CN114636938A

CN114636938A - Battery pack capacity prediction method, equipment and storage medium

Info

Publication number: CN114636938A
Application number: CN202011481654.2A
Authority: CN
Inventors: 郭磊; 蔡伟华; 王高武; 李琦; 郭启涛
Original assignee: BYD Co Ltd
Current assignee: BYD Co Ltd
Priority date: 2020-12-15
Filing date: 2020-12-15
Publication date: 2022-06-17

Abstract

The application discloses a battery pack capacity prediction method, equipment and a storage medium. The method comprises the following steps: in a charging mode, determining the minimum current electric quantity in the current electric quantities of all the single batteries of the battery pack, wherein the current electric quantity of each single battery is the product of the current charging capacity and the current charge state of each single battery; determining the current chargeable electric quantity of each single battery when each single battery reaches the respective charging cut-off voltage; determining the minimum chargeable electric quantity in the current chargeable electric quantities of all the single batteries; the sum of the minimum current capacity and the minimum chargeable capacity is determined as the charge capacity of the battery pack. According to the technical scheme provided by the embodiment of the application, the method improves the accuracy of the prediction of the battery capacity.

Description

Battery pack capacity prediction method, equipment and storage medium

Technical Field

The present application relates to the field of battery technologies, and in particular, to a method, an apparatus, and a storage medium for predicting battery capacity.

Background

Lithium batteries are widely used in the market, for example, a plurality of lithium batteries are connected in series to form a lithium battery pack for an electric vehicle. Therefore, when determining the warranty period of the electric vehicle, the service life of the battery pack needs to be predicted firstly as an important basis for determining the warranty period of the electric vehicle, and then the most important in service life prediction is to predict the capacity of the battery pack.

At present, the capacity of one single battery in a battery pack is generally predicted to be the capacity of the whole battery pack, but the capacities, the temperatures, the states of charge, the decay rates and the like of the single batteries in the battery pack are different, and the prediction of the capacity of the battery pack based on the single batteries obviously has a large error.

Disclosure of Invention

In view of the problem of inaccurate battery capacity prediction in the prior art, the application provides a battery pack capacity prediction method, device and storage medium, which can improve the battery pack capacity prediction accuracy.

In a first aspect, an embodiment of the present application provides a method for predicting battery capacity, where the method includes:

determining the minimum current electric quantity in the current electric quantities of all the single batteries of the battery pack in a charging mode, wherein the current electric quantity of each single battery is the product of the current charging capacity and the current charge state of each single battery;

determining the current chargeable electric quantity of each single battery when each single battery reaches the respective charging cut-off voltage;

determining the minimum chargeable electric quantity in the current chargeable electric quantities of all the single batteries;

the sum of the minimum current capacity and the minimum chargeable capacity is determined as the charging capacity of the battery pack.

Optionally, determining the current chargeable capacity of each single battery when each single battery reaches the respective charge cut-off voltage includes:

determining the current charging capacity of each single battery;

and determining the difference value between the current charging capacity of each single battery and the current existing electric quantity corresponding to each single battery as the chargeable electric quantity of each single battery.

Optionally, determining the current existing electric quantity of each battery cell of the battery pack includes:

acquiring the initial capacity and the current charge state of each single battery;

determining the capacity attenuation of each single battery;

determining the current charging capacity of each single battery according to the initial capacity and the capacity attenuation of each single battery;

and determining the current existing electric quantity of each single battery according to the current charging capacity and the current charge state of each single battery.

Optionally, the capacity fade amount comprises a cyclic capacity fade amount, and determining the capacity fade amount of each unit cell comprises:

acquiring circulating capacity attenuation parameters of each single battery, wherein the circulating capacity attenuation parameters comprise a temperature parameter, a capacity throughput parameter, a pressure parameter, a discharge depth parameter and a charge state parameter;

and determining the circulating capacity attenuation amount of each single battery based on the circulating capacity attenuation amount parameter of each single battery and a first preset algorithm, wherein the circulating capacity attenuation amount is the capacity attenuation amount of each single battery caused by charge and discharge factors.

Optionally, the capacity fade amount comprises a storage capacity fade amount, and determining the capacity fade amount of each unit cell comprises:

acquiring storage capacity attenuation parameters of each single battery, wherein the storage capacity attenuation parameters comprise a temperature parameter, a storage time parameter, a pressure parameter and a charge state parameter;

and determining the storage capacity attenuation amount of each single battery based on the storage capacity attenuation amount parameter of each single battery and a second preset algorithm, wherein the storage capacity attenuation amount is the single battery capacity attenuation amount caused by time and charge state factors.

Optionally, the capacity attenuation amount includes a cyclic capacity attenuation amount and a storage capacity attenuation amount, and determining the capacity attenuation amount of each single battery includes:

determining the circulating capacity attenuation of each single battery based on the circulating capacity attenuation parameter of each single battery and a first preset algorithm, wherein the circulating capacity attenuation is the capacity attenuation of each single battery caused by charge and discharge factors;

Optionally, determining the decrement of the cyclic capacity of each single battery based on the decrement of the cyclic capacity of each single battery and a first preset algorithm includes:

acquiring corresponding temperature parameter values, capacity throughput parameter values, pressure parameter values, depth of discharge parameter values and state of charge parameter values under different circulation capacity determination conditions;

and actually determining the circulating capacity attenuation of each single battery according to a first preset algorithm by using the corresponding temperature parameter value, capacity throughput parameter value, pressure parameter value, depth of discharge parameter value and charge state parameter value under different circulating capacity determination conditions.

Optionally, determining the storage capacity decrement of each single battery based on the storage capacity decrement parameter of each single battery and a second preset algorithm, including:

acquiring corresponding temperature parameter values, storage time parameter values, pressure parameter values and charge state parameter values under different storage capacity determination conditions;

and actually determining the circulating capacity attenuation of each single battery according to a second preset algorithm by using the corresponding temperature parameter value, storage time parameter value, pressure parameter value and charge state parameter value under different storage capacity determination conditions.

In a second aspect, an embodiment of the present application provides a battery management apparatus, including:

one or more processors;

a memory for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to perform a method that implements the first aspect described above.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is used to implement the method in the first aspect.

According to the method for predicting the battery capacity, the sum of the minimum current electric quantity in each single battery and the minimum chargeable electric quantity of each single battery is used as the charging capacity of the battery pack, compared with the prior art that the capacity of a single battery is used as the capacity of the battery pack, the difference between the capacities of the single batteries in the battery pack is considered, and the accuracy of predicting the battery capacity is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments or the prior art are briefly described below, and it is apparent that the drawings are only for the purpose of illustrating a preferred implementation method and are not to be considered as limiting the present application. It should be further noted that, for the convenience of description, only the relevant portions of the present application, not all of them, are shown in the drawings.

Fig. 1 is an environmental architecture diagram illustrating a method for predicting capacity of a battery pack according to an embodiment of the present disclosure;

FIG. 2 is a flow chart illustrating a method for predicting battery capacity in accordance with an embodiment of the present disclosure;

fig. 3 is a flowchart illustrating a method for predicting the current charging capacity of each single battery according to an embodiment of the present application;

fig. 4 is a flowchart illustrating a method for predicting the current chargeable capacity of each single battery according to an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating a charging curve according to an embodiment of the present application;

FIG. 6 is a schematic diagram illustrating yet another charging curve according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a discharge curve shown in accordance with an embodiment of the present application;

FIG. 8 is a schematic diagram of yet another discharge curve shown in accordance with an embodiment of the present application;

fig. 9 is a schematic structural diagram of a battery pack management system according to an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant disclosure and are not limiting of the disclosure. It should also be noted that for ease of description, only the parts relevant to the application are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 is an architecture diagram of an implementation environment of a battery capacity prediction method according to an embodiment of the present application. As shown in fig. 1, the implementation environment architecture includes: a terminal 101, a server 102, a battery pack manager 103, and a battery pack 104.

The terminal 101 includes a processor, a memory, and a display device, and further includes a user interface installed in the terminal 101, through which a user interacts with the terminal 101.

The terminal 101 is used for receiving data input by a user through a user interface. For example, the user inputs parameters of the battery pack and each battery cell, such as initial capacity, initial state of charge, and the like of the battery pack or the battery cell, through the interface.

The terminal 101 is further configured to send an instruction to the server 102, for example, to request to obtain a cell capacity fading model (following equations 1 and 2) in the server 102, a cell current charging capacity model (following equation 3), a battery pack charging capacity presetting model (following equation 4), and the like. And after the model is obtained, the parameters are input into the model to output a corresponding result.

The type of the terminal 101 includes, but is not limited to, a smart phone, a tablet computer, a television, a notebook computer, a desktop computer, and the like, which is not particularly limited in this embodiment.

The server 102 is configured to establish the above various models, and further, the server 102 receives a large amount of battery pack data sent by the terminal 101, and performs a large amount of experiments according to the battery pack data to establish a single battery capacity fading model, a single battery current charging capacity model, a battery pack charging capacity preset model, and the like.

The server 102 is further configured to store the plurality of models, and when receiving a model acquisition request sent by the terminal 101, send the requested model to the terminal 101.

The server 102 may be a server, a server cluster composed of a plurality of servers, or a cloud computing service center.

The battery pack manager 103 is configured to record temperature parameters, pressure parameters, and the like transmitted from elements such as a battery pack temperature sensor and a pressure sensor, and is further configured to record information such as a State of Charge (SOC), a Charge capacity, and a discharge capacity.

The terminal 101, the server 102, the battery pack manager 103, and the battery pack 104 establish communication connection with each other through a wired or wireless network.

In addition, it should be noted that the implementation environment may include at least two application cases, one is an implementation environment including only the terminal 101 and the server 102, and the implementation environment is used for predicting the battery pack capacity under a given condition, for example, a battery pack for supplying power to a public transportation vehicle, and the terminal 101 performs simulation of the attenuation of the battery pack capacity through the terminal 101 by loading a required model from the server 102, and predicts the charging capacity and the discharging capacity of the battery pack after each attenuation. Further, after each prediction, the terminal 101 records the capacity of the battery pack once, so that the life curve of the battery pack can be obtained, and the life of the battery pack can be predicted.

When the implementation environment further includes the battery pack manager 103 and the battery pack 104, it may be, but is not limited to, used to predict the capacity of the battery pack being used, e.g., the battery pack is used to power the vehicle. The vehicle is also provided with a battery pack manager 103, and the battery pack manager 103 is configured to record information of the battery pack 104 during use, such as a temperature parameter, a pressure parameter, a state of charge, a depth of discharge, and the like, so as to directly obtain corresponding information from the battery pack manager 103 when the terminal 101 performs battery pack capacity prediction.

Fig. 2 is a flow chart illustrating a battery capacity prediction method according to an embodiment of the present application. The method shown in fig. 2 may be performed by the terminal 101 in fig. 1, as shown in fig. 2, the method comprising the steps of:

step 201, in the charging mode, determining the minimum current electric quantity in the current electric quantities of the single batteries of the battery pack.

The battery pack comprises a plurality of single batteries, and each single battery can be connected in series.

The current time is the time when the prediction of the battery pack capacity is to be started, namely the time when the one-time charging and discharging simulation is started. The current electric quantity of the single battery is the product of the current charging capacity and the current state of charge of the single battery.

Further, referring to fig. 3, the current charge capacity of the unit battery may be obtained through steps 2011 to 2013:

in step 2011, the initial capacity of each battery cell is obtained.

Among them, the initial capacity can be acquired from a Battery Management System (BMS).

The battery management system is used for recording real-time information of each single battery included in the battery pack. The information may include information such as current charge capacity, state of charge, resistance, charge cutoff voltage, and discharge capacity, and is updated in time when each information changes.

Of course, various information of the battery pack, such as the current charge capacity, the state of charge, the resistance, the charge cut-off voltage, the discharge capacity and the like, can also be recorded in the battery management system and updated in time so as to be timely acquired when needed.

Exemplarily, the battery pack includes 4 single batteries, the identifiers of the 4 single batteries are O, P, Q and R in sequence, and the initial capacities of the 4 single batteries are all 100 ampere hours.

Step 2012, the capacity fade of each cell is determined.

Wherein the capacity attenuation amount comprises a cyclic capacity attenuation amount and a storage capacity attenuation amount. The cycle capacity fade is a fade of the capacity of the unit cell due to charge and discharge factors. The amount of memory capacity fade is the amount of cell capacity fade due to time and state of charge factors.

The cyclic capacity attenuation is obtained by the following method:

firstly, acquiring a cyclic capacity attenuation parameter of each single battery, wherein the cyclic capacity attenuation parameter comprises a temperature parameter, a capacity throughput parameter, a pressure parameter, a discharge depth parameter and a charge state parameter.

The temperature parameter, the pressure parameter, the depth of discharge parameter and the state of charge parameter can be obtained from a battery management system.

The temperature parameter, pressure parameter, depth of discharge parameter, and state of charge parameter may be an average temperature, average pressure, average depth of discharge, and average state of charge from the beginning of use of the battery pack to the current time. The capacity throughput parameter is the total discharge capacity from the beginning of the use of the battery pack to the current time. Alternatively, the capacity throughput parameter is the total charge from the beginning of the battery pack usage to the current time.

When the time for starting the battery pack is longer than the current time, the above parameters may change greatly, and an error may be caused when averaging. Therefore, the parameters can be recorded in time segments, namely, the time from the beginning of use to the current time is divided into a plurality of time segments, and the average value parameter of each time segment is recorded, so that the recording accuracy is improved. For example, if the battery pack has been used for one year so far, the parameters may be recorded in 12 groups, i.e., the average value of the parameters is recorded once every month.

And secondly, determining the circulating capacity attenuation amount of each single battery based on the circulating capacity attenuation amount parameter of each single battery and a first preset algorithm, wherein the circulating capacity attenuation amount is the single battery capacity attenuation amount caused by charge and discharge factors.

The cycling capacity fade of lithium batteries is related to factors such as temperature, capacity throughput, pressure, state of charge, and depth of discharge. The first predetermined algorithm must therefore be determined by a number of tests to find a quantitative relationship between the cyclic capacity fade and these factors.

For the first preset algorithm, a large number of cyclic capacity fading tests can be performed in a laboratory according to set test conditions. Further, the set test conditions may include:

a) at least 3 different magnifications such as 0.33C, 1C, 1.5C and the like;

b) at least 3 different DODs, such as 100-80%, 100-50%, 100-20%, 80-20%, etc.;

c) at least 3 temperatures, such as: 25 ℃, 45 ℃, 60 ℃ and the like;

d) cyclic capacity testing of at least 3 pressure levels, such as 0.05MPa, 0.1MPa, 0.2MPa, and the like.

The first preset algorithm obtained by the test may be the following formula:

wherein Q_loss，cycIs the cyclic capacity attenuation (Ah), T is absolute temperature (K), Ah is capacity throughput (Ah), σ is pressure (Pa), DOD is depth of discharge, SOC is state of charge, R is molar gas constant (J/(mol. K)), A, E_aZ, b, c, d are fitting constants.

In addition, it should be noted that the above formula is mainly used to fit the relationship between the values of the factors. After the fitting constant is determined, if the units on both sides of the formula are not uniform, the unity of the formula units can be realized by a method for setting units for the fitting constant, such as setting units for the constant a, and certainly, the units can also be directly supplemented for the formula.

After a first preset algorithm is determined, the acquired parameters are substituted into the formula (1) to calculate the cyclic capacity attenuation amount.

Further, for the case where the battery pack start-of-use time is longer than the current time, the cyclic capacity attenuation amount may be calculated by time-dividing the parameter by the following steps:

firstly, acquiring corresponding temperature parameter values, capacity throughput parameter values, pressure parameter values, depth of discharge parameter values and state of charge parameter values under different circulation capacity determination conditions;

and secondly, actually determining the circulating capacity attenuation of each single battery according to a first preset algorithm by using the corresponding temperature parameter value, capacity swallowing and spitting parameter value, pressure parameter value, discharge depth parameter value and charge state parameter value under different circulating capacity determination conditions.

The storage capacity decrement is obtained through the following steps:

the method comprises the steps of firstly, obtaining storage capacity attenuation parameters of each single battery, wherein the storage capacity attenuation parameters comprise a temperature parameter, a storage time parameter, a pressure parameter and a state of charge parameter.

The decay of the storage capacity of a lithium battery is related to factors such as temperature, storage time, pressure and state of charge. Determining the second predetermined algorithm therefore necessitates extensive testing to find a quantitative relationship between the storage capacity fade and these factors. Further, the set test conditions may include:

a) at least 3 SOC levels, typically such as 100%, 80%, 50%, 20%, etc.;

b) at least 3 different temperatures, typically 25 ℃, 45 ℃, 60 ℃, etc.;

c) cyclic capacity testing of at least 3 pressure levels, such as 0.05MPa, 0.1MPa, 0.2MPa, and the like.

The second preset algorithm may be the following formula:

wherein Q_loss，calFor the amount of storage capacity lost, T is absolute temperature (K), T is storage time (day), σ is pressure (Pa), SOC is state of charge, R is the molar gas constant (J/(mol. K)) A₂、E_a2、z₂、b₂、d₂Is a fitting constant.

In addition, it should be noted that the above formula is mainly used to fit the relationship between the values of the factors. When the fitting constant is determined, if there is a case where the units on both sides of the formula are not uniform, the fitting constant can be set by a method of setting the units for the fitting constant, such as the constant a₂And setting units to realize the unity of formula units, and certainly, the units can also be directly supplemented for formulas.

And step two, determining the storage capacity attenuation of each single battery based on the storage capacity attenuation parameter of each single battery and a second preset algorithm, wherein the storage capacity attenuation is the single battery capacity attenuation caused by time and charge state factors.

Further, for the case where the battery pack start-use time is longer than the current time, the storage capacity decrement may be calculated by time-dividing the parameters by the following method:

firstly, acquiring corresponding temperature parameter values, storage time parameter values, pressure parameter values and charge state parameter values under different storage capacity determination conditions;

secondly, the temperature parameter value, the storage time parameter value, the pressure parameter value and the state of charge parameter value which correspond to different storage capacity determination conditions are actually determined according to a second preset algorithm to determine the storage capacity attenuation of each single battery

Illustratively, still taking the above example as an example, the capacity fade amounts of the 4 unit cells determined are 13 ampere hours, 10 ampere hours and 4 ampere hours in this order.

And 2013, determining the current charging capacity of each single battery according to the initial capacity and the capacity attenuation of each single battery.

In this step, the difference between the initial capacity and the capacity decrement of each single battery is calculated, and the calculated difference is determined as the current charging capacity of each single battery. That is, the current charge capacity may be calculated as follows:

wherein, C_cellAs the current charge capacity of the unit cell,

is the initial capacity of the cell, Q_loss，cycIs the loss of the circulating capacity of the single battery, Q_loss，calIs the amount of monomer storage capacity lost.

Illustratively, still taking the above example as an example, the current charge capacities of the 4 unit cells are 87 ampere hours, 90 ampere hours and 96 ampere hours in this order.

Further, assume that the load states acquired from the battery pack management system are 6.9%, 14.9%, 13.3%, and 16.7%, respectively, in this order. The current electric quantity of the four single batteries is respectively 6 ampere hours, 13 ampere hours, 12 ampere hours and 16 ampere hours in sequence, and the minimum current electric quantity is 6 ampere hours.

Step 202, determining the current chargeable capacity of each single battery when each single battery reaches the respective charge cut-off voltage.

The current chargeable capacity of the unit cell is a capacity that can be charged when the unit cell reaches its own charge cutoff voltage when it is charged alone. Thus, referring to fig. 4, step 202 may be implemented by

steps

2021 and 2022 as follows:

at step 2021, the current charge capacity of each cell is determined.

Here, the current charging capacity may be determined through steps 2011 to 2022.

Step 2022, determining a difference between the current charging capacity of each single battery and the current existing electric quantity corresponding to each single battery as the chargeable electric quantity of each single battery.

Illustratively, taking still the above example as an example, the determined current charge capacities of the four unit cells of O, P, Q and R are 87 ampere-hours, 90 ampere-hours and 96 ampere-hours, respectively, in this order. Combining step 201, it can be known that the current electric quantity of each battery cell is respectively 6 ampere hours, 13 ampere hours, 12 ampere hours and 16 ampere hours, and the chargeable electric quantity of each battery cell is respectively 81 ampere hours, 74 ampere hours, 78 ampere hours and 80 ampere hours in sequence.

In addition, the chargeable capacity of each unit cell can also be determined by transforming the normalized charging curve when each unit cell reaches the respective charging cut-off voltage. As shown in fig. 5, (a) in fig. 5 is a normalized charging curve. The normalized charging curve is a charging curve obtained by testing a given charging strategy in a laboratory, and the normalized charging curve gives a relation between the charging electric quantity and the voltage.

The charging curves of the single batteries with different charging capacities, different resistances and different charge states can be obtained by converting the normalized charging curves.

Referring to (b) of fig. 5, the charging curves of the unit batteries with different resistances may be corrected by multiplying the difference between the resistances by the charging current; referring to fig. 5 (c), the charging curves of the batteries with different states of charge are the same, but correspond to two different charging starting points; referring to a graph (d) in fig. 5, two battery charging curves different in chargeable capacity are scaled in the charging capacity coordinate direction by the capacity size thereof.

As shown in fig. 6, O, P points represent two cells with the same resistance and chargeable capacity and different states of charge, the cell resistance represented by O, P is greater than the cell resistance represented by Q, the cell resistance represented by R is greater than the chargeable capacity represented by O, P, Q, and thus the chargeable capacity of the cell is greater than that of the cell represented by O, P, Q, so that four cellsThe chargeable capacity of the battery pack formed by the batteries is the charged capacity of each single battery from the current state to the charge cut-off voltage, and due to the series system, all the single batteries pass the same electric quantity, and the single battery which reaches the charge cut-off voltage at the earliest determines the chargeable capacity of the whole series battery pack, and in fig. 6, the single battery is

As the chargeable capacity, the resistance and the charge state of each single battery change along with the use of the battery in the use process, the charging curve of each single battery is different at different time, and the current charging curve of each single battery can be obtained according to the change of the normalized charging curve. The chargeable electric quantity of each single battery is directly obtained through the charging curve, a complex calculation process is omitted, and the prediction efficiency is improved.

And step 203, determining the minimum chargeable electric quantity in the current chargeable electric quantities of the single batteries.

Illustratively, still taking the above example as an example, the minimum value of the chargeable amount is 74 ampere-hours.

And step 204, determining the sum of the minimum current electric quantity and the minimum chargeable electric quantity as the charging capacity of the battery pack.

Step 204 may be represented by the following equation:

wherein C is_packTo charge the capacity of the battery pack,

the amount of electricity which can be charged for the single battery,

the current electric quantity of the single battery.

Still taking the above example as an example, when the minimum current electric quantity of each single battery is 6 ampere hours, and the minimum chargeable electric quantity is 74 ampere hours, the charging capacity of the battery pack is 80 ampere hours.

After the prediction of the battery capacity is finished each time, the prediction results of the battery pack and each single battery are recorded and serve as the starting point of the next prediction to record the change of the charging capacity of the battery pack and each single battery along with the time, so that the service life curve of the battery pack and each single battery can be obtained.

When the same type of battery is used under the same working condition, the user can directly obtain the service life curves of the battery pack and each single battery by checking the record without predicting, and the efficiency is improved.

The charging capacity of the battery pack is an important basis for measuring the service life of the battery pack, but the battery pack mainly serves to supply power to equipment, so that the application of the battery pack mainly depends on the discharging capacity of the battery pack, and the discharge capacity of the battery pack needs to be predicted when the service life of the battery pack is measured. Therefore, the discharge capacity of the battery pack is also an important embodiment of the battery pack capacity, and therefore, when predicting the battery pack capacity, not only the charge capacity of the battery pack but also the discharge capacity of the battery pack is predicted. Then after step 204, the battery capacity prediction method further comprises the steps of:

step one, determining the storage capacity of each single battery when the charging cut-off voltage of the battery pack is reached.

When the single batteries of the battery pack are in a series connection relationship, the single batteries are charged with the same electric quantity in unit time when the battery pack is charged, so that the charging cut-off voltage of the battery pack is the voltage of the corresponding battery pack when any single battery in the battery pack reaches the charging cut-off voltage of the battery pack when the battery pack is charged and the battery pack stops charging.

When any single battery in the battery pack reaches the charging cut-off voltage of the battery pack, the whole battery pack stops charging, and the single battery which reaches the charging cut-off voltage of the battery pack firstly is the smallest one of the chargeable electric quantities. Therefore, when the battery pack is charged, the actual charging capacity of each single battery is the minimum charging capacity in step 203.

Therefore, the sum of the current existing capacity and the minimum chargeable capacity of each unit battery is calculated, namely, the stored capacity of each unit battery when the battery pack charging cut-off voltage is reached.

Illustratively, still taking the above example as an example, when the current existing electric charge amounts of O, P, Q and R four battery cells included in the battery pack are 6 ampere-hours, 13 ampere-hours, 12 ampere-hours, and 16 ampere-hours in this order, and the minimum chargeable electric charge amount is 74 ampere-hours, the stored electric charge amount of each battery cell when the battery pack charge cutoff voltage is reached is 80 ampere-hours, 87 ampere-hours, 86 ampere-hours, and 90 ampere-hours in this order.

And step two, determining the available residual electric quantity of each single battery when the discharge cut-off voltage of the battery pack is reached.

Alternatively, the electric quantity of each single battery when the discharge cut-off voltage of the battery pack is reached can be obtained through a single battery discharge curve. The discharge curve of each single battery can be obtained by normalizing the discharge curve transformation. As shown in fig. 7, (a) in fig. 7 is a normalized discharge curve. The normalized discharge curve is a discharge curve obtained by testing a given discharge strategy in a laboratory, and gives a relation between discharge electric quantity and voltage.

The discharge curves of the single batteries with different charge capacities, different resistances and different charge states can be obtained by transforming the normalized discharge curves.

Referring to (b) of fig. 7, the discharge curve of the battery having different resistances may be corrected by multiplying the difference in resistance by the discharge current; referring to fig. 7 (c), the discharge curves of the batteries with different states of charge are the same, but correspond to two different discharge starting points; referring to (d) of fig. 7, two battery charging curves having different capacities are scaled in the capacity coordinate direction by the capacity size thereof.

Illustratively, still taking the above example as an example, when the obtained four single batteries of O, P, Q and R reach the discharge cutoff voltage of the battery pack, the available remaining capacity of each single battery is respectively 6 ampere-hour, 3 ampere-hour and 10 ampere-hour in turn.

And step three, calculating the difference value between the storage electric quantity and the residual electric quantity of each single battery to obtain the dischargeable electric quantity.

As shown in fig. 8, O, P two points of dischargeable capacity of the battery pack represent the same resistance and capacity, two cells with different states of charge have a battery resistance ratio Q represented by O, P, a battery resistance ratio R represented by R is smaller, and a cell capacity represented by R is larger than a battery capacity represented by O, P, Q, so that the dischargeable capacity of the battery pack composed of four cells is the electric quantity discharged from the current state to the discharge cut-off voltage of each cell

Illustratively, still taking the above example as an example, the obtained dischargeable electric quantities of the four unit cells of O, P, Q and R are 74 ampere-hour, 81 ampere-hour, 83 ampere-hour and 80 ampere-hour.

And step four, determining the minimum value of the dischargeable electric quantity of the single body as the discharge capacity of the battery pack.

Illustratively, the minimum value of the dischargeable electric quantity is 74 ampere hours, 81 ampere hours, 83 ampere hours and 80 ampere hours, and then the battery cell discharges 74 ampere hours, that is, the discharge capacity of the battery pack is 74 ampere hours.

As can be seen from the above steps, when the stored electricity amounts of the respective cells are 80 ampere hours, 87 ampere hours, 86 ampere hours, and 90 ampere hours in this order at the time of reaching the charge cutoff voltage of the battery pack, the actual remaining electricity amounts are 6 ampere hours, 13 ampere hours, 12 ampere hours, and 16 ampere hours in this order after the respective cells are discharged 74 ampere hours at the time of reaching the discharge cutoff voltage.

The numerical values in the above examples are only for convenience of description, and the values and the like are changed according to actual conditions.

The above-mentioned method for predicting battery capacity mainly aims at the battery pack composed of series-connected batteries, and can predict the battery pack composed of parallel-connected batteries by proper adjustment, which is not described herein.

The above-mentioned prediction method is only the charge capacity and discharge capacity of each single battery of the battery pack in the primary charging and primary discharging processes, and the specific prediction times can be determined according to the actual situation.

For example, the above-mentioned battery pack capacity prediction method may be used to predict the life of a completely new battery pack, or may also predict the remaining life of a battery pack in use.

For predicting the life of a completely new battery pack: firstly, loading a first preset algorithm and a second preset algorithm on terminal equipment as a capacity attenuation model during charging and discharging for predicting the capacity attenuation of each single battery; secondly, acquiring information of initial charging capacity, initial discharging capacity and the like of each single battery included in the battery pack; and then, simulating capacity attenuation by using a capacity attenuation model, and updating the corresponding relation between the charging capacity and the discharging capacity of the battery pack and the time after each simulation attenuation to obtain a service life curve of the battery pack. Therefore, the service life condition of the battery pack can be judged according to the service life curve.

The number of times of performing fading simulation on each single battery may be a preset number of times, or may be determined by the charging capacity and the discharging capacity after each fading simulation, for example, when the charging capacity decreases to a first preset value, or the discharging capacity decreases to a second preset value, it is determined that the service life of the battery pack is ended, and no fading simulation is performed.

For predicting the remaining life of a battery pack in use, for example, a battery pack installed in a vehicle, unlike the above-described prediction of the life of a completely new battery pack, it is necessary to acquire information required for predicting capacity fade from a battery pack management system when performing the prediction, because the temperature, the state of charge, the depth of discharge, the discharge current, the pressure, and the like of each unit battery are recorded by the battery management system during the running of the vehicle, for example, the temperature, the state of charge, the charge current, the pressure, and the like of each unit battery are recorded by the battery pack during the charging. Further, after each charging and driving is finished, calculating a one-time circulation capacity attenuation amount according to a first preset algorithm and a second preset algorithm; calculating the attenuation of the storage capacity once every day according to the parking time, the temperature and the charge state of the battery; the capacity attenuation amount of the vehicle can be calculated by adding the two. And updating the capacity of the battery pack after each attenuation, and recording the attenuation condition of the battery pack capacity in the whole life cycle to obtain a life curve of the battery pack.

In summary, according to the method for predicting the battery capacity provided by the embodiment of the present application, the sum of the minimum current electric quantity in each single battery and the minimum chargeable electric quantity of each single battery is used as the charging capacity of the battery pack, compared with the prior art that the capacity of a single battery is used as the capacity of the battery pack, the embodiment of the present application considers the difference between the capacity, temperature, state of charge and other factors of each single battery in the battery pack, and improves the accuracy of predicting the battery capacity.

The embodiments in this specification are described in a progressive manner, and similar parts between the various embodiments are referred to each other. The examples below each step focus on the specific method below that step. The above-described embodiments are merely illustrative, and the specific examples are only illustrative of the present application, and those skilled in the art can make several modifications and enhancements without departing from the principles and spirit of the examples described herein, which should be construed as being within the scope of the claims.

Optionally, as shown in fig. 9, a schematic structural diagram of a battery management system is provided, where the battery management system provided in an embodiment of the present application includes a processor and a memory, where the memory is configured to store one or more programs; the processor is configured to execute a program to implement the method as described in the above embodiments.

The one or more programs are stored in a program in a read only memory ROM or randomly accessed in a memory RAM to perform various appropriate actions and processes. In the random access memory RAM, software programs that can implement the methods described in the above embodiments in real time are included, as well as various programs and data required for the driving operation of the vehicle. The processor and the read only memory ROM, the random access memory RAM may be connected to each other via a bus, and various input/output interfaces may also be connected to the bus.

The following components are connected to an input/output interface such as a display device or the like and an output portion of a speaker or the like. The onboard controllers may also interface with the communications portion of the network interface card. The communication section performs communication processing via a network such as the internet. The memory may be a removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, and is mounted on a drive as necessary so that a computer program read out therefrom is mounted in the memory as necessary.

According to an embodiment of the present disclosure, the method of controlling switching of the target controller described in any of the above embodiments may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program containing program code for performing a train control method. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium.

In one embodiment, the processor may be configured to include:

the first determining module is used for determining the minimum current electric quantity in the current electric quantities of all the single batteries of the battery pack in a charging mode, wherein the current electric quantity of each single battery is the product of the current charging capacity and the current state of charge of each single battery;

the second determining module is used for determining the current chargeable electric quantity of each single battery when each single battery reaches the respective charging cut-off voltage;

the third determining module is used for determining the minimum chargeable electric quantity in the current chargeable electric quantities of the single batteries;

and the fourth determination module is used for determining the sum of the minimum current existing electric quantity and the minimum chargeable electric quantity as the charging capacity of the battery pack.

Optionally, the second determining module is further configured to:

determining the current charging capacity of each single battery;

Optionally, the first determining module is further configured to:

determining the capacity attenuation of each single battery;

Optionally, the capacity fade amount comprises a cyclic capacity fade amount, and the first determining module is further configured to:

Optionally, the capacity fade amount comprises a storage capacity fade amount, and the first determining module is further configured to:

Optionally, the capacity fade amount includes a cyclic capacity fade amount and a storage capacity fade amount, and the first determining module is further configured to:

Optionally, the first determining module is further configured to:

In addition, please refer to the method embodiment for related contents in the device embodiment, which are not described herein again.

In summary, the battery capacity prediction apparatus provided in the embodiment of the present application uses the sum of the minimum current electric quantity in each battery cell and the minimum chargeable electric quantity of each battery cell as the charging capacity of the battery pack, and compared with the prior art that uses the capacity of a single battery cell as the capacity of the battery pack, the embodiment of the present application considers the difference between the capacity, temperature, state of charge, and other factors of each battery cell in the battery pack, thereby improving the accuracy of battery capacity prediction.

In particular, the processes described by the flowcharts according to the embodiments of the present application may be implemented as computer software programs. For example, method embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. Which when executed by a processor performs the above-described functions defined in the system of the present application.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software, or by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves. The described units or modules may also be provided in a processor, and may be described as: a processor includes a first determination module, a second determination module, a third determination module, and a fourth determination module. The names of these units or modules do not in some cases constitute a limitation on the units or modules themselves.

As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the battery capacity prediction method as described in the above embodiments.

For example, the battery management system may implement as shown in fig. 2: step 201, in a charging mode, determining the minimum current electric quantity in the current existing electric quantities of each single battery of a battery pack; step 202, determining the current chargeable electric quantity of each single battery when each single battery reaches the respective charging cut-off voltage; step 203, determining the minimum chargeable electric quantity in the current chargeable electric quantities of each single battery; step 204, determining the sum of the minimum current electric quantity and the minimum chargeable electric quantity as the charging capacity of the battery pack.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, in accordance with the embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware.

In summary, the battery pack capacity prediction management device or the computer-readable medium provided in the embodiment of the present application uses the sum of the minimum current electric quantity in each battery cell and the minimum chargeable electric quantity of each battery cell as the charging capacity of the battery pack, and compared with the prior art that the capacity of a single battery cell is used as the capacity of the battery pack, the embodiment of the present application considers the difference between the capacities of the battery cells in the battery pack, thereby improving the accuracy of battery pack capacity prediction.

The foregoing is considered as illustrative only of the preferred embodiments of the invention and illustrative only of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application referred to in the present application is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the spirit of the application. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A method for predicting battery capacity, the method comprising:

determining a sum of the minimum current capacity and the minimum chargeable capacity as a charging capacity of the battery pack.

2. The method for predicting the capacity of a battery pack according to claim 1, wherein the determining the current chargeable capacity of each battery cell when each battery cell reaches the respective charge cut-off voltage comprises:

determining the current charging capacity of each single battery;

3. The method for predicting the capacity of a battery pack according to claim 1, wherein the determining the current electric quantity of each battery cell of the battery pack comprises:

determining the capacity attenuation of each single battery;

and determining the current electric quantity of each single battery according to the current charging capacity and the current charge state of each single battery.

4. A battery container prediction method according to claim 3, wherein the capacity fade comprises a cyclic capacity fade, and the determining a capacity fade for each cell comprises:

acquiring a circulating capacity attenuation parameter of each single battery, wherein the circulating capacity attenuation parameter comprises a temperature parameter, a capacity throughput parameter, a pressure parameter, a discharge depth parameter and a charge state parameter;

determining the circulating capacity attenuation amount of each single battery based on the circulating capacity attenuation amount parameter of each single battery and a first preset algorithm, wherein the circulating capacity attenuation amount is the single battery capacity attenuation amount caused by charge and discharge factors.

5. The battery pack capacity prediction method according to claim 3, wherein the capacity fade amount comprises a storage capacity fade amount, and the determining the capacity fade amount of each unit cell comprises:

6. The battery pack capacity prediction method according to claim 3, wherein the capacity attenuation amount includes a cyclic capacity attenuation amount and a storage capacity attenuation amount, and the determining the capacity attenuation amount of each unit battery includes:

determining the circulating capacity attenuation amount of each single battery based on the circulating capacity attenuation amount parameter of each single battery and a first preset algorithm, wherein the circulating capacity attenuation amount is the single battery capacity attenuation amount caused by charge and discharge factors;

and determining the storage capacity attenuation of each single battery based on the storage capacity attenuation parameter of each single battery and a second preset algorithm, wherein the storage capacity attenuation is the single battery capacity attenuation caused by time and charge state factors.

7. The method for predicting the capacity of the battery pack according to claim 4 or 6, wherein the determining the amount of cyclic capacity attenuation of each unit battery based on the cyclic capacity attenuation parameter of each unit battery and a first preset algorithm comprises:

and actually determining the circulating capacity attenuation of each single battery according to a first preset algorithm by using the corresponding temperature parameter value, capacity throughput parameter value, pressure parameter value, depth of discharge parameter value and state of charge parameter value under different circulating capacity determination conditions.

8. The method for predicting the capacity of the battery pack according to claim 5 or 6, wherein the determining the storage capacity decrement of each unit battery based on the storage capacity decrement parameter of each unit battery and a second preset algorithm comprises:

and actually determining the storage capacity attenuation of each single battery according to a second preset algorithm by using the corresponding temperature parameter value, storage time parameter value, pressure parameter value and charge state parameter value under the different storage capacity determination conditions.

9. A battery management apparatus, characterized in that the apparatus comprises:

one or more processors;

a memory for storing one or more programs;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-8.

10. A computer-readable storage medium, having stored thereon a computer program for:

the computer program, when executed by a processor, implements the method of any of claims 1-8.