CN115389938A - Method, system, electronic device, and medium for predicting remaining battery capacity - Google Patents
Method, system, electronic device, and medium for predicting remaining battery capacity Download PDFInfo
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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
Technical Field
The present disclosure relates to the field of battery remaining capacity prediction, and in particular, to a method, a system, an electronic device, and a medium for predicting battery remaining capacity.
Background
The battery is an indispensable product in the present life, the application scenarios of the battery are very wide, and the prediction of the remaining battery capacity of the battery is an important technical difficulty for the battery, for example, when a computer works, if the remaining battery capacity is predicted incorrectly, a user cannot accurately schedule a work process, and an immeasurable loss is caused. In the field of prediction of the residual capacity of the battery, the data volume of a database fitting equation in a traditional mode is limited, fitting is only performed on laboratory data and limited actual test data, characteristics, physique, environment and working conditions of individuals in practice are different, a traditional unified algorithm cannot perform targeted data processing on specific battery individuals and self-matching iteration of the algorithm cannot be achieved, the problem of inaccurate residual capacity prediction and the problem of drop of a displayed value of electric quantity data caused by inaccurate electric quantity calculation are prone to occur in the operation process.
Disclosure of Invention
The technical problem to be solved by the present disclosure is to provide a method, a system, an electronic device, and a medium for predicting a remaining capacity of a battery, in order to overcome a defect in the prior art that an adjustment cannot be performed with respect to a method for predicting an actual remaining capacity of an individual battery.
The technical problem is solved by the following technical scheme:
in a first aspect, a method for predicting a remaining capacity of a battery is provided, 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 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.
Preferably, the acquiring the actual remaining capacity value of the battery at the current working stage specifically includes the following steps:
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.
Preferably, the following steps are further included after the obtaining of the change value of the battery state of charge of the battery at the current working stage according to the second corresponding relationship and the change value of the voltage in the battery use start-stop interval of the battery:
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 state of charge 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 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.
Preferably, the influencing parameters comprise ambient temperature, battery temperature, charging power and discharging power.
Preferably, the step of predicting the theoretical remaining capacity value of the battery based on the updated first corresponding relation further includes:
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, and starting the next round of prediction.
Preferably, the working phase comprises a charging phase, a storing phase and a discharging phase.
Preferably, when all the influence parameters are acquired simultaneously, 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 Is the battery temperature; t is b Is ambient temperature;
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.
Preferably, the step of predicting the theoretical remaining capacity value of the battery based on the updated first corresponding relation further includes:
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 atmosphere,
after the step of updating the first corresponding relationship, the method further comprises:
and saving the first corresponding relation before updating.
In a second aspect, a system for predicting remaining battery capacity is provided, including:
the device comprises a presetting module, a judging module and a 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;
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 a theoretical remaining capacity value of the battery based on the updated first correspondence.
Preferably, the obtaining module includes:
the presetting unit is used for presetting a second corresponding relation between the battery charge state and the voltage of the battery according to the material of the battery;
the voltage acquisition unit is used for acquiring the current voltage of the battery;
the charge acquisition unit is used for acquiring a change value of the battery charge state of the battery in 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 the capacity estimation unit is used for 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.
Preferably, the charge acquisition unit includes:
the judging subunit is used for judging whether the change value of the battery charge state of the battery is greater than a preset threshold value or not;
and the execution subunit is used for continuously executing the step of obtaining the current voltage of the battery according to the actual residual capacity value of the battery obtained 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 state of charge of the battery when the change value of the battery state of charge of the battery is greater than a preset threshold value.
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 influence parameter; the update module includes:
the parameter acquisition unit is used for acquiring at least one influence parameter of the current working stage of the battery at the current moment;
an updating unit, configured to update the first corresponding relationship based on the influence parameter and a first formula.
Preferably, the influencing parameters include ambient temperature, battery temperature, charging power and discharging power.
Preferably, the prediction system further comprises:
the monitoring module is used for monitoring the working stage of the battery;
and the skip module is used for 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.
Preferably, the working phase comprises a charging phase, a storing phase and a discharging phase.
Preferably, when all the influence parameters are acquired simultaneously, 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 Is the battery temperature; t is a unit of b Is ambient temperature;
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,
the prediction system further comprises:
and the returning module is used for returning to the predicting module to start the next round of prediction when the working stage of the battery is changed when the ratio of the actual residual capacity value to the theoretical residual capacity value does not exceed a preset interval.
Preferably, the prediction system further comprises:
the capacity updating module is used for updating the theoretical residual capacity value;
the judging module is used for judging whether the theoretical residual capacity value of the battery is smaller than a preset threshold value or not;
the warning module is used for sending out a warning when the theoretical residual capacity value of the battery is smaller than a preset threshold value;
and/or the presence of a gas in the gas,
the prediction system further comprises:
and the storage module is used for storing the first corresponding relation before updating.
In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method for predicting the remaining battery capacity when executing the computer program.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the above-described method of predicting a remaining capacity of a battery.
The positive progress effect of this disclosure lies in: the method can be adjusted according to the prediction method of the actual residual capacity of the battery individual, self-updating iteration of the prediction method is achieved, the situation that residual capacity prediction is inaccurate is reduced in the operation process, the problem that the electric quantity data display value drops due to inaccurate electric quantity calculation is avoided, and the accurate prediction result of the actual residual capacity corresponding to the battery individual is provided.
Drawings
Fig. 1 is a first flowchart of a method for predicting remaining battery capacity according to embodiment 1 of the present disclosure.
Fig. 2 is a schematic diagram illustrating a relationship between a remaining life of a battery and a single-cycle capacity decay value according to embodiment 1 of the present disclosure.
Fig. 3 is a schematic diagram of a relationship between capacity SOC and voltage of a lithium iron phosphate battery provided in embodiment 1 of the present disclosure.
Fig. 4 is a schematic diagram of a fitting function of four influence parameters of the charging phase provided in embodiment 1 of the present disclosure.
Fig. 5 is a schematic diagram of a fitting function of four influencing parameters of a discharge phase provided in embodiment 1 of the present disclosure.
Fig. 6 is a schematic diagram of a fitting function of four influencing parameters in the storage stage provided in embodiment 1 of the present disclosure.
Fig. 7 is a schematic block diagram of a system for predicting remaining battery capacity according to embodiment 2 of the present disclosure.
Fig. 8 is a module schematic diagram of an electronic device provided in embodiment 3 of the present disclosure.
Detailed Description
The present disclosure is further illustrated by the following examples, but is not intended to be limited thereby in the scope of the examples described.
Example 1
In a first aspect, a method for predicting remaining battery capacity is provided, as shown in fig. 1, the method includes the following steps:
step 102, predicting a 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;
103, acquiring an actual residual capacity value of the battery in the current working stage;
104, updating the first corresponding relation according to the comparison result of the actual residual capacity value and the theoretical residual capacity value;
and 105, predicting the theoretical residual capacity value of the battery again based on the updated first corresponding relation.
In the specific implementation, it is necessary to introduce the concept of battery life, and the larger the value of battery capacity consumed in a single cycle is, the less the battery residual life is, so that the residual life of the battery can also be predicted by calculating the value of battery residual capacity; here by the theoretical attenuation value C Attenuation of Calculating the theoretical residual capacity value C Theory of the invention After the completion of a single working phase, according to the currentPredicting the theoretical residual capacity value of the current working stage according to the first corresponding relation, the theoretical residual capacity value of the previous working stage and the theoretical attenuation value of the current working stage, wherein the calculation formula of the theoretical capacity value is as follows: c n+1 ＝C n -kC Attenuation of (ii) a Here, k is a first correspondence relationship, i.e., a correction coefficient mentioned later.
It should be noted that a fitting function about a theoretical attenuation value and a residual life of the battery can be obtained through experiments, the theoretical attenuation value increases with the decrease of the residual life of the battery, the residual life of the battery is deduced according to a ratio of a maximum capacity value available in a battery full-charge state and a maximum capacity value in a battery factory state during prediction, and the larger the ratio is, the longer the residual life of the battery is, the less the battery loss is, and the healthier the battery is.
For example, as shown in FIG. 2, a set of the remaining battery life and the single-cycle capacity decay value C obtained by experiments Attenuation of The relation between the theoretical residual capacity and the theoretical attenuation value of the current working stage needs to be obtained before the theoretical residual capacity value of the current working stage is predicted each time.
The step 104 is explained here, and the principle here is that the first corresponding relationship is not updated every time the ratio of the actual remaining capacity value to the theoretical remaining capacity value changes, and if the ratio of the actual remaining capacity value to the theoretical remaining capacity value exceeds a preset interval, the first corresponding relationship is updated, and the preset interval is set here to avoid that the calculation amount is increased by unnecessary algorithm correction; for example, the preset intervals are set as follows:
if the ratio falls within the range, the algorithm is not modified, that is, the first corresponding relation is not updated, but the latest working stage (described later) and the next working stage are merged; if the ratios in the m working stages are maintained to be within the range, the change of the battery capacity loss can be ignored until a certain work is carried outWhen the ratio of the phases is out of range, i.e. up to C Theory of the invention And C Practice of Is greater than 0.5%, calculated with reference to the following equation:
C n+m ＝C n -kC attenuation of
In an optional embodiment, step 103 specifically includes the following steps:
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.
In one specific example, the general formula for energy dissipation of battery capacity is known in the art as Δ C = Σ IU Δ t; as shown in fig. 3, the graph shows the relationship between the capacity SOC (state of charge) and the voltage of the lithium iron phosphate battery obtained through experiments, 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. However, in practice, the capacity is rarely full or full, and referring to fig. 3, we can calculate the SOC variation value Δ SOC by measuring the voltage variation value Δ V in the battery usage start-stop interval, and then calculate the actual capacity of the battery by combining the capacity measurement value Δ C = Σ IU Δ t, where the actual capacity is the following formula:
in an optional embodiment, after obtaining the change value of the battery state of charge of the battery in the current operating stage according to the second corresponding relationship and the change value of the voltage in the battery use start-stop interval of the battery, the method further includes the following steps:
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 state of charge of the battery.
In connection with the above example, the principle of introducing the preset threshold here is that as observed in connection with fig. 3, the larger the numerical value change interval is, the more accurate the calculation result is, and therefore, after the Δ SOC reaches the preset threshold, if the Δ SOC is set to be greater than or equal to 20%, it is more reasonable to perform the capacity calibration calculation.
In an alternative embodiment, the first corresponding relationship is a correction factor k, and the correction factor 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 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.
In an alternative embodiment, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.
In an optional embodiment, the step 105 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, and starting the next round of prediction.
In an alternative embodiment, the working phase includes a charging phase, a storage phase, and a discharging phase.
The principle of monitoring the operating phases of the battery here is that the operating condition data at different operating phases are different and therefore corresponding fitting functions (i.e. in the following formula)Is
) Differently, as shown in fig. 4, 5, and 6, the fitting function of the four influence parameters in three different operating states can be obtained in advance through experiments, and it should be noted that t in fig. 4, 5, and 6 1 、t 2 、t 3 Only has a distinguishing function, and the three meanings are the ending time of the current working state; when the working phase is changed, for example, from the charging phase to the discharging phase, a fitting function corresponding to the discharging phase needs to be selected and substituted into an algorithm formula (for example, a first formula later) to predict the remaining capacity in a new round.
In an optional embodiment, when all the influence parameters are acquired simultaneously, the first formula is:
wherein, t 0 Is the starting time of the current working phase, t 1 As 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;
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;
it should be noted that the above algorithm formula is only for the case that the four most common influence parameters listed here are all considered, in practical application, the algorithm can be designed according to individual requirements, and the influence parameters can be more than four, or less than four; number pair based on influence parametersThe algorithm described above should be modified, for example in a specific case only involving P a (charging Power), P b (discharge power), T a (cell temperature) when the algorithm is adjusted to (where the meaning of the parameters is the same as in the first equation, this is for illustration only):
in an optional embodiment, the step 104 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.
In an alternative embodiment, the step 105 is further followed by:
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;
in practical applications, since the battery loss may cause an immeasurable change in the calculation curve after a certain period of time, the theoretical residual capacity value of the battery is usually determined by a threshold. In addition, the maximum available capacity of the battery in the fully charged state can be calculated more intuitively through a calculation equation of the theoretical residual capacity value, if the ratio of the maximum available capacity of the battery to the maximum capacity value of the battery in the factory state is lower than a preset threshold value, for example, 80%, a warning is sent to inform a user that the battery needs to be replaced with a new battery, the service life of the original battery is 0, and for user experience, the service life of the battery is not calculated when the ratio is 0%.
In an optional embodiment, the step 104 further includes:
and storing the first corresponding relation before updating.
In specific implementation, after the original algorithm is optimized, the optimized working condition is continuously calculated and predicted at the user side, and the original calculation data is not updated and covered. Original calculation data are reserved for uploading to a background, so that a user or a designer can analyze the accuracy of the algorithm and the algorithm self-optimization process conveniently, meanwhile, the user or the designer continuously optimizes the accuracy of the compensation algorithm on the control side background, and the whole set of prediction method is maintained and updated regularly.
In addition to the above, in practical application, batteries often do not appear individually, and situations in which a plurality of module BMSs are combined are common, and since the present disclosure can perform update iteration of the prediction method for each individual battery, once the number of monitored objects is large, the requirement for computing power is also increased, and based on the present disclosure, it is also considered that the storage location of the whole set of prediction method algorithm has different influences on the requirement for computing power, if the algorithm is designed locally, the background completes the optimization process, the communication frequency is low, and the computing power requirement is high; if the algorithm design and the optimization process are carried out in the background, the communication frequency is high, and the requirement on local computing power is low; the above design may be chosen as desired for different scenarios.
In the embodiment, a set of prediction method for the residual capacity of the battery, which can complete self-updating iteration, is designed by aiming at the acquisition of different working conditions of a battery at different working stages, so that the situation of inaccurate prediction of the residual capacity is reduced in the operation process, the problem of drop of the electric quantity data display value caused by inaccurate electric quantity calculation is avoided, and the accurate prediction result of the actual residual capacity corresponding to the battery is provided for an individual.
Example 2
The present embodiment provides a system 10 for predicting remaining battery capacity, where the system 10 implements the prediction method in embodiment 1, as shown in fig. 7, and includes:
the presetting module 11 is used for presetting a first corresponding relation between a theoretical residual capacity value and a theoretical attenuation value of the battery;
the prediction module 12 is configured to predict a theoretical residual capacity value of the current working stage according to the current first correspondence, the theoretical residual capacity value of the previous working stage, and the theoretical attenuation value of the current working stage;
an obtaining module 13, configured to obtain an actual remaining capacity value of the battery in a current working stage;
an updating 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 corresponding relationship.
In an optional embodiment, the obtaining module 13 includes the following:
the presetting 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 obtaining unit 132 for obtaining a current voltage of the battery;
the charge acquisition unit 133 is configured to acquire a change value of a battery charge state of the battery at the current working stage according to the second correspondence and the change value of the voltage in the battery use start-stop interval of the battery;
and a capacity estimation unit 134, configured to obtain an actual remaining capacity value of the battery according to a ratio of a theoretical consumption value of battery capacity in a battery usage start-stop interval of the battery to a change value of a battery state of charge of the battery.
In an alternative embodiment, the charge acquisition unit 133 includes the following:
a determining subunit 1331, configured to determine whether a change value of the battery state of charge of the battery is greater than a preset threshold;
and an executing subunit 1332, configured to, when the change value of the battery state of charge of the battery is greater than a preset threshold, continue to execute the step of obtaining the current voltage of the battery according to the actual remaining capacity value of the battery, where the actual remaining capacity value of the battery is obtained according to the ratio of the theoretical consumption value of the battery capacity in the battery use start-stop interval of the battery to the change value of the battery state of charge of the battery.
In an alternative embodiment, 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 update module 14 comprises the following:
a parameter obtaining unit 141, configured to obtain at least one influence parameter of a current working phase of the battery at the current time;
an updating unit 142, configured to update the first corresponding relationship based on the influence parameter and the first formula.
In an alternative embodiment, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.
In an optional embodiment, the prediction system 10 further comprises:
a monitoring module 15 for monitoring the working phase of the battery;
and the skipping module 16 is used for 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.
In an alternative embodiment, the working phase includes a charging phase, a storage phase, and a discharging phase.
In an optional embodiment, when all the influence parameters are acquired simultaneously, the first formula is:
wherein, t 0 Is the starting time, t, of the current working phase 1 As the end time of the current working phase, P a For charging power, P b Is the discharge power; t is a Is the battery temperature; t is b Is ambient temperature;
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;
in an optional embodiment, the prediction system 10 further comprises:
a returning module 17, configured to, when the ratio between the actual remaining capacity value and the theoretical remaining capacity value does not exceed a preset interval, perform the step of returning to the predicting module 12 to start the next prediction when the operating phase of the battery changes.
In an optional embodiment, the prediction system 10 further comprises:
a capacity updating module 18 for updating the theoretical residual capacity value;
the judging module 19 is used for judging whether the theoretical residual capacity value of the battery is smaller than a preset threshold value or not;
the warning module 20 is configured to send a warning when the theoretical remaining capacity value of the battery is smaller than a preset threshold;
in an optional embodiment, the prediction system 10 further comprises:
a saving module 21, configured to save the first corresponding relationship before updating.
In the embodiment, a prediction system is provided, and a set of prediction method capable of completing self-updating iteration of residual capacity of a battery is designed by aiming at acquisition of different working conditions of a battery at different working stages, so that the situation of inaccurate prediction of the residual capacity is reduced in the operation process, the problem of drop of a display value of electric quantity data caused by inaccurate electric quantity calculation is avoided, and an accurate prediction result of actual residual capacity corresponding to a battery individual is provided.
Example 3
Fig. 8 is a schematic structural diagram of an electronic device provided in this embodiment, where the electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the method for predicting the remaining battery capacity in embodiment 1 is implemented. The electronic device 80 shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure. As shown in fig. 8, the electronic device 80 may take the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 80 may include, but are not limited to: the at least one processor 81, the at least one memory 82, and a bus 83 connecting the various 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 volatile memory, such as Random Access Memory (RAM) 821 and/or cache memory 822, and may further include Read Only Memory (ROM) 823.
The memory 82 may also include a program tool 825 (or utility tool) 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 of which, or some combination thereof, may comprise an implementation of a network environment.
The processor 81 executes various functional applications and data processing, such as the prediction method of the remaining battery capacity in embodiment 1 described above, by running the computer program stored in the memory 82.
The electronic device 80 may also communicate with one or more external devices 84. Such communication may occur through input/output (I/O) interfaces 85. Also, the model-generating electronic device 80 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 86. As shown in fig. 8, the network adapter 86 communicates with the other modules of the electronic device 80 via the bus 83. It should be understood that although not shown in FIG. 8, other hardware and/or software modules may be used in conjunction with the 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, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing the method of predicting the remaining capacity of a battery in embodiment 1 described above.
More specific examples that may be employed by the readable storage medium include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible implementation, the present disclosure may also be implemented in the form of a program product including program code for causing a terminal device to execute the steps of implementing the method for predicting the remaining battery capacity in embodiment 1 described above when the program product is run on the terminal device.
Where program code for carrying out the disclosure is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device, partly on a remote device or entirely on the remote device.
While specific embodiments of the present disclosure have been described above, it will be understood by those skilled in the art that this is by way of example only, and that the scope of the disclosure is defined by the appended claims. Various changes or modifications may be made to the embodiments by those skilled in the art without departing from the principle and spirit of the disclosure, and such changes and modifications are intended to be included within the scope of the 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;
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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| CN117471328A (en) * | 2023-12-27 | 2024-01-30 | 高新兴科技集团股份有限公司 | Method, system and terminal equipment for determining capacity of lead-acid battery |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117471328A (en) * | 2023-12-27 | 2024-01-30 | 高新兴科技集团股份有限公司 | Method, system and terminal equipment for determining capacity of lead-acid battery |
| CN117471328B (en) * | 2023-12-27 | 2024-04-12 | 高新兴科技集团股份有限公司 | Method, system and terminal equipment for determining capacity of lead-acid battery |
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