Method and apparatus for determining battery SOC, and electronic device

Abstract

A method and apparatus for determining a battery SOC, and an electronic device. The method comprises: acquiring an OCV curve of a battery; extracting a capacity variation feature corresponding to the OCV curve, and on the basis of the capacity variation feature, determining a linear region and a non-linear region which correspond to the OCV curve; identifying a conversion node between the linear region and the non-linear region, and determining the conversion node as an SOC identification point; and determining the current calculated SOC value of the battery, and determining a target SOC value on the basis of an SOC corresponding to the SOC identification point, a preset SOC reference value, and the calculated SOC value.

Description

A method, apparatus, and electronic device for determining battery SOC.

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 202411823042.5, filed on December 11, 2024, entitled "A method, apparatus and electronic device for determining battery SOC", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of battery management technology, and in particular to a method, apparatus and electronic device for determining battery SOC. Background Technology

[0004] Accurate estimation of battery capacity is crucial for the effective operation of a battery management system (BMS). The widespread use of lithium-ion batteries in electric vehicles, renewable energy storage devices, and various portable electronic devices makes accurate monitoring and management of battery status particularly important. Battery status includes State of Charge (SOC) and State of Health (SOH), with SOC being a key indicator of remaining battery capacity.

[0005] Currently, SOC estimation mainly employs the ampere-hour integration method, OCV correction method, and electrochemical impedance spectroscopy. However, for some batteries, such as lithium iron phosphate (LFP) batteries, the OCV exhibits a "plateau" characteristic in certain SOC ranges, making conventional estimation methods ineffective for SOC calibration and leading to excessive estimation bias. If the battery system is not fully charged for an extended period and continues charging and discharging within the "plateau" region, the SOC will accumulate over time, causing system malfunctions and potentially resulting in overcharge or over-discharge safety incidents.

[0006] Application content

[0007] In view of this, the purpose of this disclosure is to provide a method, apparatus and electronic device for determining battery SOC, which can improve the accuracy and real-time performance of SOC estimation.

[0008] This disclosure provides a method for determining the State of Charge (SOC) of a battery, comprising: acquiring 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 based on the capacity change characteristics; identifying the transition node between the linear region and the nonlinear region, and determining the transition node as the SOC identification point; calculating the current SOC value of the battery using the ampere-hour integration method; and determining the target SOC value based on the SOC corresponding to the SOC identification point, the preset SOC reference value of the battery at the SOC identification point, and the SOC calculation value.

[0009] In one optional implementation, identifying the transition node between the linear region and the nonlinear region, and determining the transition node as the SOC identification point, specifically includes: determining the maximum capacity change value in the nonlinear region and the initial voltage value of the voltage statistical interval in which the maximum capacity change value is located; using a preset voltage change amount as the voltage statistical interval, collecting the SOC corresponding to the battery at each preset step change of the initial voltage value, and determining the real-time capacity change value corresponding to the voltage statistical interval based on the SOC; when the rate of change between the real-time capacity change value and the maximum capacity change value is greater than a preset threshold, determining the target initial voltage value of the voltage statistical interval in which the real-time capacity change value is located; determining the voltage sum value between the target initial voltage value and the preset voltage change amount as the transition node, and determining the SOC corresponding to the voltage sum value as the SOC identification point.

[0010] In one optional implementation, obtaining the battery OCV curve specifically includes: constructing a battery model estimation equation with battery terminal voltage as the output variable and battery characteristic parameters as parameters to be estimated, wherein the battery characteristic parameters include at least the battery OCV; measuring the battery terminal voltage and current values ​​in real time at each sampling moment, and constructing an input data vector based on the current value at the current sampling moment and the terminal voltage and current values ​​at the previous sampling moment; initializing the parameters to be estimated and the initial values ​​of the error covariance matrix, and determining the current sampling moment based on the error covariance matrix at the previous sampling moment, the input data vector at the current sampling moment, and a preset forgetting factor. The gain matrix of each sampling time is determined; based on the parameter to be estimated at the previous sampling time, the gain matrix at the current time, and the input data vector, the parameter to be estimated at the current sampling time is updated; based on the preset forgetting factor, the gain matrix at the current sampling time, the input data vector, and the error covariance matrix at the previous sampling time, the error covariance matrix at the current sampling time is updated, and the step of determining the gain matrix at the current sampling time is repeated to determine the parameter to be estimated at each sampling time; the corresponding battery OCV is extracted from the parameter to be estimated at each sampling time to generate the battery OCV curve.

[0011] In one optional implementation, extracting the capacity change feature corresponding to a preset voltage change in the battery OCV curve specifically includes: setting a voltage statistical interval within the battery OCV range, starting from a preset initial voltage value and having a length of a preset window; determining the battery capacity change value corresponding to the battery OCV at preset step intervals; filtering the battery capacity change values ​​within the preset voltage change range where the voltage median value in the voltage statistical interval falls; and determining the battery capacity change value as the capacity change feature within the voltage statistical interval.

[0012] In one optional implementation, after filtering the battery capacity change values ​​within the preset voltage change range that are located in the middle of the voltage statistical interval, the method further includes: determining whether there is a battery OCV less than the preset initial voltage value in the voltage statistical interval; if so, shifting the voltage statistical interval by the preset step length in the direction of decreasing the battery OCV.

[0013] In one optional implementation, determining the linear and nonlinear regions corresponding to the battery OCV curve specifically includes: determining 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; determining 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 the preset threshold, then determining that the voltage statistical interval after the movement is located in the linear region and the voltage statistical interval before the movement is located in the nonlinear region.

[0014] In one optional implementation, the target SOC value is determined based on the SOC corresponding to the SOC identification point, a preset SOC reference value, and the calculated SOC value. Specifically, this includes: determining the difference between the preset SOC reference value and the SOC corresponding to the SOC identification point; and determining the sum of the difference and the calculated SOC value as the target SOC value.

[0015] In an optional implementation, after determining the target SOC value based on the SOC corresponding to the SOC identification point, a preset SOC reference value, and the calculated SOC value, the method further includes: charging or discharging the battery until it is in a fully charged or discharged state; calculating an updated SOC value before the battery reaches the fully charged or discharged state using an ampere-hour integration method, starting from the preset SOC reference value; determining a baseline SOC value corresponding to the fully charged or discharged state; determining the difference between the baseline SOC value and the updated SOC value; and updating the battery SOC identification point based on the sum of the difference and the preset SOC reference value.

[0016] This disclosure also provides a battery SOC determination device, comprising: a battery OCV identification module for acquiring the battery's OCV curve; a capacity change feature extraction module for extracting capacity change features corresponding to the OCV curve and determining the linear and nonlinear regions corresponding to the OCV curve based on the capacity change features; an inflection point identification module for identifying the transition node between the linear and nonlinear regions and determining the SOC corresponding to the transition node as the SOC identification point; and a SOC update module for calculating the current SOC value of the battery using the ampere-hour integration method, and determining a target SOC value based on the SOC corresponding to the SOC identification point, a preset SOC reference value corresponding to the battery at the SOC identification point, and the calculated SOC value.

[0017] This disclosure also provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the above-described battery SOC determination method, or any possible implementation of the above-described battery SOC determination method.

[0018] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the battery SOC determination method described above, or any possible implementation of the battery SOC determination method described above.

[0019] This disclosure also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the above-described method for determining battery SOC, or the steps in any possible implementation of the above-described method for determining battery SOC.

[0020] This disclosure provides a method, apparatus, and electronic device for determining the State of Charge (SOC) of a battery. The method involves acquiring the battery's OCV curve; extracting the capacity change characteristics corresponding to the OCV curve; determining the linear and nonlinear regions corresponding to the OCV curve based on the capacity change characteristics; identifying the transition nodes between the linear and nonlinear regions and defining these transition nodes as SOC identification points; determining the current calculated SOC value of the battery; and determining the target SOC value based on the SOC corresponding to the SOC identification point, a preset SOC reference value, and the calculated SOC value. This method can improve the accuracy and real-time performance of SOC estimation.

[0021] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this disclosure and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 shows a flowchart of a method for determining battery SOC provided in an embodiment of this disclosure;

[0024] Figure 2 shows a flowchart of another method for determining battery SOC provided in an embodiment of this disclosure;

[0025] Figure 3 shows a schematic diagram of a battery SOC determination device provided in an embodiment of the present disclosure;

[0026] Figure 4 shows a schematic diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. Based on the embodiments of this disclosure, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this disclosure.

[0028] Research has revealed that current SOC estimation primarily employs the ampere-hour integration method, OCV correction method, and electrochemical impedance spectroscopy. However, for some batteries, such as lithium iron phosphate (LFP) batteries, the OCV exhibits a "plateau" characteristic within certain SOC ranges, rendering conventional estimation methods ineffective in calibrating the SOC and leading to excessive estimation bias. If the battery system remains partially charged for an extended period and continues charging and discharging within this "plateau," the SOC will accumulate over time, causing system malfunctions and potentially resulting in overcharging or over-discharging safety incidents.

[0029] Based on the above research, this disclosure provides a method, apparatus, and electronic device for determining battery SOC. The method involves acquiring the battery's OCV curve; extracting the capacity change characteristics corresponding to the OCV curve; determining the linear and nonlinear regions corresponding to the OCV curve based on the capacity change characteristics; identifying the transition nodes between the linear and nonlinear regions and defining these transition nodes as SOC identification points; determining the current calculated SOC value of the battery; and determining the target SOC value based on the SOC corresponding to the SOC identification point, a preset SOC reference value, and the calculated SOC value. This method can improve the accuracy and real-time performance of SOC estimation.

[0030] To facilitate understanding of this embodiment, a method for determining battery SOC disclosed in this disclosure will first be described in detail. The execution entity of the battery SOC determination method provided in this disclosure is generally a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. In some possible implementations, the battery SOC determination method can be implemented by a processor calling computer-readable instructions stored in memory.

[0031] Referring to Figure 1, which is a flowchart of a method for determining the state of charge (SOC) of a battery according to an embodiment of this disclosure, the method includes steps S101 to S104, wherein:

[0032] S101. Obtain the OCV curve of the battery.

[0033] In practical implementation, a recursive least squares method with a forgetting factor can be used to estimate the battery model, and then the battery's OCV curve can be identified. The OCV (Open Circuit Voltage) curve represents the open circuit voltage of the battery at different SOC (State of Charge). The OCV range covers the entire voltage variation range of the battery from fully discharged (SOC close to 0%) to fully charged (SOC close to 100%).

[0034] Specifically, the OCV curve of the battery can be obtained through the following steps 1-6:

[0035] Step 1: Construct a battery model estimation equation with battery terminal voltage as the output variable and battery characteristic parameters as the parameters to be estimated, wherein the battery characteristic parameters include at least the battery OCV.

[0036] Step 2: Measure the battery's terminal voltage and current values ​​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 values ​​at the previous sampling moment.

[0037] Step 3: Initialize the parameters to be estimated and the initial values ​​of the error covariance matrix. Determine the gain matrix at the current sampling time based on the error covariance matrix at the previous sampling time, the input data vector at the current sampling time, and the preset forgetting factor.

[0038] Step 4: Update the parameters to be estimated at the current sampling time based on the parameters to be estimated at the previous sampling time, the gain matrix at the current time, and the input data vector.

[0039] Step 5: Based on the preset forgetting factor, the gain matrix at the current sampling time, the input data vector, and the error covariance matrix at the previous sampling time, update the error covariance matrix at the current sampling time, and repeat the step of determining the gain matrix at the current sampling time to determine the parameter to be estimated at each sampling time.

[0040] Step 6: Extract the corresponding battery OCV from the parameters to be estimated at each sampling time to generate the battery OCV curve.

[0041] In practice, firstly, a battery model is established. The output of this model is the battery terminal voltage, and the inputs are the battery current and voltage data. The parameters to be estimated include the battery's OCV.

[0042] Here, the battery model can take the form of:

[0043] Among them, y k The system output variable at the k-th sampling time is the battery terminal voltage. The input data vector represents the voltage and current data from the current and previous time steps; θ k Represents the system parameters to be estimated, including the battery's OCV and other relevant parameters; e Ls,k This represents systematic error, typically white noise, and indicates the deviation between the model and actual battery behavior.

[0044] Here, the battery terminal voltage U is measured at each sampling time. k and current I k And based on voltage U k and current I k The measured values ​​are used to construct the input data vector. This vector will be dynamically updated at each time step, and the input data vector can be in the form of:

[0045] Among them, U k U represents the battery terminal voltage at the current sampling time k; k-1 I represents the battery terminal voltage at the previous sampling time k-1; k I represents the battery current at the current sampling time k; k-1 This represents the battery current at the previous sampling time k-1.

[0046] Furthermore, before starting the estimation, initial values ​​need to be set, including the initial values ​​θ0 of the parameters to be estimated and the error covariance matrix P. Ls,0 Then, at each sampling time, the gain matrix K is calculated using the error covariance matrix from the previous time step and the current input data vector. Ls,k Gain matrix K Ls,k This determines the extent to which the current sampled data affects parameter updates.

[0047] Here, the gain matrix can take the form of:

[0048] Among them, P Ls,k-1 This represents the error covariance matrix at the previous time step k-1; The input data vector represents the current sampling time; μ represents the forgetting factor, which is used to adjust for the influence of historical data, and its value range is usually 0 < μ ≤ 1.

[0049] Furthermore, using the gain matrix K Ls,k The parameters to be estimated at the previous time step Make corrections to obtain the parameters to be estimated at the current time. To gradually adjust the model parameters.

[0050] Here, the parameter update formula is:

[0051] Among them, y k This represents the battery terminal voltage measured at the current moment. This represents the predicted battery terminal voltage calculated by the model at the current moment.

[0052] Furthermore, at each sampling time, from the updated parameters The OCV value of the battery is extracted and accumulated one by one. The OCV values ​​at each time point are plotted on the same chart to form the battery OCV curve, which reflects the relationship between the battery SOC and voltage.

[0053] S102. Extract the capacity change features corresponding to the OCV curve, and determine the linear region and nonlinear region corresponding to the OCV curve based on the capacity change features.

[0054] In specific implementation, within the battery OCV range, a voltage statistical interval is set starting from a preset initial voltage value and with a length of a preset window; the battery capacity change value corresponding to the battery OCV at each preset step interval is determined; the battery capacity change value within the preset voltage change range where the voltage median value in the voltage statistical interval is located is filtered; and the battery capacity change value is determined as the capacity change characteristic within the voltage statistical interval.

[0055] First, select an initial voltage value on the battery's OCV curve. Starting from this value, set a voltage statistical interval with a preset window length. Within the selected voltage statistical interval, determine the battery capacity change value corresponding to each voltage point according to a preset step size. Within the voltage statistical interval, progressively increase the preset step size, calculating the battery capacity change value for each step size, for example, by determining the battery capacity change under that voltage change using the ampere-hour integration method.

[0056] Among them, the capacity change value is the change of SOC of the battery at each preset step interval within the voltage statistical range, which reflects the charge and discharge performance of the battery in this range.

[0057] Then, within each voltage statistical interval, the median value of the capacity change is calculated, and the range of the preset voltage change is moved step by step. During each movement, the capacity change within the current voltage statistical interval is calculated and summed to finally obtain a smooth capacity change value, which is determined as the capacity change characteristic within the voltage statistical interval.

[0058] Preferably, the preset step size can be 1mV, that is, the change in integral capacity for every 1mV change (decrease) in battery OCV.

[0059] For example, when the preset voltage change range is 5mV and the preset window length of the voltage statistical interval is 15mV, the middle 5mV within the statistical interval is taken as a representative value for calculating the capacity change. For instance, if the interval is set to [V, V+15mV], the middle [V+5mV, V+10mV] is used for calculation. When the initial voltage value is 4V, the first voltage statistical interval is 4.0V to 3.985V. Within the 4.0V to 3.985V interval, the median value dQ of the capacity change is calculated. With a step size of 5mV, the window moves to the 3.995V to 3.98V interval, and the median 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 values ​​within the current statistical interval are summed to obtain a smoothed capacity change value.

[0060] As one possible implementation, after filtering the battery capacity change values ​​within a preset voltage change range in the middle of the voltage statistical interval, it is determined whether there is a battery OCV less than a preset initial voltage value in the voltage statistical interval; if so, the voltage statistical interval is moved by a preset step length in the direction of decreasing battery OCV.

[0061] Here, in order to identify the inflection point in the OCV curve, it is necessary to track the change of OCV. During the battery discharge process, 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 dropped significantly by one voltage statistical interval. When this situation is detected, the statistical interval is shifted to the left by the distance of the voltage statistical interval corresponding to the voltage statistical interval, that is, the starting voltage is updated to the current OCV value.

[0062] For example, assuming the current voltage curve starts at 4.0V, the initially set starting voltage V0 is 4.0V, and the current measured OCV is 3.985V, the current OCV + 14mV = 3.985V + 0.014V = 3.999V is calculated. Comparing 3.999V with 4.0V, we find that 3.999V < 4.0V. Since the current OCV + 14mV is less than V0, it indicates that the voltage drop has exceeded 14mV. At this point: the starting voltage V0 is updated to the current 3.985V, the statistical window moves one unit to the left, and the new statistical interval begins at 3.985V.

[0063] Furthermore, the difference between the battery capacity change value corresponding to the voltage statistical interval after the move and the battery capacity change value corresponding to the voltage statistical interval before the move is determined; the ratio between this difference and the battery capacity change value corresponding to the voltage statistical interval after the move is determined; if the ratio is greater than a preset threshold, the voltage statistical interval after the move is determined to be in the linear region, and the voltage statistical interval before the move is determined to be in the nonlinear region.

[0064] Here, on the OCV curve, the region where the voltage changes rapidly with SOC is the linear region, and the region where the voltage changes slowly with SOC is the nonlinear region, i.e. the plateau region, where the battery capacity changes relatively smoothly. When the ratio between the battery capacity change value corresponding to the voltage statistical interval after the move and the battery capacity change value corresponding to the voltage statistical interval before the move exceeds the preset first threshold, it indicates that the current interval is the linear region.

[0065] It should be noted that the battery capacity change value corresponding to the voltage statistical interval after the movement is taken as the maximum value before the linear region is reached. The preset first threshold can be determined according to the characteristics of the battery material, and no specific restrictions are made here. For example, the preset first threshold corresponding to the characteristics of the lithium iron phosphate platform region is 5.

[0066] Here, when the current capacity change value is detected to drop sharply, that is, when the ratio between the difference between the battery capacity change value corresponding to the voltage statistical interval after the move and the battery capacity change value corresponding to the voltage statistical interval before the move, and the battery capacity change value corresponding to the voltage statistical interval after the move is greater than the preset second threshold, it indicates that the current statistical interval is a linear region.

[0067] 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.

[0068] S103. Identify the transition node between the linear region and the nonlinear region, and determine the transition node as the SOC identification point.

[0069] In practice, within the linear region of the battery's OCV curve, the relationship between the battery's voltage and capacity is relatively stable. However, in the nonlinear region, this relationship is no longer linear, possibly due to the complexity of the battery's chemical reactions. In this region, small changes in voltage can lead to significant changes in capacity, or vice versa, making SOC estimation more difficult.

[0070] Here, near the transition node between the linear and nonlinear regions, the sensitivity of OCV to capacity changes suddenly increases or decreases, making 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.

[0071] Specifically, referring to Figure 2, which is a flowchart of another method for determining battery SOC provided in this embodiment of the present disclosure, the method includes steps S1031 to S1034, wherein:

[0072] S1031. Determine the maximum capacity change value in the nonlinear region and the initial voltage value of the voltage statistical interval in which the maximum capacity change value is located.

[0073] S1032 uses a preset voltage change as the voltage statistical interval, collects the SOC of the battery at each preset step change of the initial voltage value, and determines the real-time capacity change value corresponding to the voltage statistical interval based on the SOC.

[0074] S1033 When the rate of change between the real-time capacity change value and the maximum capacity change value is greater than a preset threshold, determine the target initial voltage value of the voltage statistical interval in which the real-time capacity change value is located.

[0075] S1034 determines the sum of the voltages between the target initial voltage value and the preset voltage change as the conversion node, and determines the SOC corresponding to the sum of the voltages as the SOC identification point.

[0076] In specific implementation, the maximum capacity change value of the nonlinear region 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 range of the initial voltage value is recorded. The preset voltage change range is preferably 5mV.

[0077] Here, for the region where SOC is between 60% and 65%, the maximum capacity change occurs in the range where SOC > 65%. Therefore, a starting SOC threshold can be set, such as triggering calculation only when SOC > 75%. At the same time, appropriate OCV thresholds and capacity change thresholds can be set to verify the validity of the maximum capacity change.

[0078] It should be noted that when recording the SOC corresponding to different initial voltage values, the SOC may change with charging and discharging when the initial voltage value does not change. The SOC recorded here is the latest SOC under the current initial voltage value.

[0079] 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 sum of the voltage between the target initial voltage value and the preset voltage change value is determined as the conversion node between the linear region and the nonlinear region, and the SOC corresponding to the sum of the voltage is determined as the SOC identification point.

[0080] Optionally, the preset threshold can be 70% or 80%.

[0081] S104. The current SOC value of the battery is calculated by the ampere-hour integration method. Based on the SOC corresponding to the SOC identification point, the preset SOC reference value of the battery at the SOC identification point, and the SOC calculation value, the target SOC value is determined.

[0082] In practice, the current SOC of the battery is calculated by the ampere-hour integration method. Then, the difference between the preset SOC reference value and the SOC corresponding to the SOC identification point is determined. The sum of this difference and the SOC calculation value is determined as the target SOC value, and the current SOC is updated with the target SOC value.

[0083] Here, the target SOC value can be calculated using the following formula: SOC update =SOC present +SOC fixed -SOC identified

[0084] Among them, SOC present This represents the current calculated SOC value of the battery; SOC identified SOC represents the SOC corresponding to the SOC identification point; SOC fixed Represents the preset SOC reference value; SOC update This represents the target SOC value.

[0085] It should be noted that the preset SOC reference value is the reference SOC corresponding to the SOC identification point, which can be determined according to actual needs. No specific restrictions are imposed here, but 65% is preferred.

[0086] As one possible implementation, after updating the current SOC, the following steps 1-4 can also be performed to update the SOC identification points:

[0087] Step 1: Charge or discharge the battery until it is fully charged or discharged.

[0088] Step 2: Starting from the preset SOC reference value, calculate the updated SOC value before the time when the battery is in the fully charged state or the discharged state using the ampere-hour integration method.

[0089] Step 3: Determine the baseline SOC value of the battery in the fully charged state or the discharged state, and determine the difference between the baseline SOC value and the updated SOC value.

[0090] Step 4: Update the battery SOC identification point based on the sum of the difference and the preset SOC reference value.

[0091] In practical implementation, 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 fully charged (SOC = 100%) or discharged (SOC = 0%), the SOC error is calculated by pure integration at the previous moment. When the above conditions are met, the reference SOC of the current identification point is recorded. Starting from the reference SOC of the current identification point, the SOC is calculated using the pure ampere-hour integration method, i.e., the SOC update calculation value. If the system power-on time is less than a certain period, such as 6 hours, and the full charge / discharge conditions are met, the identification point SOC is updated according to the following formula: SOC tmp =SOC fixed +SOC terminal -SOC Ah

[0092] Among them, SOC tmp Represents the updated SOC (Signature Occurrence Point); SOC fixed Represents the preset SOC reference value; SOC terminalThis represents the baseline SOC value corresponding to the battery's fully charged or fully discharged state; SOC Ah This represents an update of the calculated value for the SOC.

[0093] Optional, verify SOC tmp Whether it is within a reasonable range (e.g., 55%–70%, 25%–35%), and update the preset SOC reference value using the following formula with low-pass filtering: SOC fixed (K)=a*SOC fixed (K-1)+(1-a)SOC tmp

[0094] Among them, SOC fixed Represents the preset SOC reference value; SOC tmp This represents the updated SOC (State of Origin) of the identification point; 'a' can be set according to the update frequency. A larger value can be set for more frequent triggers, such as 0.9, while a smaller value can be set for less frequent triggers, such as 0.5. Under normal circumstances, 0.5 is the preferred setting.

[0095] This disclosure provides a method for determining the State of Charge (SOC) of a battery. The method involves acquiring the battery's OCV curve; extracting the capacity change characteristics corresponding to the OCV curve; determining the linear and nonlinear regions corresponding to the OCV curve based on the capacity change characteristics; identifying the transition nodes between the linear and nonlinear regions and defining these transition nodes as SOC identification points; determining the current calculated SOC value of the battery; and determining the target SOC value based on the SOC corresponding to the SOC identification point, a preset SOC reference value, and the calculated SOC value. This method can improve the accuracy and real-time performance of SOC estimation.

[0096] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0097] Based on the same inventive concept, this disclosure also provides a battery SOC determination device corresponding to the battery SOC determination method. Since the principle of the device in this disclosure for solving the problem is similar to the battery SOC determination method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0098] Please refer to Figure 3, which is a schematic diagram of a battery SOC determination device provided in an embodiment of this disclosure. As shown in Figure 3, the battery SOC determination device 300 provided in this embodiment of the disclosure includes:

[0099] The battery OCV identification module 310 is used to acquire the battery's OCV curve.

[0100] The capacity change feature extraction module 320 is used to extract the capacity change features corresponding to the OCV curve, and determine the linear region and nonlinear region corresponding to the battery OCV curve based on the capacity change features.

[0101] The inflection point identification module 330 is used to identify the transition node between the linear region and the nonlinear region, and to determine the SOC corresponding to the transition node as the SOC identification point.

[0102] SOC update module 340 is used to calculate the current SOC value of the battery by ampere-hour integration method, and determine the target SOC value based on the SOC corresponding to the SOC identification point, the preset SOC reference value of the battery at the SOC identification point and the SOC calculation value.

[0103] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0104] This disclosure provides a battery SOC determination device that acquires the battery's OCV curve; extracts the capacity change characteristics corresponding to the OCV curve, and determines the linear and nonlinear regions corresponding to the OCV curve based on the capacity change characteristics; identifies the transition nodes between the linear and nonlinear regions, and determines the transition nodes as SOC identification points; determines the current calculated SOC value of the battery; and determines the target SOC value based on the SOC corresponding to the SOC identification point, a preset SOC reference value, and the calculated SOC value. This can improve the accuracy and real-time performance of SOC estimation.

[0105] Corresponding to the battery SOC determination method in Figures 1 and 2, this disclosure also provides an electronic device 400, as shown in Figure 4, which is a schematic diagram of the structure of the electronic device 400 provided in this disclosure, including:

[0106] The device includes a processor 41, a memory 42, and a bus 43. The memory 42 is used to store execution instructions and includes a main memory 421 and an external memory 422. The main memory 421, also known as internal memory, is used to temporarily store the computational data in the processor 41 and the data exchanged with external memory such as a hard disk. The processor 41 exchanges data with the external memory 422 through the main memory 421. When the electronic device 400 is running, the processor 41 and the memory 42 communicate through the bus 43, so that the processor 41 executes the steps of the battery SOC determination method in Figures 1 and 2.

[0107] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the battery SOC determination method described in the above method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0108] This disclosure also provides a computer program product, which includes computer instructions. When the computer instructions are executed by a processor, they can perform the steps of the battery SOC determination method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0109] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0110] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0113] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for determining the state of charge (SOC) of a battery, wherein, include: Obtain the OCV curve of the battery; Extract the capacity change characteristics corresponding to the OCV curve, and determine the linear and nonlinear regions corresponding to the OCV curve based on the capacity change characteristics; Identify the transition nodes between the linear region and the nonlinear region, and determine the transition nodes as SOC identification points; The current SOC value of the battery is calculated by the ampere-hour integration method. The target SOC value is determined based on the SOC corresponding to the SOC identification point, the preset SOC reference value of the battery at the SOC identification point, and the SOC calculation value.

2. The method according to claim 1, wherein, Identifying the transition node between the linear region and the nonlinear region, and determining the transition node as the SOC identification point, specifically includes: Determine the maximum capacity change value in the nonlinear region and the initial voltage value of the voltage statistical interval in which the maximum capacity change value is located; Using a preset voltage change as the voltage statistical interval, the SOC of the battery is collected when the initial voltage value changes by a preset step, and the real-time capacity change value corresponding to the voltage statistical interval is determined based on the SOC. 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. The sum of the voltage between the target initial voltage value and the preset voltage change is determined as the conversion node, and the SOC corresponding to the sum of the voltage is determined as the SOC identification point.

3. The method according to claim 1, wherein, Obtaining the OCV curve of the battery specifically includes: Construct a battery model estimation equation with battery terminal voltage as the output variable and battery characteristic parameters as the parameters to be estimated, wherein the battery characteristic parameters include at least the battery OCV; The battery's terminal voltage and current values ​​are measured in real time at each sampling moment, and an input data vector is constructed based on the current value at the current sampling moment and the terminal voltage and current values ​​at the previous sampling moment. Initialize the parameters to be estimated and the initial values ​​of the error covariance matrix, and determine the gain matrix at the current sampling time based on the error covariance matrix at the previous sampling time, the input data vector at the current sampling time, and the preset forgetting factor. Based on the parameter to be estimated at the previous sampling time, the gain matrix at the current sampling time, and the input data vector, update the parameter to be estimated at the current sampling time; Based on the preset forgetting factor, the gain matrix at the current sampling time, the input data vector, and the error covariance matrix at the previous sampling time, update the error covariance matrix at the current sampling time, and repeat the step of determining the gain matrix at the current sampling time to determine the parameter to be estimated at each sampling time. The corresponding battery OCV is extracted from the parameters to be estimated at each sampling time to generate the battery OCV curve.

4. The method according to claim 2, wherein, Extracting the capacity change characteristics corresponding to the preset voltage change in the OCV curve, specifically including: Within the battery OCV range, a voltage statistics interval is set starting from a preset initial voltage value and having a length equal to a preset window length. Determine the battery capacity change value corresponding to the battery OCV at each preset step interval; Filter the battery capacity change values ​​within the preset voltage change range where the median voltage value in the voltage statistical interval falls; The battery capacity change value is defined as the capacity change characteristic within the voltage statistical interval.

5. The method according to claim 4, wherein, After filtering the battery capacity change values ​​within the preset voltage change range that are located in the middle of the voltage statistical interval, the method further includes: Determine whether there is a battery OCV with a voltage lower than the preset initial voltage value within the voltage statistical interval; If it exists, the voltage statistics interval is shifted by the preset step size in the direction that reduces the battery's OCV.

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

7. The method according to claim 1, wherein, The target SOC value is determined based on the SOC corresponding to the SOC identification point, the preset SOC reference value, and the calculated SOC value, specifically including: Determine the difference between the preset SOC reference value and the SOC corresponding to the SOC identification point; The sum of this difference and the calculated SOC value is determined as the target SOC value.

8. The method according to claim 7, wherein, After determining the target SOC value based on the SOC corresponding to the SOC identification point, the preset SOC reference value, and the calculated SOC value, the method further includes: The battery is charged or discharged until it is fully charged or discharged. Starting from the preset SOC reference value, the SOC update calculation value before the time when the battery is in the fully charged state or the discharged state is calculated by the ampere-hour integration method. Determine the baseline SOC value of the battery corresponding to the fully charged state or the discharged state, and determine the difference between the baseline SOC value and the updated SOC value; The battery SOC identification point is updated based on the sum of the difference and the preset SOC reference value.

9. The method according to claim 3, wherein, The parameter to be estimated is updated based on the following formula: y represents the parameter to be estimated; k This represents the battery terminal voltage measured at the current moment. This represents the predicted battery terminal voltage calculated by the model at the current moment; K represents the parameter to be estimated at the previous time step. Ls,k This represents the gain matrix.

10. The method according to claim 8, wherein, After updating the battery SOC identification point based on the sum of the difference and the preset SOC reference value, the preset SOC reference value is updated based on the following formula: SOC fixed (K)=a*SOC fixed (K-1)+(1-a)SOC tmp SOC fixed This represents the preset SOC reference value; SOC tmp Represents the updated SOC of the identification point; 'a' is set according to the update frequency, with a larger value for points that are triggered more frequently and a smaller value for points that are triggered less frequently; 'k' represents the current sampling time.

11. The method according to claim 8, wherein, The gain matrix is ​​expressed by the following formula: K Ls,k The gain matrix representing the current sampling time k; P Ls,k-1 This represents the error covariance matrix at the previous sampling time k-1; The input data vector represents the current sampling time; μ represents the forgetting factor, which is used to adjust for the influence of historical data, and its value ranges from 0 to 1.

12. The method according to claim 3, wherein, The form of the battery model estimation equation is: y k The system output variable at the k-th sampling time is the battery terminal voltage. The input data vector represents the voltage and current data from the current and previous time steps; θ k The system parameters to be estimated include at least the battery's OCV; e Ls,k This represents systematic error, indicating the deviation between the model and actual battery behavior.

13. A device for determining the state of charge (SOC) of a battery, wherein, include: The battery OCV identification module is used to obtain the battery's OCV curve. The capacity change feature extraction module is used to extract the capacity change features corresponding to the OCV curve, and determine the linear region and nonlinear region corresponding to the OCV curve based on the capacity change features; The inflection point identification module is used to identify the transition node between the linear region and the nonlinear region, and to determine the SOC corresponding to the transition node as the SOC identification point. The SOC update module is used to calculate the current SOC value of the battery using the ampere-hour integration method, and to determine the target SOC value based on the SOC corresponding to the SOC identification point, the preset SOC reference value of the battery at the SOC identification point, and the SOC calculation value.

14. An electronic device, wherein, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the method for determining the battery SOC as described in any one of claims 1 to 12.

15. A computer-readable storage medium, wherein, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method for suppressing nonlinear distortion of an optical single-sideband transmitter based on an electroabsorption modulated laser as described in any one of claims 1 to 12.

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