Open circuit voltage (OCV) curve correction method for battery, and related product

Abstract

An open circuit voltage (OCV) curve correction method for a battery, and a related product. The curve correction method comprises: determining whether all N feature parameters of a battery cell within a set time period fall into N parameter ranges corresponding to a target working condition; and when all of the N feature parameters fall into the N parameter ranges corresponding to the target working condition, updating the current OCV curve of the battery cell to a target OCV curve corresponding to the target working condition.

Description

Battery open circuit voltage OCV curve correction method and related products

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on December 14, 2023, with application number 202311728047.5 and application name “Battery OCV Curve Correction Method and Related Products”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of battery technology, and in particular to a battery open circuit voltage (OCV) curve correction method and related products. Background Art

[0003] Open circuit voltage (OCV) is very important in a battery management system (BMS). Generally speaking, laboratory measurements record two OCV curves, one in the charging direction and one in the discharging direction. These two OCV curves are averaged to obtain an average OCV curve. Current BMSs estimate the state of charge (SOC) of battery cells based on the OCV curve. The OCV curve currently used to estimate the state of charge is the average OCV curve. However, after charging and discharging, the actual cell OCV curve deviates from the average OCV curve. Based on this curve, the SOC obtained from the OCV-SOC mapping relationship deviates from the true value.

[0004] Summary of the Invention

[0005] The embodiments of the present application provide a battery open circuit voltage (OCV) curve correction method and related products, which can correct the OCV curve so that the corrected OCV curve is closer to the actual OCV curve of the current operating conditions.

[0006] A first aspect of an embodiment of the present application provides a method for correcting a battery OCV curve, comprising:

[0007] Determine whether N characteristic parameters of the battery cell during a set period all fall within N parameter ranges corresponding to the target operating condition, where N is an integer greater than or equal to 1;

[0008] When the N characteristic parameters all fall within the N parameter ranges corresponding to the target operating condition, the current OCV curve of the battery cell is updated to the target OCV curve corresponding to the target operating condition.

[0009] Optionally, before determining whether all N characteristic parameters of the battery cell in the set time period fall within N parameter ranges corresponding to the target operating condition, the method further includes:

[0010] Periodically sampling N original characteristic parameters of the battery cell in each unit time window according to a set unit time window;

[0011] Determine the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows contained in the set time period, and use the comprehensive value of each original characteristic parameter as each characteristic parameter of the battery cell in the set time period, where the set time period is a period of time before the current time point, and M is an integer greater than or equal to 2.

[0012] Optionally, determining the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows included in the set time period includes:

[0013] If the sampled duration of the current sampling time window is equal to the duration of the unit time window, determine the M unit time windows included in the duration of the set time period, and determine the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows, where the duration of the set time period is equal to M times the duration of the unit time window;

[0014] If the sampled duration of the current sampling time window is less than the duration of the unit time window, determine the M unit time windows and the current sampling time window included in the duration of the set time period, and determine the comprehensive value of each original characteristic parameter of the battery cell sampled by the M unit time windows and the current sampling time window. The duration of the set time period is equal to M times the duration of the unit time window plus the sampled duration of the current sampling time window.

[0015] Optionally, each original feature parameter of each unit time window is determined based on each original feature data sampled at a set sampling frequency within each unit time window; each original feature parameter of each unit time window is the average value or accumulated value of all each original feature data sampled at a set sampling frequency within each unit time window, or each original feature parameter of each unit time window is one of each original feature data sampled at a set sampling frequency within each unit time window.

[0016] Optionally, the comprehensive value includes any one of an average value, an accumulated value, and a random value.

[0017] Optionally, the target operating condition is any one in a set of operating conditions, the set of operating conditions includes P operating conditions, the N parameter ranges corresponding to any two operating conditions in the P operating conditions are not completely the same, and P is an integer greater than or equal to 1.

[0018] Optionally, the characteristic parameters include: at least one of current, voltage, temperature, ampere-hour integral, battery cell aging parameters, and battery cell internal resistance.

[0019] Optionally, if the battery cell is in a charging state, the target OCV curve is determined based on a charging OCV curve and an average OCV curve corresponding to the target operating condition;

[0020] If the battery cell is in a discharging state, the target OCV curve is determined based on the discharge OCV curve and the average OCV curve corresponding to the target operating condition.

[0021] A second aspect of an embodiment of the present application provides a curve correction device, comprising:

[0022] a determination unit, configured to determine whether N characteristic parameters of the battery cell in a set time period all fall within N parameter ranges corresponding to the target operating condition, where N is an integer greater than or equal to 1;

[0023] An updating unit is configured to update the current OCV curve of the battery cell to a target OCV curve corresponding to the target operating condition when all of the N characteristic parameters fall within the N parameter ranges corresponding to the target operating condition.

[0024] A third aspect of an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions and execute the step instructions as in the first aspect of the embodiment of the present application.

[0025] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0026] A fifth aspect of the present application provides a computer program product, wherein the computer program product includes a computer program that is operable to cause a computer to perform some or all of the steps described in the first aspect of the present application. The computer program product may be a software installation package.

[0027] A sixth aspect of the present invention provides a processor configured to call program instructions and execute the steps described in the first aspect of the present invention. The processor may include any one of a chip, an integrated circuit, a microcontroller unit (MCU), and a computer terminal.

[0028] A seventh aspect of the embodiments of the present application provides a battery management system, which includes the electronic device of the third aspect of the embodiments of the present application. The battery management system can monitor the status of the battery and prevent the battery from overcharging and overdischarging to extend the battery life. The battery management system may also include a display module, a wireless communication module, an electrical device, a battery pack for powering the electrical device, and a collection module for collecting battery cell information of the battery pack.

[0029] An eighth aspect of the present application provides an electric device, comprising the electronic device of the third aspect of the present application. The electric device may be a device powered by electrical energy. For example, the electric device may include any of a vehicle, an aircraft, a ship, and an energy storage cabinet.

[0030] In an embodiment of the present application, it is determined whether all N characteristic parameters of the battery cell during a set time period fall within the N parameter ranges corresponding to the target operating condition, where N is an integer greater than or equal to 1; when all N characteristic parameters fall within the N parameter ranges corresponding to the target operating condition, the current OCV curve of the battery cell is updated to the target OCV curve corresponding to the target operating condition. In an embodiment of the present application, the target operating condition of the electric device can be determined based on the N characteristic parameters of the battery cell during a set time period, and the current OCV curve of the battery cell can be updated to the target OCV curve corresponding to the target operating condition. The current OCV curve can be corrected so that the corrected OCV curve is closer to the actual OCV curve of the current operating condition, thereby improving the estimation accuracy of the SOC. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] FIG1 is a schematic diagram of an OCV curve provided in an embodiment of the present application;

[0033] FIG2 is a flow chart of a method for correcting a battery OCV curve according to an embodiment of the present application;

[0034] FIG3 is a flow chart of another method for correcting a battery OCV curve according to an embodiment of the present application;

[0035] FIG4 is a schematic diagram of a setting period and a unit time window provided in an embodiment of the present application;

[0036] FIG5 is a schematic structural diagram of a curve correction device provided in an embodiment of the present application;

[0037] FIG6 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;

[0038] FIG7 is a schematic structural diagram of a battery management system provided in an embodiment of the present application;

[0039] FIG8 is a schematic structural diagram of an electric device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0042] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0043] In electric vehicles, in order to accurately obtain the SOC of the battery cell, the open circuit voltage (OCV) curve is generally used to sample the open circuit voltage of the battery cell, so as to obtain the SOC of the battery cell according to the OCV-SOC mapping relationship in the OCV curve. The accuracy of the OCV curve will affect the SOC value obtained according to the OCV curve. Please refer to Figure 1, which is a schematic diagram of an OCV curve provided in an embodiment of the present application. As shown in Figure 1, the horizontal axis is the value of the open circuit voltage OCV, and the vertical axis is the SOC value. It can be seen from Figure 1 that after obtaining the open circuit voltage of the battery cell, the corresponding SOC value can be obtained according to the OCV curve. It should be noted that Figure 1 is only a possible example of an OCV curve. The OCV curve can also use OCV as the horizontal axis and the SOC value as the horizontal axis.

[0044] At present, the mainstream state of charge (SOC) algorithm recalibrates the current value of SOC when the battery cell is in a static state. It is calibrated based on the OCV-SOC curve (OCV and SOC have a one-to-one correspondence). When the battery cell is not charging or discharging, the static time of the battery cell is recorded. When the static time meets the terminal voltage error range, the battery cell SOC is calibrated according to the current voltage based on the OCV-SOC mapping relationship. It can also be called a static voltage correction algorithm.

[0045] The battery characteristics of lithium-ion batteries determine that when the battery is charging or discharging, its OCV also follows the direction of charging or discharging OCV due to the hysteresis effect. The historical operating conditions will affect the hysteresis direction and size of OCV. The open circuit voltage (OCV) is the basis for the BMS to estimate the state of the battery cell. The static voltage correction algorithm is a static calibration algorithm for the current state of charge based on the SOC-OCV mapping relationship. The average OCV curve is currently used as the correction basis for the algorithm. However, after charging and discharging, the actual battery cell OCV curve has deviated from the average OCV curve. Based on this curve, the calibrated SOC obtained by terminal voltage mapping (OCV-SOC mapping relationship) deviates from the true value.

[0046] For the static voltage correction algorithm, using the average OCV, pure charge OCV or discharge OCV curve is inaccurate. From the experimental data analysis, it can be seen that the current OCV state of the battery cell is largely affected by the historical operating conditions. Therefore, the selection of the OCV curve should consider the impact of historical operating conditions more, and select the appropriate corresponding OCV curve by distinguishing specific operating conditions to correct the SOC.

[0047] For cells in the ternary system, the chemical properties result in little difference between the charge and discharge OCV curves. The SOC value error band obtained by mapping the discharge and charge OCV-SOC using the same voltage is ±2%. However, for lithium iron phosphate (chemical formula LiFePO4, abbreviated as LFP) cells, their chemical properties result in a large difference between the two OCV curves (large hysteresis effect). Within the calibrable SOC range, the SOC value error band obtained by mapping the discharge and charge OCV-SOC using the same voltage is greater than ±6%. Considering the loss of accuracy caused by factors such as sampling, the error of the SOC correction is unacceptable.

[0048] For LFP system cells, the OCV curve used for static voltage correction can be accurately corrected through historical operating conditions to eliminate the original error of terminal voltage correction SOC.

[0049] Please refer to Figure 2, which is a flow chart of a method for correcting a battery OCV curve provided by an embodiment of the present application. As shown in Figure 2, the method for correcting a battery OCV curve includes the following steps.

[0050] 201 , the electronic device determines whether N characteristic parameters of the battery cell in a set time period all fall within N parameter ranges corresponding to a target operating condition, where N is an integer greater than or equal to 1.

[0051] In the embodiment of the present application, the set period can be a historical period. For example, the set period can be a period before the current time point. Exemplarily, the set period can be a period half an hour before the current time point.

[0052] Characteristic parameters reflect the current operating conditions of an electric device. Examples include the current, voltage, and ampere-hour integral of a battery cell. The ampere-hour integral is the time integral of the battery cell's current. Characteristic parameters can be collected in real time. For example, a current sensor can periodically sample the battery cell's current, while a voltage sensor can periodically sample the battery cell's voltage. The ampere-hour integral can then be calculated based on the periodically sampled battery cell current.

[0053] Characteristic parameters may also be parameters that affect the OCV curve, such as temperature and cell aging parameters.

[0054] The electronic device may be a control module in a battery management system (BMS) on an electric device.

[0055] The set time period is a time period sufficient to determine the current working condition of the electric equipment.

[0056] Optionally, the characteristic parameters include: at least one of current, voltage, temperature, ampere-hour integral, battery cell aging parameters, and battery cell internal resistance.

[0057] Among them, the cell aging parameter can reflect the degree of cell aging. The degree of cell aging will affect the OCV curve. The cell aging parameter can be the cell's state of health (SOH). Generally speaking, the more times the cell is charged and discharged, the lower the SOH value, and the more serious the cell aging. The temperature of the cell will also have a certain impact on the OCV curve. The cell internal resistance can also reflect the degree of cell aging. Generally speaking, the more times the cell is charged and discharged, the greater the cell internal resistance and the more serious the cell aging.

[0058] In a possible example, the characteristic parameters include current, temperature, cell aging parameters, ampere-hour integral, and absolute value of ampere-hour integral.

[0059] Before executing step 201 , the electronic device may further obtain N characteristic parameters of the battery cell in a set time period.

[0060] In the embodiment of the present application, the target operating condition may correspond to N parameter ranges. If all N characteristic parameters fall within the N parameter ranges corresponding to the target operating condition, it indicates that the current operating condition of the electric device is the target operating condition. The N parameter ranges corresponding to each operating condition can be pre-set.

[0061] Operating conditions are a key concept in electric vehicle engineering and are crucial for their design, development, and testing. Operating conditions can be categorized based on different application scenarios. For example, in the case of a vehicle, operating conditions can be categorized as urban road conditions, highway conditions, mountainous conditions, and so on. A vehicle can be in one of multiple operating conditions. The target operating condition is one of these conditions.

[0062] Please refer to Table 1, which is a correspondence table between working conditions and parameter ranges provided in an embodiment of the present application.

[0063] Table 1

[0064] As shown in Table 1, the N characteristic parameters include: characteristic parameter 1, characteristic parameter 2, ... characteristic parameter N. The operating conditions include: operating condition 1, operating condition 2, operating condition 3, and operating condition 4. The N characteristic parameters all fall within the N parameter ranges corresponding to the target operating condition, which means that each characteristic parameter falls within each characteristic parameter range corresponding to the target operating condition. If characteristic parameter 1 falls within the range (A1, A2), characteristic parameter 2 falls within the range (B1, B2), ... characteristic parameter N falls within the range (C1, C2), then the N characteristic parameters all fall within the N parameter ranges corresponding to operating condition 1, and the vehicle's current operating condition is determined to be operating condition 1. If characteristic parameter 1 falls within the range (A2, A3), characteristic parameter 2 falls within the range (B2, B3), ... characteristic parameter N falls within the range (C2, C3), then the N characteristic parameters all fall within the N parameter ranges corresponding to operating condition 2, and the vehicle's current operating condition is determined to be operating condition 2. If characteristic parameter 1 falls within the range (A3, A4), characteristic parameter 2 falls within the range (B3, B4), ... characteristic parameter N falls within the range (C3, C4), then all N characteristic parameters fall within the N parameter ranges corresponding to operating condition 3, and the vehicle's current operating condition is determined to be operating condition 3. If characteristic parameter 1 falls within the range (A4, A5), characteristic parameter 2 falls within the range (B4, B5), ... characteristic parameter N falls within the range (C4, C5), then all N characteristic parameters fall within the N parameter ranges corresponding to operating condition 4, and the vehicle's current operating condition is determined to be operating condition 4.

[0065] If the N characteristic parameters do not all fall within the N parameter ranges corresponding to the aforementioned operating conditions 1, 2, 3, and 4, then the vehicle's current operating condition is not in any of these conditions, and the current OCV curve of the battery cell will not be corrected. For example, if characteristic parameter 1 falls within the range (A1, A2), characteristic parameter 2 falls within the range (B3, B4), ... characteristic parameter N falls within the range (C4, C5), then the vehicle's current operating condition is not in any of these conditions.

[0066] Optionally, the target operating condition is any one in a set of operating conditions, the set of operating conditions includes P operating conditions, the N parameter ranges corresponding to any two operating conditions in the P operating conditions are not completely the same, and P is an integer greater than or equal to 1.

[0067] In the embodiment of the present application, the electric device may be in one of a set of operating conditions. The set of operating conditions may include several typical operating conditions that the electric device may be in. For example, several typical operating conditions of a vehicle may include: urban road conditions, highway conditions, and mountainous conditions.

[0068] The N parameter ranges corresponding to any two working conditions in the P working conditions are not completely the same, which means that the N parameter ranges corresponding to any two working conditions are not completely the same.

[0069] It should be noted that in the example in Table 1 above, the range of each characteristic parameter is different for each operating condition. In actual situations, the ranges of one or more characteristic parameters may be the same for two operating conditions. It is sufficient to ensure that the ranges of the N parameters corresponding to any two of the P operating conditions are not exactly the same.

[0070] For example, referring to Table 1 above, if characteristic parameter 1 falls within the range of (A1, A2), characteristic parameter 2 falls within the range of (B1, B2), and characteristic parameter N falls within the range of (C1, C2), then the N characteristic parameters all fall within the N parameter ranges corresponding to operating condition 1, and the current operating condition of the electric equipment is determined to be operating condition 1.

[0071] 202 , when all N characteristic parameters fall within the N parameter ranges corresponding to the target operating condition, the electronic device updates the current OCV curve of the battery cell to a target OCV curve corresponding to the target operating condition.

[0072] In an embodiment of the present application, if all N characteristic parameters fall within the N parameter ranges corresponding to the target operating conditions, it indicates that the current operating condition of the electric device is the target operating condition, and the electronic device updates the current OCV curve of the battery cell to the target OCV curve corresponding to the target operating condition.

[0073] The OCV curve is a graph that plots the open-circuit voltage (OCV) against the SOC. For details, see the example in Figure 1. The OCV curve can be stored in the electronic device's memory (e.g., non-volatile memory) as a function mapping relationship. The OCV curve can also be stored in the electronic device's memory in the form of a mapping relationship table (OCV-SOC mapping relationship table).

[0074] Optionally, if the N characteristic parameters do not fall within the N parameter ranges corresponding to any of the P operating conditions, it indicates that the current operating condition of the electric device is not in any of the above P operating conditions, and the electronic device will not correct the current OCV curve of the battery cell.

[0075] Optionally, if the battery cell is in a charging state, the target OCV curve is determined based on a charging OCV curve and an average OCV curve corresponding to the target operating condition;

[0076] If the battery cell is in a discharged state, the target OCV curve is determined based on the discharge OCV curve and the average OCV curve corresponding to the target operating condition.

[0077] In the embodiment of the present application, the target operating condition may correspond to a charging OCV curve, a discharging OCV curve, and an average OCV curve. The charging OCV curve, the discharging OCV curve, and the average OCV curve corresponding to the target operating condition are all obtained under standard testing. It is possible to determine whether the battery cell is in a charging state or a discharging state, thereby obtaining a target OCV curve. To determine whether the battery cell is in a charging state or a discharging state, it can be determined based on whether the battery cell's power increases within a set period of time. If the battery cell's power increases within the set period of time, it is in a charging state. If the battery cell's power decreases within the set period of time, it is in a discharging state.

[0078] If the battery is in charging state:

[0079] OCV_use=(1-α)*OCV_avg+α*OCV_(chrg);

[0080] If the battery is in a discharged state:

[0081] OCV_use=(1-α)*OCV_avg+α*OCV_(dischrg);

[0082] Where α is a weighting factor that takes the charge and discharge states into account, and its value ranges from 0 to 1. OCV_use is the target OCV curve, OCV_avg is the average OCV curve obtained from standard testing, OCV_(chrg) is the charge OCV curve obtained from standard testing, and OCV_(dischrg) is the discharge OCV curve obtained from standard testing. If the cell is in the charging state, α is positively correlated with the increase in charge during the set period; if the cell is in the discharging state, α is positively correlated with the decrease in charge during the set period.

[0083] In an embodiment of the present application, if the battery cell is in a charging state, the target OCV curve can be determined based on the charging OCV curve and the average OCV curve corresponding to the target operating condition. Compared with simply using the average OCV curve or the charging OCV curve, determining the target OCV curve based on the charging OCV curve and the average OCV curve corresponding to the target operating condition can more accurately reflect the OCV curve under the target operating condition, so that the target OCV curve can more accurately reflect the target operating condition when the battery cell is in a charging state, thereby improving the estimation accuracy of the SOC. If the battery cell is in a discharging state, the target OCV curve can be determined based on the discharge OCV curve and the average OCV curve corresponding to the target operating condition. Compared with simply using the average OCV curve or the discharge OCV curve, determining the target OCV curve based on the discharge OCV curve and the average OCV curve corresponding to the target operating condition can more accurately reflect the OCV curve under the target operating condition, so that the target OCV curve can more accurately reflect the target operating condition when the battery cell is in a discharging state, thereby improving the estimation accuracy of the SOC.

[0084] Each operating condition corresponds to an OCV curve, which can be obtained through a large number of orthogonal experiments.

[0085] In an embodiment of the present application, the target operating condition of the electric equipment can be determined based on N characteristic parameters of the battery cell in a set time period, and the current OCV curve of the battery cell can be updated to the target OCV curve corresponding to the target operating condition. The OCV curve can be corrected so that the corrected OCV curve is closer to the actual OCV curve of the current operating condition, thereby improving the estimation accuracy of SOC.

[0086] Please refer to Figure 3, which is a flow chart of another method for correcting the battery OCV curve provided by an embodiment of the present application. As shown in Figure 3, the method for correcting the battery OCV curve includes the following steps.

[0087] 301. The electronic device periodically samples N original characteristic parameters of the battery cell in each unit time window according to a set unit time window, where N is an integer greater than or equal to 1.

[0088] In the embodiment of the present application, the unit time window is the minimum time unit for storing data, and each original characteristic parameter stores only one data in each unit time window. The unit time window is larger than the sampling period.

[0089] For example, the unit time window can be set to 5 minutes, and only one data point is stored for each original feature parameter within each 5-minute period. If the time period is set to 30 minutes, only the original feature parameters of 6 time windows are required for each feature parameter. The embodiment of the present application calculates feature parameters by calculating feature parameters in unit time windows to reduce the amount of data required for parameter calculation and storage, thereby reducing the cache space required to store feature parameters.

[0090] Optionally, each original feature parameter of each unit time window is determined based on each original feature data sampled at a set sampling frequency within each unit time window; each original feature parameter of each unit time window is the average value or accumulated value of each original feature data sampled at a set sampling frequency within each unit time window, or each original feature parameter of each unit time window is one of each original feature data sampled at a set sampling frequency within each unit time window.

[0091] In the embodiment of the present application, the unit time window is larger than the sampling period (the period corresponding to the set sampling frequency).

[0092] For example, if the sampling frequency of the system is 0.02 seconds per time and the set unit time window is 120 seconds, then a type of original feature parameter is extracted from every 6,000 original data points (one can be randomly selected from the 6,000 original feature data, or the average value of the 6,000 original feature data can be taken as the original feature parameter of this unit time window), thereby reducing the frequency of data to be stored from 50Hz to 1 / 120Hz, greatly reducing the storage space required to store the original feature parameters.

[0093] 302. The electronic device determines a comprehensive value of each original characteristic parameter of the battery cell sampled in M ​​unit time windows included in a set time period, and uses the comprehensive value of each original characteristic parameter as each characteristic parameter of the battery cell in the set time period. The set time period is a period of time before the current time point, and M is an integer greater than or equal to 2.

[0094] The comprehensive value of each original feature parameter may include any one of a mean value, a cumulative value, and a random value of the original feature parameter.

[0095] In the embodiments of the present application, the original characteristic parameters can be divided into two categories: those that vary randomly over time and those that accumulate over time. For example, the current, voltage, and temperature parameters of a battery cell vary randomly over time, while the ampere-hour integral and the absolute value of the ampere-hour integral of the battery cell accumulate over time.

[0096] For original characteristic parameters that vary randomly over time, the electronic device can determine the average value or random value of each original characteristic parameter of the battery cell sampled in the M unit time windows included in the set time period. For example, the values ​​of the original characteristic parameter sampled in the M unit time windows can be averaged to obtain the characteristic parameter of the battery cell in the set time period. For another example, a value of the original characteristic parameter sampled in the M unit time windows can be randomly selected as the characteristic parameter of the battery cell in the set time period.

[0097] For raw characteristic parameters that accumulate over time, the electronic device can determine the cumulative value of each raw characteristic parameter of the battery cell sampled over M unit time windows within a set period. For example, the values ​​of the raw characteristic parameter sampled over the M unit time windows can be added together to obtain the characteristic parameter of the battery cell during the set period.

[0098] The set time period is a period of time before the current time point, and the data of the set time period is a period of time closest to the current point, so that each characteristic parameter calculated based on the data of the battery cells sampled in M ​​unit time windows during the set time period can reflect the most recent state of the battery cell, thereby accurately obtaining the most recent operating conditions.

[0099] Optionally, in step 302, the electronic device determines the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows included in the set time period, which may include the following steps:

[0100] If the sampled duration of the current sampling time window is equal to the duration of the unit time window, determine the M unit time windows included in the duration of the set time period, and determine the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows, where the duration of the set time period is equal to M times the duration of the unit time window;

[0101] If the sampled duration of the current sampling time window is less than the duration of the unit time window, determine the M unit time windows and the current sampling time window included in the duration of the set time period, and determine the comprehensive value of each original characteristic parameter of the battery cell sampled by the M unit time windows and the current sampling time window. The duration of the set time period is equal to M times the duration of the unit time window plus the sampled duration of the current sampling time window.

[0102] In an embodiment of the present application, the duration of the set time period can be set to be greater than or equal to M times the unit time window, and less than (M+1) times the unit time window. If the sampled duration of the current sampled time window is equal to the duration of the unit time window, it indicates that the current sampled time window has just been sampled, and the set time period can be determined to be the M unit time windows sampled most recently. If the sampled duration of the current sampled time window is less than the duration of the unit time window, it indicates that the current sampled time window has not been sampled, and the set time period can be determined to be the sampled duration of the current sampled time window plus the M unit time windows sampled most recently (the M unit time windows sampled most recently refer to the M complete unit time windows sampled most recently, excluding the time window of the current sample).

[0103] Please refer to Figure 4, which is a schematic diagram of a set period and unit time window provided in an embodiment of the present application. As shown in Figure 4, the duration of each unit time window is equal. Exemplarily, the duration of the unit time window is X seconds, and the sampling period (the sampling period is the period corresponding to the sampling frequency, for example, if the sampling frequency is 50Hz, the sampling period is 0.02 seconds) is z. The set period is set to be greater than or equal to M times the unit time window and less than (M+1) times the unit time window. If the current time point is the current time 1 in Figure 4, the current sampling time window has just been sampled, then the set period is the set period 1 in Figure 4 (including M unit time windows); if the current time point is the current time 2 in Figure 4, then the current sampling time window has not been sampled, then the set period is the set period 2 in Figure 4 (including M unit time windows and the current sampling time window); if the current time point is the current time 3 in Figure 4, then the current sampling time window has just been sampled, then the set period is the set period 3 in Figure 4 (including M unit time windows).

[0104] For example, if the unit time window is 5 minutes long and M = 6, the duration from the start time of the record to the current time 1 is 30 minutes, the duration from the start time of the record to the current time 2 is 32 minutes, and the duration from the start time of the record to the current time 3 is 35 minutes. Then, the set time period 1 is 30 minutes, the set time period 2 is 32 minutes, and the set time period 3 is 30 minutes.

[0105] Exemplarily, for the 6 unit time windows included in the set time period 1, the average value of the original characteristic parameters of the battery cells sampled in the 6 unit time windows that vary randomly over time can be calculated, and the cumulative value of the original characteristic parameters of the battery cells sampled in the 6 unit time windows that accumulate over time can be calculated, thereby obtaining N characteristic parameters of the battery cells in the set time period 1. For the 6 unit time windows included in the set time period 2 and the current sampling time window, the average value of the original characteristic parameters of the battery cells sampled in the 6 unit time windows and the current sampling time window that vary randomly over time can be calculated, and the cumulative value of the original characteristic parameters of the battery cells sampled in the 6 unit time windows and the current sampling time window that accumulate over time can be calculated, thereby obtaining N characteristic parameters of the battery cells in the set time period 2. For the 6 unit time windows contained in the set time period 3, the average value of the original characteristic parameters of the battery cells sampled in the 6 unit time windows that vary randomly over time can be calculated, and the cumulative value of the original characteristic parameters of the battery cells sampled in the 6 unit time windows that accumulate over time can be calculated, thereby obtaining N characteristic parameters of the battery cells in the set time period 3.

[0106] 303 , the electronic device determines whether the N characteristic parameters of the battery cell in the set time period all fall within the N parameter ranges corresponding to the target operating condition.

[0107] 304 , when all the N characteristic parameters fall within the N parameter ranges corresponding to the target operating condition, the electronic device updates the current OCV curve of the battery cell to the target OCV curve corresponding to the target operating condition.

[0108] The specific implementation of steps 303 to 304 may refer to the above steps 201 to 202 and will not be repeated here.

[0109] In an embodiment of the present application, the target operating condition of the electric device can be determined based on the N characteristic parameters of the battery cell during a set time period. The current OCV curve of the battery cell can be updated to the target OCV curve corresponding to the target operating condition. The OCV curve can be corrected so that the corrected OCV curve is closer to the actual OCV curve of the current operating condition, thereby improving the estimation accuracy of the SOC. The N characteristic parameters of the battery cell during the set time period are calculated based on the average or cumulative value of each original characteristic parameter of the battery cell sampled over M unit time windows. This can reduce the parameter calculation and storage resolution, thereby reducing the cache space required to store the characteristic parameters.

[0110] The embodiment of the present application proposes an OCV curve correction method based on working conditions. Through a large number of orthogonal experiments, the true performance of the OCV curve under specific working conditions is obtained, and by mining the characteristic points of the specific working conditions, these working conditions are distinguished from a large number of working conditions based on the characteristic points. After identifying the specific working conditions, the OCV curve corresponding to the working conditions is considered and the algorithm OCV curve is corrected to calibrate the SOC.

[0111] First, a large number of orthogonal experiments were designed. By analyzing the experimental data, the impact of various usage and driving factors on OCV was evaluated, and the sensitivity of the OCV curve to each experimental factor was identified. For example, highly sensitive factors include discharge rate and temperature, while less sensitive factors include rest time. Subsequently, extensive testing and real-world vehicle data were processed to identify typical operating conditions that combine multiple factors with clear OCV influencing factors. These typical operating conditions have the following characteristics: 1) Under these conditions, the actual OCV curve of the battery cell has a specific position relative to the standard discharge and charge OCV curves; 2) these typical operating conditions are easily reproduced during use.

[0112] Secondly, after concluding through data analysis that typical operating conditions have typical OCV curve positions, the algorithm needs to be able to accurately identify each typical operating condition. By analyzing the data of various typical operating conditions, the average current value, ampere-hour integral value and other characteristics at a specific time can be screened out. These characteristics can be used to characterize whether the current actual vehicle operation is a typical operating condition.

[0113] The register can be used to store the current, voltage, temperature and other cell sensor signals at a specific time, and calculate the value of each characteristic parameter within the time. If the data volume (time) judgment requirement is met (for example, the amount of data stored exceeds the set time period), it will then be determined whether the calculated characteristic parameter value matches the set value of the specific working condition. If a specific working condition is met, the OCV curve used will be adjusted to the specified OCV curve under the specific working condition, and the adjusted OCV curve will be used for BMS state estimation and energy control, thereby achieving the purpose of correcting the SOC.

[0114] The embodiment of the present application proposes a method for using a correction algorithm based on working conditions to refer to the OCV curve. The idea is to calculate the characteristic parameter values ​​of a specific amount of data (time), match the specific working conditions distinguished by the experiment based on these parameter values, and use a specific OCV curve for the specific working condition. This can greatly reduce or even eliminate the initial estimation error caused by the mismatch of the reference OCV curve.

[0115] It should be noted that, considering that in actual systems, it is not advisable to spend a lot of storage space to cache information such as current, cell temperature, and cell voltage, the embodiment of the present application proposes a method for reducing the frequency to store characteristic parameters, as shown in Figure 4, that is, using a unit time of X seconds as a unit time window, calculating the characteristic value within the unit time window of X seconds, and memorizing the characteristic parameter value within a large time scale through each unit window, that is, changing the resolution of the data from the original high frequency of BMS sampling to the current low frequency, which can save a lot of cache space. For example, if the sampling frequency of the system is one data point in 0.02 seconds and the unit time window scale is set to 120 seconds, then a type of characteristic parameter value is extracted for every 6000 original data points, thereby reducing the frequency of the data to be stored from 50Hz to 1 / (120)Hz, which greatly reduces the storage space required for the algorithm.

[0116] For characteristic parameters, the parameter values ​​within the recording window Tmin (Tmin is the minimum credible time scale for recorded data) to Tmax (Tmax is the maximum credible time scale for recorded data) are defined as having high confidence. If the amount of data is less than Tmin or greater than Tmax, the calculated characteristic parameter value is not qualified to mark the current operating condition type. Define Tjug minutes as a standard judgment time window (the judgment time window Tjug can correspond to the above-mentioned set time period). When defining the characteristic parameter values ​​of special operating conditions, the standard values ​​of these characteristic parameters are calculated using this judgment time window. The calculated values ​​of this judgment time window are used for subsequent judgments and marking of the current operating condition category.

[0117] First, the high-frequency data collected is used as a time window of X seconds. The characteristic values ​​(average current, ampere-hour integral value, etc.) within this unit time window are calculated and stored at a low resolution. When the recording time is greater than Tmin and less than Tmax, the characteristic parameter values ​​within the judgment time window of Tjug seconds are calculated using the unit time window of X seconds. If the final storage window is less than X seconds and only y seconds, the judgment time window used to determine the operating condition is changed from Tjug seconds to (Tjug+y) seconds (see Figure 4 above). If the calculated characteristic parameter values ​​meet the judgment criteria for a special operating condition, the current actual vehicle operating condition is marked as this special operating condition. The OCV curve used in the correction algorithm (OCV_use) is then used to match the recorded OCV curve corresponding to this special operating condition. This OCV curve is then used as the standard for the BMS state estimation and energy control algorithm, thereby achieving the purpose of correcting the SOC.

[0118] It should be noted that the OCV curve for special working conditions is a fusion performance curve of the charge and discharge OCV curves of the standard test. This curve combines the discharge OCV (OCV_dischrg) and charge OCV (OCV_chrg) curves, while taking into account factors such as temperature, cell aging status, and internal resistance.

[0119] For example: The standard OCV curve for special working condition 1 is:

[0120] 1) If it is determined to be in the charging direction, then:

[0121] OCV_use=(1-α)*OCV_avg(SOH,Temperuture)+α*OCV_(chrg)(SOH,Temperuture);

[0122] 2) If it is determined to be the discharge direction, then:

[0123] OCV_use=(1-α)*OCV_avg(SOH,Temperuture)+α*OCV_(dischrg)(SOH,Temperuture);

[0124] Where α is the weight coefficient that takes into account the charge and discharge directions. OCV_avg: The average OCV curve obtained from the standard test; OCV_dischrg: The discharge OCV curve obtained from the standard test; OCV_chrg: The charge OCV curve obtained from the standard test.

[0125] OCV_avg(SOH,Temperature) is related to the cell's SOH and temperature. It varies at different SOHs or cell temperatures. Similarly, OCV_(chrg)(SOH,Temperature) and OCV_(dischrg)(SOH,Temperature) are related to the cell's SOH and temperature.

[0126] In the embodiment of the present application, a number of special working conditions are defined through a large number of orthogonal experiments, and characteristic parameters that can determine the special working conditions and their standard values ​​within a specific time window are defined based on data mining; a method is proposed to reduce the parameter calculation and storage resolution by calculating characteristic parameters in a unit time window, thereby reducing the required cache space; a method for adjusting the OCV curve of the BMS algorithm is defined: by matching and identifying the current working condition through characteristic parameters, the current OCV curve is corrected by the expected value of the OCV curve of the working condition under the set classification, thereby achieving the purpose of correcting the SOC. The OCV curve can be accurately corrected based on the accurate identification of the current working condition, and the OCV curve can be used as a benchmark to significantly reduce or even eliminate the original algorithm error caused by the inaccurate OCV curve, thereby improving the BMS's estimation accuracy of the battery cell state SOC, fundamentally improving the reliability of the algorithm, and having great technical advantages and market value.

[0127] The above describes the solution of the embodiment of the present application from the perspective of the execution process of the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0128] The embodiment of the present application can divide the functional units of the electronic device according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0129] Please refer to FIG5 , which is a schematic diagram of the structure of a curve correction device provided in an embodiment of the present application. The curve correction device 500 may include a determination unit 501 and an update unit 502 , wherein:

[0130] A determination unit 501 is configured to determine whether N characteristic parameters of the battery cell in a set period all fall within N parameter ranges corresponding to a target operating condition, where N is an integer greater than or equal to 1;

[0131] The updating unit 502 is configured to update the current OCV curve of the battery cell to a target OCV curve corresponding to the target operating condition when all of the N characteristic parameters fall within the N parameter ranges corresponding to the target operating condition.

[0132] Optionally, the curve correction device 500 may further include a sampling unit 503;

[0133] The sampling unit 503 is configured to periodically sample N original characteristic parameters of the battery cell in each unit time window according to a set unit time window;

[0134] The determination unit 501 is further used to determine the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows contained in the set time period, and use the comprehensive value of each original characteristic parameter as each characteristic parameter of the battery cell in the set time period, where the set time period is a period of time before the current time point, and M is an integer greater than or equal to 2.

[0135] Optionally, the determining unit 501 determines the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows included in the set time period, including:

[0136] When the sampled duration of the current sampling time window is equal to the duration of the unit time window, determine the M unit time windows included in the duration of the set time period, and determine the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows, where the duration of the set time period is equal to M times the duration of the unit time window;

[0137] When the sampled duration of the current sampling time window is less than the duration of the unit time window, determine the M unit time windows and the current sampling time window included in the duration of the set time period, and determine the comprehensive value of each original characteristic parameter of the battery cell sampled by the M unit time windows and the current sampling time window. The duration of the set time period is equal to M times the duration of the unit time window plus the sampled duration of the current sampling time window.

[0138] Optionally, each original feature parameter of each unit time window is determined based on each original feature data sampled at a set sampling frequency within each unit time window; each original feature parameter of each unit time window is the average value or accumulated value of each original feature data sampled at a set sampling frequency within each unit time window, or each original feature parameter of each unit time window is one of each original feature data sampled at a set sampling frequency within each unit time window.

[0139] Optionally, the comprehensive value includes any one of an average value, an accumulated value, and a random value.

[0140] Optionally, the target operating condition is any one in a set of operating conditions, the set of operating conditions includes P operating conditions, the N parameter ranges corresponding to any two operating conditions in the P operating conditions are not completely the same, and P is an integer greater than or equal to 1.

[0141] Optionally, the characteristic parameters include: at least one of current, voltage, temperature, ampere-hour integral, battery cell aging parameters, and battery cell internal resistance.

[0142] Optionally, if the battery cell is in a charging state, the target OCV curve is determined based on a charging OCV curve and an average OCV curve corresponding to the target operating condition;

[0143] If the battery cell is in a discharged state, the target OCV curve is determined based on the discharge OCV curve and the average OCV curve corresponding to the target operating condition.

[0144] Among them, the determining unit 501, the updating unit 502, and the sampling unit 503 in the embodiment of the present application can be processors in an electronic device.

[0145] In an embodiment of the present application, the target operating condition of the electric equipment can be determined based on N characteristic parameters of the battery cell in a set time period, and the current OCV curve of the battery cell can be updated to the target OCV curve corresponding to the target operating condition. The OCV curve can be corrected so that the corrected OCV curve is closer to the actual OCV curve of the current operating condition, thereby improving the estimation accuracy of SOC.

[0146] Please refer to Figure 6, which is a structural diagram of an electronic device provided in an embodiment of the present application. As shown in Figure 6, the electronic device 600 includes a processor 601 and a memory 602. The processor 601 and the memory 602 can be connected to each other via a communication bus 603. The communication bus 603 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus or a controller area network (CAN) bus, etc. The communication bus 603 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in Figure 6, but it does not mean that there is only one bus or one type of bus. The memory 602 is used to store computer programs, and the computer program includes program instructions. The processor 601 is configured to call program instructions. The above program includes instructions for executing some or all of the steps in the method included in Figures 1 to 4.

[0147] The processor 601 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the above program.

[0148] The memory 602 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a bus. The memory may also be integrated with the processor.

[0149] In an embodiment of the present application, the target operating condition of the electric equipment can be determined based on N characteristic parameters of the battery cell in a set time period, and the current OCV curve of the battery cell can be updated to the target OCV curve corresponding to the target operating condition. The OCV curve can be corrected so that the corrected OCV curve is closer to the actual OCV curve of the current operating condition, thereby improving the estimation accuracy of SOC.

[0150] Please refer to FIG. 7 , which is a schematic structural diagram of a battery management system 700 provided in an embodiment of the present application. As shown in FIG. 7 , the battery management system 700 includes the electronic device 600 described above.

[0151] Please refer to Figure 8, which is a structural diagram of an electric device 800 provided in an embodiment of the present application. As shown in Figure 8, the electric device 800 includes the above-mentioned battery management system 700, and the battery management system 700 includes the electronic device 600 described above.

[0152] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the battery OCV curve correction methods described in the above method embodiments.

[0153] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0154] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0156] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0157] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0158] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0159] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0160] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for correcting a battery open circuit voltage (OCV) curve, comprising: Determine whether N characteristic parameters of the battery cell in a set time period all fall within N parameter ranges corresponding to the target operating condition, where N is an integer greater than or equal to 1 (201); When the N characteristic parameters all fall within the N parameter ranges corresponding to the target operating condition, the current OCV curve of the battery cell is updated to a target OCV curve corresponding to the target operating condition (202).

2. The method according to claim 1, before determining whether the N characteristic parameters of the battery cell in the set time period all fall within the N parameter ranges corresponding to the target operating condition, the method further comprises: Periodically sampling N original characteristic parameters of the battery cell in each unit time window according to a set unit time window (301); Determine the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows included in the set time period, and use the comprehensive value of each original characteristic parameter as each characteristic parameter of the battery cell in the set time period, wherein the set time period is a period of time before the current time point, and M is an integer greater than or equal to 2 (302).

3. The method according to claim 2, wherein determining the comprehensive value of each original characteristic parameter of the battery cell sampled in the M unit time windows contained in the set time period comprises: If the sampled duration of the current sampling time window is equal to the duration of the unit time window, determine the M unit time windows included in the duration of the set time period, determine the comprehensive value of each original characteristic parameter of the battery cell sampled by the M unit time windows, and the duration of the set time period is equal to M times the duration of the unit time window; If the sampled duration of the current sampling time window is less than the duration of the unit time window, determine the M unit time windows and the current sampling time window included in the duration of the set time period, and determine the comprehensive value of each original characteristic parameter of the battery cell sampled by the M unit time windows and the current sampling time window, and the duration of the set time period is equal to M times the duration of the unit time window plus the sampled duration of the current sampling time window.

4. According to the method described in claim 2 or 3, each original feature parameter of each unit time window is determined based on each original feature data sampled at a set sampling frequency within each unit time window; each original feature parameter of each unit time window is the average value or accumulated value of each original feature data sampled at a set sampling frequency within each unit time window, or each original feature parameter of each unit time window is one of each original feature data sampled at a set sampling frequency within each unit time window.

5. The method according to any one of claims 2 to 4, wherein the comprehensive value comprises any one of an average value, an accumulated value, and a random value.

6. According to the method described in any one of claims 1 to 5, the target operating condition is any one of an operating condition set, the operating condition set includes P operating conditions, the N parameter ranges corresponding to any two operating conditions in the P operating conditions are not completely the same, and P is an integer greater than or equal to 1.

7. The method according to any one of claims 1 to 6, wherein the characteristic parameters include: At least one of current, voltage, temperature, ampere-hour integral, battery cell aging parameters, and battery cell internal resistance.

8. The method according to any one of claims 1 to 7, If the battery cell is in a charging state, the target OCV curve is determined based on the charging OCV curve and the average OCV curve corresponding to the target operating condition; If the battery cell is in a discharging state, the target OCV curve is determined based on the discharge OCV curve and the average OCV curve corresponding to the target operating condition.

9. An electronic device (600), comprising a processor (601) and a memory (602), wherein the memory (602) is used to store a computer program, wherein the computer program comprises program instructions, and the processor (601) is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, wherein the computer program comprises program instructions, and when the program instructions are executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 8.

11. A computer program product, comprising a computer program, wherein the computer program is operable to cause a computer to execute the method according to any one of claims 1 to 8.

12. A processor (601), the processor being configured to call program instructions to execute the method according to any one of claims 1 to 8.

13. A battery management system (700), comprising the electronic device (600) according to claim 9.

14. An electric device (800), comprising the electronic device (600) according to claim 9.

Images