
  • an embodiment of the present invention also provides a battery status monitoring method, including:
  • the battery management system collects real-time operation data of the battery, calculates and obtains battery state data according to the real-time operation data and battery model, and sends the real-time operation data and the battery state data to the edge processor;
  • the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters;
  • the edge processor verifies the corrected parameters according to the real-time operating data, and after the verification passes, returns the corrected parameters to the battery management system;
  • the battery management system re-verifies the corrected parameters according to the real-time operating data
  • the battery management system updates the corrected parameters to the battery model, and monitors the state of the battery by applying the updated battery model.
  • the real-time operating data includes the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery status data includes the state of charge data and health status data of the battery;
  • the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters, which specifically includes:
  • the edge processor selects the parameters of the battery model based on the current temperature of the battery cell, the state of charge data and the state of health data; wherein, the parameters of the battery model include DC resistance, short-term resistance, Long-term resistance, short-term capacitance and long-term capacitance;
  • the first equivalent circuit model is:
  • R0 DC resistance;
  • R1 is short-term resistance;
  • R2 long-term resistance;
  • C1 is short-term capacitance;
  • C2 long-term capacitance;
  • Cap is Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the least square method is used to modify the parameters of the first equivalent circuit model, that is, the parameters of the battery model are modified to obtain the modified parameters.
  • the edge processor verifies the modified parameters according to the real-time operating data, which specifically includes:
  • the edge processor applies the current current of the battery to the modified first equivalent circuit model, and calculates the cell voltage
  • the battery management system re-verifies the corrected parameters according to the real-time operating data, which specifically includes:
  • the battery management system detects the resting time of the battery
  • an embodiment of the present invention also provides an edge processor, including:
  • the first receiving module is configured to receive the real-time operating data of the battery collected by the battery management system, and the battery state data calculated by the battery management system according to the real-time operating data and the battery model;
  • the correction module is used to correct the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters;
  • the first verification module is configured to verify the corrected parameters according to the real-time operating data.
  • the first sending module is configured to return the corrected parameters to the battery management system after the verification is passed, so that the battery management system re-verifies the corrected parameters according to the real-time operating data , And after the verification is passed again, the revised parameters are updated to the battery model.
  • an embodiment of the present invention also provides a battery management system, including:
  • the data acquisition module is used to collect real-time operating data of the battery, and calculate the battery state data according to the real-time operating data and the battery model;
  • the second sending module is configured to send the real-time operating data and the battery status data to the edge processor, so that the edge processor can perform the evaluation of the battery model according to the real-time operating data and the battery status data. Correct the parameters, obtain the corrected parameters, and verify the corrected parameters according to the real-time operating data;
  • the second receiving module is configured to receive the verified parameters returned by the edge processor
  • the second verification module is configured to verify the corrected parameters again according to the real-time operating data
  • the model update module is used to update the revised parameters to the battery model after the verification is passed again;
  • the monitoring module is used to apply the updated battery model to monitor the state of the battery.
  • an embodiment of the present invention also provides a battery state monitoring system, including the above-mentioned edge processor and the above-mentioned battery management system.
  • the present invention also provides a computer-readable storage medium with a program stored on the storage medium, and when the program runs, the battery state monitoring method described in the foregoing embodiment is implemented.
  • the present invention provides a battery state monitoring method, an edge processor, a system, and a storage medium, through which the edge processor compares the battery model according to the real-time operating data and the battery state data.
  • the parameters are corrected to obtain the corrected parameters, and the corrected parameters are verified according to the real-time operating data, and after the verification is passed, the corrected parameters are returned to the battery management System, so that the battery management system re-verifies the corrected parameters according to the real-time operating data, and after the verification is passed again, the battery management system updates the corrected parameters to the battery In the model, the accuracy of the battery model is guaranteed.
  • the battery model can be used to accurately monitor the working status of the battery, effectively avoiding the battery model's accuracy decrease in the case of battery aging or extreme environments.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model.
  • the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • FIG. 1 is a schematic flowchart of a battery state monitoring method in Embodiment 1 of the present invention
  • Figure 2 is a schematic structural diagram of a battery model in an embodiment of the present invention.
  • FIG. 3 is a schematic flowchart of a least square method for modifying parameters of a first equivalent circuit model in an embodiment of the present invention
  • FIG. 4 is a schematic flowchart of a battery state monitoring method in Embodiment 2 of the present invention.
  • FIG. 5 is a schematic flowchart of a method for monitoring battery status in Embodiment 3 of the present invention.
  • FIG. 6 is a schematic structural diagram of an edge processor in Embodiment 4 of the present invention.
  • FIG. 7 is a schematic structural diagram of a battery management system in Embodiment 5 of the present invention.
  • Fig. 8 is a schematic structural diagram of a battery state monitoring system in the sixth embodiment of the present invention.
  • FIG. 1 is a schematic flowchart of a battery state monitoring method provided in Embodiment 1 of the present invention.
  • the battery state monitoring method provided by the embodiment of the present invention can be executed by an edge processor, and the edge processor is used as the execution subject for description in this embodiment.
  • the battery state monitoring method includes the following steps S11-S14:
  • the battery management system collects real-time operating data of the battery, calculates the battery status data obtained according to the real-time operating data and the battery model, and sends the real-time operating data and the battery status data to the The edge processor, the edge processor receives the real-time operating data and the battery state data.
  • the real-time operating data includes the current voltage of the battery cell, the current current of the battery, and the current temperature of the battery cell;
  • the battery state data includes the battery's state of charge data and state of health (SOH, state of health); then ,
  • the battery state data calculated by the battery management system according to the real-time operating data and the battery model specifically includes:
  • the battery management system uses the ampere-hour integration method to calculate the state of charge data and health state data of the battery according to the current voltage of the battery, the current current of the battery, the current temperature of the battery, and the battery model.
  • the embodiment of the present invention may also use other methods to calculate the state of charge data and the state of health data of the battery, which is not limited in the present invention.
  • the edge processor determines the parameters of the battery model in the battery management system according to the real-time operating data and the battery status data sent by the battery management system, so as to establish the same parameters according to the battery model parameters.
  • the battery model then, the parameters of the established battery model are corrected according to the real-time operating data, so as to obtain the corrected parameters. It is understandable that since the battery model established by the edge processor is the same as the battery model in the battery management system, the corrected parameters obtained by the edge processor can be used to update the battery model in the battery management system.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model. . Moreover, there is no need to send real-time battery operating data to a remote cloud server, so that the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • the parameters of the battery model are corrected according to the real-time operating data and the battery state data to obtain the corrected parameters, which specifically include the following Steps S121-S123:
  • S121 Select parameters of the battery model based on the current temperature of the battery cell, the state of charge data, and the state of health data; wherein, the parameters of the battery model include DC resistance, short-term resistance, and long-term resistance , Short-term capacitance and long-term capacitance;
  • R0 DC resistance;
  • R1 is short-term resistance;
  • R2 long-term resistance;
  • C1 is short-term capacitance;
  • C2 long-term capacitance;
  • Cap is Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the parameters of the first equivalent circuit model are corrected by the least square method, that is, the parameters of the battery model are corrected to obtain the corrected parameters .
  • step S121 the current temperature of the battery cell, the state-of-charge data, and the parameters of the battery model corresponding to the state-of-health data are different. Therefore, it can be based on the parameters sent by the battery management system.
  • the current temperature of the battery cell, the state of charge data, and the state of health data determine the corresponding parameters of the battery model to ensure that the parameters of the battery model obtained by the edge processor and the battery management
  • the parameters of the current battery model in the system are consistent, so as to ensure that the battery model established by the edge processor is consistent with the battery model in the battery management system, thereby ensuring that the edge processor can obtain effective corrected parameters .
  • step S122 the parameters of the battery model selected by the edge processor are used as the parameters of the first equivalent circuit model, thereby establishing the first equivalent circuit model.
  • first according to the parameters of the battery model, establish a first equivalent circuit equation:
  • the edge processor uses the least square method to calculate the corrected DC resistance R0 according to the current voltage of the battery cell and the current current of the battery.
  • the verification of the modified parameters according to the real-time operating data specifically includes the following steps S131-S132:
  • S132 Determine whether the calculated cell voltage is equal to the current voltage of the cell; if so, determine that the modified parameter verification passes; if not, determine that the modified parameter verification fails.
  • the edge processor corrects the parameters of the battery model, and after obtaining the corrected parameters, replaces the parameters of the first equivalent circuit model with the corrected parameters accordingly, thereby obtaining the corrected parameters Therefore, in step S13, the edge processor may input the current current of the battery into the corrected first equivalent circuit model to obtain the cell voltage; then The edge processor may confirm the validity of the corrected parameter by determining whether the calculated cell voltage is equal to the current voltage of the cell collected by the battery management system; specifically, When the calculated cell voltage is equal to the current voltage of the cell, it is determined that the verification of the corrected parameter is passed, which means that the edge processor confirms that the corrected parameter is valid, and can then change all the parameters. The modified parameter is returned to the battery management system; when the calculated cell voltage is not equal to the current voltage of the cell, it is determined that the verification of the modified parameter fails, which means that the edge The processor confirms that the modified parameter is invalid, and may discard the modified parameter.
  • the edge processor passes the verification of the modified parameter, it returns the modified parameter to the battery management system through the communication bus; the battery management system performs a pair of operations according to the real-time operating data.
  • the corrected parameters are verified again, and after the corrected parameters are verified again, the battery management system replaces the parameters of the battery model with the corrected parameters, thereby updating the battery Model, get the updated battery model.
  • the battery model can be used to accurately calculate the battery state data, such as charge state data, health state data, etc., and the battery can be learned from the calculated battery state data The current state, so as to accurately monitor the state of the battery.
  • the battery management system re-verifies the corrected parameters according to the real-time operating data, which specifically includes the following steps S141-S144:
  • S141 The battery management system detects the resting time of the battery
  • S143 Calculate the state-of-charge data using the ampere-hour integral method according to the second equivalent circuit model, the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • step S142 the battery management system uses the corrected parameters as the parameters of the second equivalent circuit model, thereby establishing the second equivalent circuit model. It is understandable that, because the modified parameters received by the battery management system are parameters obtained by the edge processor modifying the parameters of the first equivalent circuit model, the battery management system establishes The second equivalent circuit model of is the same as the modified first equivalent circuit model, that is, the second equivalent circuit model is:
  • R0 DC resistance;
  • R1 short-term resistance;
  • R2 long-term resistance;
  • C1 short-term capacitance;
  • C2 long-term capacitance;
  • Cap Capacitance;
  • U C1 is the voltage of the R1C1 loop;
  • U C2 is the voltage of the R2C2 loop;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the DC resistance R0, the short-term resistance R1, the long-term resistance R2, the short-term capacitance C1, and the long-term capacitance C2 in the second equivalent circuit model are the corrected parameters;
  • the process of establishing the second equivalent circuit model can be specifically referred to the process of establishing the first equivalent circuit model described above, which will not be repeated here.
  • step S143 the battery management system uses the ampere-hour integration method to calculate the state-of-charge data; of course, the embodiment of the present invention may also use other methods besides the ampere-hour integration method to calculate the state-of-charge data of the battery. There is no restriction on this.
  • the battery management system may confirm the validity of the modified parameter by determining whether the calculated state of charge data is equal to the pre-configured standard state of charge data; specifically, when When the calculated state-of-charge data is equal to the pre-configured standard state-of-charge data, it is determined that the modified parameter is verified again, which means that the battery management system confirms that the modified parameter is valid and can Update the corrected parameters to the battery model; when the calculated state-of-charge data is not equal to the pre-configured standard state-of-charge data, it is determined that the modified parameter fails the verification again, That is, it indicates that the battery management system confirms that the corrected parameter is invalid, and then discards the corrected parameter.
  • an OCV-SOC meter is pre-configured in the battery management system, therefore, the OCV-SOC meter can be queried according to the collected current voltage of the battery cell to obtain the corresponding standard state of charge Data, that is, pre-configured standard state-of-charge data.
  • the modified parameters are mutually verified by the edge processor and the battery management system, thereby ensuring the validity of the modified parameters, thereby ensuring the updated battery model
  • the accuracy of the battery model enables the application of the battery model to accurately monitor the state of the battery, effectively avoiding the problem of excessive calculation errors of the state of charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environments. Therefore, the maximum capacity output of the battery is ensured, and the safe operation of the battery is ensured, thereby improving the user experience.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model. Moreover, there is no need to send real-time battery operating data to a remote cloud server, so that the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • the parameters of the battery model are corrected through the real-time operating data of the battery and the battery status data, so that the parameters of the battery model are adjusted for a specific battery, and large errors caused by individual differences in the battery are eliminated. problem.
  • FIG. 4 is a schematic flowchart of a battery status monitoring method provided in Embodiment 2 of the present invention.
  • the battery state monitoring method provided by the embodiment of the present invention can be executed by a battery management system, and this embodiment is described with the battery management system as the execution subject.
  • the battery state monitoring method includes the following steps S21-S26:
  • S21 Collect real-time operating data of the battery, and calculate the battery state data according to the real-time operating data and the battery model.
  • the real-time operating data includes the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery state data includes the state of charge data and the state of health data of the battery;
  • the collecting real-time operating data of the battery and calculating the battery state data according to the real-time operating data and the battery model specifically include:
  • the ampere-hour integration method is used to calculate the state of charge data and the state of health data of the battery.
  • the embodiment of the present invention may also use other methods to calculate the state of charge data and the state of health data of the battery, which is not limited in the present invention.
  • the battery management system sends the real-time operating data and the battery status data to an edge processor through a communication bus, and the edge processor determines the battery according to the real-time operating data and the battery status data.
  • the parameters of the battery model in the management system are used to establish the same battery model based on the parameters of the battery model; then, the parameters of the battery model are corrected according to the real-time operating data to obtain the corrected parameters; finally, The edge processor verifies the corrected parameters according to the real-time operating data. It is understandable that since the battery model established by the edge processor is the same as the battery model in the battery management system, the corrected parameters obtained by the edge processor can be used to update the battery model in the battery management system.
  • the battery management system sends the real-time operating data and the battery status data to the edge processor, so that the edge processor can correct and verify the parameters of the battery model.
  • the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters, which specifically includes the following steps S2201-S2203:
  • the edge processor selects parameters of the battery model based on the current temperature of the battery cell, the state of charge data and the state of health data; wherein, the parameters of the battery model include DC resistance, short-term Resistance, long-term resistance, short-term capacitance and long-term capacitance;
  • R0 DC resistance;
  • R1 is short-term resistance;
  • R2 long-term resistance;
  • C1 is short-term capacitance;
  • C2 long-term capacitance;
  • Cap is Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • step S2201 the parameters of the battery model corresponding to the different current temperature of the battery cell, the state-of-charge data, and the state-of-health data are different. Therefore, the parameters sent by the battery management system may be different.
  • the current temperature of the battery cell, the state of charge data, and the state of health data determine the corresponding parameters of the battery model to ensure that the parameters of the battery model obtained by the edge processor and the battery management
  • the parameters of the battery model in the system are consistent, so as to ensure that the battery model established by the edge processor is consistent with the battery model in the battery management system, thereby ensuring that the edge processor can obtain effective corrected parameters.
  • step S2202 the parameters of the battery model selected by the edge processor are used as the parameters of the first equivalent circuit model, thereby establishing the first equivalent circuit model.
  • first according to the parameters of the battery model, establish a first equivalent circuit equation:
  • step S2203 the edge processor uses the least square method to calculate the corrected DC resistance R0, short-term resistance R1, and long-term resistance R2 according to the current voltage of the battery cell and the current current of the battery. , Short-term capacitor C1 and long-term capacitor C2, as shown in Figure 3.
  • the edge processor verifies the modified parameters according to the real-time operating data, which specifically includes the following steps S2211-S223:
  • the edge processor applies the current current of the battery to the corrected first equivalent circuit model, and calculates the cell voltage
  • S2213 Determine whether the calculated cell voltage is equal to the current voltage of the cell; if yes, determine that the modified parameter verification passes; if not, determine that the modified parameter verification fails.
  • the edge processor corrects the parameters of the battery model, and after obtaining the corrected parameters, replaces the parameters of the first equivalent circuit model with the corrected parameters accordingly, thereby obtaining the corrected parameters Therefore, the edge processor can obtain the cell voltage by inputting the current current of the battery into the modified first equivalent circuit model; then, the edge processing The device can confirm the validity of the corrected parameter by judging whether the calculated cell voltage is equal to the current voltage of the cell collected by the battery management system; specifically, when the calculated cell voltage is equal to When the cell voltage is equal to the current voltage of the cell, it is determined that the modified parameter verification is passed, which means that the edge processor confirms that the modified parameter is valid, and the modified parameter Return to the battery management system; when the calculated cell voltage is not equal to the current voltage of the cell, it is determined that the modified parameter verification has not passed, which means that the edge processor confirms the The corrected parameter is invalid, and the corrected parameter can be discarded.
  • the edge processor determines that the modified parameter verification is passed, the edge processor returns the verified parameter to the battery management system through the communication bus, and the battery management system Receiving the modified parameter.
  • the re-verification of the corrected parameters according to the real-time operating data specifically includes the following steps S241-S244:
  • S243 Calculate the state-of-charge data using the ampere-hour integral method according to the second equivalent circuit model, the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • step S242 the battery management system uses the corrected parameters as the parameters of the second equivalent circuit model, thereby establishing the second equivalent circuit model. It is understandable that, because the modified parameters received by the battery management system are parameters obtained by the edge processor modifying the parameters of the first equivalent circuit model, the battery management system establishes The second equivalent circuit model of is the same as the modified first equivalent circuit model, that is, the second equivalent circuit model is:
  • R0 DC resistance;
  • R1 short-term resistance;
  • R2 long-term resistance;
  • C1 short-term capacitance;
  • C2 long-term capacitance;
  • Cap Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the DC resistance R0, the short-term resistance R1, the long-term resistance R2, the short-term capacitance C1, and the long-term capacitance C2 in the second equivalent circuit model are the corrected parameters;
  • the process of establishing the second equivalent circuit model can be specifically referred to the process of establishing the first equivalent circuit model described above, which will not be repeated here.
  • step S243 the battery management system uses the ampere-hour integration method to calculate the state-of-charge data; of course, the embodiment of the present invention may also use other methods besides the ampere-hour integration method to calculate the state-of-charge data of the battery. There is no restriction on this.
  • the battery management system can confirm the validity of the modified parameter by judging whether the calculated state of charge data is equal to the pre-configured standard state of charge data; specifically, when When the calculated state-of-charge data is equal to the pre-configured standard state-of-charge data, it is determined that the modified parameter is verified again, which means that the battery management system confirms that the modified parameter is valid and can Update the corrected parameters to the battery model; when the calculated state-of-charge data is not equal to the pre-configured standard state-of-charge data, it is determined that the modified parameter fails the verification again, That is, it indicates that the battery management system confirms that the corrected parameter is invalid, and then discards the corrected parameter.
  • an OCV-SOC meter is pre-configured in the battery management system, therefore, the OCV-SOC meter can be queried according to the collected current voltage of the battery cell to obtain the corresponding standard state of charge Data, that is, pre-configured standard state-of-charge data.
  • the parameters of the battery model are replaced with the modified parameters, thereby updating the battery model and obtaining an updated battery model .
  • the battery state data such as charge state data, health state data, etc.
  • the battery model can be accurately calculated by using the battery model, and the calculated battery state data can be used to know the The current state of the battery, so as to accurately monitor the state of the battery.
  • the modified parameters are mutually verified by the edge processor and the battery management system, thereby ensuring the validity of the modified parameters, thereby ensuring the updated battery model
  • the accuracy of the battery model enables the application of the battery model to accurately monitor the state of the battery, effectively avoiding the problem of excessive calculation errors of the state of charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environments. Therefore, the maximum capacity output of the battery is ensured, and the safe operation of the battery is ensured, thereby improving the user experience.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model. Moreover, there is no need to send real-time battery operating data to a remote cloud server, so that the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • the parameters of the battery model are corrected through the real-time operating data of the battery and the battery status data, so that the parameters of the battery model are adjusted for a specific battery, and large errors caused by individual differences in the battery are eliminated. problem.
  • FIG. 5 is a schematic flowchart of a battery state monitoring method provided in Embodiment 3 of the present invention.
  • the battery state monitoring method includes the following steps S31-S35:
  • the battery management system collects real-time operating data of the battery, calculates and obtains battery status data according to the real-time operating data and the battery model, and sends the real-time operating data and the battery status data to the edge processor.
  • the real-time operating data includes the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery state data includes the state of charge data and the state of health data of the battery;
  • the battery management system collects real-time operating data of the battery, calculates the battery status data according to the real-time operating data and the battery model, and sends the real-time operating data and the battery status data to the edge processor, specifically including the following steps :
  • the battery management system collects the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery management system uses the ampere-hour integration method to calculate the state of charge data and the state of health data of the battery according to the current voltage of the battery, the current current of the battery, the current temperature of the battery, and the battery model;
  • the battery management system sends the current voltage of the battery cell, the current current of the battery, the current temperature of the battery cell, the state of charge data, and the state of health data to the edge processor.
  • the embodiment of the present invention may also use other methods to calculate the state of charge data and the state of health data of the battery, which is not limited in the present invention.
  • the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters.
  • the edge processor determines the parameters of the battery model in the battery management system according to the real-time operating data and the battery status data sent by the battery management system, so as to establish the same parameters according to the battery model parameters.
  • the battery model then, the parameters of the battery model are corrected according to the real-time operating data to obtain the corrected parameters. It is understandable that since the battery model established by the edge processor is the same as the battery model in the battery management system, the corrected parameters obtained by the edge processor can be used to update the battery model in the battery management system.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model. . Moreover, there is no need to send real-time battery operating data to a remote cloud server, so that the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters, Specifically, it includes the following steps S321-S323:
  • the edge processor selects parameters of the battery model based on the current temperature of the battery cell, the state of charge data, and the state of health data; wherein, the parameters of the battery model include DC resistance, short-term Resistance, long-term resistance, short-term capacitance and long-term capacitance;
  • R0 DC resistance;
  • R1 is short-term resistance;
  • R2 long-term resistance;
  • C1 is short-term capacitance;
  • C2 long-term capacitance;
  • Cap is Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the parameters of the battery model corresponding to the battery management system may be The current temperature of the battery cell, the state of charge data, and the state of health data determine the corresponding parameters of the battery model to ensure that the parameters of the battery model obtained by the edge processor and the battery management The parameters of the battery model in the system are consistent, so as to ensure that the battery model established by the edge processor is consistent with the battery model stored in the battery management system, thereby ensuring that the edge processor can obtain an effective corrected parameter.
  • step S322 the parameters of the battery model selected by the edge processor are used as the parameters of the first equivalent circuit model, thereby establishing the first equivalent circuit model.
  • first according to the parameters of the battery model, establish a first equivalent circuit equation:
  • step S123 the edge processor uses the least square method to calculate the corrected DC resistance R0, short-term resistance R1, and long-term resistance R2 according to the current voltage of the battery cell and the current current of the battery.
  • the short-term capacitor C1 and the long-term capacitor C 2 are shown in Figure 3.
  • the edge processor verifies the corrected parameter according to the real-time operating data, and after the verification passes, returns the corrected parameter to the battery management system.
  • the edge processor verifies the modified parameters according to the real-time operating data, which specifically includes the following steps S331-S332:
  • the edge processor applies the current current of the battery to the modified first equivalent circuit model, and calculates the cell voltage.
  • S332 Determine whether the calculated cell voltage is equal to the current voltage of the cell; if yes, determine that the modified parameter verification is passed; if not, determine that the modified parameter verification fails.
  • the edge processor corrects the parameters of the battery model, and after obtaining the corrected parameters, replaces the parameters of the first equivalent circuit model with the corrected parameters accordingly, thereby obtaining the corrected parameters Therefore, in step S33, the edge processor may input the current current of the battery into the modified first equivalent circuit model to obtain the cell voltage; then The edge processor may confirm the validity of the corrected parameter by determining whether the calculated cell voltage is equal to the current voltage of the cell collected by the battery management system; specifically, When the calculated cell voltage is equal to the current voltage of the cell, it is determined that the modified parameter verification is passed, which means that the edge processor confirms that the modified parameter is valid, and then passes the communication bus Return the modified parameter to the battery management system; when the calculated cell voltage is not equal to the current voltage of the cell, it is determined that the verification of the modified parameter fails, which means that all The edge processor confirms that the modified parameter is invalid, and then may discard the modified parameter.
  • the battery management system re-verifies the corrected parameters according to the real-time operating data.
  • the battery management system re-verifies the corrected parameters according to the real-time operating data, which specifically includes the following steps S341-S344:
  • S341 The battery management system detects the resting time of the battery
  • S343 Calculate the state-of-charge data using the ampere-hour integral method according to the second equivalent circuit model, the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery management system uses the corrected parameters as the parameters of the second equivalent circuit model, thereby establishing the second equivalent circuit model. It is understandable that, because the modified parameters received by the battery management system are parameters obtained by the edge processor modifying the parameters of the first equivalent circuit model, the battery management system establishes The second equivalent circuit model of is the same as the modified first equivalent circuit model, that is, the second equivalent circuit model is:
  • R0 DC resistance;
  • R1 short-term resistance;
  • R2 long-term resistance;
  • C1 short-term capacitance;
  • C2 long-term capacitance;
  • Cap Capacitance;
  • U C1 is the voltage of the R1C1 loop;
  • U C2 is the voltage of the R2C2 loop;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data;
  • the DC resistance R0, the short-term resistance R1, the long-term resistance R2, the short-term capacitance C1, and the long-term capacitance C2 in the second equivalent circuit model are the corrected parameters;
  • the process of establishing the second equivalent circuit model can be specifically referred to the process of establishing the first equivalent circuit model described above, which will not be repeated here.
  • step S343 the battery management system uses the ampere-hour integration method to calculate the state of charge data; of course, the embodiment of the present invention may also use other methods besides the ampere-hour integration method to calculate the state of charge data of the battery. There is no restriction on this.
  • the battery management system may confirm the validity of the modified parameter by determining whether the calculated state of charge data is equal to the pre-configured standard state of charge data; specifically, when When the calculated state-of-charge data is equal to the pre-configured standard state-of-charge data, it is determined that the modified parameter is verified again, which means that the battery management system confirms that the modified parameter is valid and can Update the corrected parameters to the battery model; when the calculated state-of-charge data is not equal to the pre-configured standard state-of-charge data, it is determined that the modified parameter fails the verification again, That is, it indicates that the battery management system confirms that the corrected parameter is invalid, and the corrected parameter can be discarded.
  • an OCV-SOC meter is pre-configured in the battery management system, therefore, the OCV-SOC meter can be queried according to the collected current voltage of the battery cell to obtain the corresponding standard state of charge Data, that is, pre-configured standard state-of-charge data.
  • the battery management system updates the corrected parameters to the battery model, and monitors the state of the battery by applying the updated battery model.
  • the battery management system replaces the parameters of the battery model with the corrected parameters, thereby updating the battery model to obtain The updated battery model.
  • the battery model can be used to accurately calculate the battery state data, such as charge state data, health state data, etc., and the battery can be learned from the calculated battery state data The current state, so as to accurately monitor the state of the battery.
  • the modified parameters are mutually verified by the edge processor and the battery management system, thereby ensuring the validity of the modified parameters, thereby ensuring the updated battery model
  • the accuracy of the battery model enables the application of the battery model to accurately monitor the state of the battery, effectively avoiding the problem of excessive calculation errors of the state of charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environments. Therefore, the maximum capacity output of the battery is ensured, and the safe operation of the battery is ensured, thereby improving the user experience.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model. Moreover, there is no need to send real-time battery operating data to a remote cloud server, so that the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • the parameters of the battery model are corrected through the real-time operating data of the battery and the battery status data, so that the parameters of the battery model are adjusted for a specific battery, and large errors caused by individual differences in the battery are eliminated. problem.
  • FIG. 6 is a schematic structural diagram of an edge processor provided in Embodiment 4 of the present invention.
  • the edge processor 1 includes:
  • the first receiving module 11 is configured to receive the real-time operating data of the battery collected by the battery management system, and the battery state data calculated by the battery management system according to the real-time operating data and the battery model;
  • the correction module 12 is configured to correct the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters;
  • the first verification module 13 is configured to verify the corrected parameters according to the real-time operating data.
  • the first sending module 14 is configured to return the corrected parameters to the battery management system after the verification is passed, so that the battery management system performs the corrected parameters again according to the real-time operating data Verify, and after the verification is passed again, update the corrected parameters to the battery model.
  • the first sending module 14 returns the modified parameter to the battery management via the communication bus.
  • the battery management system so that the battery management system can re-verify the modified parameters according to the real-time operating data, and after the modified parameters are verified again, the battery management system will The parameter of is replaced with the corrected parameter, thereby updating the battery model and obtaining an updated battery model.
  • the battery model can be used to accurately calculate the battery state data, such as charge state data, health state data, etc., and the battery can be learned from the calculated battery state data The current state, so as to accurately monitor the state of the battery.
  • the real-time operating data includes the current voltage of the battery, the current current of the battery, and the current temperature of the battery;
  • the battery status data includes the state of charge data and the health status data of the battery;
  • the correction module 12 specifically includes:
  • a parameter selection unit for selecting parameters of the battery model based on the current temperature of the battery cell, the state of charge data, and the state of health data; wherein, the parameters of the battery model include DC resistance and short-term resistance , Long-term resistance, short-term capacitance and long-term capacitance;
  • the first model establishing unit is configured to establish a first equivalent circuit model according to the parameters of the battery model; wherein, the first equivalent circuit model is:
  • R0 DC resistance;
  • R1 is short-term resistance;
  • R2 long-term resistance;
  • C1 is short-term capacitance;
  • C2 long-term capacitance;
  • Cap is Capacitance;
  • U C1 is the voltage of the R1C1 circuit;
  • U C2 is the voltage of the R2C2 circuit;
  • I Bat is the battery current;
  • U t is the terminal voltage;
  • U OC is the open circuit voltage;
  • SOC is the state of charge data; and,
  • the parameter correction unit is used to correct the parameters of the first equivalent circuit model by the least square method according to the current voltage of the battery cell and the current current of the battery, that is, to correct the parameters of the battery model to obtain The revised parameters.
  • the first verification module 13 in this embodiment specifically includes:
  • the cell voltage calculation unit is configured to apply the current current of the battery to the corrected first equivalent circuit model to calculate the cell voltage;
  • the first verification judgment unit is used to judge whether the calculated cell voltage is equal to the current voltage of the cell; if it is, it is judged that the verification of the revised parameter is passed; if not, it is judged that the revised parameter is Verification failed.
  • the parameters of the battery model are corrected by the correction module 12 according to the real-time operating data and the battery state data to obtain the corrected parameters, and the first verification module 13 According to the real-time operating data, the modified parameters are verified, and finally the first sending module 14 will return the modified parameters to the battery management system after the verification is passed.
  • the battery management system re-verifies the corrected parameters according to the real-time operating data, and after the re-verification is passed, updates the corrected parameters to the battery model, thereby ensuring the battery model Accuracy, the application of the battery model can accurately monitor the working state of the battery, effectively avoiding the problem of excessive calculation error of the state of charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environments.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model.
  • the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • edge processor may also include other modules/units, so that the edge processor can implement other steps of the battery state monitoring method described in the first embodiment, and no more details will be described here.
  • FIG. 7 is a schematic structural diagram of a battery management system provided by Embodiment 5 of the present invention.
  • the battery management system 2 includes:
  • the data acquisition module 21 is configured to collect real-time operating data of the battery, and calculate and obtain battery state data according to the real-time operating data and the battery model;
  • the second sending module 22 is configured to send the real-time operating data and the battery status data to an edge processor, so that the edge processor can compare the battery model according to the real-time operating data and the battery status data. Modify the parameters of, obtain the modified parameters, and verify the modified parameters according to the real-time operating data;
  • the second receiving module 23 is configured to receive the verified parameter returned by the edge processor
  • the second verification module 24 is configured to re-verify the revised parameters according to the real-time operating data
  • the model update module 25 is configured to update the revised parameters to the battery model after the verification is passed again;
  • the monitoring module 26 is used for applying the updated battery model to monitor the state of the battery.
  • the model update module 25 replaces the parameters of the battery model with the modified parameters, thereby updating The battery model obtains an updated battery model.
  • the monitoring module 26 applies the updated battery model to accurately calculate the battery state data, such as charge state data, health state data, etc., and the battery state data can be obtained through the calculated battery state data. The current state, so as to accurately monitor the state of the battery.
  • the real-time operating data includes the current voltage of the battery cell, the current current of the battery, and the current temperature of the battery cell;
  • the battery status data includes the state of charge data and health status data of the battery.
  • the second verification module 24 in this embodiment specifically includes:
  • the resting time detection unit is used to detect the resting time of the battery
  • a second model establishing unit configured to establish a second equivalent circuit model according to the corrected parameters when the resting time of the battery is greater than a preset time threshold
  • the state of charge calculation unit is configured to calculate the state of charge by using the ampere-hour integral method according to the second equivalent circuit model, the current voltage of the battery cell, the current current of the battery, and the current temperature of the battery cell data;
  • the second verification judgment unit is used to judge whether the calculated state-of-charge data is equal to the pre-configured standard state-of-charge data; if it is, it is judged that the modified parameter is verified again; if not, it is judged that the The revised parameters failed to verify again.
  • the real-time operating data and the battery status data are sent to the edge processor through the second sending module 22, so that the edge processor can be based on the real-time operating data and the battery
  • the state data corrects the parameters of the battery model to obtain the corrected parameters, and verifies the corrected parameters according to the real-time operating data, and then the second verification module 24 performs the verification according to the real-time
  • the operating data verifies the corrected parameters again, so that the model update module 25 updates the corrected parameters to the battery model after the re-verification is passed, thereby ensuring the accuracy of the battery model.
  • the battery model can be used to accurately monitor the working status of the battery, effectively avoiding the problem of excessive calculation error of the state of charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environments, thus ensuring The maximum capacity output of the battery, and to ensure the safe operation of the battery.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model.
  • the battery management system may further include other modules/units, so that the battery management system can implement other steps of the battery state monitoring method described in the second embodiment above, which will not be repeated here.
  • FIG. 8 is a schematic structural diagram of a battery state monitoring system provided by Embodiment 6 of the present invention.
  • the battery state monitoring system includes the edge processor 1 described in the fourth embodiment and the battery management system 2 described in the fifth embodiment.
  • the edge processor 1 is electrically connected to the battery management system 2.
  • the battery management system 2 is also electrically connected to the battery for collecting real-time operating data of the battery.
  • the structure and working principle of the edge processor 1 can refer to the fourth embodiment, and the structure and working principle of the battery management system 2 can refer to the fifth embodiment, which will not be described here.
  • the edge processor 1 corrects the parameters of the battery model according to the real-time operating data and the battery state data to obtain the corrected parameters, and according to the real-time operating data , Verifying the corrected parameters, and after the verification is passed, returning the corrected parameters to the battery management system 2, so that the battery management system 2 can check all the parameters according to the real-time operating data
  • the revised parameters are verified again, and after the re-verification is passed, the revised parameters are updated to the battery model, thereby ensuring the accuracy of the battery model, and the battery model can be used to accurately monitor the operation of the battery It effectively avoids the problem of excessive calculation error of the state-of-charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environments, thus ensuring the maximum capacity output of the battery and ensuring battery safety jobs.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model. Moreover, there is no need to send real-time battery operating data to a remote cloud server, so that the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.
  • the seventh embodiment of the present invention also provides a computer-readable storage medium with a program stored on the storage medium, and when the program runs, the battery status monitoring method described in the first embodiment is implemented.
  • the battery state monitoring method reference may be made to the description of the first embodiment above, and no more details will be given here.
  • the seventh embodiment of the present invention also provides another computer-readable storage medium, the storage medium stores a program, and when the program runs, the battery state monitoring method described in the second embodiment is implemented .
  • the battery state monitoring method reference may be made to the description of the second embodiment above, and no more details will be given here.
  • the present invention provides a battery state monitoring method, edge processor, system, and storage medium, through which the edge processor corrects the parameters of the battery model according to the real-time operating data and the battery state data To obtain the corrected parameters, and verify the corrected parameters according to the real-time operating data, and after the verification is passed, the corrected parameters are returned to the battery management system, so that The battery management system re-verifies the corrected parameters according to the real-time operating data, and after the re-verification is passed, updates the corrected parameters to the battery model, thereby ensuring the battery model Accuracy, the application of the battery model can accurately monitor the working state of the battery, effectively avoiding the problem of excessive calculation error of the state of charge data caused by the reduced accuracy of the battery model in the case of battery aging or extreme environments.
  • the edge processor corrects and verifies the parameters of the battery model, which solves the problem that the computing resources of the battery management system are limited and it is difficult to use the real-time operating data of the battery to correct the parameters of the battery model.
  • the cloud server can be used to modify the battery model parameters, thereby reducing the amount of remote data transmission.

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Abstract

一种电池状态监测方法、边缘处理器、系统及存储介质，该方法包括：接收电池管理系统采集的电池的实时运行数据，以及由电池管理系统根据实时运行数据和电池模型计算获得的电池状态数据（S11）；根据实时运行数据和电池状态数据对电池模型的参数进行修正，获得修正后的参数（S12）；根据实时运行数据，对修正后的参数进行验证（S13）；在验证通过后，将修正后的参数返回至电池管理系统，以使电池管理系统对修正后的参数进行再次验证，并在再次验证通过后，将修正后的参数更新到电池模型中，以应用更新后的电池模型监测所述电池的状态（S14）。提出的方法保证了电池模型的精度，应用该电池模型可准确地监测电池的工作状态，从而保证电池安全工作。

Description

电池状态监测方法、边缘处理器、系统及存储介质 技术领域
本发明涉及动力电池技术领域，特别是涉及一种电池状态监测方法、边缘处理器、系统及存储介质。
背景技术
动力电池是电动汽车的重要组成部分，尤其是纯电动汽车，其是驱动车辆的唯一动力。其中，电池的荷电状态数据(SOC，State of charge)是表征电池状态的重要参数之一，通过荷电状态数据能够估计车辆的续航里程，以防止车辆在行驶过程中抛锚或电池过度放电造成电池本身受损；因此，准确计算荷电状态数据是电池安全的重要保证。
目前，普遍采用电池算法来计算荷电状态。但是，本发明人在实施本发明的过程中，发现现有技术至少存在以下技术问题：一方面，电池算法的精度依赖于电池模型的精度，然而，在电池老化或极端环境的情况下，电池模型的参数不再匹配电芯，电池模型的外部特性与电芯特性具有较大的差异；另一方面，由于电池管理系统(BMS，Battery management system)的计算资源有限，电池管理系统难以利用电池的实时运行数据来修正电池模型的参数；因此，在电池老化或极端环境的情况下，电池模型的精度降低，造成荷电状态数据的计算误差过大，难以准确监测电池的工作状态。
发明内容
本发明的目的是提供一种电池状态监测方法、边缘处理器、系统及存储介质，其能够有效地避免在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大，不能准确监测电池的工作状态的问题。
为了解决上述技术问题，本发明实施例提供一种电池状态监测方法，包括：
接收电池管理系统采集的电池的实时运行数据，以及由所述电池管理系统根据所述实时运行数据和电池模型计算获得的电池状态数据；
根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数；
根据所述实时运行数据，对所述修正后的参数进行验证；
在验证通过后，将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中，以应用更新后的电池模型监测所述电池的状态。
作为优选方案，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
所述根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括：
基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
Figure PCTCN2020099868-appb-000001
其中，
Figure PCTCN2020099868-appb-000002
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
作为优选方案，所述根据所述实时运行数据，对所述修正后的参数进行验证，具体包括：
将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
作为优选方案，所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括：
所述电池管理系统检测电池的静置时间；
当所述电池的静置时间大于预设的时间阈值时，所述电池管理系统根据所述修正后的参数建立第二等效电路模型；
根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
为了解决相同的技术问题，本发明实施例还提供一种电池状态监测方法，包括：
采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据；
将所述实时运行数据和所述电池状态数据发送至边缘处理器，以使所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证；
接收所述边缘处理器返回的通过验证的所述修正后的参数；
根据所述实时运行数据对所述修正后的参数进行再次验证；
在再次验证通过后，将所述修正后的参数更新到所述电池模型中；
应用更新后的电池模型监测所述电池的状态。
作为优选方案，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括：
所述边缘处理器基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
Figure PCTCN2020099868-appb-000003
其中，
Figure PCTCN2020099868-appb-000004
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
作为优选方案，所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，具体包括：
所述边缘处理器将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
作为优选方案，所述根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括：
检测电池的静置时间；
当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积 分法计算荷电状态数据；
判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
为了解决相同的技术问题，本发明实施例还提供一种电池状态监测方法，包括：
电池管理系统采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据，并将所述实时运行数据和所述电池状态数据发送至边缘处理器；
所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数；
所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，并在验证通过后，将所述修正后的参数返回至所述电池管理系统；
所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证；
在再次验证通过后，所述电池管理系统将所述修正后的参数更新到所述电池模型中，以应用更新后的电池模型监测所述电池的状态。
作为优选方案，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括：
所述边缘处理器基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
Figure PCTCN2020099868-appb-000005
其中，
Figure PCTCN2020099868-appb-000006
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
作为优选方案，所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，具体包括：
所述边缘处理器将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
作为优选方案，所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括：
所述电池管理系统检测电池的静置时间；
当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
为了解决相同的技术问题，相应地，本发明实施例还提供一种边缘处理器，包括：
第一接收模块，用于接收电池管理系统采集的电池的实时运行数据，以及由所述电池管理系统根据所述实时运行数据和电池模型计算获得的电池状态数据；
修正模块，用于根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数；
第一验证模块，用于根据所述实时运行数据，对所述修正后的参数进行验证；和，
第一发送模块，用于在验证通过后，将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中。
为了解决相同的技术问题，相应地，本发明实施例还提供一种电池管理系统，包括：
数据获取模块，用于采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据；
第二发送模块，用于将所述实时运行数据和所述电池状态数据发送至边缘处理器，以使所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证；
第二接收模块，用于接收所述边缘处理器返回的通过验证的所述修正后的参数；
第二验证模块，用于根据所述实时运行数据对所述修正后的参数进行再次验证；
模型更新模块，用于在再次验证通过后，将所述修正后的参数更新到所述电池模型中；和，
监测模块，用于应用更新后的电池模型监测所述电池的状态。
为了解决相同的技术问题，本发明实施例还提供一种电池状态监测系统，包括上述的边缘处理器和上述的电池管理系统。
为了解决相同的技术问题，本发明还提供一种计算机可读存储介质，所述存储介质上存储有程序，当程序运行时，实现上述实施例所述的电池状态监测方法。
与现有技术相比，本发明提供的一种电池状态监测方法、边缘处理器、系统及存储介质，通过所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，以获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证，且在验证通过后，再将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，所述电池管理系统将所述修正后的参数更新到所述电池模型中，从而保证了电池模型的精度，应用该电池模型可准确地监测电池的工作状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此保证了电池的最大能力输出，并保证了电池安全工作。此外，本发明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
附图说明
图1是本发明实施例一中的电池状态监测方法的流程示意图；
图2是本发明实施例中的电池模型的结构示意图；
图3是本发明实施例中的采用最小二乘法修正第一等效电路模型的参数的流程示意图；
图4是本发明实施例二中的电池状态监测方法的流程示意图；
图5是本发明实施例三中的电池状态监测方法的流程示意图；
图6是本发明实施例四中的边缘处理器的结构示意图；
图7是本发明实施例五中的电池管理系统的结构示意图；
图8是本发明实施例六中的电池状态监测系统的结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例一
参见图1，是本发明实施例一提供的电池状态监测方法的流程示意图。
本发明实施例提供的电池状态监测方法，可由边缘处理器执行，且本实施例均以边缘处理器作为执行主体进行说明。
在本发明实施例中，所述电池状态监测方法，包括以下步骤S11-S14：
S11、接收电池管理系统采集的电池的实时运行数据，以及由所述电池管理系统根据所述实时运行数据和电池模型计算获得的电池状态数据。
可以理解的，所述电池管理系统采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得的电池状态数据，并通过通讯总线将所述实时运行数据和所述电池状态数据发送至所述边缘处理器，所述边缘处理器接收所述实时运行数据和所述电池状态数据。
其中，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据(SOH，state of health)；则，
所述电池管理系统根据所述实时运行数据和电池模型计算获得的电池状态数据，具体包括：
所述电池管理系统根据所述电芯的当前电压、所述电池的当前电流、所述电芯的当前温度和电池模型，采用安时积分法计算电池的荷电状态数据和健康状态数据。
当然，本发明实施例还可以采用其他方法来计算电池的荷电状态数据和健康状态数据，本发明对此不做限制。
S12、根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数。
具体地，所述边缘处理器根据所述电池管理系统发送的所述实时运行数据和所述电池状态数据确定所述电池管理系统中的电池模型的参数，以根据该电池模型的参数建立相同的电池模型；然后，根据所述实时运行数据对所建立的电池模型的参数进行修正，从而获得修正后的参数。可以理解的，由于所述边缘处理器建立的电池模型与所述电池管理系统中的电池模型相同，因而可采用边缘处理器获得的修正后的参数来更新所述电池管理系统中的电池模型。
在本发明实施例中，通过由所述边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
结合图2和图3所示，在一种优选实施方式中，所述根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括以下步骤S121-S123：
S121、基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
S122、根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
Figure PCTCN2020099868-appb-000007
其中，
Figure PCTCN2020099868-appb-000008
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
S123、根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
可以理解的，在步骤S121中，不同的所述电芯的当前温度、所述荷电状态数据和所述健康状态数据对应的电池模型的参数不同，因此，可以根据所述电池管理系统发送的所述电芯的当前温度、所述荷电状态数据和所述健康状态数据确定相应的所述电池模型的参数，以保证所述边缘处理器获得的所述电池模型的参数与所述电池管理系统中当前的电池模型的参数一致，从而保证所述边缘处理器建立的电池模型与所述电池管理系统中的电池模型的一致性，进而保证所述边缘处理器能够获得有效的修正后的参数。
另外，在步骤S122中，以所述边缘处理器选取的所述电池模型的参数作为第一等效电路模型的参数，从而建立所述第一等效电路模型。具体地，结合图2所示，首先，根据所述电池模型的参数，建立第一等效电路方程：
Figure PCTCN2020099868-appb-000009
然后，定义
Figure PCTCN2020099868-appb-000010
u＝I Bat，y＝U t，g(x 1)＝U OC，并基于所述第一等效电路方程，建立所述第一等效电路模型：
Figure PCTCN2020099868-appb-000011
如图3所示，在步骤S123中，所述边缘处理器根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法计算获得修正后的所述直流电阻R0，短时电阻R1，长时电阻R2，短时电容C1和长时电容C2。
S13、根据所述实时运行数据，对所述修正后的参数进行验证。
在一种优选实施方式中，所述根据所述实时运行数据，对所述修正后的参数进行验证，具体包括以下步骤S131-S132：
S131、将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
S132、判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
具体地，所述边缘处理器对所述电池模型的参数进行修正，获得修正后的参数后，将所述第一等效电路模型的参数相应替换为所述修正后的参数，从而获得修正后的第一等效电路模型；因此，在步骤S13中，所述边缘处理器可以通过将所述电池的当前电流输入所述修正后的第一等效电路模型中，从而获得电芯电压；接着，所述边缘处理器可以通过判断所计算出来的所述电芯电压与所述电池管理系统采集的所述电芯的当前电压是否相等来确认所述修正后的参数的有效性；具体地，当所计算出来的所述电芯电压与所述电芯的当前电压相等时，判定所述修正后的参数验证通过，即表明所述边缘处理器确认所述修正后的参数有效，进而可将所述修正后的参数返回至所述电池管理系统；当所计算出来的所述电芯电压与所述电芯的当前电压不相等时，判定所述修正后的参数验证未通过，即表明所述边缘处理器确认所述修正后的参数无效，进而可将所述修正后的参数丢弃。
S14、在验证通过后，将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中，以应用更新后的电池模型监测所述电池的状态。
具体地，所述边缘处理器在对所述修正后的参数验证通过后，通过通讯总线将所述修正后的参数返回至所述电池管理系统；所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在对所述修正后的参数再次验证通过后，所述电池管理系统将所述电池模型的参数替换为所述修正后的参数，从而更新所述电池模型，获得更新后的电池模型。获得所述更新后的电池模型后，能够应用该电池模型准确地计算出所述电池状态数据，如电荷状态数据、健康状态数据等，通过所计算出的所述电池状态数据可获知所述电池当前的状态，从而实现准确地监测所述电池的状态。
在一种优选实施方式中，所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括以下步骤S141-S144：
S141、所述电池管理系统检测电池的静置时间；
S142、当所述电池的静置时间大于预设的时间阈值时，所述电池管理系统根据所述修正后的参数建立第二等效电路模型；
S143、根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
S144、判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
具体地，在步骤S142中，所述电池管理系统以所述修正后的参数作为第二等效电路模型的参数，从而建立所述第二等效电路模型。可以理解的，由于所述电池管理系统接收的所述修正后的参数，是由所述边缘处理器修正所述第一等效电路模型的参数而获得的参数，因此，所述电池管理系统建立的所述第二等效电路模型与修正后的所述第一等效电路模型相同，即所述第二等效电路模型为：
Figure PCTCN2020099868-appb-000012
其中，
Figure PCTCN2020099868-appb-000013
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；需要说明的是，所述第二等效电路模型中的所述直流电阻R0，所述短时电阻R1，所述长时电阻R2，所述短时电容C1，所述长时电容C2为所述修正后的参数；另外，建立所述第二等效电路模型的过程具体可参考上述第一等效电路模型的建立过程，在此不做更多的赘述。
在步骤S143中，所述电池管理系统采用安时积分法计算荷电状态数据；当然，本发明实施例还可以采用除安时积分法以外的其他方法来计算电池的荷电状态数据，本发明对此不做限制。
在步骤S144中，所述电池管理系统可以通过判断所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据是否相等来确认所述修正后的参数的有效性；具体地，当所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据相等时，判定所述修正后的参数再次验证通过，即表明所述电池管理系统确认所述修正后的参数有效，进而可将所述修正后的参数更新到所述电池模型中；当所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据不相等时，判定所述修正后的参数再次验证未通过，即表明所述电池管理系统确认所述修正后的参数无效，进而将所述修正后的参数丢弃。需要说明的是，所述电池管理系统中预先配置有OCV-SOC表，因此，可以根据采集到的所述电芯的当前电压来查询所述OCV-SOC表，从而获得相应的标准荷电状态数据，即预先配置的标准荷电状态数据。
在本发明实施例中，通过所述边缘处理器和所述电池管理系统对所述修正后的参数进行相互验证，确保了所述修正后的参数的有效性，从而保证了更新后的电池模型的精度，使得能够应用该电池模型准确地监测电池的状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此确保了电池的最大能力输出，并保证了电池安全工作，从而改善了用户体验。此外，本发明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。另外，通过电池的所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，实现了针对特定的电池调整所述电池模型的参数，消除了因电池个体差异导致的大误差问题。
实施例二
参见图4，是本发明实施例二提供的电池状态监测方法的流程示意图；
本发明实施例提供的电池状态监测方法，可由电池管理系统执行，且本实施例均以电池管理系统作为执行主体进行说明。
在本发明实施例中，所述电池状态监测方法，包括以下步骤S21-S26：
S21、采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据。
其中，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池 的荷电状态数据和健康状态数据；则，
所述采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据，具体包括：
采集电芯的当前电压、电池的当前电流和电芯的当前温度；
根据所述电芯的当前电压、所述电池的当前电流、所述电芯的当前温度和电池模型，采用安时积分法计算电池的荷电状态数据和健康状态数据。
当然，本发明实施例还可以采用其他方法来计算电池的荷电状态数据和健康状态数据，本发明对此不做限制。
S22、将所述实时运行数据和所述电池状态数据发送至边缘处理器，以使所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证。
具体地，所述电池管理系统通过通讯总线将所述实时运行数据和所述电池状态数据发送至边缘处理器，所述边缘处理器根据所述实时运行数据和所述电池状态数据确定所述电池管理系统中的电池模型的参数，以根据该电池模型的参数建立相同的电池模型；然后，再根据所述实时运行数据对所述电池模型的参数进行修正，从而获得修正后的参数；最后，所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证。可以理解的，由于所述边缘处理器建立的电池模型与所述电池管理系统中的电池模型相同，因而可采用边缘处理器获得的修正后的参数来更新所述电池管理系统中的电池模型。
在本发明实施例中，所述电池管理系统通过将所述实时运行数据和所述电池状态数据发送至边缘处理器，使得能够由所述边缘处理器对电池模型的参数进行修正及校验，从而解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
在一种优选实施方式中，所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括以下步骤S2201-S2203：
S2201、所述边缘处理器基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
S2202、根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
Figure PCTCN2020099868-appb-000014
其中，
Figure PCTCN2020099868-appb-000015
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
S2203、根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
可以理解的，在步骤S2201中，不同的所述电芯的当前温度、所述荷电状态数据和所述健康状态数据对应的电池模型的参数不同，因此，可以根据所述电池管理系统发送的所述电芯的当前温度、所述荷电状态数据和所述健康状态数据确定相应的所述电池模型的参数，以保证所述边缘处理器获得的所述电池模型的参数与所述电池管理系统中的电池模型的参数一致，从而保证所述边缘处理器建立的电池模型与所述电池管理系统中的电池模型的一致性，进而保证所述边缘处理器能够获得有效的修正后的参数。
另外，在步骤S2202中，以所述边缘处理器选取的所述电池模型的参数作为第一等效电路模型的参数，从而建立所述第一等效电路模型。具体地，结合图2所示，首先，根据所述电池模型的参数，建立第一等效电路方程：
Figure PCTCN2020099868-appb-000016
然后，定义
Figure PCTCN2020099868-appb-000017
u＝I Bat，y＝U t，g(x 1)＝U OC，并根据所述第一等效电路方程，建立所述第一等效电路模型：
Figure PCTCN2020099868-appb-000018
在步骤S2203中，所述边缘处理器根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法计算获得修正后的所述直流电阻R0，短时电阻R1，长时电阻R2，短时电容C1和长时电容C2，如图3所示。
在一种优选实施方式中，所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，具体包括以下步骤S2211-S223：
S2212、所述边缘处理器将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
S2213、判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
具体地，所述边缘处理器对所述电池模型的参数进行修正，获得修正后的参数后，将所述第一等效电路模型的参数相应替换为所述修正后的参数，从而获得修正后的第一等效电路模型；因此，所述边缘处理器可以通过将所述电池的当前电流输入所述修正后的第一等效电路模型中，从而获得电芯电压；接着，所述边缘处理器可以通过判断所计算出来的所述电芯电压与所述电池管理系统采集的所述电芯的当前电压是否相等来确认所述修正后的参数的有效性；具体地，当所计算出来的所述电芯电压与所述电芯的当前电压相等时，判定所述修正后的参数验证通过，即表明所述边缘处理器确认所述修正后的参数有效，进而可将所述修正后的参数返回至所述电池管理系统；当所计算出来的所述电芯电压与所述电芯的当前电压不相等时，判定所述修正后的参数验证未通过，即表明所述边缘处理器确认所述修正后的参数无效，进而可将所述修正后的参数丢弃。
S23、接收所述边缘处理器返回的通过验证的所述修正后的参数；
具体地，在所述边缘处理器判定所述修正后的参数验证通过时，所述边缘处理器通过通讯总线向所述电池管理系统返回通过验证的所述修正后的参数，所述电池管理系统接收所述修正后的参数。
S24、根据所述实时运行数据对所述修正后的参数进行再次验证；
在一种优选实施方式中，所述根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括以下步骤S241-S244：
S241、检测电池的静置时间；
S242、当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
S243、根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
S244、判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
具体地，在步骤S242中，所述电池管理系统以所述修正后的参数作为第二等效电路模型的参数，从而建立所述第二等效电路模型。可以理解的，由于所述电池管理系统接收的所述修正后的参数，是由所述边缘处理器修正所述第一等效电路模型的参数而获得的参数，因此，所述电池管理系统建立的所述第二等效电路模型与修正后的所述第一等效 电路模型相同，即所述第二等效电路模型为：
Figure PCTCN2020099868-appb-000019
其中，
Figure PCTCN2020099868-appb-000020
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；需要说明的是，所述第二等效电路模型中的所述直流电阻R0，所述短时电阻R1，所述长时电阻R2，所述短时电容C1，所述长时电容C2为所述修正后的参数；另外，建立所述第二等效电路模型的过程具体可参考上述第一等效电路模型的建立过程，在此不做更多的赘述。
在步骤S243中，所述电池管理系统采用安时积分法计算荷电状态数据；当然，本发明实施例还可以采用除安时积分法以外的其他方法来计算电池的荷电状态数据，本发明对此不做限制。
在步骤S244中，所述电池管理系统可以通过判断所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据是否相等来确认所述修正后的参数的有效性；具体地，当所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据相等时，判定所述修正后的参数再次验证通过，即表明所述电池管理系统确认所述修正后的参数有效，进而可将所述修正后的参数更新到所述电池模型中；当所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据不相等时，判定所述修正后的参数再次验证未通过，即表明所述电池管理系统确认所述修正后的参数无效，进而将所述修正后的参数丢弃。需要说明的是，所述电池管理系统中预先配置有OCV-SOC表，因此，可以根据采集到的所述电芯的当前电压来查询所述OCV-SOC表，从而获得相应的标准荷电状态数据，即预先配置的标准荷电状态数据。
S25、在再次验证通过后，将所述修正后的参数更新到所述电池模型中。
具体地，在所述电池管理系统判定所述修正后的参数再次验证通过后，将所述电池模型的参数替换为所述修正后的参数，从而更新所述电池模型，获得更新后的电池模型。
S26、应用更新后的电池模型监测所述电池的状态。
具体地，获得更新后的电池模型后，应用该电池模型可以准确地计算出所述电池状态数据，如电荷状态数据、健康状态数据等，通过所计算出的所述电池状态数据可获知所述电池当前的状态，从而实现准确地监测所述电池的状态。
在本发明实施例中，通过所述边缘处理器和所述电池管理系统对所述修正后的参数进行相互验证，确保了所述修正后的参数的有效性，从而保证了更新后的电池模型的精度，使得能够应用该电池模型准确地监测电池的状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此确保了电池的最大能力输出，并保证了电池安全工作，从而改善了用户体验。此外，本发明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。另外，通过电池的所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，实现了针对特定的电池调整所述电池模型的参数，消除了因电池个体差异导致的大误差问题。
实施例三
参见图5，是本发明实施例三提供的电池状态监测方法的流程示意图。
在本发明实施例中，所述电池状态监测方法，包括以下步骤S31-S35：
S31、电池管理系统采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据，并将所述实时运行数据和所述电池状态数据发送至边缘处理器。
具体地，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
所述电池管理系统采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据，并将所 述实时运行数据和所述电池状态数据发送至边缘处理器，具体包括以下步骤：
所述电池管理系统采集电芯的当前电压、电池的当前电流和电芯的当前温度；
所述电池管理系统根据所述电芯的当前电压、所述电池的当前电流、所述电芯的当前温度和电池模型，采用安时积分法计算电池的荷电状态数据和健康状态数据；
所述电池管理系统将所述电芯的当前电压、所述电池的当前电流、所述电芯的当前温度、所述荷电状态数据和所述健康状态数据发送至边缘处理器。
当然，本发明实施例还可以采用其他方法来计算电池的荷电状态数据和健康状态数据，本发明对此不做限制。
S32、所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数。
具体地，所述边缘处理器根据所述电池管理系统发送的所述实时运行数据和所述电池状态数据确定所述电池管理系统中的电池模型的参数，以根据该电池模型的参数建立相同的电池模型；然后，再根据所述实时运行数据对所述电池模型的参数进行修正，从而获得修正后的参数。可以理解的，由于所述边缘处理器建立的电池模型与所述电池管理系统中的电池模型相同，因而可采用边缘处理器获得的修正后的参数来更新所述电池管理系统中的电池模型。
在本发明实施例中，通过由所述边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
结合图2和图3所示，在一种优选实施方式中，所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括以下步骤S321-S323：
S321、所述边缘处理器基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
S322、根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
Figure PCTCN2020099868-appb-000021
其中，
Figure PCTCN2020099868-appb-000022
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
S323、根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
可以理解的，在步骤S321中，不同的所述电芯的当前温度、所述荷电状态数据和所述健康状态数据对应的电池模型的参数不同，因此，可以根据所述电池管理系统发送的所述电芯的当前温度、所述荷电状态数据和所述健康状态数据确定相应的所述电池模型的参数，以保证所述边缘处理器获得的所述电池模型的参数与所述电池管理系统中的电池模型的参数一致，从而保证所述边缘处理器建立的电池模型与存储于所述电池管理系统中的电池模型的一致性，进而保证所述边缘处理器能够获得有效的修正后的参数。
另外，在步骤S322中，以所述边缘处理器选取的所述电池模型的参数作为第一等效电路模型的参数，从而建立所述第一等效电路模型。具体地，结合图2所示，首先，根据所述电池模型的参数，建立第一等效电路方程：
Figure PCTCN2020099868-appb-000023
然后，定义
Figure PCTCN2020099868-appb-000024
u＝I Bat，y＝U t，g(x 1)＝U OC，并根据所述第一等效电路方程，建立所述第一等效电路模型：
Figure PCTCN2020099868-appb-000025
在步骤S123中，所述边缘处理器根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法计算获得修正后的所述直流电阻R0，短时电阻R1，长时电阻R2，短时电容C1和长时电容C 2，如图3所示。
S33、所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，并在验证通过后，将所述修正后的参数返回至所述电池管理系统。
在一种优选实施方式中，所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，具体包括以下步骤S331-S332：
S331、所述边缘处理器将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
S332、判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
具体地，所述边缘处理器对所述电池模型的参数进行修正，获得修正后的参数后，将所述第一等效电路模型的参数相应替换为所述修正后的参数，从而获得修正后的第一等效电路模型；因此，在步骤S33中，所述边缘处理器可以通过将所述电池的当前电流输入所述修正后的第一等效电路模型中，从而获得电芯电压；接着，所述边缘处理器可以通过判断所计算出来的所述电芯电压与所述电池管理系统采集的所述电芯的当前电压是否相等来确认所述修正后的参数的有效性；具体地，当所计算出来的所述电芯电压与所述电芯的当前电压相等时，判定所述修正后的参数验证通过，即表明所述边缘处理器确认所述修正后的参数有效，进而通过通讯总线将所述修正后的参数返回至所述电池管理系统；当所计算出来的所述电芯电压与所述电芯的当前电压不相等时，判定所述修正后的参数验证未通过，即表明所述边缘处理器确认所述修正后的参数无效，进而可将所述修正后的参数丢弃。
S34、所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证。
在一种优选实施方式中，所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括以下步骤S341-S344：
S341、所述电池管理系统检测电池的静置时间；
S342、当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
S343、根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
S344、判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
具体地，在步骤S342中，所述电池管理系统以所述修正后的参数作为第二等效电路模型的参数，从而建立所述第二等效电路模型。可以理解的，由于所述电池管理系统接收的所述修正后的参数，是由所述边缘处理器修正所述第一等效电路模型的参数而获得的参数，因此，所述电池管理系统建立的所述第二等效电路模型与修正后的所述第一等效电路模型相同，即所述第二等效电路模型为：
Figure PCTCN2020099868-appb-000026
其中，
Figure PCTCN2020099868-appb-000027
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；需要说明的是，所述第二等效电路模型中的所述直流电阻R0，所述短时电阻R1，所述长时电阻R2，所述短时电容C1，所述长时电容C2为所述修正后的参数；另外，建立所述第二等效电路模型的过程具体可参考上述第一等效电路模型的建立过程，在此不做更多的赘述。
在步骤S343中，所述电池管理系统采用安时积分法计算荷电状态数据；当然，本发明实施例还可以采用除安时积分法以外的其他方法来计算电池的荷电状态数据，本发明对此不做限制。
在步骤S344中，所述电池管理系统可以通过判断所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据是否相等来确认所述修正后的参数的有效性；具体地，当所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据相等时，判定所述修正后的参数再次验证通过，即表明所述电池管理系统确认所述修正后的参数有效，进而可将所述修正后的参数更新到所述电池模型中；当所计算出来的所述荷电状态数据与预先配置的标准荷电状态数据不相等时，判定所述修正后的参数再次验证未通过，即表明所述电池管理系统确认所述修正后的参数无效，进而可将所述修正后的参数丢弃。需要说明的是，所述电池管理系统中预先配置有OCV-SOC表，因此，可以根据采集到的所述电芯的当前电压来查询所述OCV-SOC表，从而获得相应的标准荷电状态数据，即预先配置的标准荷电状态数据。
S35、在再次验证通过后，所述电池管理系统将所述修正后的参数更新到所述电池模型中，以应用更新后的电池模型监测所述电池的状态。
具体地，在所述电池管理系统判定所述修正后的参数再次验证通过后，所述电池管理系统将所述电池模型的参数替换为所述修正后的参数，从而更新所述电池模型，获得更新后的电池模型。获得所述更新后的电池模型后，能够应用该电池模型准确地计算出所述电池状态数据，如电荷状态数据、健康状态数据等，通过所计算出的所述电池状态数据可获知所述电池当前的状态，从而实现准确地监测所述电池的状态。
在本发明实施例中，通过所述边缘处理器和所述电池管理系统对所述修正后的参数进行相互验证，确保了所述修正后的参数的有效性，从而保证了更新后的电池模型的精度，使得能够应用该电池模型准确地监测电池的状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此确保了电池的最大能力输出，并保证了电池安全工作，从而改善了用户体验。此外，本发明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。另外，通过电池的所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，实现了针对特定的电池调整所述电池模型的参数，消除了因电池个体差异导致的大误差问题。
实施例四
参见图6，是本发明实施例四提供的边缘处理器的结构示意图；
在本发明实施例中，所述边缘处理器1，包括：
第一接收模块11，用于接收电池管理系统采集的电池的实时运行数据，以及由所述电池管理系统根据所述实时运行数据和电池模型计算获得的电池状态数据；
修正模块12，用于根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数；
第一验证模块13，用于根据所述实时运行数据，对所述修正后的参数进行验证；和，
第一发送模块14，用于在验证通过后，将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统 根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中。
在本发明实施例中，所述第一验证模块13在对所述修正后的参数验证通过后，由所述第一发送模块14通过通讯总线将所述修正后的参数返回至所述电池管理系统，使得所述电池管理系统能够根据所述实时运行数据对所述修正后的参数进行再次验证，并在对所述修正后的参数再次验证通过后，所述电池管理系统将所述电池模型的参数替换为所述修正后的参数，从而更新所述电池模型，获得更新后的电池模型。获得所述更新后的电池模型后，能够应用该电池模型准确地计算出所述电池状态数据，如电荷状态数据、健康状态数据等，通过所计算出的所述电池状态数据可获知所述电池当前的状态，从而实现准确地监测所述电池的状态。
进一步地，在本发明实施例中，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
所述修正模块12具体包括：
参数选取单元，用于基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
第一模型建立单元，用于根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
Figure PCTCN2020099868-appb-000028
其中，
Figure PCTCN2020099868-appb-000029
u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；和，
参数修正单元，用于根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
进一步地，本实施例中的所述第一验证模块13具体包括：
电芯电压计算单元，用于将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；和，
第一验证判决单元，用于判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
在本发明实施例中，通过所述修正模块12根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，以获得修正后的参数，并由所述第一验证模块13根据所述实时运行数据，对所述修正后的参数进行验证，最后由所述第一发送模块14在验证通过后，再将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中，从而保证了电池模型的精度，应用该电池模型可准确地监测电池的工作状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此保证了电池的最大能力输出，并保证了电池安全工作。此外，本发明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
此外，所述边缘处理器还可包括其他模块/单元，使得所述边缘处理器能够实现上述实施例一所述的电池状态监测方法的其他步骤，在此不做更多的赘述。
实施例五
参见图7，是本发明实施例五提供的电池管理系统的结构示意图；
在本发明实施例中，所述电池管理系统2，包括：
数据获取模块21，用于采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据；
第二发送模块22，用于将所述实时运行数据和所述电池状态数据发送至边缘处理器，以使所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证；
第二接收模块23，用于接收所述边缘处理器返回的通过验证的所述修正后的参数；
第二验证模块24，用于根据所述实时运行数据对所述修正后的参数进行再次验证；
模型更新模块25，用于在再次验证通过后，将所述修正后的参数更新到所述电池模型中；和，
监测模块26，用于应用更新后的电池模型监测所述电池的状态。
需要说明的是，在所述第二验证模块24判定所述修正后的参数再次验证通过后，由所述模型更新模块25将所述电池模型的参数替换为所述修正后的参数，从而更新所述电池模型，获得更新后的电池模型。最后，由监测模块26应用所述更新后的电池模型可以准确地计算出所述电池状态数据，如电荷状态数据、健康状态数据等，通过所计算出的所述电池状态数据可获知所述电池当前的状态，从而实现准确地监测所述电池的状态。
在本发明实施例中，优选地，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据。
进一步地，本实施例中所述第二验证模块24具体包括：
静置时间检测单元，用于检测电池的静置时间；
第二模型建立单元，用于当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
荷电状态计算单元，用于根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
第二验证判决单元，用于判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
在本发明实施例中，通过所述第二发送模块22将所述实时运行数据和所述电池状态数据发送至边缘处理器，以使所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，以获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证，再由所述第二验证模块24根据所述实时运行数据对所述修正后的参数进行再次验证，以使所述模型更新模块25在再次验证通过后，将所述修正后的参数更新到所述电池模型中，从而保证了电池模型的精度，应用该电池模型可准确地监测电池的工作状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此保证了电池的最大能力输出，并保证了电池安全工作。此外，本发明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
此外，所述电池管理系统还可包括其他模块/单元，使得所述电池管理系统能够实现上述实施例二所述的电池状态监测方法的其他步骤，在此不做更多的赘述。
实施例六
参见图8，是本发明实施例六提供的电池状态监测系统的结构示意图；
在本发明实施例中，所述电池状态监测系统，包括上述实施例四所述的边缘处理器1和上述实施例五所述的电池管理系统2。
具体地，如图8所示，所述边缘处理器1与所述电池管理系统2电连接。另外，所述电池管理系统2还与电池电连接，用于采集所述电池的实时运行数据。
需要说明的是，所述边缘处理器1的构造和工作原理可参考上述实施例四，所述电池管理系统2的构造和工作原理可参考上述实施例五，在此不做更多的赘述。
在本发明实施例中，通过所述边缘处理器1根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，以获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证，且在验证通过后，再将所述修正后的参数返回至所述电池管理系统2，使得所述电池管理系统2能够根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中，从而保证了电池模型的精度，应用该电池模型可准确地监测电池的工作状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此保证了电池的最大能力输出，并保证了电池安全工作。此外，本发 明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
实施例七
为了解决相同的技术问题，本发明实施例七还提供一种计算机可读存储介质，所述存储介质上存储有程序，当程序运行时，实现上述实施例一所述的电池状态监测方法。其中，该电池状态监测方法具体可参考上述实施例一的描述，在此不做更多的赘述。
为了解决相同的技术问题，本发明实施例七还提供另一种计算机可读存储介质，所述存储介质上存储有程序，当程序运行时，实现上述第二实施例所述的电池状态监测方法。其中，该电池状态监测方法具体可参考上述实施例二的描述，在此不做更多的赘述。
综上，本发明提供的一种电池状态监测方法、边缘处理器、系统及存储介质，通过所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，以获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证，且在验证通过后，再将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中，从而保证了电池模型的精度，应用该电池模型可准确地监测电池的工作状态，有效地避免了在电池老化或极端环境的情况下，电池模型的精度降低而导致的荷电状态数据的计算误差过大的问题，因此保证了电池的最大能力输出，并保证了电池安全工作。此外，本发明实施例由边缘处理器对电池模型的参数进行修正及校验，解决了电池管理系统的计算资源有限，难以利用电池的实时运行数据来修正电池模型的参数的问题。而且，也无需将电池的实时运行数据发送至远程的云服务器，以借助云服务器来修正电池模型参数，从而减少了远程数据传输量。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明技术原理的前提下，还可以做出若干改进和替换，这些改进和替换也应视为本发明的保护范围。

Claims (20)

  1. 一种电池状态监测方法，其特征在于，包括：
    接收电池管理系统采集的电池的实时运行数据，以及由所述电池管理系统根据所述实时运行数据和电池模型计算获得的电池状态数据；
    根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数；
    根据所述实时运行数据，对所述修正后的参数进行验证；
    在验证通过后，将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中，以应用更新后的电池模型监测所述电池的状态。
  2. 如权利要求1所述的电池状态监测方法，其特征在于，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
    所述根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括：
    基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
    根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
    Figure PCTCN2020099868-appb-100001
    其中，
    Figure PCTCN2020099868-appb-100002
    u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
    根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
  3. 如权利要求2所述的电池状态监测方法，其特征在于，所述根据所述实时运行数据，对所述修正后的参数进行验证，具体包括：
    将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
    判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
  4. 如权利要求2或3所述的电池状态监测方法，其特征在于，所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括：
    所述电池管理系统检测电池的静置时间；
    当所述电池的静置时间大于预设的时间阈值时，所述电池管理系统根据所述修正后的参数建立第二等效电路模型；
    根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
    判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
  5. 一种电池状态监测方法，其特征在于，包括：
    采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据；
    将所述实时运行数据和所述电池状态数据发送至边缘处理器，以使所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证；
    接收所述边缘处理器返回的通过验证的所述修正后的参数；
    根据所述实时运行数据对所述修正后的参数进行再次验证；
    在再次验证通过后，将所述修正后的参数更新到所述电池模型中；
    应用更新后的电池模型监测所述电池的状态。
  6. 如权利要求5所述的电池状态监测方法，其特征在于，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
    所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括：
    所述边缘处理器基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
    根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
    Figure PCTCN2020099868-appb-100003
    其中，
    Figure PCTCN2020099868-appb-100004
    u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
    根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
  7. 如权利要求6所述的电池状态监测方法，其特征在于，所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，具体包括：
    所述边缘处理器将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
    判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
  8. 如权利要求6或7所述的电池状态监测方法，其特征在于，所述根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括：
    检测电池的静置时间；
    当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
    根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
    判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
  9. 一种电池状态监测方法，其特征在于，包括：
    电池管理系统采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据，并将所述实时运行数据和所述电池状态数据发送至边缘处理器；
    所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数；
    所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，并在验证通过后，将所述修正后的参数返回至所述电池管理系统；
    所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证；
    在再次验证通过后，所述电池管理系统将所述修正后的参数更新到所述电池模型中，以应用更新后的电池模型监测所述电池的状态。
  10. 如权利要求9所述的电池状态监测方法，其特征在于，所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；则，
    所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，具体包括：
    所述边缘处理器基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
    根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
    Figure PCTCN2020099868-appb-100005
    其中，
    Figure PCTCN2020099868-appb-100006
    u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；
    根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
  11. 如权利要求10所述的电池状态监测方法，其特征在于，所述边缘处理器根据所述实时运行数据，对所述修正后的参数进行验证，具体包括：
    所述边缘处理器将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；
    判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
  12. 如权利要求10或11所述的电池状态监测方法，其特征在于，所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，具体包括：
    所述电池管理系统检测电池的静置时间；
    当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
    根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
    判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次 验证通过；若否，则判定所述修正后的参数再次验证未通过。
  13. 一种边缘处理器，其特征在于，包括：
    第一接收模块，用于接收电池管理系统采集的电池的实时运行数据，以及由所述电池管理系统根据所述实时运行数据和电池模型计算获得的电池状态数据；
    修正模块，用于根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数；
    第一验证模块，用于根据所述实时运行数据，对所述修正后的参数进行验证；和，
    第一发送模块，用于在验证通过后，将所述修正后的参数返回至所述电池管理系统，以使所述电池管理系统根据所述实时运行数据对所述修正后的参数进行再次验证，并在再次验证通过后，将所述修正后的参数更新到所述电池模型中。
  14. 如权利要求13所述的边缘处理器，其特征在于，包括：
    所述实时运行数据包括电芯的当前电压、电池的当前电流和电芯的当前温度；所述电池状态数据包括电池的荷电状态数据和健康状态数据；
    所述修正模块具体包括：
    参数选取单元，用于基于所述电芯的当前温度、所述荷电状态数据和所述健康状态数据选取所述电池模型的参数；其中，所述电池模型的参数包括直流电阻、短时电阻、长时电阻、短时电容和长时电容；
    第一模型建立单元，用于根据所述电池模型的参数，建立第一等效电路模型；其中，所述第一等效电路模型为：
    Figure PCTCN2020099868-appb-100007
    其中，
    Figure PCTCN2020099868-appb-100008
    u＝I Bat，y＝U t，g(x 1)＝U OC；R0为直流电阻；R1为短时电阻；R2为长时电阻；C1为短时电容；C2为长时电容；Cap为容值；U C1为R1C1回路的电压；U C2为R2C2回路的电压；I Bat为电池电流；U t为端电压；U OC为开路电压；SOC为荷电状态数据；和，
    参数修正单元，用于根据所述电芯的当前电压和所述电池的当前电流，采用最小二乘法修正所述第一等效电路模型的参数，即对所述电池模型的参数进行修正，获得修正后的参数。
  15. 如权利要求14所述的边缘处理器，其特征在于，所述第一验证模块具体包括：
    电芯电压计算单元，用于将所述电池的当前电流应用于修正后的第一等效电路模型中，计算获得电芯电压；和，第一验证判决单元，用于判断所计算出来的电芯电压是否等于所述电芯的当前电压；若是，则判定所述修正后的参数验证通过；若否，则判定所述修正后的参数验证未通过。
  16. 一种电池管理系统，其特征在于，包括：
    数据获取模块，用于采集电池的实时运行数据，根据所述实时运行数据和电池模型计算获得电池状态数据；
    第二发送模块，用于将所述实时运行数据和所述电池状态数据发送至边缘处理器，以使所述边缘处理器根据所述实时运行数据和所述电池状态数据对所述电池模型的参数进行修正，获得修正后的参数，并根据所述实时运行数据，对所述修正后的参数进行验证；
    第二接收模块，用于接收所述边缘处理器返回的通过验证的所述修正后的参数；
    第二验证模块，用于根据所述实时运行数据对所述修正后的参数进行再次验证；
    模型更新模块，用于在再次验证通过后，将所述修正后的参数更新到所述电池模型中；和，
    监测模块，用于应用更新后的电池模型监测所述电池的状态。
  17. 如权利要求16所述的电池管理系统，其特征在于，所述第二验证模块具体包括：
    静置时间检测单元，用于检测电池的静置时间；
    第二模型建立单元，用于当所述电池的静置时间大于预设的时间阈值时，根据所述修正后的参数建立第二等效电路模型；
    荷电状态计算单元，用于根据所述第二等效电路模型、所述电芯的当前电压、所述电池的当前电流以及所述电芯的当前温度，采用安时积分法计算荷电状态数据；
    第二验证判决单元，用于判断所计算出来的荷电状态数据与预先配置的标准荷电状态数据是否相等；若是，则判定所述修正后的参数再次验证通过；若否，则判定所述修正后的参数再次验证未通过。
  18. 一种电池状态监测系统，其特征在于，包括边缘处理器和电池管理系统，所述边缘处理器为权利要求13至15任一项所述的边缘处理器，所述电池管理系统为权利要求16或17所述的电池管理系统。
  19. 一种计算机可读存储介质，其特征在于，所述存储介质上存储有程序，当程序运行时，实现如权利要求1至4任一项所述的电池状态监测方法。
  20. 一种计算机可读存储介质，其特征在于，所述存储介质上存储有程序，当程序运行时，实现如权利要求5至8任一项所述的电池状态监测方法。
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