Lithium-ion battery service status monitoring system based on artificial intelligence

Abstract

The present invention discloses a service status monitoring system for lithium-ion batteries based on artificial intelligence, which belongs to the technical field of lithium-ion batteries. The service status monitoring system for lithium-ion batteries based on artificial intelligence includes an acquisition module, a processing module, a balancing module and a monitoring module. Communication connections are established among the acquisition module, the processing module, the balancing module and the monitoring module. The acquisition module is used to acquire voltage data of each battery cell to obtain a battery cell voltage data set; the processing module is used to make up for missing data of the voltage data of each battery cell through an established replenishment unit according to the battery cell voltage data set; the balancing module includes an analysis unit and a control unit, and the analysis unit is used to analyze according to the voltage data of each battery cell and a set balancing threshold. The present invention avoids problems that are easy to occur in the service status monitoring process of lithium-ion batteries by setting up a balancing module.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and in particular to a lithium-ion battery service status monitoring system based on artificial intelligence. Background Art

[0002] In order to obtain high voltage, high current and large capacity, lithium-ion battery packs are usually composed of multiple battery cells connected in series and parallel. When a lithium-ion battery is in service, when a cell experiences thermal runaway, it will quickly spread to other batteries within a few minutes, causing serious fire accidents and greatly affecting the safety performance of the battery. Due to the limitations of production technology, the consistency differences of lithium-ion battery cells are inevitable, which are typically manifested in differences in cell capacity.

[0003] The balancing function of the existing battery management system can solve the problem of monomer capacity differences to a certain extent, but its balancing ability is limited and cannot completely eliminate the difference. During the charging and discharging process of the battery pack, due to the existence of monomer capacity differences, when the monomer with smaller capacity has been fully charged or discharged, the monomer with larger capacity still has a surplus. At this time, if the battery management system identifies it as continuing to charge or discharge, it will cause overcharging or over-discharging of the monomer with smaller capacity. Overcharging or over-discharging of the battery may cause the metal inside to precipitate and form metal dendrites on the electrode surface to pierce the diaphragm, so that the positive and negative electrodes of the battery are connected internally and induce an internal short circuit. The monitoring module is generally powered by a lithium-ion battery, which makes it easy to have problems in the process of monitoring the service status of lithium-ion batteries.

[0004] Therefore, there is an urgent need to provide an artificial intelligence-based lithium-ion battery service status monitoring system to solve the above problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the problem that the equalization function of the battery management system in the prior art can solve the problem of monomer capacity difference to a certain extent, but its balancing ability is limited and cannot completely eliminate the difference. During the charging and discharging process of the battery pack, due to the existence of monomer capacity difference, when the monomer with smaller capacity has been fully charged or discharged, the monomer with larger capacity still has a surplus. At this time, if the battery management system identifies it as continuing to charge or discharge, it will cause overcharging or over-discharging of the monomer with smaller capacity. Overcharging or over-discharging of the battery may cause the precipitation of metal inside it and form metal dendrites on the electrode surface to pierce the diaphragm, so that the positive and negative electrodes of the battery are connected internally and induce internal short circuit. The monitoring module is generally powered by a lithium-ion battery, which leads to the disadvantage that problems are prone to occur in the process of monitoring the service status of lithium-ion batteries. A lithium-ion battery service status monitoring system based on artificial intelligence is provided.

[0006] In order to solve the above technical problems, a technical solution adopted by the present invention is: providing an artificial intelligence-based lithium-ion battery service status monitoring system, including an acquisition module, a processing module, a balancing module and a monitoring module, wherein communication connections are established among the acquisition module, the processing module, the balancing module and the monitoring module;

[0007] A collection module, the collection module is used to collect voltage data of each battery cell to obtain a battery cell voltage data set;

[0008] A processing module, the processing module is used to make up for missing data in the battery cell voltage data set through an established make-up unit according to the battery cell voltage data set, and wirelessly transmit the voltage data of each battery cell after the make-up to the balancing module;

[0009] A balancing module, the balancing module includes an analysis unit and a control unit, the analysis unit is used to analyze the voltage data of each battery cell and the set balancing threshold, mark the battery cells that meet the balancing conditions according to the analysis results, and obtain the voltage differential capacity data of each battery cell; the control unit is used to control the charging or discharging capacity of each marked battery cell through a set balancing register according to the voltage differential capacity data of each battery cell;

[0010] The balancing register will calibrate the voltage data when controlling the charging or discharging capacity of each battery cell marked. The calibration formula is as follows:

[0011] ;

[0012] In the formula, is the bus voltage calibration value; For the balance register Programming value, used to convert the value of the equalization register into the actual voltage in V; is the differential voltage;

[0013] The monitoring module includes a voltage balance monitoring unit and an early warning unit. The voltage balance monitoring unit is used to monitor the voltage data of each battery cell; the early warning unit is used to analyze the voltage data of each battery cell and the set standard value to obtain a differential value, and compare the differential value with the set threshold value to issue a corresponding early warning message.

[0014] The present invention is further configured as follows: the acquisition module includes an automatic data acquisition unit;

[0015] The automatic data acquisition unit is used to start the set voltage data acquisition terminal, automatically acquire the voltage data of each battery cell according to the setting information of the voltage data acquisition terminal, and transmit the acquired voltage data of each battery cell to the voltage equalization monitoring unit;

[0016] When the automatic collection of the voltage data of each battery cell is successful, the specific information of the collected voltage data of each battery cell is displayed, and the specific information of the voltage data of each battery cell is combined into the battery cell data set;

[0017] Comparing the battery cell data set with a set data set, and when the data capacity in a single battery cell data set is smaller than the data capacity in the set data set, displaying data acquisition failure;

[0018] When the number of failures in collecting the voltage data of each battery cell reaches a preset number, the automatic collection of the voltage data of each battery cell is abandoned, and specific information on the failure of automatic collection is displayed.

[0019] The present invention is further configured as follows: the processing module includes a data manual recall unit and a data soft recall unit;

[0020] The data manual re-call unit is used to receive the specific information of the automatic acquisition failure, and send a manual re-call command through the voltage data acquisition terminal, and send it to the acquisition module through a preset communication interface. The acquisition module receives the manual re-call command and re-acquires the voltage data of each battery cell;

[0021] After the manual data re-call unit fails to re-call, the data soft re-call unit automatically generates a re-call command through the established data soft re-call model, and sends it to the acquisition module through the communication interface. The acquisition module receives the re-call command and re-collects the voltage data of each battery cell.

[0022] The present invention is further configured as follows: the balancing condition in the balancing module is that the voltage data of a single battery cell is not equal to the balancing threshold;

[0023] If the voltage data of a single battery cell is higher than the equalization threshold, discharge equalization needs to be performed, and the voltage data of the single battery cell is subtracted from the equalization threshold and marked as a first verified voltage value;

[0024] If the voltage data of a single battery cell is lower than the equalization threshold, charging equalization needs to be performed, and the voltage data of the single battery cell is subtracted from the equalization threshold to be marked as the second verified voltage value.

[0025] The present invention is further configured as follows: the voltage differential capacity data of each battery cell in the equalization module is obtained by the following steps:

[0026] S1, establishing a fuzzy logic control model, and calculating according to the first voltage value to be verified and the second voltage value to be verified by the fuzzy logic control model to obtain the battery cell voltage value of the lithium-ion battery pack, the voltage values ​​to be verified of two single battery cells in a state to be balanced, and the voltage difference to be verified of the two single battery cells in a state to be balanced;

[0027] S2, calculating the difference between the voltage state of the balanced lithium-ion battery group and the voltage state of the lithium-ion battery group according to the battery cell voltage value of the lithium-ion battery group and the voltage values ​​to be verified of the two single battery cells in the state to be balanced;

[0028] S3, establishing fuzzy rules according to the battery cell voltage value of the lithium-ion battery pack, the voltage values ​​to be verified of the two single battery cells in the state to be balanced, the voltage difference to be verified of the two single battery cells in the state to be balanced, and the difference between the voltage state of the balanced lithium-ion battery pack and the voltage state of the lithium-ion battery pack.

[0029] The present invention is further configured as follows: the calculation formulas for the battery cell voltage value of the lithium-ion battery pack in S1, the voltage values ​​to be verified of the two single battery cells in a state to be balanced, and the voltage difference to be verified of the two single battery cells in a state to be balanced are as follows:

[0030] ;

[0031] ;

[0032] ;

[0033] in, is the battery cell voltage value of the lithium-ion battery pack; is the first voltage value to be checked; is the second voltage value to be verified; is the number of battery cells in the lithium-ion battery pack; are voltage values ​​to be verified of the two individual battery cells in a state to be balanced; It is the voltage difference to be verified between the two individual battery cells in the state to be balanced.

[0034] The present invention is further configured as follows: the calculation formula for the difference between the voltage state of the equalized lithium-ion battery pack and the voltage state of the lithium-ion battery pack in S2 is as follows:

[0035] ;

[0036] in, To equalize the voltage state of the lithium-ion battery pack and the difference between the voltage state of the lithium-ion battery pack; is the average voltage value of the battery cells of the lithium-ion battery pack; It is the average voltage value to be verified of the two single battery cells in the state to be balanced.

[0037] The present invention is further configured as follows: the specific content of the fuzzy rules in S3 is as follows:

[0038] Q1. If and If both are greater than the fuzzy threshold set in the fuzzy logic control model, a large balancing voltage is required to reduce the balancing time;

[0039] Q2. If and If both are smaller than the fuzzy threshold set in the fuzzy logic control model, a small balancing voltage is required to ensure the safety of the lithium-ion battery pack.

[0040] The present invention is further configured as follows: the early warning unit in the monitoring module sends out early warning information in the following specific steps:

[0041] M1. The early warning unit obtains voltage data of each battery cell of the lithium-ion battery pack that has been equalized in real time, calculates the voltage data of each battery cell and a set standard value to obtain a difference value, inputs the difference value into a set training model, and obtains a predicted voltage difference value of each battery cell;

[0042] M2. Based on the predicted voltage difference value of each battery cell, calculate the difference between the predicted voltage difference value of each battery cell and the real-time calculated difference value to obtain the voltage residual of each battery cell;

[0043] M3. Determine the fault type and level based on the voltage residual of each battery cell, and issue corresponding warning information.

[0044] The beneficial effects of the present invention are as follows:

[0045] 1. The present invention realizes real-time status monitoring and management of lithium-ion batteries by setting up four modules: acquisition, processing, balancing and monitoring. The system uses a variety of technical means, such as automatic data acquisition, manual supplementary call, soft supplementary call, voltage balancing and early warning, to ensure the safety and performance of the battery. The application of these technologies not only improves the service life of the battery, but also ensures the safe operation of the lithium-ion battery pack;

[0046] 2. The present invention sets a balancing module to mark the voltage data of a single battery cell that is higher than the balancing threshold and lower than the balancing threshold as the first verified voltage value and the second verified voltage value, respectively. The fuzzy logic control model is used to perform calculations based on the first verified voltage value and the second verified voltage value, and fuzzy rules are established to obtain appropriate balancing time and balancing voltage, thereby ensuring that the voltage state of the battery cell tends to be stable and avoiding problems that may occur during the service status monitoring of the lithium-ion battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a system flow chart of the present invention;

[0048] Figure 2 The flowchart of calculating the voltage differential capacity data of each battery cell in the equalization module of the present invention;

[0049] Figure 3 This is a flow chart of the early warning method of the monitoring module of the present invention.

[0050] In the figure: 1. Acquisition module; 2. Processing module; 3. Equalization module; 4. Monitoring module. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0052] See also Figure 1 - Figure 3 , a lithium-ion battery service status monitoring system based on artificial intelligence includes an acquisition module 1, a processing module 2, a balancing module 3 and a monitoring module 4, and communication connections are established among the acquisition module 1, the processing module 2, the balancing module 3 and the monitoring module 4;

[0053] The acquisition module 1 is used to acquire the voltage data of each battery cell to obtain a battery cell voltage data set;

[0054] The acquisition module 1 includes an automatic data acquisition unit;

[0055] The automatic data acquisition unit is used to start the set voltage data acquisition terminal, automatically acquire the voltage data of each battery cell according to the setting information of the voltage data acquisition terminal, and transmit the acquired voltage data of each battery cell to the voltage balance monitoring unit;

[0056] When the voltage data of each battery cell is automatically collected successfully, the specific information of the collected voltage data of each battery cell is displayed, and the specific information of the voltage data of each battery cell is combined into a battery cell data set;

[0057] Compare the battery cell data set with the set data set. When the data capacity in the single battery cell data set is smaller than the data capacity in the set data set, it is displayed that the data collection fails.

[0058] When the number of failures in collecting the voltage data of each battery cell reaches a preset number, the automatic collection of the voltage data of each battery cell is abandoned, and the specific information of the failure of automatic collection is displayed;

[0059] Processing module 2, processing module 2 is used to make up for missing data in the battery cell voltage data set through the established make-up unit according to the battery cell voltage data set, and wirelessly transmit the voltage data of each battery cell after the make-up to the balancing module 3;

[0060] Processing module 2 includes a data manual recall unit and a data soft recall unit;

[0061] The data manual re-call unit is used to receive specific information of automatic collection failure, and send a manual re-call command through the voltage data collection terminal, and send it to the collection module 1 through a preset communication interface. The collection module 1 receives the manual re-call command and re-collects the voltage data of each battery cell;

[0062] The data manual call unit is used to set the data type and time range of the manual call when data collection fails, and then start the manual call. When the manual call succeeds, the specific information of the called data is displayed. If the manual call fails and the number of manual call failures reaches the preset number, the manual call of the data is abandoned and the specific information of the manual call failure is displayed.

[0063] After the manual data call unit fails to call, the data soft call unit automatically generates a call command through the established data soft call model, and sends it to the acquisition module 1 through the communication interface. The acquisition module 1 receives the call command and re-collects the voltage data of each battery cell.

[0064] The data soft call unit includes: setting the data type and time period of the data soft call; establishing a data soft call model, the input of which is the voltage quality statistical data within the period from a certain historical moment to the point before the data is missing, and the output of which is the call value of the missing data; calling the missing data according to the established data soft call model; and displaying the data information after the data soft call;

[0065] The balancing module 3 includes an analysis unit and a control unit. The analysis unit is used to analyze the voltage data of each battery cell and the set balancing threshold value, mark the battery cells that meet the balancing conditions according to the analysis results, and obtain the voltage differential capacity data of each battery cell; the control unit is used to control the charging or discharging capacity of each marked battery cell through the set balancing register according to the voltage differential capacity data of each battery cell;

[0066] The balancing register will calibrate the voltage data when controlling the charging or discharging capacity of each battery cell marked. The calibration formula is as follows:

[0067] ;

[0068] In the formula, is the bus voltage calibration value; For the balance register Programming value, used to convert the value of the equalization register into the actual voltage in V; is the differential voltage;

[0069] To obtain the highest resolution for the equalization registers, use The minimum value of is calculated as follows:

[0070] ;

[0071] In the formula, is the minimum value of the least significant bit; For the balance register The maximum value of

[0072] Monitoring module 4, monitoring module 4 includes a voltage balance monitoring unit and an early warning unit. The voltage balance monitoring unit is used to monitor the voltage data of each battery cell; the early warning unit is used to analyze the voltage data of each battery cell and the set standard value to obtain a differential value, and compare the differential value with the set threshold value to issue a corresponding early warning message;

[0073] The specific steps for the early warning unit in the monitoring module 4 to issue early warning information are as follows:

[0074] M1. The early warning unit obtains the voltage data of each battery cell of the lithium-ion battery pack after equalization in real time, calculates the voltage data of each battery cell and the set standard value to obtain the difference value, inputs the difference value into the set training model, and obtains the predicted voltage difference value of each battery cell;

[0075] M2. Based on the predicted voltage difference value of each battery cell, calculate the difference between the predicted voltage difference value of each battery cell and the real-time calculated difference value to obtain the voltage residual of each battery cell;

[0076] M3. Based on the voltage residual of each battery cell, determine the fault type and level and issue corresponding warning information.

[0077] The specific steps are as follows:

[0078] (1) Determine the positive or negative value of the battery residual. If the battery residual is negative, it is an overvoltage state; if the battery residual is positive, it is an undervoltage state;

[0079] (2) At the same time, different thresholds are set according to the different charging and discharging stages of the battery. If the battery residual error is higher than the set threshold, an undervoltage fault and the corresponding alarm level are immediately triggered to locate the faulty battery cell; if the battery residual error is lower than the set threshold, an overvoltage fault and the corresponding alarm level are immediately triggered to locate the faulty battery cell.

[0080] The balancing condition in the balancing module 3 is that the voltage data of a single battery cell is not equal to the balancing threshold value;

[0081] If the voltage data of a single battery cell is higher than the equalization threshold, discharge equalization is required, and the voltage data of the single battery cell is subtracted from the equalization threshold and marked as the first verified voltage value;

[0082] If the voltage data of a single battery cell is lower than the equalization threshold, charging equalization is required, and the voltage data of the single battery cell is subtracted from the equalization threshold, and the result is marked as the second verified voltage value.

[0083] The voltage differential capacity data of each battery cell in the balancing module 3 is obtained by the following steps:

[0084] S1, establishing a fuzzy logic control model, and calculating according to the first voltage value to be verified and the second voltage value to be verified by the fuzzy logic control model to obtain the battery cell voltage value of the lithium-ion battery pack, the voltage values ​​to be verified of two single battery cells in a state to be balanced, and the voltage difference to be verified of the two single battery cells in a state to be balanced;

[0085] The calculation formulas for the battery cell voltage value of the lithium-ion battery pack in step S1, the voltage values ​​to be verified of two single battery cells in a state to be balanced, and the voltage difference to be verified of two single battery cells in a state to be balanced are as follows:

[0086] ;

[0087] ;

[0088] ;

[0089] in, is the battery cell voltage value of the lithium-ion battery pack; is the first voltage value to be checked; is the second voltage value to be verified; is the number of battery cells in the lithium-ion battery pack; are the voltage values ​​to be verified of two single battery cells in a state to be balanced; is the voltage difference to be verified between two single battery cells in a state to be balanced;

[0090] S2, calculating the difference between the voltage state of the balanced lithium-ion battery group and the voltage state of the lithium-ion battery group according to the battery cell voltage value of the lithium-ion battery group and the voltage values ​​to be verified of the two single battery cells in the state to be balanced;

[0091] The calculation formula for the difference between the voltage state of the equalized lithium-ion battery pack and the voltage state of the lithium-ion battery pack in step S2 is as follows:

[0092] ;

[0093] in, To equalize the voltage state of the lithium-ion battery pack and the difference between the voltage state of the lithium-ion battery pack; is the average voltage value of the battery cells of the lithium-ion battery pack; is the average voltage value to be verified of two single battery cells in a state to be balanced;

[0094] S3, establishing fuzzy rules according to the battery cell voltage value of the lithium-ion battery pack, the voltage values ​​to be verified of two single battery cells in a state to be balanced, the voltage difference to be verified of two single battery cells in a state to be balanced, and the difference between the voltage state of the balanced lithium-ion battery pack and the voltage state of the lithium-ion battery pack.

[0095] The specific content of the fuzzy rules in step S3 is as follows:

[0096] Q1. If and If both are greater than the fuzzy threshold set in the fuzzy logic control model, a large balancing voltage is required to reduce the balancing time;

[0097] Q2. If and If both are smaller than the fuzzy threshold set in the fuzzy logic control model, a small balancing voltage is required to ensure the safety of the lithium-ion battery pack.

[0098] The above are only embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. The artificial intelligence-based lithium-ion battery service status monitoring system is characterized by: It comprises a collection module (1), a processing module (2), a balancing module (3) and a monitoring module (4), wherein communication connections are established among the collection module (1), the processing module (2), the balancing module (3) and the monitoring module (4); A collection module (1), the collection module (1) is used to collect voltage data of each battery cell to obtain a battery cell voltage data set; A processing module (2), the processing module (2) being used to make up for missing data in the battery cell voltage data set through an established make-up unit according to the battery cell voltage data set, and to wirelessly transmit the voltage data of each battery cell after the make-up to the balancing module (3); A balancing module (3), the balancing module (3) comprising an analysis unit and a control unit, the analysis unit being used to analyze the voltage data of each battery cell and a set balancing threshold value, marking the battery cells meeting the balancing condition according to the analysis result, and obtaining the voltage differential capacity data of each battery cell; the control unit being used to control the charging or discharging capacity of each marked battery cell through a set balancing register according to the voltage differential capacity data of each battery cell; The balancing register will calibrate the voltage data when controlling the charging or discharging capacity of each battery cell marked. The calibration formula is as follows: ； Where, U is the bus voltage calibration value; CLSB is the LSB programming value of the equalization register, which is used to convert the value of the equalization register into the actual voltage with V as the unit; UR is the differential voltage; The voltage differential capacity data of each battery cell in the balancing module (3) is obtained by the following steps: S1, establishing a fuzzy logic control model, and calculating according to the first voltage value to be checked and the second voltage value to be checked by the fuzzy logic control model to obtain the battery cell voltage value of the lithium-ion battery pack, the voltage values ​​to be checked of two single battery cells in a state to be balanced, and the voltage difference to be checked of the two single battery cells in a state to be balanced; S2, calculating the difference between the voltage state of the balanced lithium-ion battery group and the voltage state of the lithium-ion battery group according to the battery cell voltage value of the lithium-ion battery group and the voltage values ​​to be verified of the two single battery cells in the state to be balanced; S3, establishing a fuzzy rule according to the battery cell voltage value of the lithium-ion battery pack, the voltage values ​​to be verified of the two single battery cells in the state to be balanced, the voltage difference to be verified of the two single battery cells in the state to be balanced, and the difference between the voltage state of the balanced lithium-ion battery pack and the voltage state of the lithium-ion battery pack; A monitoring module (4), the monitoring module (4) comprising a voltage balancing monitoring unit and an early warning unit, the voltage balancing monitoring unit being used to monitor the voltage data of each battery cell; the early warning unit being used to analyze the voltage data of each battery cell and a set standard value to obtain a differential value, and to compare the differential value with a set threshold value, and to issue a corresponding early warning message.

2. The artificial intelligence-based lithium-ion battery service status monitoring system according to claim 1 is characterized in that: The acquisition module (1) comprises an automatic data acquisition unit; The automatic data acquisition unit is used to start the set voltage data acquisition terminal, automatically acquire the voltage data of each battery cell according to the setting information of the voltage data acquisition terminal, and transmit the acquired voltage data of each battery cell to the voltage equalization monitoring unit; When the automatic collection of the voltage data of each battery cell is successful, the specific information of the collected voltage data of each battery cell is displayed, and the specific information of the voltage data of each battery cell is combined into a battery cell data set; Comparing the battery cell data set with a set data set, and when the data capacity in a single battery cell data set is smaller than the data capacity in the set data set, displaying data acquisition failure; When the number of failures in collecting the voltage data of each battery cell reaches a preset number, the automatic collection of the voltage data of each battery cell is abandoned, and specific information on the failure of automatic collection is displayed.

3. The artificial intelligence-based lithium-ion battery service status monitoring system according to claim 2 The measuring system is characterized by: The processing module (2) comprises a manual data recall unit and a soft data recall unit; The data manual re-call unit is used to receive the specific information of the automatic acquisition failure, and send a manual re-call command through the voltage data acquisition terminal, and send it to the acquisition module (1) through a preset communication interface. The acquisition module (1) receives the manual re-call command and re-acquires the voltage data of each battery cell; The data soft re-call unit automatically generates a re-call command through an established data soft re-call model after the data manual re-call unit fails to re-call, and sends the command to the acquisition module (1) through the communication interface. The acquisition module (1) receives the re-call command and re-collects the voltage data of each battery cell.

4. The artificial intelligence-based lithium-ion battery service status monitoring system according to claim 3 is characterized in that: The balancing condition in the balancing module (3) is that the voltage data of a single battery cell is not equal to the balancing threshold; If the voltage data of a single battery cell is higher than the equalization threshold, discharge equalization needs to be performed, and the voltage data of the single battery cell is subtracted from the equalization threshold and marked as a first verified voltage value; If the voltage data of a single battery cell is lower than the equalization threshold, charging equalization needs to be performed, and the voltage data of the single battery cell is subtracted from the equalization threshold to be marked as the second verified voltage value.

5. The artificial intelligence-based lithium-ion battery service status monitoring system according to claim 4 is characterized in that: The calculation formulas for the battery cell voltage value of the lithium-ion battery pack in S1, the voltage values ​​to be verified of the two single battery cells in a state to be balanced, and the voltage difference to be verified of the two single battery cells in a state to be balanced are as follows: ； ； ； Among them, SOCp is the voltage value of the battery cell of the lithium-ion battery pack; SOCi is the first voltage value to be verified; SOCj is the second voltage value to be verified; n is the number of battery cells in the lithium-ion battery pack; SOCe is the voltage value to be verified of the two single battery cells in the state to be balanced; ΔSOC is the voltage difference to be verified of the two single battery cells in the state to be balanced.

6. The artificial intelligence-based lithium-ion battery service status monitoring system according to claim 5 is characterized in that: The calculation formula for the difference between the voltage state of the balanced lithium-ion battery pack and the voltage state of the lithium-ion battery pack in S2 is as follows: ； Among them, SOCf is the voltage state of the balanced lithium-ion battery pack and the voltage of the lithium-ion battery pack Differences in status; -C-- is the average voltage value of the battery cells of the lithium-ion battery pack; -C- , It is the average voltage value to be verified of the two single battery cells in the state to be balanced.

7. The artificial intelligence-based lithium-ion battery service status monitoring system according to claim 6 is characterized in that: The specific content of the fuzzy rules in S3 is as follows: Q1. If both SOCf and ΔSOC are greater than the fuzzy threshold set in the fuzzy logic control model, a large balancing voltage is required to reduce the balancing time; Q2. If ΔSOC and SOCf are both smaller than the fuzzy thresholds set in the fuzzy logic control model, a small balancing voltage is required to ensure the safety of the lithium-ion battery pack.

8. The artificial intelligence-based lithium-ion battery service status monitoring system according to claim 7 is characterized in that: The specific steps for the early warning unit in the monitoring module (4) to issue early warning information are as follows: M1. The early warning unit obtains voltage data of each battery cell of the lithium-ion battery pack that has been equalized in real time, calculates the voltage data of each battery cell and a set standard value to obtain a difference value, inputs the difference value into a set training model, and obtains a predicted voltage difference value of each battery cell; M2. Based on the predicted voltage difference value of each battery cell, calculate the difference between the predicted voltage difference value of each battery cell and the real-time calculated difference value to obtain the voltage residual of each battery cell; M3. Determine the fault type and level based on the voltage residual of each battery cell, and issue corresponding warning information.

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