Remote capacity checking method and system for direct-current power supply storage battery pack

Abstract

The invention provides a remote capacity checking method and system for a direct-current power supply storage battery pack, and the method comprises the steps: enabling a to-be-tested storage battery to be temporarily disconnected from a network through a switch device during capacity checking, carrying out the discharging and charging experiments through remote control, and carrying out the capacity checking of the to-be-tested storage battery pack through the combination of all online measured total battery data and measurement data obtained through an offline half battery experiment. And executing an optimization algorithm to obtain the relationship between the capacity of the monomer under different decline degrees and the potential of the positive electrode and the negative electrode, and calculating the loss of the active material on the basis of the relationship, thereby realizing capacity checking, and accurately analyzing and early warning the decline and aging of the monomer and the overall potential risk of the storage battery at the same time. Compared with an existing common storage battery capacity checking mode, equipment shutdown and labor cost are reduced, and meanwhile efficiency is remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of remote capacity checking of a DC power supply battery pack, and in particular relates to a method and system for remote capacity checking of a DC power supply battery pack. Background Art

[0002] With the continuous growth of the demand for electric energy in industries such as electricity, communications, and transportation, battery systems play an important role in ensuring the normal operation of equipment, improving system reliability, and extending equipment life. Therefore, it is particularly important to accurately evaluate the capacity (i.e., nuclear capacity) of batteries to ensure that the batteries provide sufficient power support at critical moments. The nuclear capacity of batteries in traditional DC power supply systems mainly adopts manual on-site operations, which requires high skill levels of operators, high labor intensity, high costs, and high on-site safety requirements. With the improvement of the level of intelligence and digitization of power systems and the requirement for unmanned operation, remote monitoring of the operating conditions of batteries in DC power supply systems and online remote nuclear capacity are important means to ensure the safe and reliable operation of DC systems. However, existing solutions still have the disadvantages of low accuracy and efficiency, and insufficient timely warning of battery degradation and risks. Therefore, this field urgently needs new technologies that can realize remote and efficient nuclear capacity of batteries through management means such as dataization and intelligence. Summary of the invention

[0003] In view of this, in view of the technical problems existing in the prior art, the present invention provides a method for remote capacity verification of a DC power supply battery pack, which specifically includes the following steps:

[0004] Step 1: Start the remote capacity control system to perform self-test and check whether the current battery and cell voltage, cell internal resistance, and battery temperature are normal. At the same time, ensure that all electrical and communication connections are normal, there are no fault alarms, and the battery pack is not in the charging or discharging state;

[0005] Step 2: Issue the command to start capacity verification;

[0006] Step 3: Close the switch devices in sequence to connect the charge and discharge circuits between the nuclear capacity device and the battery under test;

[0007] Step 4: The charging and discharging equipment discharges the battery under test until the battery is completely discharged;

[0008] Step 5: After standing still for a specific period of time, the charging and discharging equipment charges the battery under test until the battery is fully charged;

[0009] Step 6: After standing for a certain period of time, measure various battery parameters including internal resistance;

[0010] Step 7: Perform the switch action opposite to step 3 to reset the switch device, so that the tested battery returns to normal operation;

[0011] Step 8: Upload the test data to the remote monitoring device for data preprocessing;

[0012] Step 9: extract charging stage data and calculate the full battery open circuit voltage vs. SOC curve of each cell, i.e., OCV-SOC curve;

[0013] Step 10: Based on the offline half-cell charge and discharge experiment, the open circuit potential OCP and SOC data of the single positive and negative electrode materials are measured respectively, and the optimization algorithm is used to match the parameters of the full-cell OCV-SOC curve obtained in step 9 to obtain the positive and negative electrode SOC and capacity parameters of each single full-cell corresponding to the charging process;

[0014] Step 11: Calculate the active lithium ion loss LLI and the positive electrode active material loss LAM using the parameters obtained in step 10 p And the negative electrode active material loss LAM n , and determine the aging pattern of the monomer;

[0015] Step 12: Based on the aging mode, internal resistance increment and capacity loss of the single cell, a multi-dimensional assessment of the battery aging status is performed to warn of the risk of single cell failure, consistency anomaly or performance degradation that has occurred.

[0016] Furthermore, in step 4, the triggering condition for completely discharging the battery and stopping the discharge is to meet any one of the following conditions: 1) the cell voltage reaches the lower cut-off voltage; 2) the discharge time reaches the set value; 3) the discharge capacity reaches the set value;

[0017] The triggering condition for fully charging the battery and stopping charging in step 5 is to meet any one of the following conditions: 1) the constant voltage charging time reaches 3 hours or the constant voltage charging current is less than 0.01C; 2) the charging time reaches the set value; 3) the charging capacity reaches the set value.

[0018] Furthermore, the OCV-SOC curve in step nine is denoted as f OCV (z), specifically calculated using the complete charging process test data; the test data includes the current sequence {I k , k=1,2,3,…N} and the monomer terminal voltage sequence {U k , k=1,2,3,…N}, and the sampling interval is constant, denoted as T s ; OCV-SOC curve uses the change sequence of SOC during charging {z k , k=1,2,3,…N} and the cell terminal voltage sequence are reconstructed after eliminating the time variable; the SOC sequence of the charging process is calculated using the current sequence based on the following formula:

[0019]

[0020] Among them, when charging starts, the monomer is in a completely empty state, and the initial value of SOC z0 = 0; Q max is the monomer capacity, which is calculated using the following formula:

[0021]

[0022] Furthermore, in step 10, the positive and negative open circuit potentials of the full battery during charging are specifically expressed as:

[0023]

[0024] in, For the four parameters in the model: initial SOC of the positive electrode—z PE,0 , positive electrode capacity—Q PE max , initial SOC of negative electrode—z NE,0 , Negative electrode capacity—Q NE max Using genetic algorithm calculation, the gene population is composed of various parameters and the SOC of the charging process, and the following optimization objectives are defined:

[0025]

[0026] Gene selection, random crossover, and mutation operations are performed in sequence, and the optimal gene is obtained according to the optimization goal, and the optimization parameter set θ = {z PE,0 ,Q PE max ,z NE,0 ,Q NE max}.

[0027] Furthermore, in step 11, the following formulas are used to calculate the active lithium ion loss LLI and the positive electrode active material loss LAM respectively: p And the negative electrode active material loss LAM n :

[0028]

[0029] The superscript fresh indicates new monomers, and age indicates aged monomers; Q Li Specifically calculated by the following formula:

[0030] Q Li =Q NE z NE,0 -Q PE z PE,0 .

[0031] Accordingly, the present invention also provides a remote capacity control system for a DC power supply battery pack, which is used to execute the method provided by the present invention, and is a remote capacity control system composed of a station-end device and a remote monitoring device;

[0032] Among them, the station-side equipment includes control equipment, charging and discharging equipment, switch equipment, data acquisition equipment, communication equipment and human-computer interaction equipment; the control equipment is used to control the opening and closing of the switch equipment according to the capacity control instruction, control the charging and discharging equipment to charge and discharge the battery pack, obtain and process the measurement data transmitted by the data acquisition equipment, and report the measurement data and capacity control analysis results to the human-computer interaction equipment and remote monitoring equipment; the charging and discharging equipment is used to charge, stand still and discharge the battery pack according to the control instructions issued by the control equipment; the switch equipment includes multiple switches, which are used to open and close and change the circuit structure according to the control instructions; the data acquisition equipment is used to measure the data of the battery pack including the single cell voltage, current, total voltage and temperature, and transmit the data to the control equipment; the communication equipment is used for information encryption transmission of the remote monitoring equipment and the control equipment; the human-computer interaction equipment is used to view the battery pack measurement data and capacity control on site;

[0033] The remote monitoring equipment has remote control and data analysis functions. It is used to issue capacity control instructions to the station-side equipment and obtain battery pack measurement data from the station-side equipment. By calculating the loss of battery active materials during the charging process and analyzing the aging of individual cells, the overall performance and remaining life of each cell and the battery are determined.

[0034] Furthermore, the switch device can also enable several tested batteries contained in the battery pack to form charging and discharging circuits respectively based on the instructions of the control device, so as to realize the simultaneous remote capacity verification of different batteries.

[0035] The method and system for remote capacity verification of a DC power battery pack provided by the present invention temporarily disconnects the battery under test from the grid through a switch device during capacity verification, and then performs discharge and charging experiments through remote control. The optimization algorithm is executed using various full-battery data measured online and the measurement data obtained from offline half-battery experiments to obtain the relationship between the capacity and the positive and negative electrode potentials of the single cell at different degrees of degradation, and the active material loss is calculated on this basis. While achieving capacity verification, accurate analysis and early warning are made of the single cell degradation and aging as well as the potential risks of the battery as a whole. Compared with the existing commonly used battery capacity verification method, it reduces equipment downtime and labor costs, while significantly improving efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the process provided by the present invention;

[0037] Figure 2 A framework diagram of the system provided by the present invention;

[0038] Figure 3 It is a schematic diagram of the principle of matching half-cell and full-cell data in an example based on the present invention. DETAILED DESCRIPTION

[0039] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] The DC power supply battery pack remote capacity verification method provided by the present invention is as follows: Figure 1 As shown, the specific steps include:

[0041] Step 1: Start the remote capacity control system to perform self-test and check whether the current battery and cell voltage, cell internal resistance, and battery temperature are normal. At the same time, ensure that all electrical and communication connections are normal, there are no fault alarms, and the battery pack is not in the charging or discharging state;

[0042] Step 2: Issue the command to start capacity verification;

[0043] Step 3: Close the switch devices in sequence to connect the charge and discharge circuits between the nuclear capacity device and the battery under test;

[0044] Step 4: The charging and discharging equipment discharges the battery under test until the battery is completely discharged;

[0045] Step 5: After standing for 5 minutes, the charging and discharging equipment charges the battery under test until the battery is fully charged;

[0046] Step 6: After standing for 10 minutes, measure various battery parameters including internal resistance;

[0047] Step 7: Perform the switch action opposite to step 3 to reset the switch device, so that the tested battery returns to normal operation;

[0048] Step 8: Upload the test data to the remote monitoring device for data preprocessing;

[0049] Step 9: extract charging stage data and calculate the full battery open circuit voltage vs. SOC curve of each cell, i.e., OCV-SOC curve;

[0050] Step 10: Based on the offline half-cell charge and discharge experiment, the open circuit potential OCP and SOC data of the single positive and negative electrode materials are measured respectively, and the optimization algorithm is used to match the parameters of the full-cell OCV-SOC curve obtained in step 9 to obtain the positive and negative electrode SOC and capacity parameters of each single full-cell corresponding to the charging process;

[0051] Step 11: Calculate the active lithium ion loss LLI and the positive electrode active material loss LAM using the parameters obtained in step 10 p And the negative electrode active material loss LAM n , and determine the aging pattern of the monomer;

[0052] Step 12: Based on the aging mode, internal resistance increment and capacity loss of the single cell, a multi-dimensional assessment of the battery aging status is conducted to issue early warnings for the risk of single cell failure, consistency anomaly or performance degradation.

[0053] In a preferred embodiment of the present invention, the triggering condition for completely discharging the battery and stopping the discharge in step 4 is to meet any one of the following conditions: 1) the cell voltage reaches the lower cut-off voltage; 2) the discharge time reaches the set value; 3) the discharge capacity reaches the set value;

[0054] The triggering condition for fully charging the battery and stopping charging in step 5 is to meet any one of the following conditions: 1) the constant voltage charging time reaches 3 hours or the constant voltage charging current is less than 0.01C; 2) the charging time reaches the set value; 3) the charging capacity reaches the set value.

[0055] In a preferred embodiment of the present invention, the OCV-SOC curve in step nine is denoted as f OCV (z), specifically calculated using the complete charging process test data; the test data includes the current sequence {I k , k=1,2,3,…N} and the monomer terminal voltage sequence {U k , k=1,2,3,…N}, and the sampling interval is constant, denoted as T s ; OCV-SOC curve uses the change sequence of SOC during charging {z k , k=1,2,3,…N} and the cell terminal voltage sequence are reconstructed after eliminating the time variable; the SOC sequence of the charging process is calculated using the current sequence based on the following formula:

[0056]

[0057] Among them, when charging starts, the monomer is in a completely empty state, and the initial value of SOC z0 = 0; Q max is the monomer capacity, which is calculated using the following formula:

[0058]

[0059] In a preferred embodiment of the present invention, the positive and negative open circuit potentials of the full battery during charging in step 10 are specifically expressed as:

[0060]

[0061] in, For the four parameters in the model: initial SOC of the positive electrode—z PE,0 , positive electrode capacity—Q PE max , initial SOC of negative electrode—z NE,0 , Negative electrode capacity—Q NE max Using genetic algorithm calculation, the gene population is composed of various parameters and the SOC of the charging process, and the following optimization objectives are defined:

[0062]

[0063] Gene selection, random crossover, and mutation operations are performed in sequence, and the optimal gene is searched according to the optimization goal, and the optimization parameter set θ = {z PE,0 ,Q PE max ,z NE,0 ,Q NE max}.

[0064] Based on the teachings of the present invention, those skilled in the art should know that the data obtained by performing the above-mentioned half-cell offline measurement experiment can be reused in the capacity verification process of the same model of batteries operating under the same daily usage conditions within a certain period of time, without the need to perform an offline half-cell experiment every time the capacity is verified. This can effectively improve the efficiency of remote battery capacity verification without reducing data reliability.

[0065] In a preferred embodiment of the present invention, the following formulas are used to calculate the active lithium ion loss LLI and the positive electrode active material loss LAM respectively in step 11: p And the negative electrode active material loss LAM n :

[0066]

[0067] The superscript fresh indicates new monomers, and age indicates aged monomers; Q Li Specifically calculated by the following formula:

[0068] Q Li =Q NE z NE,0 -Q PE z PE,0 .

[0069] The present invention also provides a remote capacity control system for a DC power supply battery pack, such as Figure 2 As shown, the remote nuclear capacity system consists of station-side equipment and remote monitoring equipment;

[0070] Among them, the station-side equipment includes control equipment, charging and discharging equipment, switch equipment, data acquisition equipment, communication equipment and human-computer interaction equipment; the control equipment is used to control the opening and closing of the switch equipment according to the capacity control instruction, control the charging and discharging equipment to charge and discharge the battery pack, obtain and process the measurement data transmitted by the data acquisition equipment, and report the measurement data and capacity control analysis results to the human-computer interaction equipment and remote monitoring equipment; the charging and discharging equipment is used to charge, stand still and discharge the battery pack according to the control instructions issued by the control equipment; the switch equipment includes multiple switches, which are used to open and close and change the circuit structure according to the control instructions; the data acquisition equipment is used to measure the data of the battery pack including the single cell voltage, current, total voltage and temperature, and transmit the data to the control equipment; the communication equipment is used for information encryption transmission of the remote monitoring equipment and the control equipment; the human-computer interaction equipment is used to view the battery pack measurement data and capacity control on site;

[0071] The remote monitoring equipment has remote control and data analysis functions. It is used to issue capacity control instructions to the station-side equipment and obtain battery pack measurement data from the station-side equipment. By calculating the loss of battery active materials during the charging process and analyzing the aging of individual cells, the overall performance and remaining life of each cell and the battery are determined.

[0072] The switch device can make several tested batteries contained in the battery pack form charging and discharging circuits respectively based on the instructions of the control device, so as to realize the remote capacity verification of different batteries at the same time.

[0073] In a specific example based on the present invention, the above remote charging and discharging, single cell half-cell experiment and capacity verification process were carried out for lithium iron phosphate batteries. Figure 3 The comparison between the relationship curve between the capacity, positive and negative electrode potential and voltage of a certain monomer charging process obtained by genetic algorithm optimization and the measured results is shown, which reflects the advantage of the present invention in terms of accuracy.

[0074] It should be understood that the size of the serial numbers of the steps in the embodiment of the present invention does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0075] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for remote capacity verification of a DC power supply battery pack, characterized in that: The specific steps include: Step 1: Start the remote capacity control system to perform self-test and check whether the current battery and cell voltage, cell internal resistance, and battery temperature are normal. At the same time, ensure that all electrical and communication connections are normal, there are no fault alarms, and the battery pack is not in the charging or discharging state; Step 2: Issue the command to start capacity verification; Step 3: Close the switch devices in sequence to connect the charge and discharge circuits between the nuclear capacity device and the battery under test; Step 4: The charging and discharging equipment discharges the battery under test until the battery is completely discharged; Step 5: After standing still for a specific period of time, the charging and discharging equipment charges the battery under test until the battery is fully charged; Step 6: After standing for a certain period of time, measure various battery parameters including internal resistance; Step 7: Perform the switch action opposite to step 3 to reset the switch device, so that the tested battery returns to normal operation; Step 8: Upload the test data to the remote monitoring device for data preprocessing; Step 9: extract charging stage data and calculate the full battery open circuit voltage vs. SOC curve of each cell, i.e., OCV-SOC curve; Step 10: Based on the offline half-cell charge and discharge experiment, the open circuit potential OCP and SOC data of the single positive and negative electrode materials are measured respectively, and the optimization algorithm is used to match the parameters of the full-cell OCV-SOC curve obtained in step 9 to obtain the positive and negative electrode SOC and capacity parameters of each single full-cell corresponding to the charging process; Step 11: Calculate the active lithium ion loss LLI and the positive electrode active material loss LAM using the parameters obtained in step 10 p And the negative electrode active material loss LAM n , and determine the aging pattern of the monomer; Step 12: Based on the aging mode, internal resistance increment and capacity loss of the single cell, a multi-dimensional assessment of the battery aging status is performed to warn of the risk of single cell failure, consistency anomaly or performance degradation that has occurred.

2. The method according to claim 1, characterized in that: The triggering condition for completely discharging the battery and stopping discharge in step 4 is to meet any one of the following conditions: 1) the cell voltage reaches the lower cut-off voltage; 2) the discharge time reaches the set value; 3) the discharge capacity reaches the set value; The triggering condition for fully charging the battery and stopping charging in step 5 is to meet any one of the following conditions: 1) the constant voltage charging time reaches 3 hours or the constant voltage charging current is less than 0.01C; 2) Charging time reaches the set value; 3) Charging capacity reaches the set value.

3. The method according to claim 1, characterized in that: The OCV-SOC curve in step nine is denoted as f OCV (z), specifically calculated using the complete charging process test data; the test data includes the current sequence {I k , k=1,2,3,…N} and the monomer terminal voltage sequence {U k , k=1,2,3,…N}, and the sampling interval is constant, denoted as T s ; OCV-SOC curve uses the change sequence of SOC during charging {z k , k=1,2,3,…N} and the cell terminal voltage sequence are reconstructed after eliminating the time variable; the SOC sequence of the charging process is calculated using the current sequence based on the following formula: Among them, when charging starts, the monomer is in a completely empty state, and the initial value of SOC z0 = 0; Q max is the monomer capacity, which is calculated using the following formula:

4. The method according to claim 3, characterized in that: The positive and negative open circuit potentials of the full battery during charging in step 10 are specifically expressed as: in, For the four parameters in the model: initial SOC of the positive electrode—z PE,0 , positive electrode capacity—Q PEmax , initial SOC of negative electrode—z NE,0 , Negative electrode capacity—Q NEmax The genetic algorithm is used for calculation, and the gene population is composed of various parameters and the SOC sequence of the charging process, and the following optimization objectives are defined: Gene selection, random crossover, and mutation operations are performed in sequence, and the optimal gene is searched according to the optimization goal, and the optimization parameter set θ = {z PE,0 ,Q PEmax ,z NE,0 ,Q NEmax }.

5. The method according to claim 4, characterized in that: In step 11, the following formulas are used to calculate the active lithium ion loss LLI and the positive electrode active material loss LAM respectively: p And the negative electrode active material loss LAM n : The superscript fresh indicates new monomers, and age indicates aged monomers; Q Li Specifically calculated by the following formula: Q Li =Q NE z NE,0 -Q PE z PE,0 。 6. A DC power supply battery pack remote capacity verification system, used to execute the method according to any one of claims 1 to 5, characterized in that: It consists of station-side equipment and remote monitoring equipment; Among them, the station-side equipment includes control equipment, charging and discharging equipment, switch equipment, data acquisition equipment, communication equipment and human-computer interaction equipment; the control equipment is used to control the opening and closing of the switch equipment according to the capacity control instruction, control the charging and discharging equipment to charge and discharge the tested battery, obtain and process the measurement data transmitted by the data acquisition equipment, and report the measurement data and capacity control analysis results to the human-computer interaction equipment and remote monitoring equipment; the charging and discharging equipment is used to charge, stand still and discharge the battery according to the control instructions issued by the control equipment; the switch equipment includes multiple switches, which are used to open and close and change the circuit structure according to the control instructions; the data acquisition equipment is used to measure the battery data including single cell voltage, current, total voltage and temperature, and transmit the data to the control equipment; the communication equipment is used for information encryption transmission of the remote monitoring equipment and the control equipment; the human-computer interaction equipment is used to view the battery measurement data and capacity control on site; The remote monitoring equipment has remote control and data analysis functions. It is used to issue capacity control instructions to the station-side equipment and obtain battery measurement data from the station-side equipment. By calculating the loss of battery active materials during the charging process and analyzing the aging of individual cells, the overall performance and remaining life of each cell and the battery are determined.

7. The DC power supply battery remote capacity verification system as claimed in claim 6, characterized in that: The switch device can make several tested batteries contained in the battery pack form charging and discharging circuits respectively based on the instructions of the control device, so as to realize the remote capacity verification of different batteries at the same time.

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