Method and apparatus for determining SOC of battery, and method and apparatus for determining battery charging and discharging strategy

Abstract

Provided in the present application are a method and apparatus for determining the SOC of a battery, and a method and apparatus for determining a battery charging and discharging strategy. A specific implementation of the method for determining the SOC of a battery comprises: acquiring a battery parameter, wherein the battery parameter comprises at least one of voltage, current and temperature; on the basis of the battery parameter, determining SOC estimation noise, wherein the SOC estimation noise is related to the slope of a curve representing the change of the battery parameter with SOC; and on the basis of at least one of the voltage and current of a battery to be processed, and the SOC estimation noise, determining the SOC of the battery to be processed. The method can improve the accuracy of the SOC of a battery to be processed.

Description

Method for determining SOC of battery, method and device for determining battery charging and discharging strategy Cross-reference to related applications

[0001] The present application claims priority to Chinese Patent Application No. CN202411259883.8, filed on September 9, 2024, entitled "Method for determining SOC of battery, method and device for determining battery charging and discharging strategy", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of batteries, in particular to a method for determining the SOC of a battery, a method and device for determining the battery charging and discharging strategy. BACKGROUND

[0003] Batteries can provide power for electronic devices, and the SOC (State of charge, SOC) of the battery has a crucial impact on the performance and safety of the battery, so it is often necessary to determine the SOC of the battery.

[0004] In related technologies, there are methods for determining the SOC, such as the ampere-hour integration method, the open-circuit voltage method, and the Kalman filter algorithm. However, the ampere-hour integration method leads to a gradual increase in SOC error due to current error, resulting in low accuracy of the SOC. The open-circuit voltage method requires a linear relationship between the SOC and the OCV (Open circuit voltage, OCV) and a battery resting condition to determine the SOC, but there is a nonlinear relationship for batteries such as lithium iron phosphate batteries and ternary lithium batteries, and it is not easy to meet the battery resting condition, resulting in low accuracy of the SOC. The Kalman filter algorithm determines the SOC of the battery based on a pure algorithm mechanism, which also leads to low accuracy of the SOC.

[0005] Therefore, there is a problem of low accuracy of the SOC in related technologies. SUMMARY

[0006] The purpose of the embodiments of the present application is to provide a method for determining the SOC of a battery, a method and device for determining the battery charging and discharging strategy, to improve the accuracy of the SOC of the battery to be processed.

[0007] In a first aspect, an embodiment of the present application provides a method for determining SOC of a battery, the method comprising: obtaining a battery parameter, the battery parameter comprising at least one of voltage, current, and temperature; determining an SOC estimation noise according to the battery parameter, wherein the SOC estimation noise is related to a slope of a curve of the battery parameter changing with SOC; and determining the SOC of a battery to be processed according to at least one of voltage and current of the battery to be processed and the SOC estimation noise. In this way, since the SOC estimation noise is related to the slope of the curve of the battery parameter changing with SOC, the SOC estimation noise can adaptively change with the battery parameter, thereby improving the accuracy of the SOC of the battery to be processed.

[0008] Optionally, the SOC estimation noise comprises an observation noise and / or a prediction noise. In this way, the observation noise and / or the prediction noise are considered, thereby improving the accuracy of the SOC of the battery to be processed to some extent.

[0009] Optionally, the observation noise is negatively related to the slope of the curve of the battery parameter changing with SOC. In this way, a more reliable observation noise can be obtained, thereby improving the accuracy of the SOC of the battery to be processed to some extent.

[0010] Optionally, the observation noise is a quotient of a first preset noise and the slope of the curve of the battery parameter changing with SOC. In this way, the operation rate and the accuracy of the observation noise can be improved.

[0011] Optionally, the prediction noise is positively related to the slope of the curve of the battery parameter changing with SOC. In this way, a more reliable prediction noise can be obtained, thereby improving the accuracy of the SOC of the battery to be processed to some extent.

[0012] Optionally, the prediction noise is a product of a second preset noise and the slope of the curve of the battery parameter changing with SOC. In this way, the operation rate and the accuracy of the prediction noise can be improved.

[0013] Optionally, the determining the SOC of the battery to be processed according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise comprises: determining a first SOC of the battery to be processed according to the current of the battery to be processed; determining a second SOC of the battery to be processed according to the voltage of the battery to be processed; determining a first weight corresponding to the first SOC and a second weight corresponding to the second SOC according to the SOC estimation noise; and performing weighted processing on the first SOC and the second SOC according to the first weight and the second weight to obtain the SOC of the battery to be processed. In this way, when determining the SOC of the battery to be processed, the noise caused by the current parameter and the noise caused by the voltage parameter can be considered at the same time, thereby improving the accuracy of the SOC of the battery to be processed.

[0014] Optionally, the first weight is negatively correlated with the observation noise, and the second weight is negatively correlated with the prediction noise. In this way, the accuracy of the SOC of the battery to be processed can be improved.

[0015] Optionally, the determining the SOC estimation noise according to the battery parameter comprises: if a slope of a curve of the battery parameter changing with the SOC is in any preset interval, determining a preset noise corresponding to the preset interval as the SOC estimation noise. In this way, the SOC estimation noise can be determined as the preset noise corresponding to the preset interval in which the slope of the curve of the battery parameter changing with the SOC is located, so that the SOC estimation noise is different in different preset intervals of the slope, the operation is more simple and convenient, and the accuracy of the SOC of the battery is improved to a certain extent.

[0016] Optionally, if the slope of the curve of the battery parameter changing with the SOC is in any preset interval, the preset noise corresponding to the preset interval is determined as the SOC estimation noise, comprising: if the voltage of the battery parameter is greater than a preset upper limit of voltage or the voltage of the battery parameter is less than a preset lower limit of voltage, a first noise is determined as the SOC estimation noise; and if the voltage of the battery parameter is between the preset lower limit of voltage and the preset upper limit of voltage, a second noise is determined as the SOC estimation noise. In this way, the first noise or the second noise can be adaptively determined as the SOC estimation noise according to the voltage value, so that a more accurate SOC estimation noise can be obtained, and the accuracy of the SOC is improved to a certain extent.

[0017] Optionally, if the slope of the curve of the battery parameter changing with the SOC is within any preset interval, a preset noise corresponding to the preset interval is determined as the SOC estimation noise, including: if the voltage in the battery parameter is greater than a preset upper limit of voltage, a third noise is determined as the SOC estimation noise; if the voltage in the battery parameter is less than a preset lower limit of voltage, a fourth noise is determined as the SOC estimation noise; if the voltage in the battery parameter is between the preset lower limit of voltage and the preset upper limit of voltage, a fifth noise is determined as the SOC estimation noise. In this way, a plurality of noises are set with smaller preset granularity, so that a more accurate SOC estimation noise can be determined, and the accuracy of the SOC is improved to a certain extent.

[0018] Optionally, if the slope of the curve of the battery parameter changing with the SOC is within any preset interval, a preset noise corresponding to the preset interval is determined as the SOC estimation noise, including: if the rate of change of the voltage in the battery parameter with the SOC is greater than a preset rate of change threshold, a sixth noise is determined as the SOC estimation noise; if the rate of change of the voltage in the battery parameter with the SOC is less than the preset rate of change threshold, a seventh noise is determined as the SOC estimation noise. In this way, the corresponding noise can be determined as the SOC estimation noise by the preset interval in which the rate of change of the voltage with the SOC data, so that the SOC estimation noise adaptively changes with the change of the rate of change of the voltage with the SOC data, and the accuracy of the SOC estimation noise is improved to a certain extent, thereby improving the accuracy of the battery SOC.

[0019] Optionally, before the SOC of the battery to be processed is determined according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise, the method further includes: identifying a circuit model parameter; the circuit model is used to determine the prediction noise.

[0020] Optionally, the identification of the circuit model parameter includes: identifying the circuit model parameter according to the circuit model and the SOC estimation noise. In this way, the accuracy of the SOC is improved, and a more accurate SOC is obtained.

[0021] Optionally, the identification of the circuit model parameter includes: identifying the circuit model parameter according to the circuit model and the recursive least square method. In this way, the circuit model parameter can be solved more simply and conveniently, and the calculation efficiency is improved to a certain extent.

[0022] Optionally, after the circuit model parameter is identified, the method further includes: correcting the circuit model parameter according to a correction rule, so as to improve the accuracy of the circuit model parameter.

[0023] Optionally, the correcting the circuit model parameter according to the correction rule comprises: if the circuit model parameter is greater than an upper limit value of a preset identification range, correcting the circuit model parameter to the upper limit value of the preset identification range; if the circuit model parameter is less than a lower limit value of the preset identification range, correcting the circuit model parameter to the lower limit value of the preset identification range. In this way, the circuit model parameter can be limited within the preset identification range, thereby improving the accuracy of the circuit model parameter and avoiding the dispersion of the circuit model.

[0024] Optionally, the correcting the circuit model parameter according to the correction rule comprises: if a change rate between the circuit model parameter identified in the current frame and the circuit model parameter identified in the last frame is greater than an upper limit value of a change rate range, correcting the circuit model parameter identified in the current frame according to the upper limit value of the change rate range. Here, considering that the circuit model parameter will not mutate in a short time, if the circuit model parameter is identified by using a pure algorithm mechanism, a value with a large change can be obtained. This does not conform to the actual law, and therefore, the change rate of the circuit model parameter identified in the current frame is limited, so that the circuit model parameter conforming to the actual law can be obtained, and the accuracy of the SOC of the battery to be processed is improved to a certain extent.

[0025] Optionally, the correcting the circuit model parameter according to the correction rule comprises: weighting the circuit model parameter identified in the current frame and the circuit model parameter identified in the last frame to correct the circuit model parameter identified in the current frame. In this way, the circuit model parameters identified in adjacent two frames can be weighted, so that the circuit model parameters identified in adjacent two frames will not mutate greatly, thereby improving the accuracy of the SOC.

[0026] Optionally, the battery to be processed is of the same type as the battery used to determine the SOC estimation noise. In this way, after the SOC estimation noise corresponding to a type of battery is determined, the SOC of the battery of the same type can be determined by using the SOC estimation noise, thereby reducing the calculation cost on the basis of improving the accuracy of the SOC.

[0027] In a second aspect, an embodiment of the present application provides a battery charging and discharging strategy determination method, comprising: obtaining a battery parameter in a battery charging and discharging process, the battery parameter comprising at least one of voltage, current and temperature; determining a SOC estimation noise according to the battery parameter, wherein the SOC estimation noise is related to a slope of a curve of the battery parameter changing with the SOC; determining an SOC of a battery to be processed according to at least one of voltage and current of the battery to be processed and the SOC estimation noise; and determining a charging and discharging strategy according to the SOC of the battery to be processed. In this way, the appropriate charging and discharging strategy can be determined according to the SOC of the battery to be processed, thereby improving the service life of the battery to be processed to a certain extent.

[0028] In a third aspect, an embodiment of the present application provides a device for determining SOC of a battery, the device comprising: an obtaining module configured to obtain a battery parameter, the battery parameter comprising at least one of voltage, current, and temperature; a noise determining module configured to determine an SOC estimation noise according to the battery parameter, wherein the SOC estimation noise is related to a slope of a curve of the battery parameter changing with SOC; and a SOC determining module configured to determine an SOC of a battery to be processed according to at least one of voltage and current of the battery to be processed and the SOC estimation noise. In this way, since the SOC estimation noise is related to the slope of the curve of the battery parameter changing with SOC, the SOC estimation noise can adaptively change with the battery parameter, thereby improving accuracy of the SOC of the battery to be processed.

[0029] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the steps in the method according to the first aspect are performed.

[0030] In a fifth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps in the method according to the first aspect are performed.

[0031] In a sixth aspect, an embodiment of the present application provides a computer program product, which comprises a computer program or instructions, and when the computer program or instructions are executed by a processor, the method according to the first aspect is performed.

[0032] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or be learned from the practice of the application. The purposes and other advantages of the present application can be realized and attained by the structure particularly pointed out in the written description and claims, and the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor.

[0034] FIG. 1 is a flowchart of a method for determining SOC of a battery according to an embodiment of the present application;

[0035] FIG. 2 is a schematic diagram of a change curve of a voltage of a battery varying with SOC in a charging process and a discharging process according to an embodiment of the present application;

[0036] FIG. 3 is a comparison diagram of errors of SOC determined by a traditional Kalman filtering algorithm and SOC determined based on a method for determining SOC of a battery according to an embodiment of the present application;

[0037] FIG. 4 is a structural diagram of an equivalent circuit model according to an embodiment of the present application;

[0038] FIG. 5 is a schematic diagram of a change rate of a voltage of a battery varying with SOC according to an embodiment of the present application;

[0039] FIG. 6 is a flowchart of a method for determining a battery charging and discharging strategy according to an embodiment of the present application;

[0040] FIG. 7 is a structural block diagram of a device for determining SOC of a battery according to an embodiment of the present application;

[0041] FIG. 8 is a structural diagram of an electronic device for executing a method for determining SOC of a battery or a method for determining a battery charging and discharging strategy according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0043] It should be noted that: similar reference numerals and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0044] It should be noted that: in the case of no conflict, the embodiments in the present application or the technical features in the embodiments can be combined.

[0045] In addition, the defects of the above related art solutions are the results of the inventors after practice and careful research, and therefore the discovery process of the above problems and the solutions proposed by the embodiments of the present application to the above problems should be the contributions of the inventors to the present application during the process of the present application.

[0046] It should be noted that the SOC of the battery can be determined by a server, a server cluster, or a cloud platform, etc. In other application scenarios, the SOC of the battery can also be determined by a battery management system, an on-board computer, etc., and the present application does not limit this. Exemplarily, the following text of the present application is written in the form of a server.

[0047] It should be noted that in the related art, there is a problem of low accuracy of the SOC. Therefore, according to the battery parameters, the present application determines the SOC estimation noise, so that the SOC of the battery to be processed can be determined based on the SOC estimation noise and the related parameters of the battery to be processed. In this way, since the SOC estimation noise is related to the slope of the curve of the battery parameter changing with the SOC, the SOC estimation noise will adaptively change with the battery parameter, so that the accuracy of the SOC of the battery to be processed can be improved.

[0048] Specifically, please refer to FIG. 1, which shows a flowchart of a method for determining the SOC of a battery according to an embodiment of the present application. As shown in FIG. 1, the method for determining the SOC of the battery includes the following steps 101 to 103.

[0049] Step 101: Obtain battery parameters, wherein the battery parameters include at least one of voltage, current, and temperature.

[0050] The above battery parameters can be regarded as parameters describing the state or performance of the battery, which can include, in addition to the voltage, current, and temperature of the battery, for example, the capacity of the battery, the charge-discharge rate, etc.

[0051] It should be noted that the above battery parameters can be historical battery parameters. That is, the server can obtain historical battery parameters for determining the SOC of the battery.

[0052] Step 102: Determine the SOC estimation noise according to the battery parameters, wherein the SOC estimation noise is related to the slope of the curve of the battery parameter changing with the SOC.

[0053] The above SOC estimation noise, that is, the noise existing in the process of determining the SOC of the battery.

[0054] In some application scenarios, the SOC estimation noise includes current noise, voltage noise and / or temperature noise. The current noise can be determined according to the battery internal resistance, for example. The greater the battery internal resistance, the greater the current noise. The temperature noise can be determined according to the temperature range in which the battery operates, for example. When the battery is at 25°C, the temperature noise is small, while when the battery is at less than -10°C or greater than 55°C, the temperature noise is large.

[0055] In these application scenarios, the SOC estimation noise includes noise caused by various parameters, so that the SOC estimation is more accurate in actual scenarios including current, voltage and / or temperature.

[0056] Referring to FIG. 2, it shows the change curve of the voltage of the battery (i.e., charge ocv and discharge ocv) with respect to the SOC of the battery in the charge process and the discharge process (i.e., charge ocv fitting and discharge ocv fitting). As can be seen, the voltage of the battery in the charge process and the discharge process is different for the same SOC. That is, the battery parameters can change due to the SOC of the battery.

[0057] Therefore, the battery parameters can include the battery parameters in the charge process or the discharge process, which change with the SOC. Therefore, the curve of the battery parameters with respect to the SOC can be obtained, and then the slope of the curve can be obtained. It should be noted that the slope of the curve can be obtained by dividing the voltage change value by the corresponding changed SOC.

[0058] In some application scenarios, the server can determine the state of the battery by the charge and discharge field sent by the battery management system to obtain the battery parameters in the corresponding state.

[0059] In other application scenarios, the server can also determine the state of the battery by setting a time threshold T. Specifically, if the original state of the battery is the discharge state, when the continuous charging time of the battery exceeds the time threshold T, it can be determined that the current state is the charge state, otherwise it is determined that the current state is the discharge state; if the original state of the battery is the charge state, when the continuous discharge time of the battery exceeds the time threshold T, it can be determined that the current state is the discharge state, otherwise it is determined that the current state is the charge state.

[0060] In other application scenarios, the server can also determine the state of the battery by the current data. Specifically, the current data in the charge state is generally negative, and the current data in the discharge state is generally positive, so the state of the battery can be determined according to the positive and negative of the current data.

[0061] In some application scenarios, the server can determine the charging and discharging state of the battery based on the switching state of the charging and discharging relay. For example, when the normally open contact of the charging and discharging relay is closed, it can be determined that the battery is in a charging state; when the normally open contact of the charging and discharging relay is open, it can be determined that the battery is in a discharging state.

[0062] In some application scenarios, the server can also determine the charging and discharging state of the battery by determining whether the voltage of the battery is continuously rising or continuously falling. If the voltage is continuously rising, it can be determined that the battery is in a charging state; if the voltage is continuously falling, it can be determined that the battery is in a discharging state.

[0063] Therefore, after determining the state of the battery, the server can obtain the slope of the curve of the battery parameter changing with the SOC in the state.

[0064] In some application scenarios, the SOC estimation noise is positively correlated with the slope; in other application scenarios, the SOC estimation noise is negatively correlated with the slope. Details are described below, and will not be described here.

[0065] It should be noted that when the server performs the above step 102, the SOC estimation noise can be determined according to one or more of the battery parameters. If the SOC estimation noise is determined according to multiple battery parameters, for example, the multiple battery parameters can be integrated by point multiplication or accumulation to obtain integrated data, so that the SOC estimation noise can be determined according to the integrated data, which can take into account the noise brought by multiple battery parameters, thereby improving the accuracy of the SOC estimation noise.

[0066] In some optional implementations, the determination of the SOC estimation noise according to the battery parameter in the above step 102 can include: inputting the battery parameter into a preset model to obtain a predicted SOC; then, determining the SOC estimation noise according to the predicted SOC and an expected SOC; the expected SOC is determined according to the measured historical SOC value.

[0067] The above preset model can be, for example, a neural network model, a support vector machine, etc. The preset model can be trained by one or more sample battery parameters. That is, one or more sample battery parameters are input into an initial preset model, and then the measured sample SOC is used as the expected output to train the initial preset model. When the error between the two is within a preset error range, it can be determined that the initial preset model converges to the above preset model.

[0068] In some application scenarios, the server can input the obtained battery parameter into the preset model to obtain the predicted SOC, and can determine the difference between the predicted SOC and the expected SOC as the SOC estimation noise.

[0069] It should be noted that the expected SOC can be a historical SOC value obtained by actually measuring the battery, or an average value of historical SOCs obtained by multiple measurements, etc.

[0070] In the present implementation, the SOC estimation noise can be obtained by predicting the SOC and the expected SOC, and since the expected SOC is highly accurate, the accuracy of the SOC estimation noise is also improved to a certain extent.

[0071] At step 103, the SOC of the battery to be processed is determined according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise.

[0072] The battery to be processed is the battery whose SOC is to be determined.

[0073] In some application scenarios, the battery to be processed can be the battery used to determine the SOC estimation noise. In this case, the battery parameters obtained by the server can be historical battery parameters of the battery to be processed.

[0074] In other application scenarios, the battery to be processed is of the same type as the battery used to determine the SOC estimation noise. In this case, the battery parameters obtained by the server can be historical battery parameters or current battery parameters of the battery of the same type. For example, if the battery used to determine the SOC estimation noise is a lithium iron phosphate battery, the battery to be processed can also be a lithium iron phosphate battery; if the battery used to determine the SOC estimation noise is a ternary lithium battery, the battery to be processed can also be a ternary lithium battery.

[0075] It should be noted that the application does not limit the type of applicable battery, which can be, for example, a lithium iron phosphate battery, a ternary lithium battery, or a sodium ion battery, etc.

[0076] In this way, after the SOC estimation noise corresponding to a type of battery is determined, the SOC of the battery of the same type can be determined using the SOC estimation noise, thereby reducing the calculation cost on the basis of improving the accuracy of the SOC.

[0077] In some application scenarios, the server can obtain at least one of the voltage and the current of the battery to be processed, and then determine the other based on the resistance of the battery to be processed. Therefore, the SOC of the battery to be processed can be determined according to one of them and the SOC estimation noise.

[0078] In some application scenarios, the SOC of the battery to be processed can be determined, for example, by the least square method or the Kalman filtering algorithm. In any algorithm for determining the SOC of the battery, the noise involved therein can be replaced by the SOC estimation noise for calculation.

[0079] In the present implementation, since the SOC estimation noise is related to the slope of the curve of the battery parameter varying with the SOC, the SOC estimation noise can be adapted to change, thereby improving the accuracy of the SOC of the battery to be processed.

[0080] Further, please refer to FIG. 3, which shows a comparison diagram of the error of the SOC determined based on the present application and the error of the SOC determined by the traditional Kalman filtering algorithm. As shown in FIG. 3, when the error of the SOC is 0, the accuracy of the SOC is the highest. At the same time point, the fluctuation range of the error of the SOC determined by the traditional Kalman filtering algorithm is greater than the fluctuation range of the error of the SOC determined by the present application. It can be seen that the error of the SOC determined by the traditional Kalman filtering algorithm is greater than the error of the SOC determined by the present application. Therefore, the present application can improve the accuracy of the SOC of the battery to be processed.

[0081] It should be noted that in the error comparison diagram shown in FIG. 3, the vertical coordinate is the SOC error, and the horizontal coordinate can be time (specifically, the charging and discharging time), and then the SOC error corresponding to each time point can be determined by the relevant battery data corresponding to the time point.

[0082] In some optional implementations, the SOC estimation noise includes observation noise and / or prediction noise.

[0083] The observation noise described above can represent the error existing in the observed SOC data of the battery, which can be, for example, the noise caused by the voltage parameter.

[0084] The prediction noise described above represents the error existing in the predicted SOC data of the battery, which can be, for example, the noise caused by the current parameter.

[0085] In some application scenarios, the observation noise and / or the prediction noise can be determined, for example, by the least square method or the Kalman filtering algorithm. When determined by the least square method, for example, the preset noise data can be multiplied by the slope of the curve of the battery parameter varying with the SOC, thereby obtaining the observation noise and / or the prediction noise. In addition, when the observation noise and / or the prediction noise is determined by the Kalman filtering algorithm, the observation noise and / or the prediction noise can be determined according to the battery parameter and the preset Kalman filtering noise.

[0086] In the present implementation, the observation noise and / or the prediction noise are considered, thereby the SOC of the battery to be processed can be determined in combination with one or both of them, and the accuracy of the SOC of the battery to be processed is improved to a certain extent.

[0087] In some optional implementations, the observation noise is negatively related to the slope of the curve of the battery parameter varying with the SOC.

[0088] In this way, when the slope of the curve of the battery parameter changing with the SOC becomes smaller, the value of the observation noise becomes larger; when the slope of the curve of the battery parameter changing with the SOC becomes larger, the value of the observation noise becomes smaller, so that the observation noise adaptively changes with the slope, and then a more reliable observation noise can be obtained, and the accuracy of the SOC of the battery to be processed is improved to a certain extent.

[0089] In some optional implementations, the observation noise is a quotient of a first preset noise and the slope of the curve of the battery parameter changing with the SOC.

[0090] The first preset noise may be, for example, a fixed observation value obtained based on experience, so that the first preset noise is divided by the slope to obtain the observation noise. That is, R = R' / λ. wherein R represents the observation noise, R' represents the first preset noise, and λ represents the slope of the curve of the battery parameter changing with the SOC. In this way, the operation speed and the accuracy of the observation noise can be improved.

[0091] In some optional implementations, the prediction noise is positively correlated with the slope of the curve of the battery parameter changing with the SOC.

[0092] In this way, when the slope of the curve of the battery parameter changing with the SOC becomes smaller, the value of the prediction noise becomes smaller; when the slope of the curve of the battery parameter changing with the SOC becomes larger, the value of the prediction noise becomes larger, so that the prediction noise adaptively changes with the slope, and then a more reliable prediction noise can be obtained, and the accuracy of the SOC of the battery to be processed is improved to a certain extent.

[0093] In some optional implementations, the prediction noise is a product of a second preset noise and the slope of the curve of the battery parameter changing with the SOC.

[0094] The second preset noise may be, for example, a fixed prediction value obtained based on experience, so that the second preset noise is multiplied by the slope to obtain the prediction noise. That is, Q = Q' × λ, wherein Q represents the prediction noise, Q' represents the second preset noise, and λ represents the slope of the curve of the battery parameter changing with the SOC. In this way, the operation speed and the accuracy of the prediction noise can be improved.

[0095] In some application scenarios, when the SOC of the battery to be processed is determined, the noise caused by the current parameter and the noise caused by the voltage parameter can be considered at the same time, so that the accuracy of the SOC of the battery to be processed is improved. In this way, in some optional implementations, the step 103 of determining the SOC of the battery to be processed according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise may include:

[0096] In substep 1031, a first SOC of the battery to be processed is determined according to a current of the battery to be processed.

[0097] For example, the SOC of the battery to be processed can be determined by using the ampere-hour integration method according to the current of the battery to be processed. The SOC is the first SOC mentioned above.

[0098] In substep 1032, a second SOC of the battery to be processed is determined according to a voltage of the battery to be processed.

[0099] For example, the SOC of the battery to be processed can be determined by using the open-circuit voltage method, the dynamic voltage method, etc. according to the voltage of the battery to be processed. The SOC is the second SOC mentioned above.

[0100] In substep 1033, a first weight corresponding to the first SOC and a second weight corresponding to the second SOC are determined according to the SOC estimation noise.

[0101] It can be understood that the first SOC mainly contains noise caused by the current parameter; and the second SOC mainly contains noise caused by the voltage parameter. Therefore, the first weight corresponding to the first SOC and the second weight corresponding to the second SOC can be determined, so as to determine whether the SOC of the battery to be processed is mainly determined according to the first SOC or the second SOC.

[0102] In substep 1034, the first SOC and the second SOC are weighted to obtain the SOC of the battery to be processed according to the first weight and the second weight.

[0103] In some optional implementations, the first weight is negatively correlated with the observation noise; and the second weight is negatively correlated with the prediction noise.

[0104] In this way, when the slope of the battery parameter changing with the SOC decreases, the value of the observation noise increases, the first weight decreases, and thus the proportion of the first SOC in determining the SOC of the battery to be processed decreases, thereby improving the accuracy of the SOC of the battery to be processed.

[0105] In addition, when the slope of the battery parameter changing with the SOC decreases, the value of the prediction noise decreases, the second weight increases, and thus the proportion of the second SOC in determining the SOC of the battery to be processed increases, thereby improving the accuracy of the SOC of the battery to be processed.

[0106] In some optional implementations, the SOC estimation noise is determined according to the battery parameter in step 102, and the method comprises:

[0107] If the slope of the curve of the battery parameter changing with the SOC is in any preset interval, a preset noise corresponding to the preset interval is determined as the SOC estimation noise.

[0108] In some application scenarios, the server can divide the slope of the curve of the battery parameter changing with the SOC into multiple intervals in advance, and each interval can be preconfigured with a corresponding set of noises. In this way, the corresponding noise can be determined as the SOC estimation noise according to the interval in which the slope of the curve of the battery parameter changing with the SOC is located.

[0109] In other application scenarios, the server can also set only two preset intervals, which are divided by an interval threshold. In this way, if the slope of the curve of the battery parameter changing with the SOC is greater than the interval threshold, the corresponding noise (for example, a preset prediction noise Q1 and a preset observation noise R1) is determined as the SOC estimation noise; if the slope of the curve of the battery parameter changing with the SOC is less than the interval threshold, the corresponding noise is determined as the SOC estimation noise (for example, a preset prediction noise Q2 and a preset observation noise R2, where Q2 < Q1 and R2 > R1).

[0110] In the implementation, the corresponding noise can be determined as the SOC estimation noise through the preset interval in which the slope of the curve of the battery parameter changing with the SOC is located, so that the SOC estimation noise is different in different preset intervals of the slope, the operation is more simple and convenient, and the accuracy of the battery SOC is improved to a certain extent.

[0111] Please continue to refer to FIG. 2, it can be seen that the voltage has a platform period. That is, the voltage does not change or only has a slight change with the SOC in a period of time. At this time, the slope of the curve of the voltage changing with the SOC can be regarded as not changing.

[0112] In some application scenarios, the server can also take the derivative or differential operation of the voltage value on the voltage change curve of the voltage changing with the SOC, to obtain the voltage change rate situation (that is, the slope situation of the change curve) of the voltage changing with the SOC as shown in FIG. 5, so as to more clearly reflect the change situation of the slope of the curve of the voltage changing with the SOC.

[0113] Therefore, in some optional implementation, the above-mentioned if the slope of the curve of the battery parameter changing with the SOC is in any preset interval, the preset noise corresponding to the preset interval is determined as the SOC estimation noise, includes:

[0114] If the voltage in the battery parameter is greater than a preset upper limit value of the voltage, or the voltage in the battery parameter is less than a preset lower limit value of the voltage, the first noise is determined as the SOC estimation noise.

[0115] If the voltage in the battery parameter is between the preset upper limit value of the voltage and the preset lower limit value of the voltage, the second noise is determined as the SOC estimation noise.

[0116] It should be noted that the preset upper limit value of the voltage and the preset lower limit value of the voltage can be set according to the platform period of the voltage, so that when the voltage is in the non-platform period, the corresponding first noise is determined as the SOC estimation noise, and when the voltage is in the platform period, the corresponding second noise is determined as the SOC estimation noise.

[0117] In the present implementation, the first noise or the second noise can be adaptively determined as the SOC estimation noise according to the voltage value, so that a more accurate SOC estimation noise can be obtained, and the accuracy of the SOC is improved to a certain extent.

[0118] In some optional implementations, if the slope of the curve of the battery parameter changing with the SOC is in any preset interval, the noise corresponding to the preset interval is determined as the actual Kalman filter noise, including:

[0119] If the voltage in the battery parameter is greater than the preset upper limit value of the voltage, the third noise is determined as the SOC estimation noise.

[0120] If the voltage in the battery parameter is less than the preset lower limit value of the voltage, the fourth noise is determined as the SOC estimation noise.

[0121] If the voltage in the battery parameter is between the preset lower limit value of the voltage and the preset upper limit value of the voltage, the fifth noise is determined as the SOC estimation noise.

[0122] Similarly, the preset upper limit value of the voltage and the preset lower limit value of the voltage can also be set according to the platform period of the voltage, so as to correspond to different noises when the voltage is in the platform period or the non-platform period.

[0123] Unlike the foregoing implementation, the present implementation has three preset noises (i.e., the third noise, the fourth noise, and the fifth noise), which correspond to the voltage being greater than the preset upper limit value of the voltage, the voltage being less than the preset lower limit value of the voltage, and the voltage being between the preset lower limit value of the voltage and the preset upper limit value of the voltage. Therefore, the present implementation sets multiple noises with smaller preset granularity, so that a more accurate SOC estimation noise can be determined, and the accuracy of the SOC is improved to a certain extent.

[0124] Further, since the voltage also changes with the SOC, the SOC estimation noise can be determined according to the rate of change of the voltage with the SOC data.

[0125] Therefore, in some optional implementations, if the slope of the curve of the battery parameter changing with the SOC is in any preset interval, the noise corresponding to the preset interval is determined as the SOC estimation noise, including:

[0126] If the rate of change of the voltage in the battery parameter changing with the SOC is greater than a preset rate threshold, the sixth noise is determined as the SOC estimation noise.

[0127] If the rate of change of the voltage in the battery parameter changing with the SOC is less than a preset rate threshold, the seventh noise is determined as the SOC estimation noise.

[0128] That is, the server can set two preset intervals, and the two preset intervals are divided by a preset rate threshold, for example, the preset rate threshold can include 0.1, 0.2 or 0.3, etc. In this way, if the rate of change of the voltage changing with the SOC data is greater than the preset rate threshold, the corresponding sixth noise (for example, the preset prediction noise Q1 and the preset observation noise R1) is determined as the SOC estimation noise; if the rate of change of the voltage changing with the SOC data is less than the preset rate threshold, the corresponding seventh noise (for example, the preset prediction noise Q2 and the preset observation noise R2, wherein Q2

[0129] In the implementation, the corresponding noise can be determined as the SOC estimation noise by the preset interval of the rate of change of the voltage changing with the SOC data, so that the SOC estimation noise adaptively changes with the change of the rate of change of the voltage changing with the SOC data, and the accuracy of the SOC estimation noise is improved to some extent, thereby improving the accuracy of the battery SOC.

[0130] In some optional implementations, before the SOC of the battery to be processed is determined according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise, the method further includes: identifying a circuit model parameter; the circuit model is used to determine the prediction noise.

[0131] In some application scenarios, the circuit model can be a prediction model involved in the Kalman filtering algorithm. For example, it can include an Rint model, a first-order RC model, a second-order RC model, and the like.

[0132] In some application scenarios, if the circuit model is a first-order circuit model as shown in FIG. 4, the circuit model parameters required to be identified are a first resistance parameter R0, a second resistance parameter R1, and a capacitance parameter C1. In these application scenarios, the circuit model parameters can be obtained by direct measurement.

[0133] In some optional implementations, the identifying the circuit model parameters comprises: identifying the circuit model parameters according to the circuit model and the recursive least square method.

[0134] That is, the server can substitute the voltage, the current data, and the SOC estimation noise corresponding to the battery to be processed or the same type of battery into the calculation formula of the Kalman filtering algorithm to obtain the circuit model parameters.

[0135] That is, the server can substitute the voltage, the current data, and the SOC estimation noise corresponding to the battery to be processed or the same type of battery into the calculation formula of the Kalman filtering algorithm to obtain the circuit model parameters.

[0136] O k+1 =O k +Q(1);

[0137]

[0138]

[0139] Thus, each circuit model parameter is obtained. Wherein, OCV(SOCk) represents the OCV corresponding to the current SOC, and the meanings of the remaining parameters are the same as described above. In this way, the accuracy of the SOC is improved, and a more accurate SOC is obtained.

[0140] In some optional implementations, the identifying the circuit model parameters comprises: identifying the circuit model parameters according to the circuit model and the recursive least square method.

[0141] That is, the server can substitute the voltage, the current data, and the SOC estimation noise corresponding to the battery to be processed or the same type of battery into the calculation formula of the Kalman filtering algorithm to obtain the circuit model parameters.

[0142] In the present implementation, different ways can be used to identify the circuit model parameters according to actual needs, so as to facilitate subsequent determination of the SOC of the battery to be processed.

[0143] In some application scenarios, the identified circuit model parameters can be inaccurate. For example, the identified circuit model parameters are not within a reasonable range (for example, negative) or have a large error with the actual circuit model parameters. Therefore, in some optional implementations, after identifying the circuit model parameters, the method further comprises: correcting the circuit model parameters according to a correction rule.

[0144] In some optional implementations, if the circuit model parameter is greater than an upper limit value of a preset identification range, the circuit model parameter is corrected to the upper limit value of the preset identification range; if the circuit model parameter is less than a lower limit value of the preset identification range, the circuit model parameter is corrected to the lower limit value of the preset identification range.

[0145] For example, the preset recognition range of the first resistance parameter R0 is (0.1-1) mΩ, if the first resistance parameter R0 is greater than 1 mΩ, it is corrected to 1 mΩ; if the first resistance parameter R0 is less than 0.1 mΩ, it is corrected to 0.1 mΩ.

[0146] The preset recognition range of the second resistance parameter R1 is (0.1-1) mΩ, if the second resistance parameter R1 is greater than 1 mΩ, it is corrected to 1 mΩ; if the second resistance parameter R1 is less than 0.1 mΩ, it is corrected to 0.1 mΩ.

[0147] The preset recognition range of the capacitance parameter C1 is (0.9-1.1) F, if the capacitance parameter C1 is greater than 1.1 F, it is corrected to 1.1 F; if the capacitance parameter C1 is less than 0.9 F, it is corrected to 0.9 F.

[0148] In the implementation mode, the circuit model parameters can be limited in the preset recognition range, so as to improve the accuracy of the circuit model parameters and avoid the dispersion of the circuit model.

[0149] In some optional implementation modes, if the change rate between the circuit model parameters recognized in the current frame and the circuit model parameters recognized in the last frame is greater than the upper limit value of the change rate range, the circuit model parameters recognized in the current frame are corrected according to the upper limit value of the change rate range.

[0150] The change rate may, for example, be the ratio of the circuit model parameters recognized in the current frame to the circuit model parameters recognized in the last frame, or the quotient of the difference between the two and the circuit model parameters recognized in the last frame.

[0151] In some application scenarios, the change rate range between the first resistance parameters of two adjacent frames can be (0.8-1.2) times, if the first resistance parameter R' of the current frame is 1.5 times the first resistance parameter R of the last frame, it can be limited to 1.2 times. That is, the first resistance parameter R of the current frame is corrected to R = 1.2 x R. 0k+1 0k+1 0k 0k+1 0k+1 0k 0k+1 0k 0k+1 0k+1

[0152] ​​​​​​​​​​In the present implementation, considering that the circuit model parameters will not change suddenly in a short time, if the circuit model parameters are identified by using a pure algorithm mechanism, a value that changes greatly can be obtained. This does not conform to the actual law, and therefore the change rate of the circuit model parameters identified in the current frame is limited, so that the circuit model parameters conforming to the actual law can be obtained, and the accuracy of the SOC of the battery to be processed is improved to a certain extent.

[0153] In some optional implementations, the server can also weight the circuit model parameters identified in the current frame and the circuit model parameters identified in the last frame to correct the circuit model parameters identified in the current frame.

[0154] For example, the first resistance parameter R 0k+1 , the first resistance parameter R' 0k+1 identified in the current frame, and the first resistance parameter R 0k identified in the last frame have a relationship as follows: wherein, is a weighting coefficient of the first resistance parameter R 0k identified in the last frame, which can be an empirical value, for example.

[0155] In the present implementation, the circuit model parameters identified in adjacent two frames can be weighted, so that the circuit model parameters identified in adjacent two frames will not change greatly, and the accuracy of the SOC is improved.

[0156] Those skilled in the art can understand that in the above method of the specific embodiment, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process, and the specific execution order of each step should be determined by its function and possible internal logic.

[0157] Please refer to FIG. 6, which shows a flowchart of a battery charging and discharging strategy determination method provided by an embodiment of the present application. As shown in FIG. 6, the method comprises:

[0158] Step 601, in the process of battery charging and discharging, a battery parameter is obtained, the battery parameter comprising at least one of voltage, current, and temperature;

[0159] Step 602, according to the battery parameter, a SOC estimation noise is determined, wherein the SOC estimation noise is related to the slope of a curve of the battery parameter changing with the SOC;

[0160] Step 603, according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise, the SOC of the battery to be processed is determined;

[0161] It should be noted that the implementation process and the technical effects of the above steps 601 to 603 can be the same as or similar to those of the previous steps 101 to 103, and details are not repeated here.

[0162] In step 604, a charging and discharging strategy is determined according to the SOC of the battery to be processed.

[0163] In some application scenarios, if the SOC of the battery to be processed is low, the charging current can be reduced, and the charging and discharging interval time can be extended. Similarly, if the SOC of the battery to be processed is high, the charging current can be increased, and the charging and discharging interval time can be shortened.

[0164] In other application scenarios, if the battery to be processed is a plurality of batteries, the plurality of batteries can be balanced in charging and discharging. For example, the battery with a lower SOC is charged first, and then the battery with a slightly higher SOC is charged, so that the SOCs of the batteries tend to be consistent. In addition, when discharging, the battery with a higher SOC can be discharged first, and then the battery with a slightly lower SOC is discharged. In this way, the situation that the battery with a lower SOC is damaged due to excessive discharging can be improved, and the SOCs of the batteries tend to be consistent.

[0165] In the present implementation, the appropriate charging and discharging strategy can be determined according to the SOC of the battery to be processed, which improves the service life of the battery to be processed to a certain extent.

[0166] Please refer to FIG. 7, which shows a structural block diagram of a battery SOC determination device provided by an embodiment of the present application. The battery SOC determination device can be a module, a program segment or code on an electronic device. It should be understood that the device corresponds to the above-mentioned method embodiment of FIG. 1, and can perform each step involved in the method embodiment of FIG. 1.

[0167] Optionally, the battery SOC determination device includes an acquisition module 701, a noise determination module 702 and an SOC determination module 703. The acquisition module 701 is configured to acquire battery parameters, the battery parameters including at least one of voltage, current and temperature. The noise determination module 702 is configured to determine an SOC estimation noise according to the battery parameters, wherein the SOC estimation noise is related to the slope of the curve of the battery parameters changing with the SOC. The SOC determination module 703 is configured to determine the SOC of the battery to be processed according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise.

[0168] Optionally, the SOC estimation noise includes observation noise and / or prediction noise.

[0169] Optionally, the observation noise is negatively related to the slope of the curve of the battery parameters changing with the SOC.

[0170] Optionally, the observation noise is a quotient of a first preset noise and a slope of the curve of the battery parameter varying with the SOC.

[0171] Optionally, the prediction noise is positively related to the slope of the curve of the battery parameter varying with the SOC.

[0172] Optionally, the prediction noise is a product of a second preset noise and the slope of the curve of the battery parameter varying with the SOC.

[0173] Optionally, the SOC determination module 703 is further configured to: determine a first SOC of the battery to be processed according to a current of the battery to be processed; determine a second SOC of the battery to be processed according to a voltage of the battery to be processed; determine a first weight corresponding to the first SOC and a second weight corresponding to the second SOC according to the SOC estimation noise; and perform weighted processing on the first SOC and the second SOC according to the first weight and the second weight to obtain the SOC of the battery to be processed.

[0174] Optionally, the first weight is negatively related to the observation noise; and the second weight is negatively related to the prediction noise.

[0175] Optionally, the noise determination module 702 is further configured to: if the slope of the curve of the battery parameter varying with the SOC is in any preset interval, determine a preset noise corresponding to the preset interval as the SOC estimation noise.

[0176] Optionally, the noise determination module 702 is further configured to: if the voltage of the battery parameter is greater than a preset upper limit of voltage or the voltage of the battery parameter is less than a preset lower limit of voltage, determine a first noise as the SOC estimation noise; and if the voltage of the battery parameter is between the preset lower limit of voltage and the preset upper limit of voltage, determine a second noise as the SOC estimation noise.

[0177] Optionally, the noise determination module 702 is further configured to: if the voltage of the battery parameter is greater than a preset upper limit of voltage, determine a third noise as the SOC estimation noise; if the voltage of the battery parameter is less than a preset lower limit of voltage, determine a fourth noise as the SOC estimation noise; and if the voltage of the battery parameter is between the preset lower limit of voltage and the preset upper limit of voltage, determine a fifth noise as the SOC estimation noise.

[0178] Optionally, the noise determination module 702 is further configured to: if a rate of change of the voltage with respect to the SOC in the battery parameter is greater than a preset rate of change threshold, determine a sixth noise as the SOC estimation noise; and if the rate of change of the voltage with respect to the SOC in the battery parameter is less than the preset rate of change threshold, determine a seventh noise as the SOC estimation noise.

[0179] Optionally, the apparatus further comprises an identification module configured to identify the circuit model parameter before determining the SOC of the battery to be processed according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise, wherein the circuit model is configured to determine the prediction noise.

[0180] Optionally, the identification module is further configured to identify the circuit model parameter according to the circuit model and the SOC estimation noise.

[0181] Optionally, the identification module is further configured to identify the circuit model parameter according to the circuit model and a recursive least square method.

[0182] Optionally, the apparatus further comprises a first correction module configured to correct the circuit model parameter according to a correction rule after identifying the circuit model parameter.

[0183] Optionally, the first correction module is further configured to correct the circuit model parameter to an upper limit value of a preset identification range if the circuit model parameter is greater than the upper limit value of the preset identification range, and correct the circuit model parameter to a lower limit value of the preset identification range if the circuit model parameter is less than the lower limit value of the preset identification range.

[0184] Optionally, the first correction module is further configured to correct the circuit model parameter identified in a current frame according to an upper limit value of a rate of change range if a rate of change between the circuit model parameter identified in the current frame and a circuit model parameter identified in a previous frame is greater than the upper limit value of the rate of change range.

[0185] Optionally, the first correction module is further configured to weight the circuit model parameter identified in the current frame and the circuit model parameter identified in the previous frame to correct the circuit model parameter identified in the current frame.

[0186] Optionally, the battery to be processed is of the same type as the battery used to determine the SOC estimation noise.

[0187] It should be noted that, for the convenience and brevity of description, the specific working process of the apparatus described above can refer to the corresponding process in the foregoing method embodiments, which will not be described herein.

[0188] Please refer to FIG. 8, which is a structural schematic diagram of an electronic device for performing the method for determining the SOC of a battery provided in an embodiment of the present application. The electronic device can include at least one processor 801, such as a CPU, at least one communication interface 802, at least one memory 803, and at least one communication bus 804. The communication bus 804 is used to realize direct connection communication among the components. The communication interface 802 of the device in the embodiment of the present application is used to perform signaling or data communication with other node devices. The memory 803 can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The memory 803 can alternatively be at least one storage device located away from the aforementioned processor. The memory 803 stores computer readable instructions. When the computer readable instructions are executed by the processor 801, the electronic device can perform the method processes shown in FIG. 1 or FIG. 6.

[0189] It can be understood that the structure shown in FIG. 8 is only schematic. The electronic device can further include more or fewer components than those shown in FIG. 8 or have a different configuration from that shown in FIG. 8. The components shown in FIG. 8 can be realized by hardware, software, or a combination thereof.

[0190] The embodiment of the present application provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the computer program can perform the method processes performed by the electronic device in the method embodiment shown in FIG. 1 or FIG. 6.

[0191] The embodiment of the present application provides a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can perform the method provided in each method embodiment, for example, the method can include: obtaining battery parameters, the battery parameters including at least one of voltage, current, and temperature; determining a SOC estimation noise according to the battery parameters, wherein the SOC estimation noise is related to a slope of a curve of the battery parameters changing with the SOC; and determining the SOC of the battery to be processed according to at least one of voltage and current of the battery to be processed and the SOC estimation noise.

[0192] In the embodiments of the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. The embodiments described above are merely exemplary, for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation; for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0193] In addition, the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0194] In addition, the functional modules in the various embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0195] In this article, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or sequence between the entities or operations.

[0196] The above description is only some embodiments of the present application, and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of determining SOC of a battery, characterized by, The method comprises: acquiring a battery parameter, the battery parameter comprising at least one of voltage, current, and temperature; determining a SOC estimation noise according to the battery parameter, wherein the SOC estimation noise is related to a slope of a curve of the battery parameter varying with SOC; determining an SOC of a battery to be processed according to at least one of voltage and current of the battery to be processed and the SOC estimation noise.

2. The method of claim 1, wherein, The SOC estimation noise comprises observation noise and / or prediction noise.

3. The method of claim 2, wherein, The observation noise is negatively related to the slope of the curve of the battery parameter varying with SOC.

4. The method of claim 3, wherein, The observation noise is a quotient of a first preset noise and the slope of the curve of the battery parameter varying with SOC.

5. The method according to any one of claims 2-4, characterized in that, The prediction noise is positively related to the slope of the curve of the battery parameter varying with SOC.

6. The method of claim 3, wherein, The prediction noise is a product of a second preset noise and the slope of the curve of the battery parameter varying with SOC.

7. The method according to any one of claims 2-6, characterized in that, The determining of the SOC of the battery to be processed according to at least one of voltage and current of the battery to be processed and the SOC estimation noise comprises: determining a first SOC of the battery to be processed according to current of the battery to be processed; determining a second SOC of the battery to be processed according to voltage of the battery to be processed; determining a first weight corresponding to the first SOC and a second weight corresponding to the second SOC according to the SOC estimation noise; performing weighted processing on the first SOC and the second SOC according to the first weight and the second weight to obtain the SOC of the battery to be processed.

8. The method of claim 7, wherein, The first weight is negatively related to the observation noise, and the second weight is negatively related to the prediction noise.

9. The method according to any one of claims 2-8, characterized in that, The determining of the SOC estimation noise according to the battery parameter comprises: if the slope of the curve of the battery parameter varying with SOC is in any preset interval, determining a preset noise corresponding to the preset interval as the SOC estimation noise.

10. The method of claim 9, wherein, The if the slope of the curve of the battery parameter varying with SOC is in any preset interval, determining a preset noise corresponding to the preset interval as the SOC estimation noise comprises: if the voltage in the battery parameter is greater than a preset upper limit of voltage or the voltage in the battery parameter is less than a preset lower limit of voltage, determining a first noise as the SOC estimation noise; if the voltage in the battery parameter is between the preset lower limit of voltage and the preset upper limit of voltage, determining a second noise as the SOC estimation noise.

11. The method of claim 9, wherein, The if the slope of the curve of the battery parameter varying with SOC is in any preset interval, determining a preset noise corresponding to the preset interval as the SOC estimation noise comprises: if the voltage in the battery parameter is greater than a preset upper limit of voltage, determining a third noise as the SOC estimation noise; if the voltage in the battery parameter is less than a preset lower limit of voltage, determining a fourth noise as the SOC estimation noise; if the voltage in the battery parameter is between the preset lower limit of voltage and the preset upper limit of voltage, determining a fifth noise as the SOC estimation noise.

12. The method of claim 9, wherein, If the slope of the curve of the battery parameter changing with the SOC is within any preset interval, a preset noise corresponding to the preset interval is determined as the SOC estimation noise, including: If the rate of change of the voltage in the battery parameter changing with the SOC is greater than a preset rate threshold, a sixth noise is determined as the SOC estimation noise; If the rate of change of the voltage in the battery parameter changing with the SOC is less than a preset rate threshold, a seventh noise is determined as the SOC estimation noise.

13. The method according to any one of claims 2-12, characterized in that, Before the SOC of the battery to be processed is determined according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise, the method further includes: Identifying a circuit model parameter; the circuit model is used to determine the prediction noise.

14. The method of claim 13, wherein, The identification of the circuit model parameter includes: Identifying the circuit model parameter according to the circuit model and the SOC estimation noise.

15. The method of claim 13, wherein, The identification of the circuit model parameter includes: Identifying the circuit model parameter according to the circuit model and the recursive least square method.

16. The method according to any of claims 14-15, characterized by, After identifying the circuit model parameter, the method further includes: According to the correction rule, the circuit model parameter is corrected.

17. The method of claim 16, wherein, The correction of the circuit model parameter according to the correction rule includes: If the circuit model parameter is greater than the upper limit value of the preset identification range, the circuit model parameter is corrected to the upper limit value of the preset identification range; If the circuit model parameter is less than the lower limit value of the preset identification range, the circuit model parameter is corrected to the lower limit value of the preset identification range.

18. The method of claim 16, wherein, The correction of the circuit model parameter according to the correction rule includes: If the change rate between the circuit model parameter identified in the current frame and the circuit model parameter identified in the last frame is greater than the upper limit value of the change rate range, the circuit model parameter identified in the current frame is corrected according to the upper limit value of the change rate range.

19. The method of claim 16, wherein, The correction of the circuit model parameter according to the correction rule includes: The circuit model parameter identified in the current frame and the circuit model parameter identified in the last frame are weighted to correct the circuit model parameter identified in the current frame.

20. The method of any one of claims 1-19, wherein, The battery to be processed is of the same type as the battery used to determine the SOC estimation noise.

21. A method for determining a battery charging and discharging strategy, characterized in that, Including: During the charging and discharging process of the battery, a battery parameter is acquired, the battery parameter including at least one of voltage, current, and temperature; According to the battery parameter, a SOC estimation noise is determined, wherein the SOC estimation noise is related to the slope of the curve of the battery parameter changing with the SOC; According to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise, the SOC of the battery to be processed is determined; According to the SOC of the battery to be processed, a charging and discharging strategy is determined.

22. A device for determining the SOC of a battery, characterized in that Including: An acquisition module is configured to acquire a battery parameter, the battery parameter including at least one of voltage, current, and temperature; A noise determination module is configured to determine a SOC estimation noise according to the battery parameter, wherein the SOC estimation noise is related to the slope of the curve of the battery parameter changing with the SOC; An SOC determination module is configured to determine the SOC of the battery to be processed according to at least one of the voltage and the current of the battery to be processed and the SOC estimation noise.

23. An electronic device, comprising: A computer program product comprising a computer readable medium storing instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1-21.

24. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to perform the method of any one of claims 1-21.

25. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions are executed by a processor to perform the method of any one of claims 1-21.

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