Method and device for determining SOC (State of Charge) of battery and electronic equipment

Abstract

The invention provides a battery SOC determination method and apparatus, and an electronic device. The method comprises the steps of obtaining an OCV curve of a battery; extracting capacity change characteristics corresponding to the OCV curve, and determining a linear region and a nonlinear region corresponding to the OCV curve according to the capacity change characteristics; identifying a conversion node between the linear region and the nonlinear region, and determining the conversion node as an SOC identification point; and determining a current SOC calculation value of the battery, and determining a target SOC value according to the SOC corresponding to the SOC identification point, a preset SOC reference value and the SOC calculation value. The accuracy and the real-time performance of SOC estimation can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of battery management, and in particular to a method, device and electronic device for determining a battery SOC. Background Art

[0002] Accurate estimation of battery charge is crucial for the effective operation of the battery management system (BMS). The widespread use of lithium-ion batteries in electric vehicles, renewable energy storage devices, and various portable electronic devices makes it particularly important to accurately monitor and manage the battery status. The battery status includes the state of charge (SOC), state of health (SOH), etc., among which SOC is an important indicator to measure the remaining capacity of the battery.

[0003] At present, SOC estimation mainly adopts the ampere-hour integration method, OCV correction method and electrochemical impedance spectroscopy method. However, for some batteries, such as lithium iron phosphate (LFP) batteries, due to the "platform area" characteristics of their OCV in some SOC ranges, conventional estimation cannot effectively calibrate the SOC, resulting in large estimation deviations. If the battery system is not fully charged for a long time and is charged and discharged in the "platform area", the SOC will accumulate over time, causing system malfunctions, and serious safety accidents such as overcharging or over-discharging may occur. Summary of the invention

[0004] The embodiments of the present disclosure at least provide a method, device and electronic device for determining the SOC of a battery, which can improve the accuracy and real-time performance of SOC estimation.

[0005] The present disclosure provides a method for determining a battery SOC, including:

[0006] Get the OCV curve of the battery;

[0007] Extracting a capacity variation characteristic corresponding to the OCV curve, and determining a linear region and a nonlinear region corresponding to the OCV curve according to the capacity variation characteristic;

[0008] identifying a transition node between the linear region and the nonlinear region, and determining the transition node as a SOC identification point;

[0009] The current SOC calculated value of the battery is calculated by the ampere-hour integration method, and the target SOC value is determined according to the SOC corresponding to the SOC identification point, the preset SOC reference value corresponding to the battery at the SOC identification point, and the SOC calculated value.

[0010] In an optional implementation manner, identifying a transition node between the linear region and the nonlinear region, and determining the transition node as an SOC identification point, specifically includes:

[0011] Determine a maximum capacity change value in the nonlinear region and an initial voltage value of a voltage statistical interval in which the maximum capacity change value is located;

[0012] Taking a preset voltage change as a voltage statistical interval, collecting the SOC of the battery corresponding to each change of the initial voltage value by a preset step length, and determining a real-time capacity change value corresponding to the voltage statistical interval according to the SOC;

[0013] When the change rate between the real-time capacity change value and the maximum capacity change value is greater than a preset threshold, determining a target initial voltage value of a voltage statistical interval in which the real-time capacity change value is located;

[0014] A voltage sum value between the target initial voltage value and the preset voltage change amount is determined as the conversion node, and an SOC corresponding to the voltage sum value is determined as the SOC identification point.

[0015] In an optional implementation manner, the obtaining of the battery OCV curve specifically includes:

[0016] Constructing a battery model estimation equation with the battery terminal voltage as the output variable and the battery characteristic parameters as the parameters to be estimated, wherein the battery characteristic parameters at least include the battery OCV;

[0017] At each sampling moment, the terminal voltage and current value of the battery are measured in real time, and an input data vector is constructed according to the current value at the current sampling moment and the terminal voltage and current value at the previous sampling moment;

[0018] Initialize the initial values ​​of the parameters to be estimated and the error covariance matrix, and determine the gain matrix at the current sampling moment according to the error covariance matrix at the previous sampling moment, the input data vector at the current sampling moment, and a preset forgetting factor;

[0019] Update the parameter to be estimated at the current sampling moment according to the parameter to be estimated at the previous sampling moment, and the gain matrix and the input data vector at the current moment;

[0020] According to the preset forgetting factor, the gain matrix at the current sampling moment and the input data vector, and the error covariance matrix at the previous sampling moment, the error covariance matrix at the current sampling moment is updated, and the step of determining the gain matrix at the current sampling moment is repeated to determine the parameter to be estimated at each sampling moment;

[0021] The corresponding battery OCV is extracted from the parameters to be estimated at each sampling moment to generate the battery OCV curve.

[0022] In an optional implementation, extracting the capacity change characteristics of the battery OCV curve corresponding to a preset voltage change amount specifically includes:

[0023] In the battery OCV range, set a voltage statistical interval starting from a preset initial voltage value and with a length equal to a preset window length;

[0024] Determine the battery capacity change value corresponding to the battery OCV at each preset step interval;

[0025] Filtering the battery capacity change value within the preset voltage change range where the voltage middle value in the voltage statistical interval is located;

[0026] The battery capacity change value is determined as the capacity change feature within the voltage statistical interval.

[0027] In an optional implementation, after screening the battery capacity change values ​​within the preset voltage change range in the middle of the voltage statistical interval, the method further includes:

[0028] Determine whether there is a battery OCV less than the preset initial voltage value in the voltage statistical interval;

[0029] If so, the voltage statistical interval is moved in a direction of reducing the battery OCV by the preset step length.

[0030] In an optional implementation manner, determining the linear region and the nonlinear region corresponding to the battery OCV curve specifically includes:

[0031] Determine a difference between a battery capacity change value corresponding to the voltage statistical interval after the movement and a battery capacity change value corresponding to the voltage statistical interval before the movement;

[0032] Determine a ratio between the difference and a battery capacity change value corresponding to the voltage statistical interval after the movement;

[0033] If the ratio is greater than the preset threshold, it is determined that the voltage statistical interval is located in the linear region after the movement, and the voltage statistical interval is located in the nonlinear region before the movement.

[0034] In an optional implementation manner, determining a target SOC value according to the SOC corresponding to the SOC identification point, a preset SOC reference value, and the SOC calculation value specifically includes:

[0035] determining a difference between the preset SOC reference value and the SOC corresponding to the SOC identification point;

[0036] The sum of the difference and the SOC calculated value is determined as the target SOC value.

[0037] In an optional implementation manner, after determining the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value and the SOC calculated value, the method further includes:

[0038] The battery is charged or discharged until it is fully charged or discharged;

[0039] Taking the preset SOC reference value as a starting point, calculating the SOC update calculation value before the moment when the battery is in the full state or the empty state by the ampere-hour integration method;

[0040] Determine a reference SOC value of the battery corresponding to the full state or the empty state, and determine a difference between the reference SOC value and the updated SOC calculated value;

[0041] The battery SOC identification point is updated according to the sum of the difference and the preset SOC reference value.

[0042] The present disclosure also provides a device for determining a battery SOC, including:

[0043] Battery OCV identification module, used to obtain the battery OCV curve;

[0044] A capacity variation feature extraction module, used to extract the capacity variation feature corresponding to the OCV curve, and determine the linear region and the nonlinear region corresponding to the OCV curve according to the capacity variation feature;

[0045] An inflection point identification module, used to identify a conversion node between the linear region and the nonlinear region, and determine the SOC corresponding to the conversion node as an SOC identification point;

[0046] The SOC update module is used to calculate the current SOC calculated value of the battery by the ampere-hour integration method, and determine the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value corresponding to the battery at the SOC identification point, and the SOC calculated value.

[0047] An embodiment of the present disclosure also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the above-mentioned method for determining the battery SOC is performed, or steps in any possible implementation of the above-mentioned method for determining the battery SOC is performed.

[0048] The embodiment of the present disclosure also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program executes the above-mentioned method for determining the battery SOC, or the steps of any possible implementation of the above-mentioned method for determining the battery SOC.

[0049] The embodiments of the present disclosure also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for determining the battery SOC, or the steps in any possible implementation of the above-mentioned method for determining the battery SOC.

[0050] The disclosed embodiments provide a method, device and electronic device for determining the SOC of a battery, which obtains the OCV curve of the battery; extracts the capacity change characteristics corresponding to the OCV curve, and determines the linear region and nonlinear region corresponding to the OCV curve according to the capacity change characteristics; identifies the conversion node between the linear region and the nonlinear region, and determines the conversion node as the SOC identification point; determines the current SOC calculation value of the battery, and determines the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value and the SOC calculation value. The accuracy and real-time performance of SOC estimation can be improved.

[0051] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following is a brief introduction to the drawings required for use in the embodiments. The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can also be obtained based on these drawings without creative work.

[0053] Figure 1 A flow chart of a method for determining a battery SOC provided by an embodiment of the present disclosure is shown;

[0054] Figure 2 A flow chart showing another method for determining battery SOC provided by an embodiment of the present disclosure;

[0055] Figure 3 A schematic diagram of a battery SOC determination device provided by an embodiment of the present disclosure is shown;

[0056] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present disclosure.

[0058] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0059] The term "and / or" herein only describes an association relationship, indicating that three relationships may exist. For example, A and / or B may represent the following three situations: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set consisting of A, B, and C.

[0060] According to research, at present, SOC estimation mainly adopts the ampere-hour integration method, OCV correction method and electrochemical impedance spectroscopy method. However, for some batteries, such as lithium iron phosphate (LFP) batteries, due to the "platform area" characteristics of their OCV in some SOC ranges, conventional estimation cannot effectively calibrate the SOC, resulting in excessive estimation deviation. If the battery system is not fully charged for a long time and is charged and discharged in the "platform area", the SOC will accumulate over time, resulting in abnormal system function, and serious safety accidents such as overcharging or over-discharging may occur.

[0061] Based on the above research, the present disclosure provides a method, device and electronic device for determining the SOC of a battery, which obtains the OCV curve of the battery; extracts the capacity change characteristics corresponding to the OCV curve, and determines the linear region and nonlinear region corresponding to the OCV curve according to the capacity change characteristics; identifies the conversion node between the linear region and the nonlinear region, and determines the conversion node as the SOC identification point; determines the current SOC calculation value of the battery, and determines the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value and the SOC calculation value. The accuracy and real-time performance of SOC estimation can be improved.

[0062] To facilitate understanding of this embodiment, a method for determining a battery SOC disclosed in an embodiment of the present disclosure is first introduced in detail. The execution subject of the method for determining a battery SOC provided in an embodiment of the present disclosure is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the method for determining a battery SOC may be implemented by a processor calling a computer-readable instruction stored in a memory.

[0063] See also Figure 1 FIG. 1 is a flow chart of a method for determining a battery SOC provided by an embodiment of the present disclosure, the method comprising steps S101 to S104, wherein:

[0064] S101. Obtain an OCV curve of a battery.

[0065] In the specific implementation, the recursive least square method with forgetting factor can be used to estimate the battery model, and then identify the OCV curve of the battery. The OCV (Open Circuit Voltage) curve represents the open circuit voltage of the battery at different SOC (State of Charge), and the OCV range covers the entire voltage change range of the battery from full discharge (SOC close to 0%) to full charge (SOC close to 100%).

[0066] Specifically, the OCV curve of the battery can be obtained through the following steps 1 to 6:

[0067] Step 1: construct a battery model estimation equation with the battery terminal voltage as the output variable and the battery characteristic parameters as the parameters to be estimated, wherein the battery characteristic parameters at least include the battery OCV.

[0068] Step 2: Measure the terminal voltage and current value of the battery in real time at each sampling moment, and construct an input data vector based on the current value at the current sampling moment and the terminal voltage and current value at the previous sampling moment.

[0069] Step 3: Initialize the initial values ​​of the parameters to be estimated and the error covariance matrix, and determine the gain matrix at the current sampling moment according to the error covariance matrix at the previous sampling moment, the input data vector at the current sampling moment, and a preset forgetting factor.

[0070] Step 4: Update the parameter to be estimated at the current sampling moment according to the parameter to be estimated at the previous sampling moment, and the gain matrix and the input data vector at the current moment.

[0071] Step 5: Update the error covariance matrix at the current sampling moment according to the preset forgetting factor, the gain matrix at the current sampling moment, the input data vector, and the error covariance matrix at the previous sampling moment, and repeat the step of determining the gain matrix at the current sampling moment to determine the parameters to be estimated at each sampling moment.

[0072] Step 6: extract the corresponding battery OCV from the parameters to be estimated at each sampling moment, and generate the battery OCV curve.

[0073] In a specific implementation, first, a battery model is established, the output of which is the battery terminal voltage, the input is the battery current and voltage data, and the parameters to be estimated include the battery OCV.

[0074] Here, the battery model can be in the form of:

[0075]

[0076] Among them, y k represents the system output variable at the kth sampling moment, i.e., the terminal voltage of the battery; Represents the input data vector, which contains the voltage and current data of the current and previous moments; θ k represents the system parameters to be estimated, including the battery's OCV and other related parameters; e Ls,k Represents the systematic error, usually white noise, indicating the deviation between the model and the actual battery behavior.

[0077] Here, the battery terminal voltage U is measured at each sampling moment. k and current I k , and based on the voltage Uk and current I k The input data vector is constructed from the measured values ​​of This vector will be dynamically updated at each moment, and the input data vector can be in the form of:

[0078]

[0079] Among them, U k represents the battery terminal voltage at the current sampling time k; U k-1 represents the battery terminal voltage at the previous sampling time k-1; I k represents the battery current at the current sampling time k; I k-1 Represents the battery current at the previous sampling time k-1.

[0080] Furthermore, before starting the estimation, it is necessary to set the initial values, including the initial values ​​θ0 of the parameters to be estimated and the error covariance matrix P Ls,0 , then at each sampling moment, the error covariance matrix of the previous moment and the current input data vector are used to calculate the gain matrix K Ls,k , the gain matrix K Ls,k Determines the impact of the current sampling data on parameter updates.

[0081] Here, the gain matrix can be in the form of:

[0082]

[0083] Among them, P Ls,k-1 Represents the error covariance matrix of the previous moment k-1; Represents the input data vector at the current sampling moment; μ represents the forgetting factor, which is used to adjust the influence of historical data, and its value range is usually 0<μ≤1.

[0084] Furthermore, using the gain matrix K Ls,k The parameters to be estimated at the previous moment Make corrections to obtain the parameters to be estimated at the current moment To gradually adjust the model parameters.

[0085] Here, the parameter update formula is:

[0086]

[0087] Among them, y k Represents the battery terminal voltage measured at the current moment; Represents the predicted value of the battery terminal voltage calculated by the model at the current moment.

[0088] Furthermore, at each sampling moment, from the updated parameters The OCV value of the battery is extracted, and the extracted OCV values ​​are accumulated one by one, and the OCV values ​​at each moment are plotted on the same graph to form a battery OCV curve to reflect the relationship between the battery SOC and voltage.

[0089] S102: extracting a capacity variation characteristic corresponding to the OCV curve, and determining a linear region and a nonlinear region corresponding to the OCV curve according to the capacity variation characteristic.

[0090] In a specific implementation, within the battery OCV range, a voltage statistical interval starting from a preset initial voltage value and having a length of a preset window length is set; the battery capacity change value corresponding to the battery OCV of each preset step length is determined; the battery capacity change value within the preset voltage change range where the voltage middle value in the voltage statistical interval is located is screened; and the battery capacity change value is determined as the capacity change feature within the voltage statistical interval.

[0091] First, an initial voltage value is selected on the battery's OCV curve, and a voltage statistical interval with a preset window length is set with this value as the starting point. Within the selected voltage statistical interval, the battery capacity change value corresponding to each voltage point is determined in sequence according to the preset step length. Within the voltage statistical interval, the preset step length is gradually advanced, and the battery capacity change value corresponding to each step length is calculated, for example, the battery capacity change under the voltage change is determined by the ampere-hour integration method.

[0092] The capacity change value is the SOC change of the battery at each preset step within the voltage statistical interval, reflecting the charging and discharging performance of the battery in this interval.

[0093] Afterwards, in each voltage statistical interval, the middle value of the capacity change is calculated, and the value is moved step by step in units of a preset voltage change range. In each movement process, the capacity change in the current voltage statistical interval is calculated and summed up, and finally a smooth capacity change value is obtained to determine the capacity change characteristic in the voltage statistical interval.

[0094] Preferably, the preset step size may be 1 mV, that is, the change in the integrated capacity is determined every time the battery OCV changes (decreases) by 1 mV.

[0095] Exemplarily, when the preset voltage change range is 5mV and the preset window length of the voltage statistical interval is 15mV, within the statistical interval, the middle 5mV is taken as a representative to calculate the capacity change value. For example, the interval is set to [V, V+15mV], and the middle [V+5mV, V+10mV] is taken for calculation. When the initial voltage value is 4V, the first voltage statistical interval is 4.0V to 3.985V. Within the interval of 4.0V to 3.985V, the intermediate value dQ of the capacity change is calculated. With a step size of 5mV, the window is moved to the interval of 3.995V to 3.98V, and the intermediate value of the capacity change is calculated again. The above steps are repeated, and the statistical window continues to move. In each moving step, the capacity change value within the current statistical interval is summed to obtain a smooth capacity change value.

[0096] As a possible implementation, after screening the battery capacity change value within the preset voltage change range in the middle of the voltage statistical interval, determine whether there is a battery OCV less than the preset initial voltage value in the voltage statistical interval; if so, move the voltage statistical interval in the direction of reducing the battery OCV by a preset step length.

[0097] Here, in order to identify the inflection point in the OCV curve, it is necessary to track the change of OCV. During the discharge process of the battery, the OCV will gradually decrease. The current OCV value of the battery is monitored in real time, and the relationship between the current OCV value plus the voltage value corresponding to the voltage statistical interval and the preset initial voltage value is calculated. If the current OCV value plus the voltage value corresponding to the voltage statistical interval is less than the preset initial voltage value, it means that the current voltage has obviously dropped by a voltage statistical interval. When this situation is detected, the statistical interval is moved to the left by the distance of the voltage value corresponding to the voltage statistical interval, that is, the starting voltage is updated to the current OCV value.

[0098] For example, assuming that the current voltage curve starts from 4.0V, the initial starting voltage V0 is set to 4.0V, and the OCV measured at the current moment is 3.985V, the current OCV+14mV=3.985V+0.014V=3.999V is calculated, and 3.999V is compared with 4.0V, and it is found that 3.999V<4.0V. Since the current OCV+14mV is less than V0, it means that the voltage drop has exceeded 14mV. At this time: update the starting voltage V0 to the current 3.985V, move the statistics window one grid to the left, and the new statistical interval starts at 3.985V.

[0099] Further, determine the difference between the battery capacity change value corresponding to the voltage statistical interval after the movement and the battery capacity change value corresponding to the voltage statistical interval before the movement; determine the ratio between the difference and the battery capacity change value corresponding to the voltage statistical interval after the movement; if the ratio is greater than a preset threshold, it is determined that the voltage statistical interval after the movement is in the linear region and the voltage statistical interval before the movement is in the nonlinear region.

[0100] Here, on the OCV curve, the area where the voltage changes faster with SOC is the linear area, and the area where the voltage changes slower with SOC is the nonlinear area, that is, the platform area. The battery capacity changes relatively slowly. When the ratio of the battery capacity change value corresponding to the voltage statistical interval after movement to the battery capacity change value corresponding to the voltage statistical interval before movement exceeds the set preset first threshold, it indicates that the current interval is a linear area.

[0101] It should be noted that the battery capacity change value corresponding to the voltage statistical interval after movement takes the maximum value before the linear region is reached. The preset first threshold can be determined according to the battery material characteristics, and no specific limitation is made here. For example, the preset first threshold corresponding to the lithium iron phosphate platform region characteristics is 5.

[0102] Here, when it is monitored that the current capacity change value shows a cliff-like drop, that is, the difference between the battery capacity change value corresponding to the voltage statistical interval after movement and the battery capacity change value corresponding to the voltage statistical interval before movement, and the ratio of the battery capacity change value corresponding to the voltage statistical interval after movement is greater than the preset second threshold, it indicates that the current statistical interval is a linear area.

[0103] It should be noted that the preset second threshold is preferably 80% of the battery capacity change value corresponding to the voltage statistical interval after the movement.

[0104] S103 , identifying a conversion node between the linear region and the nonlinear region, and determining the conversion node as an SOC identification point.

[0105] In a specific implementation, in the linear region of the battery OCV curve, the relationship between the battery voltage and its capacity is relatively stable. In the nonlinear region, the relationship between the battery voltage and capacity is no longer linear, which may be due to the complexity of the battery chemical reaction. In this region, a small change in voltage may lead to a significant change in capacity or vice versa, making SOC estimation more difficult.

[0106] Here, near the transition node between the linear region and the nonlinear region, the sensitivity of OCV to capacity changes will suddenly increase or decrease, which makes it an important reference point for identifying SOC. The SOC value determined at the transition node can accurately reflect the state of the battery and serve as an identification point for SOC estimation.

[0107] For details, see Figure 2 FIG. 1 is a flow chart of another method for determining a battery SOC provided by an embodiment of the present disclosure, the method comprising steps S1031 to S1034, wherein:

[0108] S1031. Determine a maximum capacity change value in the nonlinear region and an initial voltage value of a voltage statistical interval in which the maximum capacity change value is located.

[0109] S1032 takes a preset voltage change as a voltage statistical interval, collects the SOC of the battery corresponding to each change of the initial voltage value by a preset step length, and determines a real-time capacity change value corresponding to the voltage statistical interval according to the SOC.

[0110] S1033: When the change rate between the real-time capacity change value and the maximum capacity change value is greater than a preset threshold, determine a target initial voltage value of a voltage statistical interval in which the real-time capacity change value is located.

[0111] S1034 determines the voltage sum value between the target initial voltage value and the preset voltage change amount as the conversion node, and determines the SOC corresponding to the voltage sum value as the SOC identification point.

[0112] In a specific implementation, the maximum capacity change value of the nonlinear region statistics is identified. When a valid maximum capacity change value appears, the initial voltage value of the voltage statistical interval in which the maximum capacity change value is located is recorded, and the SOC within the preset voltage change interval of the initial voltage value is recorded. The preset voltage change interval is preferably 5mV.

[0113] Here, for the area between 60% and 65% of SOC, the maximum capacity change value appears in the interval of SOC>65%. Therefore, a starting SOC threshold can be set, such as SOC>75% to trigger the calculation, and a suitable OCV threshold and capacity change threshold can also be set to verify the validity of the maximum capacity change.

[0114] It should be noted that when recording the SOC corresponding to different initial voltage values, when the initial voltage value does not change, the SOC may change with charging and discharging. The SOC here records the latest change in the SOC under the current initial voltage value.

[0115] Furthermore, when the rate of change between the real-time capacity change value and the maximum capacity change value is greater than a preset threshold, the target initial voltage value of the voltage statistical interval in which the real-time capacity change value is located is determined, and the voltage sum value between the target initial voltage value and the preset voltage change amount is determined as the conversion node between the linear region and the nonlinear region, and the SOC corresponding to the voltage sum value is determined as the SOC identification point.

[0116] Optionally, the preset threshold may be 70% or 80%.

[0117] S104, obtaining the current SOC calculated value of the battery by the ampere-hour integration method, and determining the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value corresponding to the battery at the SOC identification point, and the SOC calculated value.

[0118] In a specific implementation, the current SOC calculated value of the battery is calculated by the ampere-hour integration method, and then the difference between the preset SOC reference value and the SOC corresponding to the SOC identification point is determined; the sum of the difference and the SOC calculated value is determined as the target SOC value, and the current SOC is updated with the target SOC value.

[0119] Here, the target SOC value can be calculated by the following formula:

[0120] SOC update =SOC present +SOC fixed -SOC identified

[0121] Among them, SOC present Represents the current SOC calculated value of the battery; SOC identified Represents the SOC corresponding to the SOC identification point; SOC fixed Represents the preset SOC reference value; SOC update Represents the target SOC value.

[0122] It should be noted that the preset SOC reference value is a reference SOC corresponding to the SOC identification point, which can be determined according to actual needs and is not specifically limited here. It is preferably 65%.

[0123] As a possible implementation, after updating the current SOC, the following steps 1 to 4 may be performed to update the SOC identification point:

[0124] Step 1: Charge or discharge the battery until the battery is fully charged or discharged.

[0125] Step 2: Taking the preset SOC reference value as a starting point, the SOC update calculation value before the moment when the battery is in the full state or the empty state is calculated by the ampere-hour integration method.

[0126] Step 3: Determine a reference SOC value corresponding to the battery in the full state or the empty state, and determine a difference between the reference SOC value and the SOC update calculation value.

[0127] Step 4: Update the battery SOC identification point according to the sum of the difference and the preset SOC reference value.

[0128] In the specific implementation, in order to ensure the accuracy and dynamic adjustment of the SOC identification point, it is necessary to update the SOC identification point according to the battery charging and discharging process. When the battery is full (SOC = 100%) or empty (SOC = 0%), the SOC error is calculated by pure integration of the previous moment. When the above conditions are met, the reference SOC of the current identification point is recorded, and the reference SOC of the current identification point is used as the starting point to calculate the SOC by pure ampere-hour integration method, that is, the SOC update calculation value. If the system power-on time is less than a period of time, such as 6 hours, and the full / empty conditions are met, the identification point SOC is updated according to the following formula:

[0129] SOC tmp =SOC fixed +SOC terminal -SOC A

[0130] Among them, SOC tmp Represents the updated identification point SOC; SOC fixed Represents the preset SOC reference value; SOC terminal Represents the benchmark SOC value corresponding to the battery in a fully charged or discharged state; SOC A Indicates the SOC updated calculation value.

[0131] Optional, verify SOC tmp Is it within a reasonable range (such as 55% to 70%, 25% to 35%), and use the following formula to low-pass filter to update the preset SOC reference value:

[0132] SOC fixed (K) = a*SOC fixed (K-1)+(1-a)SOC tmp

[0133] Among them, SOC fixed Represents the preset SOC reference value; SOC tmp Represents the updated identification point SOC; a can be set according to the update frequency. A larger value, such as 0.9, can be set for a higher number of triggers. A smaller value, such as 0.5, can be set for a lower trigger frequency. Under normal circumstances, it is preferably set to 0.5.

[0134] The disclosed embodiment provides a method for determining the SOC of a battery, which includes obtaining the OCV curve of the battery; extracting the capacity change characteristics corresponding to the OCV curve, and determining the linear region and nonlinear region corresponding to the OCV curve according to the capacity change characteristics; identifying the conversion node between the linear region and the nonlinear region, and determining the conversion node as the SOC identification point; determining the current SOC calculation value of the battery, and determining the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value, and the SOC calculation value. The accuracy and real-time performance of SOC estimation can be improved.

[0135] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0136] Based on the same inventive concept, the embodiment of the present disclosure also provides a device for determining the battery SOC corresponding to the method for determining the battery SOC. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the method for determining the battery SOC in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0137] See also Figure 3 Schematic diagram of a device for determining battery SOC provided by an embodiment of the present disclosure. Figure 3 As shown in , the battery SOC determination device 300 provided in the embodiment of the present disclosure includes:

[0138] The battery OCV identification module 310 is used to obtain the OCV curve of the battery.

[0139] The capacity variation characteristic extraction module 320 is used to extract the capacity variation characteristic corresponding to the OCV curve, and determine the linear region and the nonlinear region corresponding to the battery OCV curve according to the capacity variation characteristic.

[0140] The inflection point identification module 330 is used to identify a transition node between the linear region and the nonlinear region, and determine the SOC corresponding to the transition node as an SOC identification point.

[0141] The SOC update module 340 is used to calculate the current SOC calculated value of the battery by the ampere-hour integration method, and determine the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value corresponding to the battery at the SOC identification point, and the SOC calculated value.

[0142] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0143] The present disclosure provides a device for determining the SOC of a battery, which obtains the OCV curve of the battery; extracts the capacity change characteristics corresponding to the OCV curve, and determines the linear region and nonlinear region corresponding to the OCV curve according to the capacity change characteristics; identifies the conversion node between the linear region and the nonlinear region, and determines the conversion node as the SOC identification point; determines the current SOC calculation value of the battery, and determines the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value and the SOC calculation value. The accuracy and real-time performance of SOC estimation can be improved.

[0144] Corresponds to Figure 1 and Figure 2 The present disclosure also provides an electronic device 400, such as Figure 4 FIG. 4 is a schematic diagram of the structure of an electronic device 400 provided in an embodiment of the present disclosure, including:

[0145] Processor 41, memory 42, and bus 43; memory 42 is used to store execution instructions, including memory 421 and external memory 422; memory 421 here is also called internal memory, which is used to temporarily store operation data in processor 41 and data exchanged with external memory 422 such as hard disk. Processor 41 exchanges data with external memory 422 through memory 421. When the electronic device 400 is running, the processor 41 communicates with the memory 42 through bus 43, so that the processor 41 executes Figure 1 and Figure 2 Steps of the method for determining the battery SOC.

[0146] The present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for determining the battery SOC described in the above method embodiment are executed. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0147] The embodiments of the present disclosure also provide a computer program product, which includes computer instructions. When the computer instructions are executed by a processor, the steps of the method for determining the battery SOC described in the above method embodiment can be executed. For details, please refer to the above method embodiment, which will not be repeated here.

[0148] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0149] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present disclosure, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, 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 communication interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0153] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for determining battery SOC, characterized in that: include: Get the OCV curve of the battery; Extracting a capacity variation characteristic corresponding to the OCV curve, and determining a linear region and a nonlinear region corresponding to the OCV curve according to the capacity variation characteristic; identifying a transition node between the linear region and the nonlinear region, and determining the transition node as a SOC identification point; The current SOC calculated value of the battery is calculated by the ampere-hour integration method, and the target SOC value is determined according to the SOC corresponding to the SOC identification point, the preset SOC reference value corresponding to the battery at the SOC identification point, and the SOC calculated value.

2. The method according to claim 1, characterized in that Identifying a transition node between the linear region and the nonlinear region, and determining the transition node as an SOC identification point, specifically includes: Determine a maximum capacity change value in the nonlinear region and an initial voltage value of a voltage statistical interval in which the maximum capacity change value is located; Taking a preset voltage change as a voltage statistical interval, collecting the SOC of the battery corresponding to each change of the initial voltage value by a preset step length, and determining a real-time capacity change value corresponding to the voltage statistical interval according to the SOC; When the change rate between the real-time capacity change value and the maximum capacity change value is greater than a preset threshold, determining a target initial voltage value of a voltage statistical interval in which the real-time capacity change value is located; A voltage sum value between the target initial voltage value and the preset voltage change amount is determined as the conversion node, and an SOC corresponding to the voltage sum value is determined as the SOC identification point.

3. The method according to claim 1, characterized in that Get the OCV curve of the battery, including: Constructing a battery model estimation equation with the battery terminal voltage as the output variable and the battery characteristic parameters as the parameters to be estimated, wherein the battery characteristic parameters at least include the battery OCV; At each sampling moment, the terminal voltage and current value of the battery are measured in real time, and an input data vector is constructed according to the current value at the current sampling moment and the terminal voltage and current value at the previous sampling moment; Initialize the initial values ​​of the parameters to be estimated and the error covariance matrix, and determine the gain matrix at the current sampling moment according to the error covariance matrix at the previous sampling moment, the input data vector at the current sampling moment, and a preset forgetting factor; Update the parameter to be estimated at the current sampling moment according to the parameter to be estimated at the previous sampling moment, and the gain matrix and the input data vector at the current sampling moment; According to the preset forgetting factor, the gain matrix at the current sampling moment and the input data vector, and the error covariance matrix at the previous sampling moment, the error covariance matrix at the current sampling moment is updated, and the step of determining the gain matrix at the current sampling moment is repeated to determine the parameter to be estimated at each sampling moment; The corresponding battery OCV is extracted from the parameters to be estimated at each sampling moment to generate the battery OCV curve.

4. The method according to claim 2, characterized in that: Extracting the capacity change characteristics of the OCV curve corresponding to the preset voltage change amount specifically includes: In the battery OCV range, set a voltage statistical interval starting from a preset initial voltage value and with a length equal to a preset window length; Determine the battery capacity change value corresponding to the battery OCV at each preset step interval; Filtering the battery capacity change value within the preset voltage change range where the voltage middle value in the voltage statistical interval is located; The battery capacity change value is determined as the capacity change feature within the voltage statistical interval.

5. The method according to claim 4, characterized in that After screening the battery capacity change values ​​within the preset voltage change range in the middle of the voltage statistical interval, the method further includes: Determine whether there is a battery OCV less than the preset initial voltage value in the voltage statistical interval; If so, the voltage statistical interval is moved in a direction of reducing the battery OCV by the preset step length.

6. The method according to claim 5, characterized in that Determining the linear region and the nonlinear region corresponding to the battery OCV curve specifically includes: Determine a difference between a battery capacity change value corresponding to the voltage statistical interval after the movement and a battery capacity change value corresponding to the voltage statistical interval before the movement; Determine a ratio between the difference and a battery capacity change value corresponding to the voltage statistical interval after the movement; If the ratio is greater than the preset threshold, it is determined that the voltage statistical interval is located in the linear region after the movement, and the voltage statistical interval is located in the nonlinear region before the movement.

7. The method according to claim 1, characterized in that Determining a target SOC value according to the SOC corresponding to the SOC identification point, a preset SOC reference value, and the SOC calculation value specifically includes: determining a difference between the preset SOC reference value and the SOC corresponding to the SOC identification point; The sum of the difference and the SOC calculated value is determined as the target SOC value.

8. The method according to claim 7, characterized in that After determining the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value and the SOC calculation value, the method further includes: The battery is charged or discharged until it is fully charged or discharged; Taking the preset SOC reference value as a starting point, calculating the SOC update calculation value before the moment when the battery is in the full state or the empty state by the ampere-hour integration method; Determine a reference SOC value of the battery corresponding to the full state or the empty state, and determine a difference between the reference SOC value and the updated SOC calculated value; The battery SOC identification point is updated according to the sum of the difference and the preset SOC reference value.

9. A device for determining battery SOC, characterized in that: include: Battery OCV identification module, used to obtain the battery OCV curve; A capacity variation feature extraction module, used to extract the capacity variation feature corresponding to the OCV curve, and determine the linear region and the nonlinear region corresponding to the OCV curve according to the capacity variation feature; An inflection point identification module, used to identify a conversion node between the linear region and the nonlinear region, and determine the SOC corresponding to the conversion node as an SOC identification point; The SOC update module is used to calculate the current SOC calculated value of the battery by the ampere-hour integration method, and determine the target SOC value according to the SOC corresponding to the SOC identification point, the preset SOC reference value corresponding to the battery at the SOC identification point, and the SOC calculated value.

10. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method for determining the battery SOC as described in any one of claims 1 to 8 are performed.

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