CN115389938A - Method, system, electronic device and medium for predicting battery remaining capacity - Google Patents

Abstract

The present disclosure provides a method, a system, an electronic device, and a medium for predicting a remaining capacity of a battery, the method including the steps of: presetting a first corresponding relation between a theoretical residual capacity value and a theoretical attenuation value of a battery; predicting the theoretical residual capacity value of the current working stage according to the current first corresponding relation, the theoretical residual capacity value of the previous working stage and the theoretical attenuation value of the current working stage; acquiring an actual residual capacity value of the battery in a current working stage; updating the first corresponding relationship according to the comparison result of the actual residual capacity value and the theoretical residual capacity value; and re-predicting the theoretical residual capacity value of the battery based on the updated first corresponding relation. The method and the device realize adjustment of the prediction method aiming at the actual residual capacity of the battery individual, realize self-updating iteration of the prediction method, and provide the accurate prediction result of the actual residual capacity corresponding to the battery individual.

Description

Method, system, electronic device and medium for predicting battery remaining capacity

technical field

The present disclosure relates to the field of prediction of battery remaining capacity, and in particular to a method, system, electronic device and medium for predicting battery remaining capacity.

Background technique

As an indispensable product in today's life, batteries have a wide range of application scenarios. For batteries, the prediction of their remaining battery capacity is an important technical difficulty. Users cannot accurately arrange the work process, resulting in immeasurable losses. In the field of prediction of battery remaining capacity, the data volume of the traditional database fitting equation is limited, and it only fits the laboratory data and limited actual test data. In practice, the characteristics, constitution, environment, and working conditions of the individual There are differences, and the traditional unified algorithm cannot perform targeted data processing for a specific battery individual, nor can it achieve self-matching iterations of the algorithm. The power data display value drops.

Contents of the invention

The technical problem to be solved in the present disclosure is to provide a method, system, electronic device and medium for predicting the remaining capacity of a battery in order to overcome the defect that the method for predicting the actual remaining capacity of an individual battery cannot be adjusted in the prior art.

The present disclosure solves the above-mentioned technical problems through the following technical solutions:

In a first aspect, a method for predicting the remaining capacity of a battery is provided, and the method for predicting includes the following steps:

Preset the first corresponding relationship between the theoretical remaining capacity value of the battery and the theoretical attenuation value;

Predicting the theoretical remaining capacity value of the current working stage according to the current first corresponding relationship, the theoretical remaining capacity value of the previous working stage, and the theoretical attenuation value of the current working stage;

Obtain the actual remaining capacity value of the battery in the current working stage;

updating the first corresponding relationship according to a comparison result between the actual remaining capacity value and the theoretical remaining capacity value;

Re-predicting the theoretical remaining capacity value of the battery based on the updated first correspondence relationship.

Preferably, the obtaining the actual remaining capacity value of the battery in the current working stage specifically includes the following steps:

According to the material of the battery, preset a second corresponding relationship between the state of charge of the battery and the voltage of the battery;

obtaining the current voltage of the battery;

Acquiring the change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and the change value of the battery voltage within the battery use start and end interval;

The actual remaining capacity value of the battery is obtained according to the ratio of the theoretical consumption value of the battery capacity and the change value of the battery state of charge of the battery during the battery use start and end intervals of the battery.

Preferably, after obtaining the change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and the change value of the battery voltage within the battery use start and end interval, the following steps are further included:

judging whether the change value of the battery state of charge of the battery is greater than a preset threshold;

If it is greater than that, continue to execute the step of deriving the actual remaining capacity of the battery according to the ratio between the theoretical consumption value of the battery capacity and the change value of the battery state of charge of the battery within the battery use interval.

Preferably, the first correspondence is a correction coefficient k, and the correction coefficient k is determined by the duration of the current working phase of the battery and at least one influencing parameter; the updating of the first correspondence specifically includes the following steps :

Obtain at least one influencing parameter of the current working stage of the battery;

The first corresponding relationship is updated based on the influencing parameter and a first formula.

Preferably, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

Preferably, after the step of re-predicting the theoretical remaining capacity of the battery based on the updated first correspondence based on the updated first correspondence, the step further includes:

monitoring the operating phase of said battery;

When the working stage of the battery changes, return to the step of predicting the theoretical remaining capacity value of the current working stage according to the current first corresponding relationship to start the next round of prediction.

Preferably, the working phase includes a charging phase, a storage phase, and a discharging phase.

Preferably, when all influencing parameters are obtained at the same time, the first formula is:

Among them, t ₀ is the start time of the current working stage, t ₁ is the end time of the current working stage, _Pa is the charging power, P _b is the discharging power; T _a is the battery temperature; T _b is the ambient temperature;

分别为对应工作阶段下的拟合函数；

are the fitting functions in the corresponding working stages;

p _a , p _b , t _a , t _b are preset reference parameters;

j _a , j _b , j _c , j _d are preset weight correction coefficients;

and / or,

After the step of updating the first correspondence according to the comparison result between the actual remaining capacity value and the theoretical remaining capacity value, the step further includes:

If the ratio of the actual remaining capacity value to the theoretical remaining capacity value does not exceed the preset interval, when the working stage of the battery changes, return to the first corresponding relationship based on the current, previous The theoretical remaining capacity value of the working stage and the theoretical decay value of the current working stage The step of predicting the theoretical remaining capacity value of the current working stage at the current moment starts the step of predicting the next round.

Preferably, after the step of re-predicting the theoretical remaining capacity of the battery based on the updated first correspondence based on the updated first correspondence, the step further includes:

updating the theoretical remaining capacity value;

Judging whether the theoretical remaining capacity value of the battery is less than a preset threshold;

If less than, a warning is issued;

and / or,

After the step of updating the first correspondence, it also includes:

The first corresponding relationship before updating is saved.

In the second aspect, a prediction system for remaining battery capacity is provided, including:

The preset module is used to preset the first corresponding relationship between the theoretical remaining capacity value and the theoretical attenuation value of the battery;

A prediction module, configured to predict the theoretical remaining capacity value of the current working stage according to the current first corresponding relationship, the theoretical remaining capacity value of the previous working stage, and the theoretical attenuation value of the current working stage;

An acquisition module, configured to acquire the actual remaining capacity value of the battery in the current working stage;

An updating module, configured to update the first corresponding relationship according to a comparison result between the actual remaining capacity value and the theoretical remaining capacity value;

The prediction module is further configured to re-predict the theoretical remaining capacity value of the battery based on the updated first correspondence.

Preferably, the acquisition module includes:

a preset unit, configured to preset a second corresponding relationship between the battery state of charge and the voltage of the battery according to the material of the battery;

a voltage acquisition unit, configured to acquire the current voltage of the battery;

The charge acquisition unit is configured to acquire the change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and the change value of the voltage in the battery use start and end interval of the battery;

The capacity estimation unit is used to calculate the actual remaining capacity of the battery according to the ratio between the theoretical consumption value of the battery capacity and the change value of the battery state of charge of the battery within the battery usage interval of the battery.

Preferably, the charge acquisition unit includes:

A judging subunit, configured to judge whether the change value of the battery state of charge of the battery is greater than a preset threshold;

The execution subunit is used to continue to execute the battery charge based on the theoretical consumption value of the battery capacity in the battery use start and stop interval of the battery and the battery charge of the battery when the change value of the battery state of charge of the battery is greater than a preset threshold value. The step of obtaining the current voltage of the battery is obtained from the ratio of the change value of the electric state to obtain the actual remaining capacity value of the battery.

Preferably, the first corresponding relationship is a correction coefficient k, and the correction coefficient k is determined by the duration of the current working phase of the battery and at least one influencing parameter; the update module includes:

A parameter acquisition unit, configured to acquire at least one influencing parameter of the current working stage of the battery at the current moment;

An updating unit, configured to update the first correspondence based on the influencing parameter and the first formula.

Preferably, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

Preferably, the forecasting system also includes:

a monitoring module, configured to monitor the working phase of the battery;

A jump module, configured to return to predicting the current moment based on the current first correspondence, the theoretical remaining capacity value of the previous working stage, and the theoretical attenuation value of the current working stage when the working stage of the battery changes. The next round of forecasting starts with the step of the theoretical remaining capacity value of the current working phase.

Preferably, the working phase includes a charging phase, a storage phase, and a discharging phase.

Preferably, when all influencing parameters are obtained at the same time, the first formula is:

Among them, t ₀ is the start time of the current working stage, t ₁ is the end time of the current working stage, _Pa is the charging power, P _b is the discharging power; T _a is the battery temperature; T _b is the ambient temperature;

分别为对应工作阶段下的拟合函数；

are the fitting functions in the corresponding working stages;

p _a , p _b , t _a , t _b are preset reference parameters;

j _a , j _b , j _c , j _d are preset weight correction coefficients;

and / or,

The predictive system also includes:

A return module, configured to return to the prediction module to start the next round when the working stage of the battery changes when the ratio of the actual remaining capacity value to the theoretical remaining capacity value does not exceed a preset interval Predicted steps.

Preferably, the forecasting system also includes:

a capacity update module, configured to update the theoretical remaining capacity value;

A judging module, configured to judge whether the theoretical remaining capacity of the battery is less than a preset threshold;

A warning module, configured to send a warning when the theoretical remaining capacity of the battery is less than a preset threshold;

and / or,

The predictive system also includes:

A saving module, configured to save the first correspondence before updating.

In a third aspect, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and operable on the processor, and the above-mentioned method for predicting the remaining battery capacity is implemented when the processor executes the computer program .

In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above method for predicting the remaining capacity of a battery is implemented.

The positive progress effect of the present disclosure is that it can adjust the prediction method of the actual remaining capacity of the individual battery, and realize the self-updating iteration of the prediction method, so that the situation of inaccurate prediction of the remaining capacity can be reduced during the operation process, and the situation caused by inaccurate power calculation can be avoided. The power data display value drop problem provides accurate prediction results of actual remaining capacity corresponding to individual batteries.

Description of drawings

FIG. 1 is a schematic flow chart of a first method for predicting the remaining capacity of a battery provided in Embodiment 1 of the present disclosure.

FIG. 2 is a schematic diagram of the relationship between the remaining battery life and the capacity fading value of a single cycle provided by Embodiment 1 of the present disclosure.

3 is a schematic diagram of the relationship between the capacity SOC and the voltage of a lithium iron phosphate battery provided in Example 1 of the present disclosure.

FIG. 4 is a schematic diagram of fitting functions of four influencing parameters of the charging stage provided by Embodiment 1 of the present disclosure.

FIG. 5 is a schematic diagram of the fitting function of the four influencing parameters of the discharge stage provided by Embodiment 1 of the present disclosure.

FIG. 6 is a schematic diagram of fitting functions of four influencing parameters of the storage stage provided by Embodiment 1 of the present disclosure.

FIG. 7 is a block diagram of a system for predicting the remaining battery capacity provided by Embodiment 2 of the present disclosure.

FIG. 8 is a schematic diagram of modules of an electronic device provided by Embodiment 3 of the present disclosure.

Detailed ways

The present disclosure is further illustrated below by means of examples, but the present disclosure is not limited to the scope of the examples.

Example 1

In a first aspect, a method for predicting the remaining capacity of a battery is provided, as shown in FIG. 1 , the method for predicting includes the following steps:

Step 101, preset the first corresponding relationship between the theoretical remaining capacity value of the battery and the theoretical attenuation value;

Step 102. Predict the theoretical remaining capacity value of the current working stage according to the first corresponding relationship, the theoretical remaining capacity value of the previous working stage, and the theoretical attenuation value of the current working stage;

Step 103, acquiring the actual remaining capacity value of the battery in the current working stage;

Step 104, updating the first corresponding relationship according to the comparison result between the actual remaining capacity value and the theoretical remaining capacity value;

Step 105: Re-predict the theoretical remaining capacity value of the battery based on the updated first correspondence relationship.

Step 101 and step 102 are described here. In the actual implementation, the concept of battery life needs to be introduced here. The larger the battery capacity value consumed in a single cycle, the less the remaining battery life. Therefore, the remaining battery capacity can also be calculated by value to predict the remaining life of the battery; here, the theoretical remaining capacity value C _theory is calculated by the theoretical attenuation value C _attenuation . After a single working stage is completed, according to the current first corresponding relationship, the theoretical remaining capacity value and The theoretical attenuation value of the current working stage predicts the theoretical remaining capacity value of the current working stage. The calculation formula of the theoretical capacity value is: C _n+1 =C _n -kC _attenuation ; here k is the first corresponding relationship, that is, the And the correction factor.

It should be noted that the fitting function about the theoretical attenuation value and the remaining battery life can be obtained through experiments. The theoretical attenuation value increases with the decrease of the remaining battery life. The ratio between the maximum capacity value and the maximum capacity value in the factory state of the battery is inferred. The larger the ratio, the longer the remaining battery life, the smaller the battery loss, and the healthier the battery.

For example, as shown in Figure 2, the relationship between the remaining life of a group of batteries obtained from experiments and the decay value C of the single-cycle capacity _decay is shown. Before predicting the theoretical remaining capacity value of the current working stage, it is necessary to obtain the theoretical value of the current working stage decay value.

Step 104 is described here. The principle here is that the first corresponding relationship does not have to be updated every time the ratio of the actual remaining capacity value to the theoretical remaining capacity value changes. If the actual remaining capacity Value and the ratio of the theoretical remaining capacity value exceeds the preset range, then update the first corresponding relationship, the purpose of setting the preset range here is to avoid unnecessary algorithm correction and increase the amount of calculation; for example, setting The preset intervals are as follows:

If the ratio falls within the range, the algorithm will not be corrected, that is, the first corresponding relationship will not be updated, but the latest working stage (described later) will be merged with the next working stage; if m working stages are maintained All ratios fall within this range, indicating that the change in battery capacity loss is negligible until the ratio exceeds the range in a certain working stage, that is, until the error between C _theory and C _actual exceeds 0.5%, refer to the following formula The relationship is calculated:

C _n+m = C _n -kC _decay

In an optional implementation manner, step 103 specifically includes the following steps:

According to the material of the battery, preset a second corresponding relationship between the state of charge of the battery and the voltage of the battery;

obtaining the current voltage of the battery;

Acquiring the change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and the change value of the battery voltage within the battery use start and end interval;

The actual remaining capacity value of the battery is obtained according to the ratio of the theoretical consumption value of the battery capacity and the change value of the battery state of charge of the battery during the battery use start and end intervals of the battery.

In a specific example, the general formula of the energy consumption of battery capacity known in the art is ΔC=ΣIUΔt; state) and the voltage, that is, when the voltage is 3.65V, the current capacity is 100%, and when the voltage is about 2.75V, the capacity is 0. But in practice, it is rare to encounter the situation that the capacity is fully charged or completely used up. Referring to Figure 3, we can calculate the SOC change value ΔSOC by measuring the voltage change value ΔV between the start and end of battery use, and then combine the measurement of capacity Value ΔC=ΣIUΔt to calculate the actual capacity of the battery, the actual capacity is the following formula:

In an optional implementation manner, after the change value of the battery state of charge of the battery in the current working stage is obtained according to the second corresponding relationship and the change value of the voltage of the battery in the battery use start and end interval, Also includes the following steps:

judging whether the change value of the battery state of charge of the battery is greater than a preset threshold;

If it is greater than that, continue to execute the step of deriving the actual remaining capacity of the battery according to the ratio of the theoretical consumption value of the battery capacity and the change value of the battery state of charge of the battery within the battery use start and end interval.

Combining the above example, the principle of introducing the preset threshold here is that it can be observed in conjunction with Figure 3 that the larger the range of numerical changes, the more accurate the calculation result. Therefore, after ΔSOC reaches the preset threshold, if ΔSOC≥20%, It is more reasonable to perform volume calibration calculations.

In an optional implementation manner, the first corresponding relationship is a correction coefficient k, and the correction coefficient k is determined by the duration of the current working phase of the battery and at least one influencing parameter; the step 104 specifically includes the following step:

Obtain at least one influencing parameter of the current working stage of the battery;

The first corresponding relationship is updated based on the influencing parameter and a first formula.

In an optional implementation manner, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

In an optional implementation manner, after the step 105, it also includes:

monitoring the operating phase of said battery;

When the working stage of the battery changes, return to the step of predicting the theoretical remaining capacity value of the current working stage according to the current first corresponding relationship to start the next round of prediction.

In an optional implementation manner, the working phase includes a charging phase, a storage phase, and a discharging phase.

)是不同的，如图4、图5、图6所示，通过实验，可以提前获得上述四个影响参数分别在三种不同工作状态下的拟合函数，需要注意的是图4、图5和图6中的t₁、t₂、t₃仅起区分作用，三者含义皆为所述当前工作状态的结束时间；当工作阶段改变时，例如从充电阶段变为放电阶段时，就需要选择放电阶段相对应的拟合函数代入算法公式(例如后文的第一公式)进行新一轮的剩余容量的预测。The principle of monitoring the working stage of the battery here is that the working condition data in different working stages are different, so the corresponding fitting function (that is, the

) are different, as shown in Figure 4, Figure 5, and Figure 6. Through experiments, the fitting functions of the above four influencing parameters under three different working conditions can be obtained in advance. It should be noted that Figure 4, Figure 5 and t ₁ , t ₂ , and t ₃ in Fig. 6 are only used to distinguish, and the meanings of all three are the end time of the current working state; when the working stage changes, for example, from the charging stage to the discharging stage, it is necessary to A fitting function corresponding to the discharge stage is selected and substituted into an algorithm formula (such as the first formula below) to predict the remaining capacity of a new round.

In an optional implementation manner, when all influencing parameters are acquired at the same time, the first formula is:

Among them, t ₀ is the start time of the current working stage, t ₁ is the end time of the current working stage, _Pa is the charging power, P _b is the discharging power; T _a is the battery temperature; T _b is the ambient temperature;

分别为对应工作阶段下的拟合函数；

are the fitting functions in the corresponding working stages;

p _a , p _b , t _a , t _b are preset reference parameters;

j _a , j _b , j _c , j _d are preset weight correction coefficients;

It should be noted that the above algorithm formula is only for the case where the four most common influencing parameters listed here are considered. In practical applications, the algorithm can be designed according to individual needs, and the influencing parameters can be greater than four, or , less than four; change the above algorithm based on the number of influencing parameters, for example, in a specific example, only three influences of P _a (charging power), P _b (discharging power), and T _a (battery temperature) are involved parameters, at this time the algorithm formula is adjusted to (the meaning of the parameters is the same as that in the first formula, which is only used as an example here):

In an optional implementation manner, after the step 104, it also includes:

If the ratio of the actual remaining capacity value to the theoretical remaining capacity value does not exceed the preset interval, when the working stage of the battery changes, return to the first corresponding relationship based on the current, previous The theoretical remaining capacity value of the working stage and the theoretical decay value of the current working stage The step of predicting the theoretical remaining capacity value of the current working stage at the current moment starts the step of predicting the next round.

In an optional implementation manner, after the step 105, it also includes:

updating the theoretical remaining capacity value;

Judging whether the theoretical remaining capacity value of the battery is less than a preset threshold;

If less than, a warning is issued;

In actual implementation, since the calculation curve will change immeasurably when the battery is depleted to a certain stage, the theoretical remaining capacity value of the battery is usually judged on the threshold. In addition, the maximum available capacity of the battery at this time in the fully charged state can be calculated more intuitively through the calculation equation of the theoretical remaining capacity value. Set a threshold, such as 80%, at this time, a warning will be issued to inform the user that a new battery needs to be replaced, and the life of the original battery is 0. For the user experience, it is not set that the battery life is 0 when the ratio is 0%.

In an optional implementation manner, after the step 104, it also includes:

The first corresponding relationship before updating is saved.

In the specific implementation, after the original algorithm is optimized, the optimized working conditions will continue to be calculated and predicted on the user side, and the original calculation data will not be updated and covered. The original calculation data is saved for uploading to the background, which is convenient for users or designers to analyze the accuracy of the algorithm and the self-optimization process of the algorithm. At the same time, the user or designer continuously optimizes the accuracy of the compensation algorithm in the control side background, and regularly checks the entire set of prediction methods. Perform maintenance updates.

In addition to the above content, in practical applications, often the battery does not appear as an individual, and the combination of multiple module BMSs is more common. Since this disclosure can update and iterate the prediction method for each individual battery, once When the number of monitored objects is large, the requirements for computing power will also increase. Based on this, this disclosure also considers that the storage location of the entire set of prediction method algorithms will have different impacts on the computing power requirements. If the algorithm is designed locally, the optimization will be completed in the background In the process, the communication frequency is low and the computing power requirement is high; if the algorithm design and optimization process are carried out in the background, the communication frequency is high and the local computing power requirement is low; the above design can be selected according to different scenarios.

In this embodiment, through the collection of different working conditions in different working stages of a battery, a set of battery remaining capacity prediction methods that can complete self-renewal iterations are designed, so that the inaccurate prediction of remaining capacity can be reduced during operation and avoid The power data display value drop problem caused by inaccurate power calculation provides accurate prediction results of the actual remaining capacity corresponding to the individual battery.

Example 2

This embodiment provides a prediction system 10 for the remaining battery capacity. The prediction system 10 implements the prediction method in Embodiment 1, as shown in FIG. 7 , including:

The preset module 11 is used to preset the first corresponding relationship between the theoretical remaining capacity value and the theoretical attenuation value of the battery;

A prediction module 12, configured to predict the theoretical remaining capacity value of the current working stage according to the current first corresponding relationship, the theoretical remaining capacity value of the previous working stage, and the theoretical attenuation value of the current working stage;

An acquisition module 13, configured to acquire the actual remaining capacity value of the battery in the current working stage;

An update module 14, configured to update the first corresponding relationship according to a comparison result between the actual remaining capacity value and the theoretical remaining capacity value;

The prediction module 12 is further configured to re-predict the theoretical remaining capacity value of the battery based on the updated first correspondence.

In an optional implementation manner, the acquisition module 13 includes the following:

The preset unit 131 is configured to preset a second corresponding relationship between the battery state of charge and the voltage of the battery according to the material of the battery;

a voltage acquisition unit 132, configured to acquire the current voltage of the battery;

The charge acquisition unit 133 is configured to acquire the change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and the change value of the voltage within the battery use start and end interval of the battery;

The capacity estimation unit 134 is configured to calculate the actual remaining capacity of the battery according to the ratio between the theoretical consumption value of the battery capacity and the change value of the battery state of charge of the battery within the battery usage interval of the battery.

In an optional implementation manner, the charge acquisition unit 133 includes the following:

A judging subunit 1331, configured to judge whether the change value of the battery state of charge of the battery is greater than a preset threshold;

Executing subunit 1332, configured to continue to execute the battery operation according to the theoretical consumption value of the battery capacity in the battery use start and end interval of the battery and the battery capacity of the battery when the change value of the battery state of charge of the battery is greater than a preset threshold value. The step of obtaining the current voltage of the battery is obtained from the ratio of the change value of the state of charge to obtain the actual remaining capacity value of the battery.

In an optional implementation manner, the first corresponding relationship is a correction coefficient k, and the correction coefficient k is determined by the duration of the current working phase of the battery and at least one influencing parameter; the update module 14 includes the following :

A parameter acquisition unit 141, configured to acquire at least one influencing parameter of the current working stage of the battery at the current moment;

An updating unit 142, configured to update the first correspondence based on the influencing parameter and the first formula.

In an optional implementation manner, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

In an optional embodiment, the prediction system 10 also includes:

A monitoring module 15, configured to monitor the working phase of the battery;

The jump module 16 is used to return the predicted current capacity according to the current first corresponding relationship, the theoretical remaining capacity value of the previous working stage and the theoretical attenuation value of the current working stage when the working stage of the battery changes. The next round of forecasting starts at the step of the theoretical remaining capacity value of the current working stage.

In an optional implementation manner, the working phase includes a charging phase, a storage phase, and a discharging phase.

In an optional implementation manner, when all influencing parameters are acquired at the same time, the first formula is:

Among them, t ₀ is the start time of the current working stage, t ₁ is the end time of the current working stage, _Pa is the charging power, P _b is the discharging power; T _a is the battery temperature; T _b is the ambient temperature;

分别为对应工作阶段下的拟合函数；

are the fitting functions in the corresponding working stages;

p _a , p _b , t _a , t _b are preset reference parameters;

j _a , j _b , j _c , j _d are preset weight correction coefficients;

In an optional embodiment, the prediction system 10 also includes:

return module 17, used to return to the prediction module 12 to start the next step when the working stage of the battery changes when the ratio of the actual remaining capacity value to the theoretical remaining capacity value does not exceed the preset range; round of prediction steps.

In an optional embodiment, the prediction system 10 also includes:

A capacity update module 18, configured to update the theoretical remaining capacity value;

Judging module 19, for judging whether the theoretical remaining capacity value of the battery is less than a preset threshold;

A warning module 20, configured to issue a warning when the theoretical remaining capacity of the battery is less than a preset threshold;

In an optional embodiment, the prediction system 10 also includes:

The saving module 21 is configured to save the first corresponding relationship before updating.

In this embodiment, a prediction system is provided. The system designs a set of prediction methods for the remaining capacity of the battery that can complete self-renewal iterations through the collection of different working conditions in different working stages of a battery, so that the reduction in the operating process is reduced. In the case of inaccurate prediction of remaining capacity, it avoids the problem of power data display value drop caused by inaccurate power calculation, and provides accurate prediction results of actual remaining capacity corresponding to individual batteries.

Example 3

This embodiment provides an electronic device. FIG. 8 is a schematic structural diagram of an electronic device provided by this embodiment. The electronic device includes a memory, a processor, and a computer program stored in the memory and operable on the processor. When the computer executes the computer program, the method for predicting the remaining capacity of the battery in Embodiment 1 above is realized. The electronic device 80 shown in FIG. 8 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure. As shown in FIG. 8, the electronic device 80 may be in the form of a general-purpose computing device, for example, it may be a server device. Components of the electronic device 80 may include, but are not limited to: at least one processor 81 , at least one memory 82 , and a bus 83 connecting different system components (including the memory 82 and the processor 81 ).

The bus 83 includes a data bus, an address bus and a control bus.

The memory 82 may include a volatile memory, such as a random access memory (RAM) 821 and/or a cache memory 822 , and may further include a read only memory (ROM) 823 .

Memory 82 may also include a program tool 825 (or utility) having a set (at least one) of program modules 824, such program modules 824 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include the implementation of the network environment.

The processor 81 executes various functional applications and data processing by running the computer program stored in the memory 82 , such as the method for predicting the remaining capacity of the battery in the first embodiment above.

Electronic device 80 may also communicate with one or more external devices 84 . Such communication may occur through input/output (I/O) interface 85 . Also, the model-generating electronic device 80 may also communicate with one or more networks (eg, a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) via a network adapter 86 . As shown in FIG. 8 , the network adapter 86 communicates with other modules of the electronic device 80 through the bus 83 . It should be appreciated that although not shown in FIG. 8 , other hardware and/or software modules may be used in conjunction with electronic device 80, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array ) systems, tape drives, and data backup storage systems.

It should be noted that although several units/modules or subunits/modules of an electronic device are mentioned in the above detailed description, such division is only exemplary and not mandatory. Actually, according to the embodiments of the present disclosure, the features and functions of two or more units/modules described above may be embodied in one unit/module. Conversely, the features and functions of one unit/module described above can be further divided to be embodied by a plurality of units/modules.

Example 4

This embodiment provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the method for predicting the remaining capacity of the battery in Embodiment 1 above is implemented.

Wherein, the readable storage medium may more specifically include but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device or any of the above-mentioned the right combination.

In a possible implementation manner, the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product runs on the terminal device, the program code is used to make the terminal device execute the implementation of the above-mentioned embodiment 1. The steps of the method for predicting the remaining capacity of the battery.

Wherein, the program code for executing the present disclosure may be written in any combination of one or more programming languages, and the program code may be completely executed on the user equipment, partially executed on the user equipment, or used as an independent software Package execution, partly on the user device and partly on the remote device, or entirely on the remote device.

Although the specific implementations of the present disclosure have been described above, those skilled in the art should understand that this is only an example, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principle and essence of the present disclosure, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims (12)

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

presetting a first corresponding relation between a theoretical residual capacity value and a theoretical attenuation value of a battery;

predicting the theoretical residual capacity value of the current working stage according to the current first corresponding relation, the theoretical residual capacity value of the previous working stage and the theoretical attenuation value of the current working stage;

acquiring an actual residual capacity value of the battery in a current working stage;

updating the first corresponding relation according to a comparison result of the actual residual capacity value and the theoretical residual capacity value;

and predicting the theoretical residual capacity value of the battery again based on the updated first corresponding relation.

2. The method for predicting remaining battery capacity according to claim 1, wherein the step of obtaining the actual remaining capacity value of the battery at the current operating stage specifically comprises the steps of:

presetting a second corresponding relation between the battery charge state and the voltage of the battery according to the material of the battery;

acquiring the current voltage of the battery;

acquiring a change value of the battery state of charge of the battery at the current working stage according to the second corresponding relation and the change value of the voltage in the battery use starting and stopping interval of the battery;

and obtaining the actual residual capacity value of the battery according to the ratio of the theoretical consumption value of the battery capacity in the battery use starting and stopping interval of the battery to the change value of the battery charge state of the battery.

3. The method according to claim 2, wherein the step of obtaining the change value of the battery state of charge of the battery at the current operating stage according to the second correspondence and the change value of the voltage in the battery use start-stop interval further comprises:

judging whether the change value of the battery charge state of the battery is larger than a preset threshold value or not;

and if the current value is larger than the preset value, continuing to execute the step of obtaining the actual residual capacity value of the battery according to the ratio of the theoretical consumption value of the battery capacity in the battery use starting and stopping interval of the battery to the change value of the battery charge state of the battery.

4. The method according to claim 1, wherein the first corresponding relationship is a correction factor k, and the correction factor k is determined by the duration of the current operation phase of the battery and at least one influence parameter; the updating the first corresponding relationship specifically includes the following steps:

acquiring at least one influence parameter of the current working stage of the battery;

updating the first correspondence based on the impact parameter and a first formula.

5. The method according to claim 4, wherein the influence parameters include an ambient temperature, a battery temperature, a charging power, and a discharging power.

6. The method for predicting the remaining battery capacity according to claim 4, wherein the step of predicting the theoretical remaining capacity value of the battery based on the updated first correspondence is further followed by:

monitoring the working phase of the battery;

and when the working stage of the battery is changed, returning to the step of predicting the theoretical residual capacity value of the current working stage according to the current first corresponding relation, the theoretical residual capacity value of the previous working stage and the theoretical attenuation value of the current working stage, and starting the next round of prediction.

7. The method according to claim 6, wherein the operation phase includes a charge phase, a storage phase, and a discharge phase.

8. The method of predicting the remaining battery capacity according to claim 7, wherein when all the influence parameters are acquired at the same time, the first formula is:

wherein, t ₀ Is the starting time of the current working phase, t ₁ For the end time of the current working phase, P _a For charging power, P _b Is the discharge power; t is a unit of _a Is the battery temperature; t is _b Is ambient temperature;

respectively as fitting functions in corresponding working stages;

p _a ，p _b ，t _a ，t _b presetting a reference parameter;

j _a ，j _b ，j _c ，j _d correcting the coefficient for the preset weight;

and/or the presence of a gas in the gas,

after the step of updating the first corresponding relationship according to the comparison result between the actual remaining capacity value and the theoretical remaining capacity value, the method further includes:

and if the ratio of the actual residual capacity value to the theoretical residual capacity value does not exceed a preset interval, executing the step of returning to the step of predicting the theoretical residual capacity value of the current working stage at the current moment according to the current first corresponding relation, the theoretical residual capacity value of the previous working stage and the theoretical attenuation value of the current working stage to start the next round of prediction when the working stage of the battery is changed.

9. The method for predicting the remaining capacity of a battery according to claim 1, wherein the step of predicting again the theoretical remaining capacity value of the battery based on the updated first correspondence further includes, after the step of:

updating the theoretical residual capacity value;

judging whether the theoretical residual capacity value of the battery is smaller than a preset threshold value or not;

if the current value is less than the preset value, a warning is sent out;

and/or the presence of a gas in the gas,

after the step of updating the first corresponding relationship, the method further comprises:

and saving the first corresponding relation before updating.

10. A system for predicting a remaining battery capacity, comprising:

the device comprises a presetting module, a first control module and a second control module, wherein the presetting module is used for presetting a first corresponding relation between a theoretical residual capacity value and a theoretical attenuation value of a battery;

the prediction module is used for predicting the theoretical residual capacity value of the current working stage according to the current first corresponding relation, the theoretical residual capacity value of the previous working stage and the theoretical attenuation value of the current working stage;

the acquisition module is used for acquiring the actual residual capacity value of the battery in the current working stage;

the updating module is used for updating the first corresponding relation according to a comparison result of the actual residual capacity value and the theoretical residual capacity value;

the prediction module is further configured to re-predict a theoretical remaining capacity value of the battery based on the updated first correspondence.

11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for predicting the remaining battery capacity according to any one of claims 1 to 9 when executing the computer program.

12. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the method for predicting the remaining capacity of a battery according to any one of claims 1 to 9.

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