Charge state calibration method, device, medium, product, system and equipment

Abstract

The embodiment of the invention provides a state of charge calibration method and device, a medium, a product, a system and equipment. The state of charge calibration method comprises the following steps: acquiring first measurement terminal voltages corresponding to a battery at a plurality of sampling time points, and first model terminal voltages corresponding to each sampling time point; determining a first voltage error corresponding to the sampling time point based on the first measurement terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage; and under the condition that the first voltage errors corresponding to the plurality of sampling time points are in a monotonous change trend, the estimated state of charge of the battery is calibrated. Therefore, whether the current state of charge of the battery needs to be calibrated or not can be more accurately identified, the correction opportunity of the state of charge is increased, the correction effect of the SOC is improved, and the problem of overvoltage or undervoltage of the battery caused by deviation of the state of charge is reduced.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to methods, devices, media, products, systems and equipment for calibrating the state of charge. Background Art

[0002] This section is intended to provide a background or context for the embodiments of the present application. No description herein is admitted to be prior art by virtue of its inclusion in this section.

[0003] As the battery is used, there will be a deviation between the estimated state of charge (SOC) and the actual state of charge. If the state of charge is not corrected in time, it will cause unexpected phenomena such as overvoltage or undervoltage in the battery, thereby affecting the battery performance.

[0004] Therefore, a method for correcting the battery state of charge is needed to improve battery performance and further enhance user experience. Summary of the invention

[0005] In view of this, the embodiments of the present application hope to provide a state of charge calibration method, device, medium, product, system and equipment, which can correct the SOC deviation generated by battery use, reduce problems such as battery overvoltage or battery undervoltage caused by SOC deviation, thereby improving the performance of battery products.

[0006] The present application provides a method for calibrating a state of charge, the method comprising: Obtaining first measurement terminal voltages corresponding to the battery at multiple sampling time points, and first model terminal voltages corresponding to each sampling time point; the first model terminal voltages corresponding to each sampling time point are determined based on an estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery; Determine a first voltage error corresponding to the sampling time point based on a first measurement terminal voltage corresponding to the sampling time point and a corresponding first model terminal voltage; When the first voltage errors corresponding to the plurality of sampling time points show a monotonically changing trend, the estimated state of charge of the battery is calibrated.

[0007] In an embodiment of the present application, firstly, the first measurement terminal voltage corresponding to the battery at multiple sampling time points and the first model terminal voltage corresponding to each sampling time point are obtained; wherein, the first model terminal voltage corresponding to each sampling time point is determined based on the estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery; then, based on the first measurement terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage, the first voltage error corresponding to the sampling time point is determined; when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, the estimated state of charge of the battery is calibrated. In this way, on the one hand, since there is a specific corresponding relationship between the OCV and SOC of the battery, by introducing the equivalent circuit model of the battery, the voltage error between the battery terminal voltage calculated by the model and the actually measured battery terminal voltage can be used to reflect the deviation of the SOC; on the other hand, since the rate of change of the battery's OCV with SOC will be different in different SOC ranges, and when there is a deviation between the battery's estimated state of charge and the actual SOC, the battery's estimated state of charge and the actual SOC may be in two SOC ranges with different rates of change of OCV with SOC, respectively. In this way, there will be a trend that the deviation between the battery's estimated state of charge and the actual SOC changes monotonically over time. Therefore, by determining the changing trend of the first voltage error corresponding to multiple sampling time points, it is possible to more accurately judge whether there is a deviation between the battery's estimated state of charge and the actual SOC, thereby more accurately identifying whether the battery's estimated state of charge needs to be calibrated. In this way, when the first voltage error corresponding to multiple sampling time points shows a monotonic change trend, by calibrating the current state of charge of the battery, the chance of SOC correction can be increased and the SOC correction effect can be improved, thereby reducing the problem of battery overvoltage or undervoltage due to SOC deviation and further improving battery performance.

[0008] In some embodiments, the estimated state of charge of the battery is determined using a target estimation model; Calibrate the battery's estimated state of charge, including: Adjusting the model parameters of the target estimation model to obtain the target estimation model after parameter adjustment; The target estimation model after parameter adjustment is used to estimate the state of charge of the battery to obtain a calibrated estimated state of charge.

[0009] In the above embodiment, the model parameters of the target estimation model for determining the estimated state of charge of the battery are adjusted to obtain the target estimation model after parameter adjustment; and the state of charge of the battery is estimated using the target estimation model after parameter adjustment to obtain the calibrated estimated state of charge. In this way, by adjusting the model parameters of the target estimation model for estimating the state of charge of the battery, the estimated state of charge can be calibrated during the process of estimating the state of charge of the battery, thereby improving the accuracy of the estimated state of charge output by the target estimation model.

[0010] In some embodiments, the target estimation model is used to perform weighted processing on the state prediction quantity and the state observation quantity of the battery to obtain the estimated state of charge of the battery, and the model parameters include a first weight coefficient of the state prediction quantity and a second weight coefficient of the state observation quantity; Adjusting the model parameters of the target estimation model includes at least one of the following: reducing the first weight coefficient; Increase the second weight coefficient.

[0011] In the above embodiment, by reducing the first weight coefficient of the state prediction quantity used to determine the estimated state of charge in the target estimation model, and / or increasing the second weight coefficient of the state observation quantity, the estimated state of charge can be made closer to the actual state of charge corresponding to the state observation quantity, thereby improving the accuracy of battery SOC calibration.

[0012] In some embodiments, calibrating the estimated state of charge of the battery includes: Get the current estimated state of charge of the battery; When the current estimated state of charge of the battery is within a preset state of charge range, the current estimated state of charge of the battery is calibrated; and the rate of change of the open circuit voltage of the battery with the state of charge within the preset state of charge range is greater than a rate of change threshold.

[0013] In the above embodiment, when the rate of change of the open circuit voltage corresponding to the current estimated state of charge of the battery with the state of charge is greater than the rate of change threshold, the current estimated state of charge of the battery is calibrated. In this way, considering that the rate of change of the open circuit voltage of the battery with the state of charge exceeds the rate of change threshold, the change of the open circuit voltage with the state of charge is usually more obvious (for example, the OCV corresponding to the state of charge of the battery is in the non-platform area), in this case, the change trend of the error between the model terminal voltage and the measurement terminal voltage of the battery will be more obvious, so that the monotonic change trend of the first voltage error is easier to be accurately identified. Therefore, when the rate of change of the open circuit voltage corresponding to the current estimated state of charge of the battery with the state of charge is greater than the rate of change threshold, the SOC is calibrated, which can reduce the situation of miscalibration caused by inaccurate identification of the change trend of the first voltage error, and further improve the accuracy and reference of the calibration result.

[0014] In some embodiments, the state of charge calibration method further includes: For each sampling time point, the equivalent circuit model of the battery is used to determine the first model terminal voltage corresponding to the sampling time point based on the estimated state of charge of the battery at the sampling time point and the first operating condition indicator of the battery collected at the sampling time point.

[0015] In the above embodiment, for each sampling time point, the equivalent circuit model of the battery is used, based on the estimated state of charge of the battery at the sampling time point, and the first operating condition index of the battery collected at the sampling time point, to determine the first model terminal voltage corresponding to the sampling time point. In this way, by combining the estimated state of charge and the operating condition index of the battery, a more accurate first model terminal voltage can be determined, thereby obtaining a more accurate first voltage error, and further more accurately identifying the situation where the estimated SOC needs to be calibrated.

[0016] In some embodiments, the state of charge calibration method further includes: When a first voltage error corresponding to at least one sampling time point exceeds a reference voltage error range, calibrating the estimated state of charge of the battery; The reference voltage error range represents the error range of the model terminal voltage determined based on the equivalent circuit model when the estimated state of charge of the battery meets the target credibility condition.

[0017] In the above-mentioned embodiment, in the embodiment of the present application, when the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range, the estimated state of charge of the battery is calibrated. In this way, since the reference voltage error range represents the voltage error range of the model terminal voltage when the estimated state of charge of the battery meets the target trustworthy condition, by judging whether the first voltage error corresponding to the estimated SOC exceeds the reference voltage error range, it is possible to judge whether the estimated SOC meets the target trustworthy condition, and thus it is possible to more accurately identify the situation where the estimated SOC needs to be calibrated.

[0018] In some embodiments, the state of charge calibration method further includes: Acquire at least one second measurement terminal voltage of the battery during a target test, and a second operating condition indicator and a measured state of charge corresponding to each second measurement terminal voltage; For each second measurement terminal voltage, based on the equivalent circuit model and the corresponding measured state of charge, determine the second model terminal voltage of the battery under the corresponding second operating condition indicator; Determining a second voltage error of each second model terminal voltage based on each second measurement terminal voltage and the corresponding second model terminal voltage; A reference voltage error range is determined based on the second voltage errors of the second model terminal voltages.

[0019] In the above embodiment, at least one second measurement terminal voltage collected during the target test process of the battery, and the second operating condition index and the measured charge state corresponding to each second measurement terminal voltage are used; for each second measurement terminal voltage, each second model terminal voltage of the battery under the corresponding second operating condition index is determined, and the second voltage error of each second model terminal voltage is determined; further based on the second voltage error of each second model terminal voltage, the reference voltage error range is determined. In this way, the reference voltage error range can be quickly and accurately found through the target test process, thereby improving the accuracy and efficiency of judging whether the estimated SOC is reliable based on the reference voltage error range.

[0020] The present application provides a state of charge calibration device, which includes: An acquisition module, used to acquire the first measurement terminal voltage corresponding to each of the battery at multiple sampling time points, and the first model terminal voltage corresponding to each sampling time point; the first model terminal voltage corresponding to each sampling time point is determined based on the estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery; A determination module, configured to determine a first voltage error corresponding to a sampling time point based on a first measurement terminal voltage corresponding to the sampling time point and a corresponding first model terminal voltage; The calibration module is used to calibrate the estimated state of charge of the battery when the first voltage errors corresponding to multiple sampling time points show a monotonically changing trend.

[0021] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. The computer program can be executed by a processor to implement the above-mentioned state of charge calibration method.

[0022] An embodiment of the present application provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements some or all of the steps in the above-mentioned state of charge calibration method.

[0023] An embodiment of the present application provides a battery system, which includes: at least one battery and a battery management system; the battery management system is used to implement some or all of the steps in the above-mentioned state of charge calibration method.

[0024] An embodiment of the present application provides an electrical device, which includes the above-mentioned battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the implementation process of a charge state calibration method provided in an embodiment of the present application Figure 1 ; Figure 2 A schematic diagram of the structure of an equivalent circuit model provided in an embodiment of the present application; Figure 3 A schematic diagram of the implementation process of a charge state calibration method provided in an embodiment of the present application Figure 2 ; Figure 4 A schematic diagram showing the effect of a method for calibrating a state of charge provided in an embodiment of the present application; Figure 5 A schematic diagram of the composition structure of a charge state calibration device provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of a battery system provided in an embodiment of the present application; Figure 7 A schematic diagram of the composition structure of an electrical device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] It should be noted that, in the absence of conflict, the embodiments and technical features in the embodiments of the present application can be combined with each other, and the detailed description in the specific implementation method should be understood as an explanation of the purpose of the present application and should not be regarded as an improper limitation on the present application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in this application are intended to cover non-exclusive inclusions.

[0028] In the description of the embodiments of the present application, the technical terms "first", "second", "third", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0031] The present application embodiment provides a method for calibrating the state of charge, such as Figure 1 As shown, the calibration method may include the following steps S101 to S103: Step S101, obtaining the first measurement terminal voltage corresponding to the battery at multiple sampling time points, and the first model terminal voltage corresponding to each sampling time point; the first model terminal voltage corresponding to each sampling time point is determined based on the estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery.

[0032] Here, the type of battery may include but is not limited to at least one of a lithium-ion battery, a zinc-manganese battery, a lead-acid battery, and the like.

[0033] The first measurement terminal voltage may be an actual terminal voltage obtained by measuring the battery.

[0034] In some embodiments, the first measurement terminal voltage of the battery and the first model terminal voltage corresponding to the first measurement terminal voltage can be obtained in real time; the first measurement terminal voltage of the battery and the first model terminal voltage corresponding to the first measurement terminal voltage can also be obtained periodically based on a target time interval.

[0035] In some embodiments, the time intervals between the multiple sampling time points may be the same or different.

[0036] Here, the equivalent circuit model of the battery can be used as an open-loop model of the battery to simulate the dynamic characteristics of the battery and reflect the voltage change characteristics of the battery during the charging and discharging process, such as ohmic voltage drop, electrochemical polarization, and concentration polarization. In implementation, the equivalent circuit model of the battery can be any suitable open-loop model, which is not limited in the embodiments of the present application.

[0037] In some embodiments, the equivalent circuit model of the battery may include, but is not limited to, at least one of a first-order resistance-capacitance (RC) equivalent circuit model, a second-order RC equivalent circuit model, and the like.

[0038] In some embodiments, the first model terminal voltage may be a battery terminal voltage estimated based on model parameters of an equivalent circuit model and an estimated state of charge of the battery; wherein the model parameters may include but are not limited to at least one of the open circuit voltage of the battery, the equivalent internal resistance of the battery, the polarization resistance, the voltage of the polarization resistance, etc.

[0039] In some embodiments, based on the estimated state of charge of the battery, a first correspondence between the open circuit voltage and the state of charge of the battery can be queried to obtain the open circuit voltage corresponding to the estimated state of charge; after establishing an equivalent circuit model of the battery, the open circuit voltage corresponding to the estimated state of charge is input into the equivalent circuit model to obtain the first model terminal voltage.

[0040] For example, Figure 2 As shown, taking the first-order RC equivalent circuit model 10 as an example, the equivalent circuit model 10 includes a battery equivalent internal resistance 11, a polarization resistor 12, a polarization capacitor 13 corresponding to the polarization resistor 12, an equivalent voltage source 14, a battery positive electrode 15 and a battery negative electrode 16.

[0041] According to Kirchhoff's law, the input-output relationship of the equivalent circuit model 10 can be seen from formulas (1) to (2): (1); (2); in, U tis the first model terminal voltage corresponding to the equivalent circuit model 10 between the battery positive electrode 15 and the battery negative electrode 16, U OC is the open circuit voltage of the equivalent voltage source 14, that is, the open circuit voltage of the battery, R 0 is the internal resistance value of the battery equivalent internal resistance 11, U D1 is the voltage value of the polarization resistor 12, R D1 is the resistance value of the polarization resistor 12, C D1 is the capacitance value of the polarized capacitor 13, i L is the current value. U OC The state of charge may be determined based on the estimated state of charge of the battery and the corresponding relationship between the estimated state of charge and the open circuit voltage.

[0042] It can be understood that based on formula (1), when there is no deviation in SOC, U OC Without error, the voltage at the first model terminal of the equivalent circuit model 10 is U t The error comes from U D1 , and / or i L R 0 error, where U D1 The error and i L R The error of 0 is usually related to operating conditions such as current ratio and / or temperature.

[0043] In some implementations, the model parameters of the equivalent circuit model may be identified offline by using a target optimization algorithm through test experiments.

[0044] For example, the least square method can be used to analyze the Figure 2 The model parameters of the equivalent circuit model 10 in U OC , R 0. R D1 as well as U D1 The fitting is performed to complete the off-line identification process of the model parameters of the equivalent circuit model 10 .

[0045] The estimated state of charge of the battery can be estimated based on the target parameters of the battery at the sampling time point; wherein the target parameters may include but are not limited to at least one of the open circuit voltage of the battery, the current of the battery, the discharge capacity of the battery, the rated capacity of the battery, etc.

[0046] For example, the open circuit voltage of the battery at the sampling time point can be measured, and the estimated state of charge of the battery can be estimated by the open circuit voltage method.

[0047] For another example, the current of the battery at the sampling time point and the historical sampling time points before the sampling time point can be measured, and the estimated state of charge of the battery can be estimated by the ampere-hour integration method.

[0048] In some embodiments, the estimated state of charge of the battery may be estimated by a management component of the battery, for example, by a battery management system (BMS).

[0049] Step S102: determining a first voltage error corresponding to the sampling time point based on a first measurement terminal voltage corresponding to the sampling time point and a corresponding first model terminal voltage.

[0050] Here, the first voltage error may represent an error between a battery terminal voltage calculated by an equivalent circuit model and a battery terminal voltage actually measured.

[0051] In some implementations, the first voltage error may be determined based on a difference between the first model terminal voltage and the first measurement terminal voltage.

[0052] For example, the first voltage error may be a difference between the first model terminal voltage and the first measurement terminal voltage.

[0053] For another example, the first voltage error may be an average value of differences between the first model terminal voltage and the corresponding first measurement terminal voltage at multiple moments within the target time period.

[0054] For another example, the first voltage error may be a difference between an average model terminal voltage obtained by averaging first model terminal voltages at multiple moments within the target duration and an average measurement terminal voltage obtained by averaging first measurement terminal voltages at corresponding multiple moments.

[0055] Exemplarily, determining the first voltage error E The process can be seen in formula (3): (3); in, V t1 is the voltage at the first model terminal, V t2is the voltage at the first measuring terminal.

[0056] Step S103: When the first voltage errors corresponding to the plurality of sampling time points show a monotonically changing trend, calibrate the estimated state of charge of the battery.

[0057] Here, the monotonically changing trend may include a monotonically increasing trend or a monotonically decreasing trend.

[0058] For the relationship curve between OCV and SOC, when OCV is in the platform area, the slope of the curve is small, that is, the rate of change of OCV with SOC is small; when OCV is in the non-platform area, the slope of the curve is large, that is, the rate of change of OCV with SOC is large. Therefore, when the OCV corresponding to the SOC enters the platform area from the non-platform area, or enters the platform area from the non-platform area, if there is a deviation between the estimated SOC and the actual SOC, at this time, the first voltage error will show a monotonically increasing or monotonically decreasing trend.

[0059] For example, during the discharge process of the battery, if the estimated SOC is falsely high, the OCV corresponding to the actual SOC has entered the non-platform area (for example, the actual SOC is less than 30%), and the first measurement terminal voltages corresponding to multiple sampling time points will decrease as the actual SOC decreases, and show a faster downward trend; while the OCV corresponding to the estimated SOC is still in the platform area (for example, the estimated SOC is between 30% and 60%), the first model terminal voltages corresponding to multiple sampling time points do not change much as the estimated SOC decreases, and show a relatively stable trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measurement terminal voltage, the first voltage error tends to increase.

[0060] For another example, during the charging process of the battery, if the estimated SOC is too low and the OCV corresponding to the actual SOC has entered the non-platform area (e.g., the actual SOC is greater than 60%), the first measurement terminal voltages corresponding to multiple sampling time points will increase with the increase of the actual SOC, and show a faster rising trend; while the OCV corresponding to the estimated SOC is still in the platform area (e.g., the estimated SOC is between 30% and 60%), the first model terminal voltages corresponding to multiple sampling time points do not change much with the increase of the estimated SOC, and show a relatively stable trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measurement terminal voltage, the first voltage error shows a decreasing trend.

[0061] For example, during the discharge process of the battery, if the estimated SOC is falsely low, the OCV corresponding to the actual SOC has entered the platform area (for example, the actual SOC is greater than 30% and less than 60%), and the first measurement terminal voltage corresponding to multiple sampling time points does not change much as the actual SOC decreases, showing a relatively stable trend; while the OCV corresponding to the estimated SOC is still in the non-platform area (for example, the estimated SOC is greater than 60%), the first model terminal voltage corresponding to multiple sampling time points will decrease as the estimated SOC decreases, and show a faster downward trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measurement terminal voltage, the first voltage error tends to decrease.

[0062] For another example, during the charging process of the battery, if the estimated SOC is falsely high, the OCV corresponding to the actual SOC is in the platform area (for example, the actual SOC is greater than 30% and less than 60%), and the first measurement terminal voltage corresponding to multiple sampling time points does not change much with the increase of the actual SOC, and shows a relatively stable trend; while the OCV corresponding to the estimated SOC has entered the non-platform area (for example, the estimated SOC is greater than 60%), the first model terminal voltage corresponding to multiple sampling time points will increase with the increase of the estimated SOC, and show a faster rising trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measurement terminal voltage, the first voltage error tends to increase.

[0063] It can be understood that when the first voltage error corresponding to multiple sampling time points shows a monotonic change trend, it indicates that the estimated SOC and the actual SOC are not in the platform area or non-platform area of ​​​​the OCV at the same time, that is, there is a deviation between the estimated SOC and the actual SOC, and calibration is required.

[0064] In some implementations, a plurality of sampling time points may correspond to a plurality of state of charge intervals, and when the plurality of state of charge intervals present a monotonically changing trend, the estimated state of charge of the battery is calibrated.

[0065] For example, when multiple continuous state-of-charge intervals show a monotonically increasing or decreasing trend, it can be determined that there is a deviation between the estimated SOC corresponding to the continuous state-of-charge interval and the actual SOC, and the estimated SOC is calibrated. Among the multiple continuous state-of-charge intervals, the interval range of each state-of-charge interval can be different.

[0066] For example, the estimated SOC may be calibrated when the first voltage errors corresponding to the first interval of the estimated SOC from 27% to 25%, the second interval of the estimated SOC from 24% to 18%, and the third interval of the estimated SOC from 17% to 10% respectively show an increasing trend.

[0067] For example, when the estimated SOC is in the range of 80% to 90%, the first voltage error corresponding to each 2% SOC interval segment can be determined, and when the first voltage error of each SOC interval segment shows a monotonically changing trend, the estimated SOC can be calibrated.

[0068] In an embodiment of the present application, firstly, the first measurement terminal voltage corresponding to the battery at multiple sampling time points and the first model terminal voltage corresponding to each sampling time point are obtained; wherein, the first model terminal voltage corresponding to each sampling time point is determined based on the estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery; then, based on the first measurement terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage, the first voltage error corresponding to the sampling time point is determined; when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, the estimated state of charge of the battery is calibrated. In this way, on the one hand, since there is a specific corresponding relationship between the OCV and SOC of the battery, by introducing the equivalent circuit model of the battery, the voltage error between the battery terminal voltage calculated by the model and the actually measured battery terminal voltage can be used to reflect the deviation of the SOC; on the other hand, since the rate of change of the battery's OCV with SOC will be different in different SOC ranges, and when there is a deviation between the battery's estimated state of charge and the actual SOC, the battery's estimated state of charge and the actual SOC may be in two SOC ranges with different rates of change of OCV with SOC, respectively. In this way, there will be a trend that the deviation between the battery's estimated state of charge and the actual SOC changes monotonically over time. Therefore, by determining the changing trend of the first voltage error corresponding to multiple sampling time points, it is possible to more accurately judge whether there is a deviation between the battery's estimated state of charge and the actual SOC, thereby more accurately identifying whether the battery's estimated state of charge needs to be calibrated. In this way, when the first voltage error corresponding to multiple sampling time points shows a monotonic change trend, by calibrating the current state of charge of the battery, the chance of SOC correction can be increased and the SOC correction effect can be improved, thereby reducing the problem of battery overvoltage or undervoltage due to SOC deviation and further improving battery performance.

[0069] In some embodiments, the estimated state of charge of the battery is determined using a target estimation model, and the calibration of the estimated state of charge of the battery in step S103 may include the following steps S201 to S202: Step S201: adjust the model parameters of the target estimation model to obtain the target estimation model after parameter adjustment.

[0070] Here, the target estimation model after parameter adjustment is used to reduce the deviation between the estimated SOC and the actual SOC.

[0071] In some embodiments, the method of estimating the state of charge is different and the target estimation model may be different.

[0072] For example, the target estimation model may include an open circuit voltage model that estimates the state of charge according to an open circuit voltage method.

[0073] As another example, the target estimation model may include a current integration model that estimates the state of charge according to a current integration method.

[0074] For example, the target estimation model may include a Kalman filter model that estimates the state of charge based on the Kalman filter method.

[0075] In some implementations, the target estimation model may include at least one of a neural network model, a recursive model, a convergence model, and the like.

[0076] The model parameters of the target estimation model may include, but are not limited to, at least one of coefficients, weights, number of iterations, and the like.

[0077] In some implementations, the model parameters of the target estimation model may be adjusted based on the model parameters obtained in a pre-test, or may be adjusted based on empirical values, which is not limited in the embodiments of the present application.

[0078] Step S202: using the target estimation model after parameter adjustment, estimate the state of charge of the battery to obtain a calibrated estimated state of charge.

[0079] Here, the state of charge of the battery is estimated by using the target estimation model after parameter adjustment, so that the deviation between the estimated state of charge and the actual state of charge can be reduced.

[0080] In some implementations, the deviation between the estimated state of charge and the actual state of charge may be reduced by causing the estimated state of charge to converge toward the actual state of charge.

[0081] In the embodiment of the present application, the model parameters of the target estimation model for determining the estimated state of charge of the battery are adjusted to obtain the target estimation model after parameter adjustment; and the target estimation model after parameter adjustment is used to estimate the state of charge of the battery to obtain the calibrated estimated state of charge. In this way, by adjusting the model parameters of the target estimation model for estimating the state of charge of the battery, the estimated state of charge can be calibrated during the process of estimating the state of charge of the battery, thereby improving the accuracy of the estimated state of charge output by the target estimation model.

[0082] In some embodiments, the target estimation model is used to perform weighted processing on the state prediction quantity and the state observation quantity of the battery to obtain the estimated state of charge of the battery, and the model parameters include a first weight coefficient of the state prediction quantity and a second weight coefficient of the state observation quantity. The adjustment of the model parameters of the target estimation model in the above step S201 may include at least one of the following steps S301 to S302: Step S301: Reduce the first weight coefficient.

[0083] Here, the state prediction variable may be a variable obtained by predicting the battery state, for example, the battery state may include SOC, and the state observation variable may be an estimated SOC obtained by predicting the SOC without weighting processing.

[0084] The state observable may be a variable obtained by observing the battery. For example, the state observable may include voltage.

[0085] Since the first weight coefficient is the weight coefficient of the state prediction quantity, when the first weight coefficient is reduced, the influence of the state prediction quantity on the estimated state of charge can be reduced, that is, the estimated state of charge can be made closer to the actual state of charge corresponding to the state observation quantity.

[0086] Taking the Kalman filter model as an example, the Kalman filter algorithm is a state estimation algorithm that can be used to estimate the state of charge of the battery. The Kalman filter algorithm is a recursive estimation algorithm, and its core idea is to combine the predicted value and the observed value of the battery to obtain a more accurate state estimation value; wherein the predicted value, the observed value and the state estimation value can correspond to the state prediction value, the state observation value and the estimated state of charge in the embodiment of the present application, respectively.

[0087] The Kalman filter algorithm needs to determine the battery state equation and observation equation, as well as the initial state and variance matrix of the battery during initialization. In the prediction step, the state and variance matrix of the next moment are predicted based on the battery state equation and control variables (such as current, temperature, etc.); in the update step, the state and variance matrix of the system are updated based on the observed values ​​and predicted values. By repeating the two steps of prediction and update, the Kalman filter algorithm can update the state estimate in real time and handle systems with noise.

[0088] In some embodiments, the method for estimating the SOC by measuring the current and voltage of the battery using the Kalman filter model may include the following steps S3011 to S3014: Step S3011, determine the state transfer equation and observation equation.

[0089] Among them, the battery state transfer equation can correspond to the above-mentioned battery state equation, which is used to describe the change law of battery state (such as SOC, internal resistance, etc.) over time, and the observation equation is used to describe the relationship between the observed value (such as voltage) and the battery state.

[0090] For example, the state transition equation of the battery can be referred to in formula (4): (4); in, k Can correspond to the next moment, k It can correspond to the current moment. x k is a state variable (such as SOC), u k To control the amount, ω k is the process noise, A and B is the coefficient matrix.

[0091] For example, the observation equation of the battery can be referred to as formula (5): (5); in, y k is the observed output (such as OCV), v k To measure noise, C is the coefficient matrix.

[0092] Step S3012, initialization.

[0093] During the initialization process, the initial SOC and initial variance matrix of the battery need to be set. The initial SOC can be determined by methods such as the open circuit voltage method.

[0094] Step S3013: prediction.

[0095] Among them, the battery state (such as SOC) at the next moment can be predicted based on the battery state equation and control quantities (such as current and temperature).

[0096] For example, the battery status The prediction process can be seen in formula (6): (6); A priori estimate of error covariance P k The process can be seen in formula (7): (7); in, is the a priori estimated error covariance,Q is the process noise covariance matrix, A T For the matrix A The transposed matrix of .

[0097] Step S3014, update.

[0098] The observed and predicted values ​​of the voltage at the current moment can be used to update the battery's SOC estimate and variance matrix. In this process, the Kalman gain matrix can be used to adjust the weights of the predicted and observed values ​​to obtain a more accurate SOC estimate.

[0099] For example, the Kalman gain matrix K The determination process of can be seen in formula (8): (8); Among them, the coefficient matrix C can be used as the state observation matrix, and , C T For the matrix C The transposed matrix of G is the measurement noise covariance matrix; Based on the Kalman gain matrix K Update battery status The process can be seen in formula (9): (9); Based on the Kalman gain matrix K Update error covariance P k The process can be seen in formula (10): (10); in, I is the identity matrix, P k is the error covariance of the posterior estimate.

[0100] For example, It can correspond to the state prediction quantity in the above charge state calibration method at the next moment. Based on formula (9), it can be determined that 1- KC It can correspond to the first weight coefficient in the above-mentioned state of charge calibration method, and the first weight coefficient can be reduced, further The weight of the state prediction is reduced, making the state estimate Closer to the real state.

[0101] In some embodiments, based on formula (8), it can be determined that K Follow CTherefore, by increasing C or K , which can make KC Increase, so that the first weight coefficient 1- KC Reduce.

[0102] Step S302: Increase the second weight coefficient.

[0103] Here, since the second weight coefficient is the weight coefficient of the state observation quantity, when the first weight coefficient is increased, the influence of the state observation quantity on the estimated state of charge can be increased, that is, the estimated state of charge can be made closer to the actual state of charge corresponding to the state observation quantity.

[0104] For example, in the process of estimating SOC by the above Kalman filter model, it can be determined based on formula (9): K It can correspond to the second weight coefficient in the above-mentioned state of charge calibration method, by increasing K , which can increase the second weight coefficient and further make y k The weight of the observation increases, making the state estimate It is closer to the actual state corresponding to the observed value.

[0105] In some embodiments, based on formula (8), it can be determined that K Follow C Therefore, by increasing C , the second weight coefficient can be K Increase.

[0106] In some embodiments, by increasing C It is used to describe the rate of change of OCV with the change of SOC ,make C Increase.

[0107] It can be understood that by reducing the first weight coefficient and increasing the second weight coefficient at the same time, the influence of the state prediction quantity on the estimated state of charge can be reduced while further improving the influence of the state observation quantity on the estimated state of charge, making the estimated state of charge closer to the actual state of charge corresponding to the state observation quantity.

[0108] In the embodiment of the present application, by reducing the first weight coefficient of the state prediction quantity used to determine the estimated state of charge in the target estimation model, and / or increasing the second weight coefficient of the state observation quantity, the estimated state of charge can be made closer to the actual state of charge corresponding to the state observation quantity, thereby improving the accuracy of battery SOC calibration.

[0109] In some embodiments, the above step S103 may include the following steps S401 to S402: Step S401: Obtain the current estimated state of charge of the battery.

[0110] Step S402: When the current estimated state of charge of the battery is within a preset state of charge range, calibrate the current estimated state of charge of the battery; the rate of change of the open circuit voltage of the battery with the state of charge within the preset state of charge range is greater than a rate of change threshold.

[0111] Here, the rate of change of the open circuit voltage with the state of charge exceeds the rate of change threshold, which can indicate that the OCV of the battery changes significantly with the change of SOC. Considering that the first model terminal voltage is determined based on the state of charge of the battery, when the OCV does not change significantly with the change of SOC, the accuracy of the first model terminal voltage determined based on the OCV will be reduced, and the accuracy of judging whether to calibrate the current state of charge based on the first voltage error corresponding to the first model terminal voltage will be further reduced. Therefore, when the current estimated state of charge of the battery is within the preset state of charge range, the current estimated state of charge of the battery is calibrated.

[0112] Exemplarily, when the rate of change of the open circuit voltage of the battery with the state of charge exceeds a change rate threshold, the preset state of charge interval may correspond to at least one of the state of charge intervals such as SOC less than 30%, SOC greater than 60% and less than 70%, and SOC greater than 95%.

[0113] In the embodiment of the present application, when the rate of change of the open circuit voltage corresponding to the current estimated state of charge of the battery with the state of charge is greater than the rate of change threshold, the current estimated state of charge of the battery is calibrated. In this way, considering that the rate of change of the open circuit voltage of the battery with the state of charge exceeds the rate of change threshold, the change of the open circuit voltage with the state of charge is usually more obvious (for example, the OCV corresponding to the state of charge of the battery is in the non-platform area), in this case, the change trend of the error between the model terminal voltage and the measurement terminal voltage of the battery will be more obvious, so that the monotonic change trend of the first voltage error is easier to be accurately identified. Therefore, when the rate of change of the open circuit voltage corresponding to the current estimated state of charge of the battery with the state of charge is greater than the rate of change threshold, the SOC is calibrated, which can reduce the situation of miscalibration caused by inaccurate identification of the change trend of the first voltage error, and further improve the accuracy and reference of the calibration result.

[0114] In some embodiments, the above-mentioned state of charge calibration method may further include the following step S501: Step S501: for each sampling time point, using an equivalent circuit model of the battery, based on an estimated state of charge of the battery at the sampling time point and a first operating condition indicator of the battery collected at the sampling time point, determine a first model terminal voltage corresponding to the sampling time point.

[0115] Here, the first operating condition indicator can be used to characterize the working environment and / or working state of the battery at the first measuring terminal voltage, and can be an operating condition indicator collected when measuring the first measuring terminal voltage; wherein the operating condition indicator may include but is not limited to at least one of current, current rate, temperature, humidity, etc.

[0116] In some embodiments, the first measurement terminal voltage of the battery and the first operating condition indicator corresponding to the first measurement terminal voltage can be obtained during the use of the battery; the first measurement terminal voltage of the battery and the first operating condition indicator corresponding to the first measurement terminal voltage can also be obtained when the battery is in a non-use state (for example, a static state).

[0117] In some embodiments, the first measurement terminal voltage of the battery and the first operating condition indicator corresponding to the first measurement terminal voltage can be obtained in real time; the first measurement terminal voltage of the battery and the first operating condition indicator corresponding to the first measurement terminal voltage can also be obtained periodically based on a target time interval.

[0118] The first model terminal voltage may be a battery terminal voltage estimated based on model parameters of the equivalent circuit model and an estimated state of charge of the battery under a first operating condition indicator.

[0119] In some implementations, for each sampling point, a first model terminal voltage corresponding to the battery under the first operating condition indicator may be determined based on an equivalent circuit model of the battery and an estimated state of charge of the battery.

[0120] It can be understood that since the first measurement terminal voltage and the corresponding first model terminal voltage are both in the first operating condition, the first voltage error determined based on the first measurement terminal voltage and the first model terminal voltage can characterize the error between the battery terminal voltage calculated by the equivalent circuit model and the actual measured battery terminal voltage under the first operating condition indicator.

[0121] In some implementations, a first voltage error under a first operating condition indicator corresponding to each sampling time point may be determined based on a first measurement terminal voltage and a first model terminal voltage corresponding to each sampling time point.

[0122] In the embodiment of the present application, for each sampling time point, the equivalent circuit model of the battery is used, based on the estimated state of charge of the battery at the sampling time point, and the first operating condition index of the battery collected at the sampling time point, to determine the first model terminal voltage corresponding to the sampling time point. In this way, by combining the estimated state of charge and the operating condition index of the battery, a more accurate first model terminal voltage can be determined, so that a more accurate first voltage error can be obtained, and then the situation where the estimated SOC needs to be calibrated can be more accurately identified.

[0123] In some embodiments, the above-mentioned state of charge calibration method may further include the following step S601: Step S601: calibrate the estimated state of charge of the battery when a first voltage error corresponding to at least one sampling time point exceeds a reference voltage error range; The reference voltage error range represents the error range of the model terminal voltage determined based on the equivalent circuit model when the estimated state of charge of the battery meets the target credibility condition.

[0124] Here, the target credible condition may be a condition indicating that the estimated state of charge of the battery is within a credible interval.

[0125] In some implementations, the estimated state of charge may be determined to satisfy the target trustworthy condition when the absolute value of the difference between the estimated state of charge of the battery and the corresponding actual state of charge is less than an absolute value threshold.

[0126] For example, when the estimated state of charge of the battery is 28% and the actual state of charge is 25%, the absolute value of the difference between the estimated state of charge and the corresponding actual state of charge is 3%, which is less than the absolute value threshold of 5%, and the estimated state of charge meets the target trust condition.

[0127] When the estimated state of charge of the battery meets the target credibility condition, it can be characterized that the estimated state of charge is credible, and further, the model terminal voltage corresponding to the equivalent circuit model determined based on the estimated state of charge is credible.

[0128] Therefore, when the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range, it can be determined that the estimated state of charge corresponding to the sampling time point is unreliable and needs to be calibrated.

[0129] For example, when the OCV corresponding to any one of the actual SOC and the estimated SOC is in the platform area, the other corresponding OCV is in the non-platform area, and the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range, it can be determined that the estimated state of charge corresponding to the sampling time point is unreliable and needs to be calibrated.

[0130] For another example, when the OCV corresponding to any one of the actual SOC and the estimated SOC is in the non-platform area, the other corresponding OCV is in the platform area, and the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range, it can be determined that the estimated state of charge corresponding to the sampling time point is unreliable and needs to be calibrated.

[0131] For example, when the OCV corresponding to the actual SOC and the estimated SOC are both in the non-platform area, and the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range, it can be determined that the estimated state of charge corresponding to the sampling time point is unreliable and needs to be calibrated.

[0132] It can be understood that since the OCV changes slowly with the SOC when it is in the platform area, when the OCV corresponding to the actual SOC and the estimated SOC are both in the platform area, the first voltage error is small and it is difficult to exceed the reference voltage error range. Therefore, when the OCV corresponding to the actual SOC and the estimated SOC are both in the platform area, there is no need to calibrate the estimated SOC.

[0133] Exemplarily, the reference voltage error range may include but is not limited to [-30mV, 30mV], [-25mV, 25mV], [-20mV, 20mV], etc.

[0134] In the embodiment of the present application, when the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range, the estimated state of charge of the battery is calibrated. In this way, since the reference voltage error range represents the voltage error range of the model terminal voltage when the estimated state of charge of the battery meets the target trustworthy condition, by judging whether the first voltage error corresponding to the estimated SOC exceeds the reference voltage error range, it is possible to judge whether the estimated SOC meets the target trustworthy condition, and thus it is possible to more accurately identify the situation where the estimated SOC needs to be calibrated.

[0135] In some embodiments, the above-mentioned state of charge calibration method may further include the following steps S701 to S704: Step S701, obtaining at least one second measurement terminal voltage of the battery during a target test, and a second operating condition indicator and a measured state of charge corresponding to each second measurement terminal voltage.

[0136] Here, the target test process may include a usage test process of the electric device.

[0137] In some embodiments, the operation data of the electrical equipment in different scenarios can be collected by performing usage tests on the electrical equipment in advance; wherein the operation data may include but is not limited to current, current rate, voltage, temperature, SOC, etc.

[0138] In some embodiments, during the target test process, the battery may be in a used state or in a non-used state.

[0139] The second operating condition indicator may be used to characterize the operating environment and / or operating state of the battery at the second measuring terminal voltage, and may be an operating condition indicator acquired when measuring the second measuring terminal voltage.

[0140] The measured state of charge may be the state of charge of the battery acquired when measuring the voltage of the second measuring terminal.

[0141] Step S702: for each second measurement terminal voltage, based on the equivalent circuit model and the corresponding measured state of charge, determine the second model terminal voltage of the battery under the corresponding second operating condition indicator.

[0142] Here, the second model terminal voltage may be a battery terminal voltage estimated based on model parameters of the equivalent circuit model and the state of charge of the battery under the second operating condition indicator.

[0143] Step S703: Determine a second voltage error of each second model terminal voltage based on each second measurement terminal voltage and the corresponding second model terminal voltage.

[0144] Exemplarily, the first voltage error E’ The process can be seen in formula (11): (11); in, V t3 is the voltage at the second model terminal, V t4 is the voltage at the second measuring terminal.

[0145] Step S704: determining a reference voltage error range based on the second voltage errors of the second model terminal voltages.

[0146] Here, the second voltage error can be screened to determine the second operating condition indicator corresponding to the second voltage error within the first voltage error range as the candidate operating condition, and the first voltage error range can be determined as the candidate voltage error range corresponding to the candidate operating condition.

[0147] For example, the second operating condition indicators corresponding to the second voltage errors when at least one second voltage error is in the range of [-30mV, 30mV] can be sorted into candidate operating conditions. For example, when the second voltage error is in the range of [-30mV, 30mV], the minimum temperature in each second operating condition indicator is greater than 30 degrees, then when the candidate voltage error range is [-30mV, 30mV], the corresponding candidate operating condition may be that the minimum temperature is greater than 30 degrees.

[0148] In some embodiments, actual vehicle tests can be conducted on vehicles using batteries to collect operating data of the vehicles in different scenarios such as high-speed conditions and urban conditions; based on the operating data corresponding to each scenario, a model terminal voltage determined based on an equivalent circuit model is determined for each scenario; based on the measurement terminal voltage corresponding to each scenario, a voltage error of the model terminal voltage is determined for each scenario; based on the operating data corresponding to each scenario, operating conditions in which the voltage error of the model terminal voltage is within a preset threshold range are determined as candidate operating conditions; the range of the voltage error of the model terminal voltage corresponding to the operating data under the candidate operating conditions is determined as a candidate voltage error range corresponding to the candidate operating condition; and a reference voltage error range is further determined based on the candidate voltage error range.

[0149] In the embodiment of the present application, at least one second measurement terminal voltage collected during the target test process of the battery, and the second operating condition index and the measured charge state corresponding to each second measurement terminal voltage are used; for each second measurement terminal voltage, each second model terminal voltage of the battery under the corresponding second operating condition index is determined, and the second voltage error of each second model terminal voltage is determined; further based on the second voltage error of each second model terminal voltage, the reference voltage error range is determined. In this way, the reference voltage error range can be quickly and accurately found through the target test process, thereby improving the accuracy and efficiency of judging whether the estimated SOC is reliable based on the reference voltage error range.

[0150] In some embodiments, the above step S101 may include the following steps S801 to S802: Step S801: collecting the voltage of the first measuring terminal of the battery corresponding to a plurality of sampling time points within a target state of charge variation interval.

[0151] Step S802: For each sampling time point, obtain a first model terminal voltage corresponding to the first measurement terminal voltage.

[0152] The above step S102 may include the following steps S803 to S805: Step S803 , averaging a plurality of first measurement terminal voltages corresponding to a plurality of sampling time points to obtain an average measurement terminal voltage.

[0153] Step S804 , averaging the plurality of first model terminal voltages corresponding to the plurality of first measurement terminal voltages to obtain an average model terminal voltage.

[0154] Step S805: determine the difference between the average model terminal voltage and the average measurement terminal voltage as a first voltage error.

[0155] Here, the target state of charge change interval may be an interval in which the state of charge of the battery changes during or after use.

[0156] For example, if the SOC of the battery changes from 15% to 24% during or after use, the target state of charge change interval may include the complete range of the SOC change from 15% to 24%, that is, a 9% change interval.

[0157] For another example, if the SOC of the battery changes from 15% to 24% during or after use, the target state of charge change interval may include every 3% change interval in the process of the SOC changing from 15% to 24%, which may include but is not limited to at least one of the interval segments of 15% to 18%, 16% to 19%, 18% to 21%, 21% to 24%, etc.

[0158] In some implementations, the voltage at the first measurement terminal may be sampled multiple times at sampling time points with the same or different time intervals.

[0159] It can be understood that the “ V t1 – V t2 " can be replaced by the first voltage error obtained in step S805 based on the difference between the average model terminal voltage and the average measurement terminal voltage.

[0160] In the embodiment of the present application, the first measurement terminal voltages corresponding to different moments and the first measurement terminal voltages corresponding to each first measurement terminal voltage are collected multiple times at multiple sampling time points within the target state of charge change interval; and the multiple first measurement terminal voltages collected within the target state of charge change interval and the first model terminal voltages corresponding to the multiple first measurement terminal voltages are averaged; the difference between the obtained average model terminal voltage and the average measurement terminal voltage is determined as the first voltage error. In this way, based on the average values ​​of the first measurement terminal voltage and the first model terminal voltage within the target state of charge change interval, the influence of abnormal data on the determination of the first voltage error can be reduced, and the accuracy and reference of the first voltage error can be improved.

[0161] As the battery is used, the SOC deviation will gradually accumulate. If the long-term accumulated SOC deviation is not corrected, it will cause the battery to have unexpected phenomena such as overvoltage or undervoltage.

[0162] In the related technologies, battery SOC estimation technology covers a variety of methods such as open circuit voltage method, ampere-hour integration method, internal resistance measurement method, Kalman filter method and neural network algorithm. Although these methods have their own applications in the field of SOC estimation, the Kalman filter algorithm stands out for its wide applicability. The Kalman filter algorithm achieves the optimal estimation of the output by minimizing the covariance between the estimated value and the observed value.

[0163] Lithium iron phosphate (LFP) batteries have become an ideal power and energy source for electric vehicles due to their long life, good safety performance and low cost. However, LFP batteries have the problem that the change with the state of charge is not obvious when the open circuit voltage is in the platform area. It is difficult to accurately estimate the state of charge when the open circuit voltage is in the platform area. The current Kalman filter algorithm cannot correct the SOC in the voltage platform period or may make an incorrect correction, affecting the accuracy of correcting the battery state of charge.

[0164] On this basis, the embodiment of the present application provides a method for calibrating the state of charge, and takes the scenario of calibrating the state of charge of a lithium iron phosphate battery as an example to illustrate the method for calibrating the state of charge provided by the embodiment of the present application. Figure 3 As shown, the state of charge calibration method includes the following steps S1001 to S1006: Step S1001, establishing a model for a lithium iron phosphate battery.

[0165] Here, an open-loop model is established for lithium iron phosphate batteries.

[0166] In implementation, the model may include a first order RC equivalent circuit model.

[0167] Step S1002: Through OCV test experiments and pulse current test experiments, an optimization algorithm is used to complete the offline parameter identification process.

[0168] Here, the parameters for completing the offline identification process may correspond to the model parameters of the equivalent circuit model in the above-mentioned state of charge calibration method.

[0169] Step S1003: Determine a target estimation model based on a Kalman filter algorithm.

[0170] Here, the method of estimating SOC using the target estimation model can refer to the above steps S3011 to S3014.

[0171] Step S1004: determine the reference voltage error range.

[0172] In some embodiments, candidate operating conditions in which the second voltage error is within a candidate voltage error range can be screened out by trying multiple operating condition combinations; and a reference voltage error range is determined from the candidate voltage error range based on the first operating condition indicator of the battery collected at the sampling time point.

[0173] For example, the battery current I can be collected in high-speed conditions, urban conditions, and other scenarios through actual vehicle testing of a car using the battery. t0 to I tn , voltage U t0 to U tn, temperature T0 to T n and SOC0 to SOC n and other data; taking the current, temperature, SOC and other parameters as the input of the model in step S1001, and taking the calculated model terminal voltage as the output, the calculation process can refer to formula (1), wherein the calculated model terminal voltage can correspond to the second model terminal voltage in the above-mentioned state of charge calibration method; calculating the second voltage error within the 3% SOC change interval for each 3% SOC change interval, wherein the second voltage error is determined based on the average value of the second measurement terminal voltage collected at multiple times and the difference between the average values ​​of the second model terminal voltages corresponding to each second measurement terminal voltage; and statistically calculating the second operating condition index within the 3% SOC change interval Indicators, such as the average current multiplier of the 3% SOC change segment, the minimum temperature of the 3% SOC change segment, etc.; by combining multiple second operating condition indicators within [-30mV, 30mV] whose second voltage error is within [-30mV, 30mV], candidate operating conditions are screened out, such as the average current multiplier is less than 0.5C and the minimum temperature is greater than 30 degrees, and [-30mV, 30mV] is used as the candidate voltage error range corresponding to the candidate operating condition; further, when the first operating condition indicator corresponding to the first voltage error is that the average current multiplier is less than 0.5C and the minimum temperature is greater than 30 degrees, [-30mV, 30mV] is used as the reference voltage error range.

[0174] Step S1005: during the operation of the battery, determine the first voltage errors corresponding to the plurality of sampling time points respectively; and when the first voltage error satisfies the target error condition, adjust the model parameters of the target estimation model.

[0175] Here, the first voltage error meeting the target error condition may include but is not limited to at least one of the following: a plurality of consecutive first voltage errors showing a monotonic change trend, the first voltage error exceeding a reference voltage error range, etc.

[0176] Exemplarily, during the operation of the battery, starting from the estimated SOC of n%, the average measurement terminal voltage and the average model terminal voltage in each z% (such as 2%, 3%) SOC change interval are calculated to obtain the first voltage error in each z% SOC change interval, and the average current multiplier and the minimum temperature corresponding to each z% SOC change interval are determined; When the average current multiplier corresponding to each z% SOC variation interval is less than 0.5C and the minimum temperature is greater than 30 degrees, it can be determined that the first voltage error in the z% SOC variation interval should be within [-30mV, 30mV], that is, within the reference voltage error range.

[0177] When the first voltage error corresponding to h consecutive z%SOC variation intervals continues to increase or decrease, and / or is greater than 30 mV or less than -30 mV, the state observation matrix C in the Kalman filter algorithm is adjusted.

[0178] Step S1006: using the target estimation model after parameter adjustment, estimate the state of charge of the battery to obtain a calibrated estimated state of charge.

[0179] Here, the target estimation model based on the Kalman filter algorithm can converge the estimated SOC towards the actual SOC in each subsequent iteration process after parameter adjustment.

[0180] In some implementations, when it is determined that the estimated SOC needs to be calibrated, The target number is magnified by multiples to increase the state observation matrix C, so that the weight of the state observation quantity increases and the weight of the state prediction quantity decreases, so that the estimated SOC converges to the direction of the real SOC, achieving the purpose of correcting the estimated SOC.

[0181] Exemplarily, the target number times may be a preset value or an empirical value, for example, 10 times, which is not limited to the embodiments of the present application.

[0182] In the embodiment of the present application, during the operation of the battery, when the first voltage error meets the target error condition, the model parameters of the target estimation model based on the Kalman filter algorithm are adjusted, and the current state of charge of the battery is corrected by the target estimation model after the parameter adjustment. In this way, on the one hand, when there is a deviation such as a false high or false low current state of charge of the battery, the state of charge of the battery can be calibrated to reduce the problem of overvoltage or undervoltage of the battery due to SOC deviation, and further improve the equipment performance of the power-consuming equipment supported by the battery; on the other hand, when the SOC is estimated based on the Kalman filter algorithm, a higher convergence effect can be achieved.

[0183] For example, Figure 4 As shown, Figure 4 The horizontal axis in the figure can represent time, and the vertical axis can represent the value corresponding to the SOC; at time t0, the estimated SOC is 80%, and the actual SOC is 70%, and the estimated SOC is 10% higher than the actual SOC; when the estimated SOC is 32%, the actual SOC is already less than 30%, at this time, the OCV corresponding to the estimated SOC is in the platform area, and the OCV corresponding to the actual SOC is in the non-platform area; at time t1, after detecting that the first voltage error continues to increase and exceeds the reference voltage error range, the state observation matrix of the Kalman filter algorithm is adjusted, and the estimated SOC is corrected; it can be seen that from time t2, the estimated SOC and the actual SOC tend to be close.

[0184] The present application embodiment provides a charge state calibration device, such as Figure 5 As shown, the state of charge calibration device 30 includes: An acquisition module 31 is used to acquire a first measurement terminal voltage corresponding to each of the battery at a plurality of sampling time points, and a first model terminal voltage corresponding to each of the sampling time points; the first model terminal voltage corresponding to each of the sampling time points is determined based on an estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery; A determination module 32, configured to determine a first voltage error corresponding to the sampling time point based on a first measurement terminal voltage corresponding to the sampling time point and a corresponding first model terminal voltage; The calibration module 33 is used to calibrate the estimated state of charge of the battery when the first voltage errors corresponding to the plurality of sampling time points show a monotonically changing trend.

[0185] In some embodiments, the estimated state of charge of the battery is determined using a target estimation model, and the calibration module can also be used to: adjust model parameters of the target estimation model to obtain a target estimation model with adjusted parameters; estimate the state of charge of the battery using the target estimation model with adjusted parameters to obtain a calibrated estimated state of charge.

[0186] In some embodiments, the target estimation model is used to perform weighted processing on the state prediction quantity and the state observation quantity of the battery to obtain the estimated state of charge of the battery, and the model parameters include a first weight coefficient of the state prediction quantity and a second weight coefficient of the state observation quantity; the calibration module can also be used for at least one of the following: reducing the first weight coefficient; increasing the second weight coefficient.

[0187] In some embodiments, the acquisition module may also be used to: acquire the current estimated state of charge of the battery; The calibration module can also be used to: calibrate the current estimated state of charge of the battery when the current estimated state of charge of the battery is within a preset state of charge range; the rate of change of the open circuit voltage of the battery in the preset state of charge range with the state of charge is greater than a change rate threshold.

[0188] In some embodiments, the determination module can also be used to: for each of the sampling time points, use the equivalent circuit model of the battery, based on the estimated state of charge of the battery at the sampling time point, and collect the first operating condition indicator of the battery at the sampling time point, to determine the first model terminal voltage corresponding to the sampling time point.

[0189] In some embodiments, the calibration module can also be used to: calibrate the estimated state of charge of the battery when the first voltage error corresponding to at least one of the sampling time points exceeds a reference voltage error range; wherein the reference voltage error range represents the error range of the model terminal voltage determined based on the equivalent circuit model when the estimated state of charge of the battery meets the target trust condition.

[0190] In some embodiments, the acquisition module may also be used to: acquire at least one second measurement terminal voltage of the battery during the target test process, and a second operating condition indicator and a measured state of charge corresponding to each second measurement terminal voltage; The determination module can also be used to: for each second measurement terminal voltage, based on the equivalent circuit model and the corresponding measured state of charge, determine the second model terminal voltage of the battery under the corresponding second operating condition indicator; based on each second measurement terminal voltage and the corresponding second model terminal voltage, determine the second voltage error of each second model terminal voltage; based on the second voltage error of each second model terminal voltage, determine the reference voltage error range.

[0191] In some embodiments, the acquisition module may also be used to: collect the first measurement terminal voltages corresponding to a plurality of sampling time points of the battery within a target state of charge change interval; and for each sampling time point, acquire the first model terminal voltage corresponding to the first measurement terminal voltage; The determination module can also be used to: average the multiple first measurement terminal voltages corresponding to the multiple sampling time points to obtain an average measurement terminal voltage; average the multiple first model terminal voltages corresponding to the multiple first measurement terminal voltages to obtain an average model terminal voltage; and determine the difference between the average model terminal voltage and the average measurement terminal voltage as the first voltage error.

[0192] An embodiment of the present application provides a computer program, including a computer-readable code. When the computer-readable code is executed in a computer device, a processor in the computer device executes part or all of the steps in the above-mentioned state of charge calibration method.

[0193] An embodiment of the present application provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements some or all of the steps in the above-mentioned state of charge calibration method.

[0194] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. The computer program can be executed by a processor to implement the above-mentioned state of charge calibration method.

[0195] The present application embodiment provides a battery system, such as Figure 6 As shown, the battery system 40 includes at least one battery 41 and a battery management system 42. The battery management system 42 is used to implement part or all of the steps in the above-mentioned state of charge calibration method.

[0196] The present application embodiment provides an electrical device, such as Figure 7 As shown, the electrical device 50 includes the battery system 40 in the above-mentioned application embodiment.

[0197] Here, electrical equipment may include but is not limited to automobiles, airplanes, electric bicycles, electric motorcycles, electric motorboats, ships, etc.

[0198] In the description of the present application, the description with reference to the terms "in one embodiment", "in some embodiments", "in other embodiments", "in yet other embodiments", or "exemplary" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of the present application. In the present application, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine the different embodiments or examples described in the present application and the features of the different embodiments or examples without contradiction.

[0199] The above is only an exemplary embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are included in the protection scope of the present application.

Claims

1. A method for calibrating a state of charge, characterized in that: The calibration method comprises: Obtaining first measurement terminal voltages corresponding to the battery at multiple sampling time points, and first model terminal voltages corresponding to each sampling time point; the first model terminal voltage corresponding to each sampling time point is determined based on an estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery; Determine a first voltage error corresponding to the sampling time point based on a first measurement terminal voltage corresponding to the sampling time point and a corresponding first model terminal voltage; When the first voltage errors corresponding to the plurality of sampling time points show a monotonically changing trend, the estimated state of charge of the battery is calibrated.

2. The method for calibrating the state of charge according to claim 1, characterized in that: The estimated state of charge of the battery is determined using a target estimation model; The calibrating the estimated state of charge of the battery includes: Adjusting the model parameters of the target estimation model to obtain the target estimation model after parameter adjustment; The target estimation model after the parameter adjustment is used to estimate the state of charge of the battery to obtain a calibrated estimated state of charge.

3. The method for calibrating the state of charge according to claim 2, characterized in that: The target estimation model is used to perform weighted processing on the state prediction quantity and the state observation quantity of the battery to obtain the estimated state of charge of the battery, and the model parameters include a first weight coefficient of the state prediction quantity and a second weight coefficient of the state observation quantity; The adjusting of the model parameters of the target estimation model includes at least one of the following: reducing the first weight coefficient; Increase the second weight coefficient.

4. The method for calibrating the state of charge according to any one of claims 1 to 3, characterized in that: The calibrating the estimated state of charge of the battery includes: Obtaining a current estimated state of charge of the battery; When the current estimated state of charge of the battery is within a preset state of charge range, the current estimated state of charge of the battery is calibrated; and the rate of change of the open circuit voltage of the battery with the state of charge within the preset state of charge range is greater than a rate of change threshold.

5. The method for calibrating the state of charge according to any one of claims 1 to 3, characterized in that: The method further comprises: For each of the sampling time points, an equivalent circuit model of the battery is used to determine a first model terminal voltage corresponding to the sampling time point based on an estimated state of charge of the battery at the sampling time point and a first operating condition indicator of the battery collected at the sampling time point.

6. The method for calibrating the state of charge according to any one of claims 1 to 3, characterized in that: The method further comprises: When a first voltage error corresponding to at least one of the sampling time points exceeds a reference voltage error range, calibrating the estimated state of charge of the battery; The reference voltage error range represents an error range of the model terminal voltage determined based on the equivalent circuit model when the estimated state of charge of the battery meets a target trust condition.

7. The method for calibrating the state of charge according to claim 6, characterized in that: The method further comprises: Acquire at least one second measurement terminal voltage of the battery during a target test, and a second operating condition indicator and a measured state of charge corresponding to each second measurement terminal voltage; For each second measurement terminal voltage, determining a second model terminal voltage of the battery under a corresponding second operating condition indicator based on the equivalent circuit model and the corresponding measured state of charge; Determine a second voltage error of each second model terminal voltage based on each second measurement terminal voltage and a corresponding second model terminal voltage; The reference voltage error range is determined based on the second voltage errors of the second model terminal voltages.

8. A charge state calibration device, characterized in that: The calibration device comprises: An acquisition module, used to acquire first measurement terminal voltages of the battery corresponding to a plurality of sampling time points, and first model terminal voltages corresponding to each of the sampling time points; the first model terminal voltages corresponding to each of the sampling time points are determined based on an estimated state of charge of the battery at the sampling time point using an equivalent circuit model of the battery; A determination module, configured to determine a first voltage error corresponding to the sampling time point based on a first measurement terminal voltage corresponding to the sampling time point and a corresponding first model terminal voltage; The calibration module is used to calibrate the estimated state of charge of the battery when the first voltage errors corresponding to the plurality of sampling time points show a monotonically changing trend.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

11. A battery system, characterized in that: The invention comprises at least one battery and a battery management system; the battery management system is used to implement the steps in the method according to any one of claims 1 to 7.

12. An electrical equipment, characterized in that: Comprising the battery system as claimed in claim 11.

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