A power battery balancing method and balancing system based on cloud data

Abstract

The present invention discloses a power battery equalization method and an equalization system based on cloud data, including the following steps: S1: The cloud server calculates the SOH of a single string of battery cells according to the battery cell data uploaded by the vehicle-mounted device; S2: Determine the charging threshold VOLchg of the current battery cell according to the SOH of the single string of battery cells; S3: Judge whether the current battery cell voltage meets the charging threshold VOLchg; S4: Use the difference Qdiffi between the minimum-capacity battery cell and the current charge amount as the equalization value to equalize all battery cells; S5: The vehicle-mounted module uploads the equalization value Qdiffi to the cloud server through the communication unit. By calculating the SOH of each string of battery cells by the cloud server, obtaining the TEMP_VOLchg relationship table according to the SOH of each string of battery cells, and precisely equalizing each battery cell, the dependence on in-vehicle chips is reduced. By locking the charging capacity when the battery cell voltage reaches VOLchg to calculate the capacity difference between battery cells, the problems of the huge operation system and heavy operation burden of intelligent vehicles are solved; it is not affected by the voltage platform interval and can be used for different types of battery cells, with wide applicability.

Description

Technical Field

[0001] The present invention relates to the field of balanced charging of battery packs, and particularly to a power battery balancing method based on cloud data. Background Art

[0002] With the increasing market share of new energy vehicles year by year, the market's demand for high safety and long life of new energy is becoming stronger and stronger.

[0003] Battery balancing technology is one of the important functions of the battery management system of new energy vehicles. Perfect battery balancing can achieve good consistency of the power battery pack, avoid the problem that the battery pack cannot be fully charged or discharged due to inconsistent battery cells, thus affecting the vehicle's endurance.

[0004] The application of cloud computing and 5G communication technologies in new energy vehicles has solved the problem that some complex algorithms cannot run on the vehicle-end controller, thus achieving precise control of the vehicle. The state of health of the power battery, hereinafter referred to as SOH, is an important indicator of the power battery of electric vehicles. SOH directly affects the calculation of the energy and capacity that the battery can use.

[0005] Traditional balancing calculation methods mainly obtain the SOC (State of Charge) of each series of battery cells by querying the OCV-SOC table through the OCV (Open Circuit Voltage) of a single series of battery cells, and control the balancing actuator through the SOC difference to reduce the SOC difference. This method is applicable to battery cells with a good linear relationship between OCV and SOC. For battery cells with a voltage plateau interval, the deviation obtained by looking up the SOC table through OCV is relatively large, causing the problem of misbalancing.

[0006] For example, a "Balanced Monitoring and Control System and Method for a Power Battery" disclosed in a Chinese patent application document, with the publication number CN113054706A, includes a control system and method. The system includes an input module, a balanced monitoring and control module, and an execution module; the balanced monitoring and control module includes a battery pack balance pre-judgment unit, a single-cell capacity difference balance control unit, a single-cell voltage difference redundancy control unit, a balance loop diagnosis and monitoring unit, and a balance watchdog monitoring unit; the single-cell capacity difference balance control unit determines whether to turn on the balance switch of the single-cell according to the difference between the single-cell capacity and the minimum single-cell capacity; the single-cell voltage difference redundancy control unit monitors the voltage of the single-cell in real time to determine whether to turn off the balance switch; the balance loop diagnosis and monitoring unit determines whether to turn off the balance switch according to the voltage at the sampling point of the balance loop; the balance watchdog monitoring unit monitors the program operation of the system to determine whether to turn off the balance switch.

[0007] The above solution obtains the SOC of each battery cell through OCV, and then obtains the cell capacity difference according to the SOC difference. Summary of the Invention

[0008] The present invention is to solve the problem that the existing technology's balancing method obtains the SOC of each string of battery cells by querying the OCV-SOC table through the OCV of a single string of battery cells, and controls the balancing actuator through the SOC difference to reduce the SOC difference; for battery cells with a voltage plateau interval, the deviation obtained by looking up the SOC through the OCV is relatively large, causing misbalancing. The present invention provides a power battery balancing method and a balancing system based on cloud data that can accurately calculate the capacity difference between each string of battery cells and are applicable to different types of battery cells.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A power battery balancing method based on cloud data includes the following steps:

[0011] S1: The cloud server calculates the SOH of a single string of battery cells according to the battery cell data uploaded by the vehicle-mounted device.

[0012] S2: Determine the current battery cell's charging threshold VOLchg according to the SOH of a single string of battery cells.

[0013] S3: Judge whether the current battery cell voltage meets the charging threshold VOLchg.

[0014] S4: Use the difference Qdiffi between the minimum-capacity battery cell and the current charge amount as the balancing value to balance all battery cells.

[0015] S5: The vehicle-mounted module uploads the balancing value Qdiffi to the cloud server through the communication unit for battery cell consistency statistics. The charging threshold VOLchg is a voltage threshold during the charging process of the battery cell. The capacity difference between battery cells is obtained by comparing the charging capacities when each battery cell reaches the charging threshold voltage during the charging process.

[0016] Preferably, the step S1 includes the following steps:

[0017] S11: After the cloud server receives the data uploaded by the vehicle-mounted device and the battery management system, it starts the operation, and selects the battery cell voltage VOCV(i,c) uploaded when the vehicle starts again after the battery cell's static time >= N, where N is the static time threshold of the battery cell, and the unit is h; i = 1, 2, 3... n, i is the battery cell string label, and n is the total number of battery cell strings;

[0018] Obtain the calibrated SOC as SOC(i,c) by looking up the OCV-SOC table through the OCV method, and record the current total discharge capacity as DisQc at the same time; where: i = 1, 2, 3... n, i is the battery cell string label, and n is the total number of battery cell strings; c is the number of times the static condition is met, and the initial value is 1.

[0019] S12: When the battery cell meets Condition 1 again, the battery cell voltage VOCV(i,c+1), calibrate the SOC to SOC(i,c+1); at the same time, record the current total discharge capacity as DisQc+1 and record the total charge capacity ChgQc+1;

[0020] S13: Calculate the difference in SOC between two adjacent static calibrations as △SOCi = SOC(i,c+1) - SOC(i,c), and the change in the total charge and discharge capacity of the battery cell between two adjacent static calibrations is:

[0021] △Dis_ChgQ = (DisQc+1) - (DisQc) + (ChgQc+1) - (ChgQc), and obtain the SOH of the battery cell's charge and discharge as SOHOCVi = 100%△Dis_ChgQ / △SOCi*Qcap, where Qcap is the rated capacity of the battery cell; i = 1,2,3…n, i is the battery cell string label, and n is the total number of battery cell strings; c is the number of times the static condition is met, and the initial value is 1;

[0022] S14: Obtain the charge data of the battery cell of the battery management system, and judge that the battery cell temperature >= TempChgL and the battery cell temperature <= TempChgT, where TempChgL is the first temperature threshold and TempChgT is the second temperature threshold, and the first temperature threshold is less than the second temperature threshold; if so,

[0023] then judge whether the battery cell voltage during the charging process passes through VolChgL and VolChgH, where VolChgL is the first voltage threshold and VolChgH is the second voltage threshold, and the first voltage threshold is lower than the second voltage threshold; if so,

[0024] then calculate the charging capacity VChgQi between VolChgL and VolChgH, and obtain the SOH of the battery cell's charge as SOHChgi = 100%*VChgQi / VsetChgQ, where VsetChgQ is the calibrated capacity value, and i = 1,2,3…n, i is the battery cell string label, and n is the total number of battery cell strings;

[0025] S15: According to Step S13 and Step S14, obtain the SOHi of the battery cell as SOHi = x*SOHOCVi + (1 - x)* SOHChgi, where x is the weight value; i = 1,2,3…n, i is the battery cell string label, and n is the total number of battery cell strings.

[0026] Preferably, the step S2 of determining the current battery cell's charging threshold VOLchg according to the SOH of a single battery cell string includes the following steps:

[0027] S21: Query the SOH-TEMP_VOLchg relationship table according to the cell SOH, obtain the relationship table TEMP_VOLchg of the corresponding temperature and charging threshold VOLchg under the current cell SOH, and transmit the TEMP_VOLchg relationship table to the vehicle battery management system through the communication unit.

[0028] Preferably, the step S3 of determining whether the current cell voltage meets the charging threshold VOLchg includes:

[0029] S31: Query the TEMP-VOLchg table according to the current cell temperature Tempcelli to obtain VOLchgi, and determine whether the cell voltage VOLi >= VOLchgi. If it is satisfied, go to step S32; if not, return to step S2.

[0030] S32: Lock the charging amount Qi of the current cell.

[0031] S33: Determine whether the voltages of all cells are all >= VOLchg. If it is satisfied, execute step S4; if not, return to step S31. By locking the charging capacity when the cell voltage reaches VOLchg, the capacity difference between cells is calculated, so as to balance each cell. The calculation amount is small, the calculation complexity is reduced, and the problem of the huge operation system and heavy operation burden of intelligent vehicles is solved; when charging to VOLchg, the charging capacity of the cell is locked. After locking, the cell capacity no longer changes, and charging continues. While not affecting the charging efficiency, the cells are precisely balanced.

[0032] Preferably, the step S4 of balancing all cells with the difference Qdiffi between the minimum-capacity cell and the current charging amount as the balancing value includes:

[0033] S41: QMax = MAX(Qi), Qdiffi = QMax - Qi; QMax is the charging capacity corresponding to the minimum-capacity cell when reaching the voltage threshold, and Qdiffi is the capacity that each string of cells has more than the minimum-capacity cell. Among them, i = 1, 2, 3...n, i is the cell string label, and n is the total number of cell strings.

[0034] S42: The balancing execution module balances according to Qdiffi. During the balancing execution process, the cell capacity difference Qcelldiffi = Qdiffi - ∫Idt, where I is the balancing current, positive for discharging and negative for charging. The balancing method of this solution is not affected by the voltage platform interval and can be used for different types of cells, with wide applicability.

[0035] A power battery balancing system based on cloud data adopts the battery balancing method described in the present invention.

[0036] Preferably, it includes a cloud server, a communication unit, a vehicle head unit device, and a vehicle-mounted battery management system; the vehicle head unit device and the vehicle-mounted battery management system are communicatively connected to the cloud server through the communication unit;

[0037] Cloud server: Receives the data uploaded by the vehicle head unit device and the vehicle-mounted battery management system, calculates the balancing data of each string of battery cells, and transmits the balancing data of each string of battery cells to the vehicle-mounted battery management system through the communication unit;

[0038] Vehicle head unit device: Reads the vehicle head unit startup data in real time, and transmits the vehicle head unit startup data to the cloud server through the communication unit;

[0039] Vehicle-mounted battery management system: Controls the charging and discharging of the battery cells, monitors the charging and discharging data of the battery cells, transmits the detected charging and discharging data of the battery cells to the cloud server through the communication unit, and balances each string of battery cells according to the battery balancing data fed back by the cloud server.

[0040] Preferably, the vehicle-mounted battery management system includes a balancing execution module, and the balancing execution module balances the battery cells according to Qdiffi. During the balancing execution process, the battery cell capacity difference Qcelldiffi = Qdiffi - ∫Idt, where I is the balancing current, positive for discharging and negative for charging. Here, i = 1, 2, 3…n, i is the battery cell string label, and n is the total number of battery cell strings. The cloud server calculates the SOH of each string of battery cells, obtains the relationship table between the temperature and the charging threshold VOLchg corresponding to each section of battery cells according to the SOH of each string of battery cells, balances each battery cell, and precisely balances each battery cell to reduce the dependence on vehicle-mounted chips.

[0041] Preferably, the vehicle head unit startup data includes the battery cell static time.

[0042] Preferably, the charging and discharging data of the battery cells includes the battery cell temperature, the battery cell voltage, the charging power, and the charging duration.

[0043] Therefore, the present invention has the following beneficial effects:

[0044] (1) The cloud server calculates the SOH of each string of battery cells, obtains the relationship table between the temperature and the charging threshold VOLchg corresponding to each section of battery cells according to the SOH of each string of battery cells, precisely balances each battery cell, and reduces the dependence on vehicle-mounted chips.

[0045] (2) By locking the charging capacity when the battery cell voltage reaches VOLchg to calculate the capacity difference between battery cells, thereby balancing each battery cell, the calculation amount is small, the calculation complexity is reduced, and the problem of the large operation system and heavy operation burden of intelligent vehicles is solved;

[0046] (3) The balancing method of this solution is not affected by the voltage platform interval and can be used for different types of battery cells, with wide applicability. Description of the Drawings

[0047] Figure 1 It is a flowchart of a power battery balancing method based on cloud data according to an embodiment of the present invention. Detailed Embodiments

[0048] The present invention will be further described below in conjunction with the drawings and detailed embodiments.

[0049] Embodiment:

[0050] As Figure 1 A power battery balancing method based on cloud data as shown includes the following steps: S1: The cloud server calculates the SOH of a single string of battery cells according to the battery cell data uploaded by the vehicle-mounted device.

[0051] S11: After the cloud server receives the data uploaded by the vehicle-mounted device and the battery management system, it starts the operation, and selects the battery cell voltage VOCV(i,c) uploaded when the vehicle starts again after the battery cell has been static for >=N hours, where N is the time threshold for the battery cell to be static, and the unit is h; i = 1, 2, 3... n, i is the battery cell string label, and n is the total number of battery cell strings;

[0052] The calibrated SOC is obtained as SOC(i,c) by looking up the OCV-SOC table through the OCV method, and at the same time, the current total discharge capacity is recorded as DisQc; where: i = 1, 2, 3... n, i is the battery cell string label, and n is the total number of battery cell strings; c is the number of times the static condition is met, and the initial value is 1;

[0053] S12: When the battery cell meets condition 1 again, the battery cell voltage is VOCV(i,c + 1), and the calibrated SOC is SOC(i,c + 1); at the same time, the current total discharge capacity is recorded as DisQc + 1, and the total charge capacity ChgQc + 1 is recorded;

[0054] S13: Calculate the difference in SOC between two adjacent static calibrations as △SOCi = SOC(i,c + 1) - SOC(i,c), and the change in the total charge and discharge capacity of the battery between two adjacent static calibrations is:

[0055] △Dis_ChgQ = (DisQc + 1) - (DisQc) + (ChgQc + 1) - (ChgQc), and the charge and discharge SOH of the battery cell is obtained as SOHOCVi = 100%△Dis_ChgQ / △SOCi*Qcap, where Qcap is the rated capacity of the battery cell; i = 1, 2, 3... n, i is the battery cell string label, and n is the total number of battery cell strings; c is the number of times the static condition is met, and the initial value is 1;

[0056] S14: Obtain the charging data of the battery cells in the battery management system, and determine whether the cell temperature >= TempChgL and the cell temperature <= TempChgT, where TempChgL is the first temperature threshold, TempChgT is the second temperature threshold, and the first temperature threshold is less than the second temperature threshold; if so,

[0057] then determine whether the cell voltage during the charging process passes through VolChgL and VolChgH, where VolChgL is the first voltage threshold, VolChgH is the second voltage threshold, and the first voltage threshold is lower than the second voltage threshold; if so,

[0058] then calculate the charging capacity VChgQi between VolChgL and VolChgH, and obtain the SOH of the cell charging as SOHChgi = 100% * VChgQi / VsetChgQ, where VsetChgQ is the calibrated capacity value, and i = 1, 2, 3…n, i is the cell string label, and n is the total number of cell strings;

[0059] S15: According to steps S13 and S14, obtain SOHi of the cell as SOHi = x * SOHOCVi + (1 - x) * SOHChgi, where x is the weight value; i = 1, 2, 3…n, i is the cell string label, and n is the total number of cell strings.

[0060] S2: Determine the charging threshold VOLchg of the current cell according to the SOH of the single - string cell,

[0061] S21: Query the SOH - TEMP_VOLchg relationship table according to the cell SOH, obtain the relationship table TEMP_VOLchg of the temperature and the charging threshold VOLchg corresponding to the current cell SOH, and transmit the TEMP_VOLchg relationship table to the vehicle - mounted battery management system through the communication unit.

[0062] S3: Determine whether the current cell voltage meets the charging threshold VOLchg;

[0063] S31: Query the TEMP - VOLchg table according to the current cell temperature Tempcelli to obtain VOLchgi, and determine whether the cell voltage VOLi >= VOLchgi. If it is satisfied, go to step S32; if not, return to step S2;

[0064] S32: Lock the charging amount Qi of the current cell;

[0065] S33: Determine whether all cell voltages meet >= VOLchg. If it is satisfied, execute step S4; if not, return to step S31.

[0066] When VOLchg is 3500 mV, the charging capacity of the battery cell is locked when it is charged to 3500 mV. After locking, the capacity of the battery cell no longer changes, and charging continues. While not affecting the charging efficiency, precise equalization of the battery cells is performed.

[0067] S4: Use the difference Qdiffi between the minimum-capacity battery cell and the current charging amount as the equalization value to equalize all battery cells;

[0068] S41: QMax = MAX(Qi), Qdiffi = QMax - Qi; QMax is the charging capacity corresponding to when the lowest-capacity battery cell reaches the voltage threshold, and Qdiffi is the capacity that each string of battery cells has more than the lowest-capacity battery cell. Here, i = 1, 2, 3…n, i is the label of the battery cell string, and n is the total number of battery cell strings;

[0069] S42: The equalization execution module equalizes according to Qdiffi. During the equalization execution process, the battery cell capacity difference Qcelldiffi = Qdiffi - ∫Idt, where I is the equalization current, positive for discharging and negative for charging.

[0070] S5: The vehicle-mounted device module uploads the equalization value Qdiffi to the cloud server through the communication unit for battery cell consistency statistics.

[0071] The present invention also discloses a power battery equalization system based on cloud data, including a cloud server, a communication unit, a vehicle-mounted device, and an in-vehicle battery management system; the vehicle-mounted device and the in-vehicle battery management system are communicatively connected to the cloud server through the communication unit;

[0072] Cloud server: Receive the data uploaded by the vehicle-mounted device and the in-vehicle battery management system, calculate the equalization data of each string of battery cells, and transmit the equalization data of each string of battery cells to the in-vehicle battery management system through the communication unit;

[0073] Vehicle-mounted device: Read the vehicle-mounted startup data in real time, where the vehicle-mounted startup data includes the battery cell static time, and transmit the vehicle-mounted startup data to the cloud server through the communication unit;

[0074] In-vehicle battery management system: Control the charging and discharging of the battery cells, monitor the charging and discharging data of the battery cells, transmit the detected charging and discharging data of the battery cells to the cloud server through the communication unit, and equalize each string of battery cells according to the battery equalization data fed back by the cloud server. The charging and discharging data of the battery cells includes the battery cell temperature, battery cell voltage, charging amount, and charging duration.

[0075] The vehicle-mounted battery management system includes a balancing execution module. The balancing execution module balances the battery cells according to Qdiffi. During the balancing execution process, the battery cell capacity difference Qcelldiffi = Qdiffi - ∫Idt, where I is the balancing current, positive for discharging and negative for charging. Here, i = 1, 2, 3…n, i is the battery cell string label, and n is the total number of battery cell strings.

[0076] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Those skilled in the art to which the present invention pertains can make various modifications or supplements to the described specific embodiments or use similar ways to substitute, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

[0077] Although terms such as balancing, voltage platform, vehicle-mounted device, charging threshold, vehicle-mounted battery management threshold, etc. are used more frequently herein, the possibility of using other terms is not excluded. The use of these terms is only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional limitation is contrary to the spirit of the present invention.

Claims

1. A power battery equalization method based on cloud data, characterized in that, It includes the following steps: S1: The cloud server selects the cell voltage after static calibration of the cell according to the cell data uploaded by the in-vehicle device, looks up the calibrated SOC in a table, and records the current total charge and discharge capacity; the SOH of the cell during charge and discharge is obtained by multiplying the ratio of the change in the total charge and discharge capacity of the battery between two adjacent static calibrations to the difference in SOC between two adjacent static calibrations by the rated capacity of the cell; the SOH of the cell during charging is calculated by the ratio of the charging capacity between the first voltage threshold and the second voltage threshold of the cell during the charging process to the calibrated capacity value; the SOH of the single string of cells is calculated by weighted addition of the SOH of the cell during charge and discharge and the SOH of the cell during charging; S2: Determine the charging threshold VOLchg of the current cell according to the SOH of the single string of cells, S3: Determine whether the current cell voltage meets the charging threshold VOLchg; S4: Use the difference Qdiffi between the cell with the minimum capacity and the current charge amount as the balancing value to balance all cells, where i is the cell string label; S5: The in-vehicle module uploads the balancing value Qdiffi to the cloud server through the communication unit.

2. The power battery equalization method based on cloud data according to claim 1, characterized in that, The step S1 includes the following steps: S11: After the cloud server receives the data uploaded by the in-vehicle device and the battery management system, it starts the operation, and selects the cell voltage VOCV(i,c) uploaded when the vehicle starts again after the cell has been stationary for a time >= N, where N is the time threshold for cell stationary, in hours; i = 1, 2, 3…n, i is the cell string label, and n is the total number of cell strings; The calibrated SOC, i.e., SOC(i,c), is obtained by looking up the OCV-SOC table through the OCV method, and the current total discharge capacity DisQ is recorded simultaneously. c where: i = 1, 2, 3…n, i is the serial number of the battery cell string, n is the total number of battery cell strings; c is the number of times the static condition is satisfied, and the initial value is 1. S12: When the battery cell meets Condition 1 again, the battery cell voltage VOCV(i, c + 1), calibrate the SOC to SOC(i, c + 1); at the same time, record the current total discharge capacity as DisQ c+1 , record the total charge capacity ChgQ c+1 ; S13: Calculate the difference in SOC between two adjacent static calibrations as △SOCi = SOC(i,c+1) - SOC(i,c), and the change in the total charge and discharge capacity of the battery between two adjacent static calibrations is: △Dis_ChgQ=(DisQ c+1 )-(DisQ c )+(ChgQ c+1 )-(ChgQ c ), the SOH of the battery cell for charge and discharge is SOHOCV i =100%△Dis_ChgQ / △SOC i *Qcap, where Qcap is the rated capacity of the battery cell; i = 1, 2, 3…n, i is the battery cell string label, n is the total number of battery cell strings; c is the number of times the static condition is met, and the initial value is 1; S14: Obtain the cell charging data of the battery management system, and determine whether the cell temperature >= TempChg L and the cell temperature <= TempChg T , where TempChg L is the first temperature threshold, and TempChg T is the second temperature threshold, and the first temperature threshold is less than the second temperature threshold; if so, Then it is judged whether the cell voltage during the charging process passes through VolChg L and VolChg H , where VolChg L is the first voltage threshold, and VolChg H is the second voltage threshold, and the first voltage threshold is lower than the second voltage threshold; if so, Then calculate VolChg L , VolChg H The charging capacity VChgQ between i , and obtain the SOH of the battery cell during charging as SOHChg i = 100% * VChgQ i / VsetChgQ, where VsetChgQ is the calibrated capacity value, where i = 1, 2, 3…n, i is the battery cell string label, and n is the total number of battery cell strings; S15: Obtain the SOH of the battery cell according to Step S13 and Step S14 i =x*SOHOCV i +(1 - x)*SOHChg i , where x is the weight value; i = 1, 2, 3…n, i is the battery cell string label, and n is the total number of battery cell strings.

3. A power battery equalization method based on cloud data according to claim 2, characterized in that, The step S2 of determining the charging threshold VOLchg of the current cell according to the SOH of the single string of cells includes the following steps: S21: Query the SOH-TEMP_VOLchg relationship table according to the cell SOH, obtain the relationship table TEMP_VOLchg between the temperature and the charging threshold VOLchg corresponding to the current cell SOH, and transmit the TEMP_VOLchg relationship table to the in-vehicle battery management system through the communication unit.

4. A power battery equalization method based on cloud data according to claim 3, characterized in that, The S3: Determining whether the current cell voltage meets the charging threshold VOLchg includes: S31: According to the current cell temperature Tempcell i Query the TEMP-VOLchg table to obtain VOLchg i , and determine the cell voltage VOL i >=VOLchg i , if satisfied, proceed to step S32, if not satisfied, return to step S2; S32: Lock the charging amount Q of the current battery cell i ; S33: Determine whether the voltages of all cells are all >= VOLchg. If so, execute step S4. If not, return to step S31.

5. A power battery equalization method based on cloud data according to claim 4, characterized in that The step S4 of using the difference Qdiffi between the cell with the minimum capacity and the current charge amount as the balancing value to balance all cells includes: S41: QMax = MAX(Q i ), Qdiffi = QMax - Q i ; QMax is the charging capacity corresponding to when the cell with the lowest capacity reaches the voltage threshold, and Qdiff i is the capacity that each string of cells has more than the cell with the lowest capacity. Here, i = 1, 2, 3... n, i is the cell string label, and n is the total number of cell strings; S42: The balancing execution module performs balancing according to Qdiffi. During the balancing execution process, the cell capacity difference Qcelldiffi = Qdiffi - ∫Idt, where I is the balancing current, positive for discharge and negative for charge.

6. A power battery equalization system based on cloud data, characterized in that Adopt the battery balancing method described in any one of claims 1-5.

7. A power battery balancing system based on cloud data according to claim 6, characterized in that, It includes a cloud server, a communication unit, an in-vehicle device and an in-vehicle battery management system; the in-vehicle device and the in-vehicle battery management system are communicatively connected to the cloud server through the communication unit; Cloud server: Receives the data uploaded by the vehicle head unit device and the in-vehicle battery management system, calculates the balancing data for each string of battery cells, and transmits the balancing data for each string of battery cells to the in-vehicle battery management system through the communication unit; Vehicle head unit device: Reads the vehicle head unit startup data in real time and transmits the vehicle head unit startup data to the cloud server through the communication unit; In-vehicle battery management system: Controls the charging and discharging of the battery cells, monitors the charging and discharging data of the battery cells, transmits the detected charging and discharging data of the battery cells to the cloud server through the communication unit, and balances each string of battery cells according to the battery balancing data fed back by the cloud server.

8. A power battery equalization system based on cloud data according to claim 7, characterized in that, The in-vehicle battery management system includes an equalization execution module, and the equalization execution module performs equalization on the battery cells according to Qdiff i During the equalization execution process, the capacity difference of the battery cells Qcelldiff i = Qdiff i -∫Idt, where I is the equalization current, positive for discharging and negative for charging, and where i = 1, 2, 3…n, i is the battery cell string label, and n is the total number of battery cell strings.

9. A power battery equalization system based on cloud data according to claim 7 or 8, characterized in that, The vehicle head unit startup data includes the static time of the battery cells.

10. A power battery equalization system based on cloud data according to claim 9, characterized in that, The charging and discharging data of the battery cells includes the battery cell temperature, battery cell voltage, charging power, and charging duration.

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