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

Abstract

The present disclosure provides a method, system, electronic device and medium for predicting the remaining capacity of a battery. The prediction method includes the following steps: presetting a first correspondence between a theoretical remaining capacity value and a theoretical attenuation value of a battery; predicting a theoretical remaining capacity value of a current working stage based on the current first correspondence, the theoretical remaining capacity value of a previous working stage and the theoretical attenuation value of a current working stage; obtaining an actual remaining capacity value of the battery in the current working stage; updating the first correspondence based on a comparison result between the actual remaining capacity value and the theoretical remaining capacity value; and re-predicting the theoretical remaining capacity value of the battery based on the updated first correspondence. The present disclosure implements an adjustment of a prediction method for the actual remaining capacity of an individual battery, achieves self-update and iteration of the prediction method, and provides an accurate prediction result of the actual remaining capacity corresponding to the individual battery.

Description

Technical Field

[0001] The present disclosure relates to the field of battery remaining capacity prediction, and in particular to a battery remaining capacity prediction method, system, electronic device and medium. Background Art

[0002] As an indispensable product in today's life, batteries have a wide range of application scenarios. For batteries, the prediction of their remaining capacity is an important technical difficulty. For example, when working on a computer, if the remaining battery capacity is predicted incorrectly, users will often be unable to accurately arrange their work progress, resulting in immeasurable losses. In the field of battery remaining capacity prediction, the amount of data in the traditional database fitting equation is limited, and only laboratory data and limited actual test data are fitted. In practice, individual characteristics, physiques, environments, and working conditions are different, and traditional unified algorithms cannot perform targeted data processing on specific battery individuals, nor can they achieve self-matching iteration of the algorithm. During operation, inaccurate remaining capacity predictions and power data display value drops due to inaccurate power calculations are prone to occur. Summary of the invention

[0003] The technical problem to be solved by the present disclosure is to overcome the defect in the prior art that the prediction method for the actual remaining capacity of an individual battery cannot be adjusted, and to provide a method, system, electronic device and medium for predicting the remaining capacity of a battery.

[0004] The present invention solves the above technical problems through the following technical solutions:

[0005] In a first aspect, a method for predicting a remaining battery capacity is provided, the method comprising the following steps:

[0006] A first corresponding relationship between a theoretical remaining capacity value and a theoretical attenuation value of a preset battery;

[0007] 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;

[0008] Obtaining the actual remaining capacity value of the battery in the current working stage;

[0009] updating the first corresponding relationship according to a comparison result between the actual remaining capacity value and the theoretical remaining capacity value;

[0010] The theoretical remaining capacity value of the battery is re-predicted based on the updated first corresponding relationship.

[0011] Preferably, the obtaining of the actual remaining capacity value of the battery in the current working stage specifically comprises the following steps:

[0012] Presetting a second corresponding relationship between the battery state of charge and the voltage of the battery according to the material of the battery;

[0013] Obtaining the current voltage of the battery;

[0014] Obtaining a change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and a change value of the voltage within a battery use start and end interval of the battery;

[0015] The actual remaining capacity value of the battery is obtained according to the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

[0016] 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 voltage in the battery use start and end interval, the method further includes the following steps:

[0017] Determining whether a change value of the battery state of charge of the battery is greater than a preset threshold;

[0018] If it is greater, continue to execute the step of calculating the actual remaining capacity value of the battery based on the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

[0019] 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 updating of the first corresponding relationship specifically comprises the following steps:

[0020] Obtain at least one influencing parameter of the current working stage of the battery;

[0021] The first corresponding relationship is updated based on the influencing parameter and the first formula.

[0022] Preferably, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

[0023] Preferably, after the step of re-predicting the theoretical remaining capacity value of the battery based on the updated first corresponding relationship, the step further includes:

[0024] monitoring the operating stage of the battery;

[0025] When the working stage of the battery changes, the step of predicting the theoretical remaining capacity value of the current working stage according to the current first corresponding relationship is returned to start the next round of prediction.

[0026] Preferably, the working phases include a charging phase, a storage phase, and a discharging phase.

[0027] Preferably, when all influencing parameters are obtained simultaneously, the first formula is:

[0028]

[0029] Among them, t0 is the start time of the current working stage, t1 is the end time of the current working stage, P a is the charging power, P b is the discharge power; T a is the battery temperature; T b is the ambient temperature;

[0030] are the fitting functions in the corresponding working stages respectively;

[0031] p a , p b , t a , t b is the preset benchmark parameter;

[0032] j a , j b , j c , j d is the preset weight correction coefficient;

[0033] and / or,

[0034] 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 following step further includes:

[0035] If the ratio of the actual remaining capacity value to the theoretical remaining capacity value does not exceed the preset interval, then when the working stage of the battery changes, the step of returning to the step of predicting the theoretical remaining capacity value of the current working stage at the current moment based on 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 is executed to start the next round of prediction.

[0036] Preferably, after the step of re-predicting the theoretical remaining capacity value of the battery based on the updated first corresponding relationship, the method further comprises:

[0037] Updating the theoretical remaining capacity value;

[0038] Determine whether the theoretical remaining capacity of the battery is less than a preset threshold;

[0039] If it is less than, a warning will be issued;

[0040] and / or,

[0041] After the step of updating the first corresponding relationship, the following step further includes:

[0042] The first corresponding relationship before updating is saved.

[0043] In a second aspect, a system for predicting remaining battery capacity is provided, comprising:

[0044] A preset module, used to preset a first corresponding relationship between a theoretical remaining capacity value and a theoretical attenuation value of a battery;

[0045] A prediction module, used for predicting a theoretical remaining capacity value in a current working stage according to the current first corresponding relationship, a theoretical remaining capacity value in a previous working stage and a theoretical attenuation value in a current working stage;

[0046] An acquisition module, used to acquire the actual remaining capacity value of the battery in the current working stage;

[0047] 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;

[0048] The prediction module is further used to re-predict the theoretical remaining capacity value of the battery based on the updated first corresponding relationship.

[0049] Preferably, the acquisition module includes:

[0050] a preset unit, used for preset a second corresponding relationship between the battery state of charge and the voltage of the battery according to the material of the battery;

[0051] A voltage acquisition unit, used to acquire the current voltage of the battery;

[0052] A charge acquisition unit, configured to acquire a change value of the battery state of charge of the battery in a current working stage according to the second corresponding relationship and a change value of the voltage within a battery use start and end interval of the battery;

[0053] The capacity estimation unit is used to calculate the actual remaining capacity value of the battery according to the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

[0054] Preferably, the charge acquisition unit comprises:

[0055] A judging subunit, used to judge whether a change value of the battery state of charge of the battery is greater than a preset threshold;

[0056] An execution subunit is used to continue to execute the step of obtaining the actual remaining capacity value of the battery based on the ratio of the theoretical consumption value of the battery capacity within the start and end interval of battery use of the battery and the change value of the battery state of charge of the battery to obtain the current voltage of the battery when the change value of the battery state of charge of the battery is greater than a preset threshold.

[0057] 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 updating module includes:

[0058] A parameter acquisition unit, used to acquire at least one influencing parameter of the battery at the current working stage at the current moment;

[0059] An updating unit is used to update the first corresponding relationship based on the influencing parameter and the first formula.

[0060] Preferably, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

[0061] Preferably, the prediction system further comprises:

[0062] A monitoring module, used for monitoring the working stage of the battery;

[0063] The jump module is used to return to the step of predicting the theoretical remaining capacity value of the current working stage at the current moment based on 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 to start the next round of prediction when the working stage of the battery changes.

[0064] Preferably, the working phases include a charging phase, a storage phase, and a discharging phase.

[0065] Preferably, when all influencing parameters are obtained simultaneously, the first formula is:

[0066]

[0067] Among them, t0 is the start time of the current working stage, t1 is the end time of the current working stage, P a is the charging power, P b is the discharge power; T a is the battery temperature; T b is the ambient temperature;

[0068] are the fitting functions in the corresponding working stages respectively;

[0069] p a , p b , t a , tb is the preset benchmark parameter;

[0070] j a , j b , j c , j d is the preset weight correction coefficient;

[0071] and / or,

[0072] The prediction system also includes:

[0073] The return module is used to execute the step of returning to the prediction module to start the next round of prediction 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 interval.

[0074] Preferably, the prediction system further comprises:

[0075] A capacity updating module, used for updating the theoretical remaining capacity value;

[0076] A judgment module is used to judge whether the theoretical remaining capacity value of the battery is less than a preset threshold;

[0077] A warning module is used to issue a warning when the theoretical remaining capacity of the battery is less than a preset threshold;

[0078] and / or,

[0079] The prediction system also includes:

[0080] A saving module is used to save the first corresponding relationship before updating.

[0081] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for predicting the remaining battery capacity when executing the computer program.

[0082] 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-mentioned method for predicting the remaining battery capacity is implemented.

[0083] The positive and progressive effect of the present disclosure is that it can adjust the prediction method for the actual remaining capacity of individual batteries, achieve self-updating and iteration of the prediction method, reduce the inaccurate prediction of the remaining capacity during operation, avoid the problem of power data display value drops due to inaccurate power calculation, and provide the corresponding accurate actual remaining capacity prediction results for the individual batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1A first flow chart of a method for predicting remaining battery capacity provided in Embodiment 1 of the present disclosure.

[0085] Figure 2 A schematic diagram of the relationship between the remaining life of a battery and the single-cycle capacity attenuation value provided in Example 1 of the present disclosure.

[0086] Figure 3 A schematic diagram of the relationship between the capacity SOC and voltage of a lithium iron phosphate battery provided in Example 1 of the present disclosure.

[0087] Figure 4 A schematic diagram of the fitting functions of the four influencing parameters in the charging stage provided in Example 1 of the present disclosure.

[0088] Figure 5 A schematic diagram of the fitting functions of the four influencing parameters in the discharge stage provided in Example 1 of the present disclosure.

[0089] Figure 6 A schematic diagram of the fitting functions of the four influencing parameters of the storage stage provided in Example 1 of the present disclosure.

[0090] Figure 7 A schematic diagram of the modules of a system for predicting the remaining battery capacity provided in Example 2 of the present disclosure.

[0091] Figure 8 A module schematic diagram of an electronic device provided in Embodiment 3 of the present disclosure. DETAILED DESCRIPTION

[0092] The present disclosure is further described below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0093] Example 1

[0094] In a first aspect, a method for predicting the remaining capacity of a battery is provided. Figure 1 As shown, the prediction method comprises the following steps:

[0095] Step 101, presetting a first corresponding relationship between a theoretical remaining capacity value and a theoretical attenuation value of a battery;

[0096] Step 102: 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;

[0097] Step 103, obtaining the actual remaining capacity value of the battery in the current working stage;

[0098] Step 104: updating the first corresponding relationship according to the comparison result between the actual remaining capacity value and the theoretical remaining capacity value;

[0099] Step 105: re-predict the theoretical remaining capacity value of the battery based on the updated first corresponding relationship.

[0100] Here, step 101 and step 102 are explained. In the specific implementation, the concept of battery life needs to be introduced. The larger the battery capacity consumed in a single cycle, the shorter the remaining battery life. Therefore, the remaining battery life can also be predicted by calculating the remaining battery capacity. Here, the theoretical attenuation value C 衰减 Calculate the theoretical remaining capacity value C 理论 After a single working stage is completed, the theoretical remaining capacity value of the current working stage is predicted based on 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. The calculation formula of the theoretical capacity value is: C n+1 =C n -kC 衰减 ; The k here refers to the first corresponding relationship, which is the correction coefficient mentioned later.

[0101] It should be noted that through experiments, a fitting function can be obtained about the theoretical attenuation value and the remaining battery life. The theoretical attenuation value increases as the remaining battery life decreases. The remaining battery life is inferred based on the ratio of the maximum capacity value available when the battery is fully charged during prediction and the maximum capacity value of the battery when it leaves the factory. The larger the ratio, the longer the remaining battery life, the smaller the battery loss, and the healthier the battery.

[0102] For example, Figure 2 The figure shows the remaining life of a battery and the capacity decay value C of a single cycle obtained from the experiment. 衰减 The relationship between them is that before predicting the theoretical remaining capacity value of the current working stage each time, it is necessary to obtain the theoretical attenuation value of the current working stage.

[0103] Step 104 is described here. The principle here is that the first corresponding relationship does not need to be updated every time the ratio of the actual remaining capacity value to the theoretical remaining capacity value changes. If the ratio of the actual remaining capacity value to the theoretical remaining capacity value exceeds the preset interval, the first corresponding relationship is updated. The preset interval is set here to avoid unnecessary algorithm correction and increase the amount of calculation; for example, the preset interval is set as follows:

[0104]

[0105] If the ratio falls within the interval, the algorithm is not corrected, that is, the first corresponding relationship is not updated, but the most recent working stage (see below) is combined with the next working stage for processing; if the ratios in m working stages are kept within the interval, it means that the change in battery capacity loss can be ignored until the ratio in a certain working stage exceeds the range, that is, until C 理论 and C 实际 If the error value exceeds 0.5%, refer to the following formula for calculation:

[0106] C n+m =C n -kC 衰减

[0107] In an optional implementation manner, step 103 specifically includes the following steps:

[0108] Presetting a second corresponding relationship between the battery state of charge and the voltage of the battery according to the material of the battery;

[0109] Obtaining the current voltage of the battery;

[0110] Obtaining a change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and a change value of the voltage within a battery use start and end interval of the battery;

[0111] The actual remaining capacity value of the battery is obtained according to the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

[0112] In a specific example, the general formula for energy consumption of battery capacity known in the art is ΔC=ΣIUΔt; Figure 3 As shown in the figure, this coordinate relationship diagram shows the relationship between the SOC (battery state of charge) and 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, it is rare to encounter a situation where the capacity is fully charged or fully used up. Figure 3 , we can calculate the SOC change value ΔSOC by measuring the voltage change value ΔV in the start and end interval of battery use, and then calculate the actual capacity of the battery by combining the capacity measurement value ΔC=ΣIUΔt. The actual capacity is as follows:

[0113]

[0114] In an optional implementation manner, 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 voltage in the battery use start and end interval, the following steps are also included:

[0115] Determining whether a change value of the battery state of charge of the battery is greater than a preset threshold;

[0116] If it is greater, continue to execute the step of calculating the actual remaining capacity value of the battery based on the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

[0117] Combined with the above example, the principle of introducing the preset threshold here is to combine Figure 3 It can be observed that the larger the range of value variation, the more accurate the calculation result. Therefore, after ΔSOC reaches a preset threshold, such as setting ΔSOC ≥ 20%, it is more reasonable to perform capacity calibration calculation.

[0118] 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 steps:

[0119] Obtain at least one influencing parameter of the current working stage of the battery;

[0120] The first corresponding relationship is updated based on the influencing parameter and the first formula.

[0121] In an optional implementation manner, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

[0122] In an optional implementation manner, the step 105 further includes:

[0123] monitoring the operating stage of the battery;

[0124] When the working stage of the battery changes, the step of predicting the theoretical remaining capacity value of the current working stage according to the current first corresponding relationship is returned to start the next round of prediction.

[0125] In an optional implementation, the working phase includes a charging phase, a storage phase, and a discharging phase.

[0126] 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 (i.e. ) are different, such as Figure 4 , Figure 5 , Figure 6 As shown in the figure, 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 Figure 6 The t1, t2, and t3 in the figure only serve to distinguish, and all of them mean 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 select the fitting function corresponding to the discharging stage and substitute it into the algorithm formula (such as the first formula below) to predict the remaining capacity in a new round.

[0127] In an optional implementation manner, when all influencing parameters are obtained simultaneously, the first formula is:

[0128]

[0129] Among them, t0 is the start time of the current working stage, t1 is the end time of the current working stage, P a is the charging power, P b is the discharge power; T a is the battery temperature; T b is the ambient temperature;

[0130] are the fitting functions in the corresponding working stages respectively;

[0131] p a , p b , t a , t b is the preset benchmark parameter;

[0132] j a , j b , j c , j d is the preset weight correction coefficient;

[0133] It should be noted that the above algorithm formula is only for the case where the four most common influencing parameters listed here are taken into account. In actual applications, the algorithm can be designed according to personal needs, and the influencing parameters can be greater than four, or less than four; the above algorithm can be changed accordingly based on the number of influencing parameters. For example, in a specific example, only P is involved. a (charging power), P b (discharge power), T a (battery temperature) these three influencing parameters, the algorithm formula is adjusted to (the meaning of the parameters is the same as in the first formula, and is only used as an example here):

[0134]

[0135] In an optional implementation manner, after step 104, the step further includes:

[0136] If the ratio of the actual remaining capacity value to the theoretical remaining capacity value does not exceed the preset interval, then when the working stage of the battery changes, the step of returning to the step of predicting the theoretical remaining capacity value of the current working stage at the current moment based on 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 is executed to start the next round of prediction.

[0137] In an optional implementation manner, the step 105 further includes:

[0138] Updating the theoretical remaining capacity value;

[0139] Determine whether the theoretical remaining capacity of the battery is less than a preset threshold;

[0140] If it is less than, a warning will be issued;

[0141] In specific implementation, since battery loss to a certain stage will cause immeasurable changes in the calculation curve, a threshold value is usually used to determine the theoretical remaining capacity of the battery. In addition, the maximum available capacity of the battery at this time in the fully charged state can be more intuitively calculated through the calculation equation of the theoretical remaining capacity value. If the ratio of the maximum available capacity of the battery at this time to the maximum capacity value of the battery at the factory state is lower than a preset threshold, such as 80%, 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 sake of user experience, it is not set that the battery life is 0 only when the ratio is 0%.

[0142] In an optional implementation manner, after step 104, the step further includes:

[0143] The first corresponding relationship before updating is saved.

[0144] In 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 overwritten. The original calculation data is retained for uploading to the background, which is convenient for users or designers to analyze the accuracy of the algorithm and the process of algorithm self-optimization. At the same time, users or designers continuously optimize the accuracy of the compensation algorithm on the control side background and regularly maintain and update the entire prediction method.

[0145] In addition to the above content, in actual applications, batteries often do not appear as individuals, and it is more common to see multiple module BMS combinations. Since the present disclosure can update and iterate the prediction method for each individual battery, once the number of monitored objects is large, the requirements for computing power will also increase. Based on this, the present disclosure also takes into account that the storage location of the entire prediction method algorithm will have different effects on the requirements for computing power. If the algorithm is designed locally and the optimization process is completed in the background, the communication frequency is low and the computing power requirement is high; if the algorithm design and optimization process are both performed in the background, the communication frequency is high and the local computing power requirement is low; the above design can be selected as needed for different scenarios.

[0146] In this embodiment, by collecting data on different working conditions of a battery in different working stages, a method for predicting the remaining capacity of the battery that can complete self-updating and iteration is designed, so as to reduce the inaccuracy in the remaining capacity prediction during operation, avoid the problem of the value drop of the power data display due to inaccurate power calculation, and provide the battery with an accurate prediction result of the actual remaining capacity corresponding to the individual battery.

[0147] Example 2

[0148] This embodiment provides a battery remaining capacity prediction system 10, the prediction system 10 implements the prediction method in embodiment 1, such as Figure 7 As shown, including:

[0149] A preset module 11 is used to preset a first corresponding relationship between a theoretical remaining capacity value and a theoretical attenuation value of a battery;

[0150] A prediction module 12, configured to predict a theoretical remaining capacity value in a current working stage according to the current first corresponding relationship, a theoretical remaining capacity value in a previous working stage, and a theoretical attenuation value in a current working stage;

[0151] The acquisition module 13 is used to obtain the actual remaining capacity value of the battery in the current working stage;

[0152] 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;

[0153] 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.

[0154] In an optional implementation manner, the acquisition module 13 includes the following:

[0155] A preset unit 131, 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;

[0156] A voltage acquisition unit 132, used to acquire the current voltage of the battery;

[0157] A charge acquisition unit 133, configured to acquire a change value of the battery state of charge of the battery in a current working stage according to the second corresponding relationship and a change value of the voltage within a battery use start and end interval of the battery;

[0158] The capacity estimating unit 134 is used to calculate the actual remaining capacity value of the battery according to the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

[0159] In an optional implementation manner, the charge acquisition unit 133 includes the following:

[0160] The judging subunit 1331 is used to judge whether the change value of the battery state of charge of the battery is greater than a preset threshold;

[0161] The execution subunit 1332 is used to continue to execute the step of obtaining the actual remaining capacity value of the battery based on the ratio of the theoretical consumption value of the battery capacity within the start and end interval of battery use of the battery and the change value of the battery state of charge of the battery to obtain the current voltage of the battery when the change value of the battery state of charge of the battery is greater than a preset threshold.

[0162] In an optional implementation, 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 updating module 14 includes the following:

[0163] A parameter acquisition unit 141 is used to acquire at least one influencing parameter of the current working stage of the battery at the current moment;

[0164] The updating unit 142 is configured to update the first corresponding relationship based on the influencing parameter and the first formula.

[0165] In an optional implementation manner, the influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

[0166] In an optional implementation, the prediction system 10 further includes:

[0167] A monitoring module 15, used to monitor the working stage of the battery;

[0168] The jump module 16 is used to return to the step of predicting the theoretical remaining capacity value of the current working stage at the current moment based on 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 to start the next round of prediction when the working stage of the battery changes.

[0169] In an optional implementation, the working phase includes a charging phase, a storage phase, and a discharging phase.

[0170] In an optional implementation manner, when all influencing parameters are obtained simultaneously, the first formula is:

[0171]

[0172] Among them, t0 is the start time of the current working stage, t1 is the end time of the current working stage, P a is the charging power, P b is the discharge power; T a is the battery temperature; T b is the ambient temperature;

[0173] are the fitting functions in the corresponding working stages respectively;

[0174] p a , p b , t a , t b is the preset benchmark parameter;

[0175] j a , j b , j c , j d is the preset weight correction coefficient;

[0176] In an optional implementation, the prediction system 10 further includes:

[0177] The return module 17 is used to execute the step of returning to the prediction module 12 to start the next round of prediction 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 interval.

[0178] In an optional implementation, the prediction system 10 further includes:

[0179] A capacity updating module 18, used for updating the theoretical remaining capacity value;

[0180] The judging module 19 is used to judge whether the theoretical remaining capacity of the battery is less than a preset threshold value;

[0181] The warning module 20 is used to issue a warning when the theoretical remaining capacity of the battery is less than a preset threshold;

[0182] In an optional implementation, the prediction system 10 further includes:

[0183] The saving module 21 is used to save the first corresponding relationship before updating.

[0184] In the present embodiment, a prediction system is provided. The system collects data on different working conditions of a battery in different working stages, and designs a battery remaining capacity prediction method that can complete self-updating iterations, thereby reducing inaccurate predictions of the remaining capacity during operation, avoiding the problem of a drop in the displayed value of the power data due to inaccurate power calculation, and providing accurate prediction results of the actual remaining capacity corresponding to the individual battery.

[0185] Example 3

[0186] This embodiment provides an electronic device, Figure 8 This is a schematic diagram of the structure of an electronic device provided in this embodiment. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for predicting the remaining battery capacity in the above-mentioned embodiment 1 is implemented. Figure 8 The electronic device 80 shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure. Figure 8 As shown, the electronic device 80 may be in the form of a general-purpose computing device, for example, it may be a server device. The 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).

[0187] The bus 83 includes a data bus, an address bus, and a control bus.

[0188] 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 .

[0189] The 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 of which or some combination may include an implementation of a network environment.

[0190] The processor 81 executes computer programs stored in the memory 82 to perform various functional applications and data processing, such as the method for predicting the remaining battery capacity in the first embodiment.

[0191] The electronic device 80 may also communicate with one or more external devices 84. Such communication may be performed via an input / output (I / O) interface 85. Furthermore, 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 a network adapter 86. Figure 8 As shown, the network adapter 86 communicates with other modules of the electronic device 80 via the bus 83. It should be understood that although Figure 8 Not shown, 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.

[0192] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0193] Example 4

[0194] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for predicting the remaining battery capacity in the above-mentioned embodiment 1 is implemented.

[0195] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0196] In a possible implementation, the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of the method for predicting the remaining battery capacity in the above-mentioned embodiment 1.

[0197] Among them, the program code for executing the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as a separate software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0198] Although the specific embodiments of the present disclosure are 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 may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A method for predicting remaining battery capacity, characterized in that: The prediction method comprises the following steps: A first corresponding relationship between a theoretical remaining capacity value and a theoretical attenuation value of a preset battery; 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; Obtaining 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 a theoretical remaining capacity value of the battery based on the updated first corresponding relationship; The obtaining of the actual remaining capacity value of the battery in the current working stage specifically comprises the following steps: Presetting a second corresponding relationship between the battery state of charge and the voltage of the battery according to the material of the battery; Obtaining the current voltage of the battery; Obtaining a change value of the battery state of charge of the battery in the current working stage according to the second corresponding relationship and a change value of the voltage within a battery use start and end interval of the battery; The actual remaining capacity value of the battery is obtained according to the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

2. The method for predicting the remaining battery capacity according to claim 1, characterized in that: 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 voltage in the battery use start and end interval, the following steps are also included: Determining whether a change value of the battery state of charge of the battery is greater than a preset threshold; If it is greater, continue to execute the step of calculating the actual remaining capacity value of the battery based on the ratio of the theoretical consumption value of the battery capacity within the battery use start and end interval of the battery and the change value of the battery state of charge of the battery.

3. The method for predicting the remaining battery capacity according to claim 1, wherein: The said One-to-one relationship is repair The correction factor k is determined by the duration of the current working phase of the battery. time and at least one influencing parameter; the updating of the first corresponding relationship specifically comprises the following steps: Obtain at least one influencing parameter of the current working stage of the battery; Update the first corresponding relationship based on the influencing parameter and the first formula; When all influencing parameters are obtained at the same time, the first formula is: Among them, t0 is the start time of the current working stage, t1 is the end time of the current working stage, P a is the charging power, P b is the discharge power; T a is the battery temperature; T b is the ambient temperature; are the fitting functions in the corresponding working stages respectively; p a , p b , t a , t b is the preset benchmark parameter; j a , j b , j c , j d is the preset weight correction coefficient.

4. The method for predicting the remaining battery capacity according to claim 3, characterized in that: The influencing parameters include ambient temperature, battery temperature, charging power, and discharging power.

5. The method for predicting the remaining battery capacity according to claim 3, characterized in that: After the step of re-predicting the theoretical remaining capacity value of the battery based on the updated first corresponding relationship, the step further includes: monitoring the operating stage of the 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 based on 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 to start the next round of prediction.

6. The method for predicting the remaining battery capacity according to claim 5, characterized in that: The working phases include a charging phase, a storage phase, and a discharging phase.

7. The method for predicting the remaining battery capacity according to claim 6, characterized in that: 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 following step further includes: If the ratio of the actual remaining capacity value to the theoretical remaining capacity value does not exceed the preset interval, then when the working stage of the battery changes, the step of returning to the step of predicting the theoretical remaining capacity value of the current working stage at the current moment based on 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 is executed to start the next round of prediction.

8. The method for predicting the remaining battery capacity according to claim 1, wherein: After the step of re-predicting the theoretical remaining capacity value of the battery based on the updated first corresponding relationship, the step further includes: Updating the theoretical remaining capacity value; Determine whether the theoretical remaining capacity of the battery is less than a preset threshold; If it is less than, a warning will be issued; and / or, After the step of re-predicting the theoretical remaining capacity value of the battery based on the updated first corresponding relationship, the step further includes: The first corresponding relationship before updating is saved.

9. A battery remaining capacity prediction system, characterized in that: include: A preset module, used to preset a first corresponding relationship between a theoretical remaining capacity value and a theoretical attenuation value of a battery; A prediction module, used for predicting a theoretical remaining capacity value in a current working stage according to the current first corresponding relationship, a theoretical remaining capacity value in a previous working stage and a theoretical attenuation value in a current working stage; An acquisition module, used 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 used to re-predict the theoretical remaining capacity value of the battery based on the updated first corresponding relationship; The acquisition module is specifically used 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; obtain the current voltage of the battery; and The corresponding relationship between the two and the battery The battery uses the voltage change value in the start and end interval to obtain the current working The actual remaining capacity value of the battery is obtained according to the ratio of the theoretical consumption value of the battery capacity within the start and end interval of the battery use of the battery and the change value of the battery state of charge of the battery.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for predicting the remaining battery capacity according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the remaining battery capacity according to any one of claims 1 to 8 is implemented.

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