CN117172028A - An efficient equilibrium calculation method based on the lithium iron phosphate cell imbalance model - Google Patents

Abstract

The invention discloses an efficient equilibrium calculation method based on the imbalance model of lithium iron phosphate battery cells. Connect multiple battery packs in series and allocate the capacity of each battery in the battery pack according to the actual situation; analyze the reasons for battery imbalance in the battery pack based on the allocation of each battery capacity; based on the analysis results of the reasons for battery imbalance, implement real-time total discharge capacity of the battery cells Estimation; Based on the estimation adjustment, single-cell discharge balancing is performed when the discharge capacity of different cells has a large difference; in order to achieve a balance of the total discharge of each battery, and realize that all batteries in the battery pack are fully charged at the same time. By introducing the imbalance model of lithium iron phosphate cells, the present invention estimates each cell and can balance the unbalanced cells when the capacity is greater than 30%, not only during charging, but also during discharging and idle periods. Full-time balancing is performed at all times, with extremely high balancing efficiency. Greatly improve the discharge capacity of lithium iron phosphate battery pack.

Description

An efficient equilibrium calculation method based on the lithium iron phosphate cell imbalance model

Technical field

The invention belongs to the field of batteries, and is specifically an efficient equalization calculation method based on an imbalance model of lithium iron phosphate cells.

Background technique

Lithium-ion battery packs generally consist of multiple small-capacity single cells connected in parallel, expanded to the target capacity, and then connected in series to the required high voltage to form a high-voltage and high-capacity module. Together with BMS and corresponding structures, the lithium-ion battery pack is formed. Battery. Single cells may have inconsistent capacity, inconsistent self-consumption current, or imbalance of each string of cells caused by inconsistent leakage current of each BMS cell. This imbalance during the charging and discharging process may result in insufficient discharge capacity of the battery pack. At this time, the BMS needs to control the balancing circuit to balance and bring the unbalanced cells to a balanced state. Balancing generally uses passive discharge, which controls the balancing circuit between cells to passively discharge individual cells, and independently discharges batteries with higher voltages to the same level as other cells. Then charge again, and finally realize that all the cells are fully charged to reach the maximum discharge capacity. Filling all inconsistent cells is the main goal of balancing.

Existing technology generally uses voltage equalization during resting or charging. For example, when the cell voltage of a ternary cell is >3.8V and the voltage difference is >50mV, equalization is started and equalization is stopped if the voltage is <20mV. Lithium iron phosphate batteries generally use cell voltage >3.4V, voltage difference >50mV to start equalization, and <20mV to stop equalization. This balancing method has a certain effect on ternary batteries, because the voltage to start balancing is 3.8V, corresponding to SOC not exceeding 70%, and there is a long balancing time. However, if it is lithium iron phosphate because the middle curve is too smooth, refer to the figure 1. When the capacity of lithium iron phosphate is below 98, the voltage is almost stable and cannot be balanced by the voltage in this range. If it exceeds 3.4V, the power is more than 99%. After balancing a short period of capacity, it returns to the stable area of the platform. , it is impossible to continue balancing, and the range and time that can be balanced are too short. At the same time, for some products that require fast operation, such as the battery replacement market, the battery is often taken out and put into the market when it is full or close to full, and the effective balancing time is very long. Short, resulting in poor balancing effect, long-term use and the capacity of the battery pack cannot be fully released. In view of these shortcomings, the present invention mainly solves the problem that equalization can only be done at the end during equalization, the time is short, and the equalization effect is poor.

Contents of the invention

The present invention provides an efficient equilibrium calculation method based on the imbalance model of lithium iron phosphate batteries. By introducing the imbalance model of lithium iron phosphate batteries, the capacity, charge and discharge current, self-consumption, etc. of each battery cell are estimated. When the capacity is greater than Unbalanced cells can be balanced at 30% of the time, and not only during charging, but also during discharging and idle times, full-time balancing is achieved, with extremely high balancing efficiency. Greatly improve the discharge capacity of lithium iron phosphate battery pack.

The present invention is realized through the following technical solutions:

An efficient equalization calculation method based on the lithium iron phosphate cell imbalance model. The efficient equalization calculation method includes the following steps:

Step 1: Connect multiple battery packs in series and allocate the capacity of each battery in the battery pack according to actual conditions;

Step 2: Based on the capacity allocation of each battery in step 1, analyze the causes of battery imbalance in the battery pack;

Step 3: Based on the analysis results of the cause of battery imbalance in Step 2, estimate the real-time total discharge capacity of the battery cell;

Step 4: Based on the estimated adjustment in step 3, perform single-cell discharge balancing when the discharge capacity of different cells has a large difference; to achieve a balance of the total discharge of each battery and achieve full charging of each battery in the battery pack at the same time.

Further, step 1 is specifically as follows: the first battery in the battery pack has a basic capacity, the second battery in the battery pack has 3% more power than the battery with the lowest capacity of the battery pack; the third battery in the battery pack has The lowest capacity battery in this battery pack has 5% more power.

Further, the specific reasons for the imbalance of batteries in the battery pack in step 2 are:

The individual cells assembled into a battery pack have differences in cell capacity;

When the battery pack is used continuously, different cells will experience different levels of capacity attenuation.

Furthermore, the causes of battery imbalance in the battery pack in step 2 also include uneven self-consumption of battery cells.

Further, the reasons for the imbalance of batteries in the battery pack in step 2 also include inconsistent current and power consumption of single cells on the BMS board.

Further, step 3 implements the real-time estimation of the total discharge capacity of the battery cell, which is a real-time estimation of the full capacity of each cell, and at the same time, the accumulated charge and discharge current, and the self-consumption current of each cell A real-time estimate is made and the two currents are Coulomb-summed.

Further, the step 3 is to estimate the real-time total discharge capacity of the battery core as follows:

Step 3.1: Set the initial value of the full capacity value AFCCx of each cell;

Step 3.2: Set the initial value of the self-consumption current value I(K)x of each cell;

Step 3.3: Set the initial value of the cumulative charge and discharge capacity QC(CD)x of each battery cell;

Step 3.4: Based on the initial value of I(K)x set in step 3.2 and the initial value of QC(CD)x set in step 3.3, after the software is run, QC(CD)x and QC(Ik)x perform real-time accumulation of working parameters;

Step 3.5: Based on the accumulation of working parameters in step 3.4, use the SOC algorithm for equalization;

Step 3.6: When the AFCCx and I(K)x of the single cell continue to change with the degradation cycle of the cell, AFCCx and I(K)x will also be tracked and calibrated in real time.

Further, the step 3.5 uses the SOC algorithm for equalization. Specifically, when the total SOC of the battery pack is >30%, and the maximum QCx-any QCx>2%*AFCC of the total battery, the string of cells exceeding this parameter needs to be balanced. Balance. At the same time, I(balace)x is calculated during equilibrium, and it is accumulated to QC(Ibalance)x. The QC(CD)x value will also continue to grow until the equation is no longer satisfied, and the equilibrium will stop.

Furthermore, the AFCCx real-time tracking calibration in step 3.6 is as follows: if there is enough time to stand still to obtain the OCV before charging, and the OCV is also in one of the three intervals, the OCV is considered valid, and the software will calculate the OCV according to the OCV/SOC The table obtains the corresponding SOCx1 and records the QC(CD)x1 at this time, enters the charging process, and then stops;

If the charged OCV can also be obtained, and the OCV also falls in any of these three intervals, the OCV is considered valid, and the software obtains the corresponding SOCx2 based on the OCV/SOC table, and records the QC at this time. (CD)x2;

If SOCx2-SOCx1>30%, it is considered that real-time calibration is possible;

The calibration formula is,

AFCCx calibration=ΔQC(CD)/ΔSOC=(QC(CD)x1-QC(CD)x2)/(SOCx2-SOCx 1);

The three intervals are interval 1: SOC 0% to 26%;

Interval 2: SOC 58~62%;

Area 3: SOC 98% ~ 100%.

Further, the I(K)x real-time tracking calibration in step 3.6 is specifically: the corresponding SOCx will be obtained during OCV calibration, and QCx can be calculated at this time,

QCx=AFCC*(100%-SOCx);

QCx=QC(CD)x+QC(Ik)x,

Among them, QC(CD)x is the accurate accumulation of discharge capacity, and QCx is also calibrated;

can be launched

QC(Ik)x1＝QCx-QC(CD)x

And record the QC(Ik)x1, and clear the accumulated running time of QC(Ik)x to 0, that is, T(Ik)x=0,

If the second OCV calibration is obtained after continuing to run, T(Ik)x>48 hours,

Using calibration, the corresponding QC(Ik)x2 can be derived,

Divide (QC(Ik)x2-QC(Ik)x1) to get the accumulated ΔQC(Ik)x per unit time,

Differentiating ΔQC(Ik)x can be obtained

I(K)x calibration=dΔQC(Ik)x/dT(Ik)x;

After the calibration is completed, T(Ik)x is cleared to 0, accumulated again, and the current I(K)x calibration is recorded as QC(Ik)x1, and the next round of calibration is entered.

The beneficial effects of the present invention are:

1. The present invention models the balancing algorithm based on the imbalance causes of lithium iron phosphate batteries. The reasons for imbalance include uneven capacity of a single section and uneven power consumption of a single section.

2. The present invention conducts software modeling for each single battery cell and introduces the single battery core capacity AFCCx, the single battery cell leakage quantity I(K)x parameter, and the single battery cell total charge and discharge capacity QC (CD )x, total leakage capacity QC(Ik)x parameter. The total charge and discharge capacity of a single cell QC(CD)x includes the capacity formed by the charge and discharge of the battery pack, the balanced capacity of a single cell, and the capacity of the BMS power consumption.

3. The present invention uses the real-time operation process of software algorithm on the basis of 1 to perform the cumulative calculation of the charge and discharge capacity QC(CD)x of the single unit and the self-consumption power capacity QC(Ik)x: QCx=QC(CD)x +QC(Ik)x, real-time balancing of the single unit that satisfies the total SOC>30% and the maximum QCx-any QCx>2%*total AFCC.

4. The present invention performs real-time calibration of AFCCx on the basis of 2, divides the lithium iron phosphate OCV into 3-stage platforms, and performs real-time degradation of AFCCx when the two OCVs that meet the process of Figure 5 are calibrated and meet ΔSOC>30%. Calibration, calibration formula AFCCx calibration = (QC(CD)x1-QC(CD)x2)/(SOCx2-SOCx 1). Long-term tracking of AFCCx degradation due to battery cycling.

5. The present invention implements real-time calibration of I(K)x on the basis of 2, and performs calibration that conforms to the process of Figure 6. Formula I(K)x calibration=d(QC(Ik)x2-QC(Ik)x1)/dT(Ik)x. Long-term tracking of I(K)x degradation due to battery cycling.

6. The present invention can greatly improve the balancing efficiency of lithium iron phosphate batteries. When the power is greater than 30%, the lithium iron phosphate batteries can be balanced in real time to ensure consistent charging, reduce charging time, increase the discharge capacity of the battery, and improve Battery cycle life.

Description of drawings

Figure 1 is a schematic diagram comparing the SOC voltage of the battery cell and the OCV voltage of the battery cell according to the present invention.

Figure 2 is an explanatory diagram of the causes of battery pack imbalance analyzed by the present invention.

Figure 3 is a schematic diagram of the voltage change interval of lithium iron phosphate according to the present invention.

Figure 4 is a flow chart of the method of the present invention.

Figure 5 is an AFCCx calibration flow chart of the present invention.

Figure 6 is the I(K)x calibration flow chart of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

AFCC is: a battery that is fully charged under standard conditions (standard temperature, voltage, current), and the capacity released under standard conditions (standard temperature, voltage, current) is the standard capacity of the battery. This standard capacity is called AFCC (Absolute Full Charge Capacity) here.

AFCCx is the full capacity of a certain single battery, where x represents the corresponding cell. For example, AFCC1 represents the absolute full capacity of the first string of cells.

SOC is the percentage of charge of the battery pack's total power.

SOCx is the charge percentage of a single string of cells in the battery pack.

I(K) in I(K)x represents the self-consumption current of the battery core, which includes the sum of the self-discharge current of the battery core and the power consumption of a single BMS cell. I(K)x represents the total self-consumption current of a single cell.

I(bms) is the total operating power consumption of BMS, including the operating power consumption value in each mode.

I(balace)x is the balance current value of a single cell BMS. In actual operation, I(balance)x=current cell voltage/balancing resistance

QC(Ibms) is the Coulomb integral of the total discharge current of BMS operation. That is, QC(Ibms)=∫tI(bms). This parameter will be cleared to 0 when the battery is fully charged, and it will accumulate positively at other times.

QC(Ibalance)x is the Coulomb integral of the total balanced current of a single section of the BMS. That is, QC(Ibalance)x=∫tI(balance)x. This parameter will be cleared to 0 when the battery is fully charged, and it will accumulate positively at other times.

QC(CD)x represents the accumulated total charge and discharge current of a certain single cell battery (discharge current is a positive value, charging current is a negative value), the total power consumption current of the BMS, and the integrated coulomb quantity of the single balanced current, unit is mAh. QC(CD)x=Total charge and discharge current cumulative capacity+QC(Ibms)+QC(Ibalance)x. This parameter will be cleared to 0 when the battery is fully charged, and it will accumulate positively at other times.

Among QC(Ik)x, QC(K)x is the cumulative capacity of self-consumption current of a certain single cell, in mAh. QC(Ik)x is positive. That is, QC(Ibalance)=∫tI(K)x. This parameter will be cleared to 0 when the battery is fully charged, and it will accumulate positively at other times.

QCx is the coulomb accumulation of the total power of a single battery. The current per unit time is accumulated to form the capacity in mAh. It is positive accumulation when discharging and negative accumulation when charging. The value of QCx is 0 when fully charged, and the maximum value when discharged is AFCCx. QCx=QC(CD)x+QC(Ik)x. That is, the total discharge coulombs include the charge and discharge coulombs, the BMS operating current coulombs, the balancing current coulombs, and the self-consumption coulombs of a single cell. This value represents the accumulation of all power consumed by a fully charged battery.

An efficient equalization calculation method based on the lithium iron phosphate cell imbalance model. The efficient equalization calculation method includes the following steps:

Step 1: Connect multiple battery packs in series and allocate the capacity of each battery in the battery pack according to actual conditions;

Step 2: Based on the capacity allocation of each battery in step 1, analyze the causes of battery imbalance in the battery pack;

Step 3: Based on the analysis results of the cause of battery imbalance in Step 2, estimate the real-time total discharge capacity of the battery cell;

Step 4: Based on the estimated adjustment in step 3, perform single-cell discharge balancing when the discharge capacity of different cells has a large difference; to achieve a balance of the total discharge of each battery and achieve full charging of each battery in the battery pack at the same time.

Further, step 1 is specifically as follows: the first battery in the battery pack has a basic capacity, the second battery in the battery pack has 3% more power than the battery with the lowest capacity of the battery pack; the third battery in the battery pack has The lowest capacity battery in this battery pack has 5% more power.

Take a battery pack with a rated capacity of 10Ah and a rated voltage of 9.6V as an example. It is also assumed that the battery pack is composed of three lithium iron phosphate batteries with a rated capacity of 10Ah connected in series. The actual capacity of the first string of cells is 10Ah, the actual capacity of the second string is 10.3Ah (3% more power than the lowest capacity battery in this battery pack), and the actual capacity of the third string of cells is 10.5Ah ( 5% more power than the lowest capacity cell of this battery pack).

Further, the specific reasons for the imbalance of batteries in the battery pack in step 2 are:

The individual cells assembled into a battery pack have different cell capacities; according to the above example, the first string of cells with a low capacity will be charged to above 3.4V first, while the other cells are still in the capacity plateau area below 3.34V. An imbalance will occur at this point;

When the battery pack is continuously used, different cells will have different degrees of capacity attenuation; for example, lithium iron phosphate generally has a capacity of attenuating to less than 80% after 2,000 cycles. Cell 1 may decay to 80%, and cell 2 may decay to 85%. This kind of situation leads to the expansion of capacity imbalance during use.

Furthermore, the causes of battery imbalance in the battery pack in step 2 also include uneven self-consumption of battery cells. Battery cells will also consume electricity when they are stored and not in use, resulting in a reduction in capacity. Different battery cells cannot be completely consistent due to production and manufacturing, resulting in inconsistent self-consumption current. After long-term storage, the remaining capacity may be different. The greater the difference in self-consumption levels, the more severe the battery imbalance will be after long-term storage. After continuous recycling of batteries, the self-consumption level will also be different due to different usage environments (there are different positions of the battery pack, different hot water levels), and the self-consumption parameters will change with use. offset.

Further, the reasons for the imbalance of batteries in the battery pack in step 2 also include inconsistent current and power consumption of single cells on the BMS board. BMS needs to collect the voltage of each battery. If there is inconsistent power consumption on the board, the remaining capacity of the battery will be inconsistent for a long time, eventually leading to imbalance. The degree of difference in power consumption will be controlled during BMS design, but there is still a certain difference. This imbalance is reflected in the increase in the self-consumption power of a single cell. In order to simplify the software model, this parameter is classified into the self-consumption parameters of the cell.

Further, step 3 implements the real-time estimation of the total discharge capacity of the battery cell, which is a real-time estimation of the full capacity of each cell, and at the same time, the accumulated charge and discharge current, and the self-consumption current of each cell A real-time estimate is made and the two currents are Coulomb-summed.

Further, the step 3 is to estimate the real-time total discharge capacity of the battery core as follows:

Step 3.1: Set the initial value of AFCCx, the full capacity value of each battery cell; the default value of this parameter can be extracted from the data provided by the battery cell manufacturer. The battery cell manufacturer has a record of the production aging capacity of each battery cell, and the full capacity of each battery cell can be written into the software through automated means during production;

Step 3.2: Set the initial value of the self-consumption current value I(K)x of each cell; this value can be calculated through long-term static experiments of the cell, or written into a fixed and reasonable estimated value. The software will be corrected in real time during actual operation;

Step 3.3: Set the initial value of the cumulative charge and discharge capacity QC(CD)x of each cell; this value is the full capacity value of the cell - the factory capacity value, that is, QC(CD)x = AFCCx - the factory capacity. The factory capacity is the same amount of capacity that the cell manufacturer charges into a completely empty battery. If the battery core is stored for a long time after leaving the factory, the self-consumption power capacity can be deducted according to the self-consumption power K value parameter, and then the value can be written into QC(CD)x. This value is the discharge capacity value from the fully charged state;

Step 3.4: The I(bms) value of the software is multiple sets of fixed values. The power consumption value in each mode is tested during the development process, based on the initial value of I(K)x set in step 3.2 and the QC(CD) set in step 3.3. Initial value of x, after the software is run, QC(CD)x, QC(Ik)x accumulate real-time working parameters;

Step 3.5: Based on the accumulation of working parameters in step 3.4, use the SOC algorithm for equalization;

Step 3.6: With continuous recycling, when the AFCCx and I(K)x of a single battery cell will continue to change with the degradation cycle of the battery cell, AFCCx and I(K)x will also be tracked and calibrated in real time. Referring to Figure 3, we can see that although the overall OCV curve of lithium iron phosphate is relatively smooth, there are still three obvious voltage change intervals. In this example, interval 1: SOC 0% ~ 26% corresponds to a voltage range of 3140mV ~ 3273mV. The voltage change range is 133mV. This interval is sufficient to use the OCV table lookup method to find the corresponding accurate SOC. The voltage range of interval 2SOC 58~62% is 3317mV~3296mV, and the variation range is 21mV. In this interval, it is enough to use the OCV table lookup method to find the corresponding accurate SOC. Region 3 SOC 98% ~ 100% corresponds to a voltage range of 3331mV ~ 3431mV, and a transformation amplitude of 100mV. This range is sufficient to use the OCV lookup table method to find the corresponding accurate SOCx.

Further, the step 3.5 uses the SOC algorithm for equalization. Specifically, when the total SOC of the battery pack is >30%, and the maximum QCx-any QCx>2%*AFCC of the total battery, the string of cells exceeding this parameter needs to be balanced. Balance. At the same time, I(balace)x is calculated during equilibrium, and it is accumulated to QC(Ibalance)x. The QC(CD)x value will also continue to grow until the equation is no longer satisfied, and the equilibrium will stop. This equalization is not limited to charging and discharging conditions; because errors cannot be avoided when calculating and accumulating parameters, a 2% calculation error is reserved. An imbalance of more than 2% can be balanced efficiently in real time. The balance within 2% is achieved by the traditional method of single string cell voltage > 3.4V, and (cell voltage exceeding 3.4V - minimum cell voltage) voltage difference exceeds 20mV. When balancing is started, since the imbalance has been greatly reduced when it is not full, the time required for terminal balancing will be greatly shortened, ensuring that all cells can be filled uniformly.

Further, the AFCCx real-time tracking calibration in step 3.6 is specifically: to ensure the calibration accuracy, this correction is performed during the charging process. The charging current during the charging process is more stable and continuous than during the discharging process. If there is enough time to stand still (generally more than 15 minutes) before charging to obtain the OCV, and the OCV is also in one of the three intervals as shown in Figure 3, the OCV is considered valid, and the software obtains the corresponding value based on the OCV/SOC table. SOCx1, and QC(CD)x1 recorded at this time, enter the charging process and then stop;

If the charged OCV can also be obtained, and the OCV also falls in any of these three intervals, the OCV is considered valid, and the software obtains the corresponding SOCx2 based on the OCV/SOC table, and records the QC at this time. (CD)x2;

If SOCx2-SOCx1>30%, it is considered that real-time calibration is possible;

The calibration formula is,

AFCCx calibration=ΔQC(CD)/ΔSOC=(QC(CD)x1-QC(CD)x2)/(SOCx2-SOCx 1);

The three intervals are interval 1: SOC 0% to 26% corresponds to a voltage range of 3140mV to 3273mV. The voltage change amplitude is 133mV. In this interval, it is enough to use the OCV table lookup method to find the corresponding accurate SOC;

Interval 2: SOC 58~62% voltage range 3317mV~3296mV, change amplitude is 21mV. In this interval, it is enough to use the OCV table lookup method to find the corresponding accurate SOC;

Area 3: SOC 98% to 100% corresponds to a voltage range of 3331mV to 3431mV, with a conversion amplitude of 100mV. This range is sufficient to use the OCV lookup table method to find the corresponding accurate SOCx.

Further, the I(K)x real-time tracking calibration in step 3.6 is specifically: the corresponding SOCx will be obtained during OCV calibration, and QCx can be calculated at this time,

QCx=AFCC*(100%-SOCx);

QCx=QC(CD)x+QC(Ik)x,

Among them, QC(CD)x is the accurate accumulation of discharge capacity, and QCx is also calibrated;

can be launched

QC(Ik)x1＝QCx-QC(CD)x

And record the QC(Ik)x1, and clear the accumulated running time of QC(Ik)x to 0, that is, T(Ik)x=0,

If the second OCV calibration is obtained after continuing to run, T(Ik)x>48 hours,

Using calibration, the corresponding QC(Ik)x2 can be derived,

Divide (QC(Ik)x2-QC(Ik)x1) to get the accumulated ΔQC(Ik)x per unit time,

Differentiating ΔQC(Ik)x can be obtained

I(K)x calibration=dΔQC(Ik)x/dT(Ik)x;

After the calibration is completed, T(Ik)x is cleared to 0, accumulated again, and the current I(K)x calibration is recorded as QC(Ik)x1, and the next round of calibration is entered.

Pay special attention to the time between two calibrations, or when full power occurs before calibration is obtained and QCx, QC(CD)x, QC(Ik)x are cleared to 0, QC(Ik)x1, QC(Ik)x2, T( Ik)x should also be cleared to 0 and the correction process should be restarted.

I(K)x also changes very slowly in the time process. During the software processing, the newly calibrated I(K)x can be expanded by accumulating the accumulation time of QC(Ik)x. At the same time, the results obtained multiple times can be The method of limiting the change range and multiple averages is adopted to ensure the accuracy and reliability of the calibration.

An efficient equilibrium calculation system based on the lithium iron phosphate cell imbalance model. The efficient equilibrium calculation system uses the above-mentioned efficient equilibrium calculation system based on the lithium iron phosphate cell imbalance model. The efficient equilibrium calculation system includes an estimation module and a calculation control module. ;

The estimation module estimates the real-time total discharge capacity of the battery core based on the analysis results of the cause of battery imbalance;

The calculation control module, based on the estimation adjustment of the estimation module, performs single-cell discharge equalization when the discharge capacity of different cells has a large difference; in order to achieve the balance of the total discharge of each battery and realize that all batteries in the battery pack are fully charged at the same time.

Claims (10)

1. The efficient balance calculation method based on the lithium iron phosphate cell unbalance model is characterized by comprising the following steps of:

step 1: a plurality of battery packs are connected in series, and the capacities of the batteries in the battery packs are distributed according to the actual situation;

step 2: analyzing the reason of unbalance of the batteries in the battery pack based on the capacity allocation of each battery in the step 1;

step 3: based on the analysis result of the cell unbalance reason in the step 2, estimating the real-time total discharge capacity of the battery cell;

step 4: based on the estimation adjustment in the step 3, single-section discharge equalization is carried out when the discharge capacity difference of different electric cores is large; so as to achieve the balance of the total discharge quantity of each battery and realize the simultaneous filling of each battery in the battery pack.

2. The method for efficient balance calculation based on lithium iron phosphate cell unbalance model according to claim 1, wherein in the step 1, a first battery in a battery pack is a base capacity, and two batteries Bao Nadi are 3% more than a battery with the lowest capacity in the battery pack; the third cell in the pack was 5% more charged than the lowest capacity cell of the pack.

3. The method for efficient balance calculation based on lithium iron phosphate cell unbalance model of claim 1, wherein the reason for the unbalance of the battery in the battery pack in step 2 is specifically,

the single battery cells assembled into the battery pack have the capacity difference of the battery cells;

the battery pack is continuously used, and different attenuation degrees of different capacities of the battery cells can appear.

4. The efficient balance calculation method based on the lithium iron phosphate cell unbalance model of claim 1, wherein the step 2 further comprises the step of causing the cell unbalance in the battery pack to have uneven power consumption of the cell.

5. The efficient balance calculation method based on the lithium iron phosphate cell unbalance model of claim 1, wherein the step 2 further includes that the current power consumption of a single cell on a BMS board is inconsistent for the reason of the cell unbalance in the battery pack.

6. The method for efficient balance calculation based on lithium iron phosphate cell unbalance model according to claim 1, wherein the step 3 is to estimate the real-time total discharge capacity of the cell by estimating the full capacity of each cell in real time, and simultaneously estimating the charge-discharge accumulated current, the self-consumed current of each cell in real time, and performing coulomb accumulation on the two currents.

7. The method of claim 6, wherein the step 3 is implemented to estimate the real-time total discharge capacity of the battery cell specifically,

step 3.1: setting an initial value of a full capacity value AFCCx of each battery cell;

step 3.2: setting an initial value of a self-consumption current value I (K) x of each battery cell;

step 3.3: setting an initial value of accumulated charge-discharge capacity QC (CD) x of each battery cell;

step 3.4: based on the initial value of the I (K) x set in the step 3.2 and the initial value of the QC (CD) x set in the step 3.3, after the software is operated, the QC (CD) x and the QC (Ik) x are accumulated with real-time working parameters;

step 3.5: based on the accumulation of the working parameters in the step 3.4, carrying out equalization by adopting an SOC algorithm;

step 3.6: when AFCCx and I (K) x of the single cell change continuously along with the degradation cycle of the cell, the AFCCx and I (K) x also perform real-time tracking calibration.

8. The efficient equalization calculation method based on the lithium iron phosphate cell imbalance model according to claim 7, wherein the step 3.5 adopts the SOC algorithm to perform equalization, specifically, when the total SOC of the battery pack is greater than 30%, and the maximum QCx-arbitrary QCx is greater than 2% of the AFCC of the total battery, the string of cells exceeding the parameter needs to perform equalization, and at the same time, calculate I (band) x, and at the same time, the value of QC (CD) x accumulated to QC (Ibalance) x will also increase continuously until the equation is not satisfied, and the equalization is stopped.

9. The method of claim 7, wherein the real-time AFCCx tracking calibration in step 3.6 is specifically that if enough time is allowed to stand for obtaining OCV before charging, and the OCV is also in one of three intervals, the OCV is considered to be valid, the software obtains the corresponding SOCx1 according to the OCV/SOC table, records the QC (CD) x1 at the moment, enters the charging process, and then stops;

if the OCV after charging can be obtained as well, and the OCV also falls in any one of the three sections, the OCV is considered to be valid, and software obtains the corresponding SOCx2 according to the OCV/SOC table and records QC (CD) x2 at the moment;

if SOCx2-SOCx1>30%, then it is considered that real-time calibration is possible;

the calibration formula is given by the following formula,

AFCCx calibration = Δqc (CD)/Δsoc= (QC (CD) x1-QC (CD) x 2)/(SOCx 2-SOCx 1); the three intervals are interval 1: SOC 0% -26%;

interval 2: SOC 58-62%;

region 3: SOC 98-100%.

10. The method for efficient balance calculation based on lithium iron phosphate cell unbalance model according to claim 7, wherein the real-time tracking calibration of I (K) x in step 3.6 is specifically that the OCV calibration will obtain the corresponding SOCx, at which time QCx can be calculated,

QCx＝AFCC*(100％-SOCx)；

QCx＝QC(CD)x+QC(Ik)x，

wherein QC (CD) x is the exact discharge capacity accumulation, QCx also gets calibrated;

can be pushed out

QC(Ik)x1＝QCx-QC(CD)x

And records the QC (Ik) x1, and clears the cumulative run time of QC (Ik) x to 0, i.e. T (Ik) x=0,

continuing to run if a second OCV calibration is obtained, T (Ik) x >48 hours,

the corresponding QC (Ik) x2 can be deduced using calibration,

(QC (Ik) x2-QC (Ik) x 1) to obtain accumulated DeltaQC (Ik) x in unit time,

differentiating DeltaQC (Ik) x to obtain

I (K) x calibration = dΔqc (Ik) x/dT (Ik) x;

after the calibration is finished, T (Ik) x is clear of 0, accumulation is carried out again, the current I (K) x is recorded and calibrated to be QC (Ik) x1, and the next round of calibration links is carried out.