Calibration Method, Device, Medium, Product, System and Equipment for State of Charge

Abstract

An embodiment of the present application provides a method, device, medium, product, system and equipment for calibrating the state of charge; the method for calibrating the state of charge includes: obtaining a first measured terminal voltage corresponding to a battery at multiple sampling time points, and a first model terminal voltage corresponding to each sampling time point; determining a first voltage error corresponding to the sampling time point based on the first measured terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage; and calibrating the estimated state of charge of the battery when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend. In this way, it is possible to more accurately identify whether the current state of charge of the battery needs to be calibrated, increase the opportunity for correcting the state of charge, improve the correction effect of the SOC, and reduce the problems of overvoltage or undervoltage of the battery caused by the deviation of the state of charge.

Description

Technical Field

[0001] The present application relates to the technical field of batteries, and particularly to a method, device, medium, product, system and equipment for calibrating the state of charge. Background Art

[0002] This section aims to provide background or context for the implementation manners of the present application. The description herein is not admitted to be prior art merely because it is included in this section.

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

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

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

[0006] Embodiments of the present application provide a method for calibrating the state of charge, and the calibration method includes:

[0007] Obtain the first measured 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 by using the equivalent circuit model of the battery based on the estimated state of charge of the battery at the sampling time point;

[0008] Based on the first measured terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage, determine the first voltage error corresponding to the sampling time point;

[0009] When the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, calibrate the estimated state of charge of the battery.

[0010] In the embodiments of the present application, first, the first measured 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 by using the equivalent circuit model of the battery based on the estimated state of charge of the battery at the sampling time point; then, based on the first measured 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 correspondence 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 OCV of the battery with respect to the SOC varies in different SOC interval ranges, and when there is a deviation between the estimated state of charge of the battery and the true SOC, the estimated state of charge of the battery and the true SOC may be respectively in two SOC interval ranges with different rates of change of the OCV with respect to the SOC. In this way, a monotonic change trend of the deviation between the estimated state of charge of the battery and the true SOC over time will occur. Therefore, by determining the change trend of the first voltage errors corresponding to multiple sampling time points, it is possible to more accurately determine whether there is a deviation between the estimated state of charge of the battery and the true SOC, and thus more accurately identify whether the estimated state of charge of the battery needs to be calibrated. In this way, when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, by calibrating the current state of charge of the battery, the opportunity for SOC correction can be increased, the correction effect of the SOC can be improved, thereby reducing the problems of overvoltage or undervoltage of the battery caused by the SOC deviation, and further improving the battery performance.

[0011] In some embodiments, the estimated state of charge of the battery is determined by using a target estimation model;

[0012] Calibrating the estimated state of charge of the battery includes:

[0013] Adjusting the model parameters of the target estimation model to obtain a target estimation model with adjusted parameters;

[0014] Using the target estimation model with adjusted parameters to estimate the state of charge of the battery to obtain a calibrated estimated state of charge.

[0015] In the above embodiments, by adjusting the model parameters of the target estimation model for determining the estimated state of charge of the battery, the target estimation model with adjusted parameters is obtained; and the estimated state of charge of the battery is estimated by using the target estimation model with adjusted parameters, and the calibrated estimated state of charge is obtained. 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.

[0016] In some embodiments, the target estimation model is used to perform a weighted process 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 the first weight coefficient of the state prediction quantity and the second weight coefficient of the state observation quantity.

[0017] Adjusting the model parameters of the target estimation model includes at least one of the following:

[0018] Reducing the first weight coefficient;

[0019] Increasing the second weight coefficient.

[0020] In the above embodiments, by reducing the first weight coefficient of the state prediction quantity for determining the estimated state of charge in the target estimation model, and / or increasing the second weight coefficient of the state observation quantity. In this way, the estimated state of charge can be made closer to the true state of charge corresponding to the state observation quantity, thereby improving the accuracy of the battery SOC calibration.

[0021] In some embodiments, calibrating the estimated state of charge of the battery includes:

[0022] Obtaining the current estimated state of charge of the battery;

[0023] When the current estimated state of charge of the battery is within the preset state of charge interval, calibrating the current estimated state of charge of the battery; the open circuit voltage of the battery within the preset state of charge interval has a change rate with respect to the state of charge greater than the change rate threshold.

[0024] In the above embodiments, when the rate of change of the open-circuit voltage corresponding to the currently estimated state of charge (SOC) of the battery with respect to the SOC is greater than a rate-of-change threshold, the currently estimated SOC of the battery is calibrated. In this way, considering that when the rate of change of the open-circuit voltage of the battery with respect to the SOC exceeds the rate-of-change threshold, the change of the open-circuit voltage with respect to the SOC is usually more obvious (for example, the OCV corresponding to the SOC of the battery is in a non-plateau region). In this case, the change trend of the error between the model terminal voltage and the measured terminal voltage of the battery will be more obvious, so that the monotonic change trend of the first voltage error is more easily and accurately identified. Therefore, calibrating the SOC when the rate of change of the open-circuit voltage corresponding to the currently estimated SOC of the battery is greater than the rate-of-change threshold can reduce the situation of mis-calibration caused by inaccurate identification of the change trend of the first voltage error, and further improve the accuracy and referenceability of the calibration result.

[0025] In some embodiments, the method for calibrating the state of charge further includes:

[0026] For each sampling time point, using the equivalent circuit model of the battery, 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, determine the first model terminal voltage corresponding to the sampling time point.

[0027] In the above embodiments, for each sampling time point, using the equivalent circuit model of the battery, 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, 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 further, the situation that needs to calibrate the estimated SOC can be identified more accurately.

[0028] In some embodiments, the method for calibrating the state of charge further includes:

[0029] When the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range, calibrate the estimated state of charge of the battery;

[0030] 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 credibility condition.

[0031] In the above embodiments, in the embodiments 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 credibility condition, therefore, by determining whether the first voltage error corresponding to the estimated SOC exceeds the reference voltage error range, it can be determined whether the estimated SOC meets the target credibility condition, and thus the situation where the estimated SOC needs to be calibrated can be identified more accurately.

[0032] In some embodiments, the method for calibrating the state of charge further includes:

[0033] Obtaining at least one second measured terminal voltage of the battery during a target test process, and the second operating condition index and the measured state of charge corresponding to each second measured terminal voltage respectively;

[0034] For each second measured terminal voltage, based on the equivalent circuit model and the corresponding measured state of charge, determining the second model terminal voltage of the battery under the corresponding second operating condition index;

[0035] Based on each second measured terminal voltage and the corresponding second model terminal voltage, determining the second voltage error of each second model terminal voltage;

[0036] Based on the second voltage errors of the respective second model terminal voltages, determining the reference voltage error range.

[0037] In the above embodiments, using at least one second measured terminal voltage collected during the target test process of the battery, and the second operating condition index and the measured state of charge corresponding to each second measured terminal voltage respectively; for each second measured terminal voltage, determining each second model terminal voltage of the battery under the corresponding second operating condition index, and determining the second voltage error of each second model terminal voltage; further based on the second voltage errors of the respective second model terminal voltages, determining the reference voltage error range. 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 credible based on the reference voltage error range.

[0038] The embodiments of the present application provide a device for calibrating the state of charge, and the calibration device includes:

[0039] An acquisition module, configured to acquire the first measured terminal voltage corresponding to each of a plurality of sampling time points of the battery, and the first model terminal voltage corresponding to each sampling time point respectively; 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 by using the equivalent circuit model of the battery;

[0040] A determination module, configured to determine a first voltage error corresponding to a sampling time point based on a first measured terminal voltage and a corresponding first model terminal voltage at the sampling time point;

[0041] A calibration module, configured to calibrate the estimated state of charge of the battery when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend.

[0042] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be executed by a processor to implement the above-mentioned state of charge calibration method.

[0043] An embodiment of the present application provides a computer program product, including a computer program or instruction, which when executed by a processor, implements some or all of the steps in the above-mentioned state of charge calibration method.

[0044] 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 configured to implement some or all of the steps in the above-mentioned state of charge calibration method.

[0045] An embodiment of the present application provides an electrical device, which includes the above-mentioned battery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the implementation process of a state of charge calibration method provided by an embodiment of the present application Figure 1 ;

[0047] Figure 2 Schematic diagram of the composition structure of an equivalent circuit model provided by an embodiment of the present application;

[0048] Figure 3 Schematic diagram of the implementation process of a state of charge calibration method provided by an embodiment of the present application Figure 2 ;

[0049] Figure 4 Schematic diagram of the effect of a state of charge calibration method provided by an embodiment of the present application;

[0050] Figure 5 Schematic diagram of the composition structure of a state of charge calibration device provided by an embodiment of the present application;

[0051] Figure 6 Schematic diagram of the composition structure of a battery system provided by an embodiment of the present application;

[0052] Figure 7 Schematic diagram of the composition structure of an electrical device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] It should be noted that, without conflict, the embodiments in the present application and the technical features in the embodiments may be combined with each other. The detailed description in the specific implementation manners should be understood as an explanatory illustration of the gist of the present application and should not be regarded as an improper limitation to the present application.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill 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 inclusion.

[0055] In the description of the embodiments of the present application, technical terms such as "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 quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality of" is more than two, unless otherwise specifically defined.

[0056] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments may be included in at least one embodiment of this application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0057] 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 there can be three relationships. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0058] The embodiments of the present application provide a method for calibrating the state of charge, as Figure 1 shown, the calibration method may include the following steps S101 to step S103:

[0059] Step S101, obtain the first measured 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 by using the equivalent circuit model of the battery based on the estimated state of charge of the battery at the sampling time point.

[0060] Here, the types of batteries may include but are not limited to at least one of lithium-ion batteries, zinc-manganese batteries, lead-acid batteries, etc.

[0061] The first measured terminal voltage may be the actual terminal voltage measured for the battery.

[0062] In some embodiments, the first measured terminal voltage of the battery and the first modeled terminal voltage corresponding to the first measured terminal voltage may be obtained in real time; alternatively, the first measured terminal voltage of the battery and the first modeled terminal voltage corresponding to the first measured terminal voltage may be obtained at regular intervals based on a target time interval.

[0063] In some embodiments, the time intervals between multiple sampling time points may be the same or different.

[0064] Here, the equivalent circuit model of the battery may 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 charge and discharge processes, such as effects like ohmic voltage drop, electrochemical polarization, and concentration polarization. In implementation, the equivalent circuit model of the battery may be any suitable open-loop model, and the embodiments of the present application do not limit this.

[0065] In some embodiments, the equivalent circuit model of the battery may include at least one of, but is not limited to, a first-order resistance-capacitance (RC) equivalent circuit model, a second-order RC equivalent circuit model, etc.

[0066] In some embodiments, the first modeled terminal voltage may be the terminal voltage of the battery estimated based on the model parameters of the equivalent circuit model and the estimated state of charge of the battery; wherein, the model parameters may include at least one of, but are not limited to, the open-circuit voltage of the battery, the equivalent internal resistance of the battery, the polarization resistance, the voltage of the polarization resistance, etc.

[0067] In some embodiments, based on the estimated state of charge of the battery, the first correspondence between the open-circuit voltage and the state of charge of the battery may be queried to obtain the open-circuit voltage corresponding to the estimated state of charge; after establishing the 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 modeled terminal voltage.

[0068] Exemplarily, as Figure 2 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 resistance 12, a polarization capacitor 13 corresponding to the polarization resistance 12, an equivalent voltage source 14, a battery positive electrode 15, and a battery negative electrode 16.

[0069] According to Kirchhoff's law, the input-output relationship of the equivalent circuit model 10 can be referred to in formulas (1) to (2):

[0070] (1);

[0071] (2);

[0072] Wherein, U t is 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 resistance 12, R D1 is the resistance value of the polarization resistance 12, C D1 is the capacitance value of the polarization capacitor 13, i L is the current value. Wherein, U OC can 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.

[0073] It can be understood that, based on formula (1), in the case where there is no deviation in the SOC, the U OC obtained based on the SOC has no error, and the error of the first model terminal voltage U t of the equivalent circuit model 10 comes from U D1 , and / or i L R 0 error, wherein, U D1 error and i L R 0 error is usually related to operating condition indicators such as current rate and / or temperature.

[0074] In some embodiments, the model parameters of the equivalent circuit model can be identified offline through test experiments and by using a target optimization algorithm.

[0075] For example, through OCV test experiments and pulse current test experiments, the least squares method can be used to Figure 2 the model parameters U OC , R 0, R D1 and U D1Perform fitting to complete the offline identification process of the model parameters of the equivalent circuit model 10.

[0076] 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 can 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.

[0077] 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.

[0078] Again, for 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.

[0079] In some embodiments, the estimated state of charge of the battery can be estimated by the management component of the battery. For example, the estimated state of charge of the battery can be estimated by the Battery Management System (BMS).

[0080] Step S102, based on the first measured terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage, determine the first voltage error corresponding to the sampling time point.

[0081] Here, the first voltage error can characterize the error between the battery terminal voltage calculated by the equivalent circuit model and the actually measured battery terminal voltage.

[0082] In some embodiments, the first voltage error can be determined based on the difference between the first model terminal voltage and the first measured terminal voltage.

[0083] For example, the first voltage error can be the difference between the first model terminal voltage and the first measured terminal voltage.

[0084] Again, for example, the first voltage error can be the average value of the differences between the first model terminal voltages at multiple moments within the target duration and the corresponding first measured terminal voltages.

[0085] Once again, for example, the first voltage error can be the difference between the average model terminal voltage obtained by averaging the first model terminal voltages at multiple moments within the target duration and the average measured terminal voltage obtained by averaging the corresponding first measured terminal voltages at multiple moments.

[0086] Exemplarily, the process of determining the first voltage error E can be seen in formula (3):

[0087] (3);

[0088] Among them, V t1 is the first model terminal voltage, V t2 is the first measured terminal voltage.

[0089] Step S103, when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, calibrate the estimated state of charge of the battery.

[0090] Here, the monotonic change trend may include a monotonic increasing trend or a monotonic decreasing trend.

[0091] For the relationship curve between OCV and SOC, when OCV is in the plateau region, the slope of the curve is small, that is, the change rate of OCV with respect to SOC is small; when OCV is in the non-plateau region, the slope of the curve is large, that is, the change rate of OCV with respect to SOC is large. Therefore, during the process where the OCV corresponding to SOC enters the plateau region from the non-plateau region, or enters the non-plateau region from the plateau region, if there is a deviation between the estimated SOC and the true SOC, at this time, the first voltage error will show a monotonic increasing or monotonic decreasing trend.

[0092] For example, during the discharge process of the battery, if the estimated SOC is falsely high and the OCV corresponding to the true SOC has entered the non-plateau region (for example, the true SOC is less than 30%), the first measured terminal voltages corresponding to multiple sampling time points will decrease as the true SOC decreases, and show a relatively fast decreasing trend; while the OCV corresponding to the estimated SOC is still in the plateau region (for example, the estimated SOC is between 30% and 60%), the first model terminal voltages corresponding to multiple sampling time points change little as the estimated SOC decreases, showing a relatively stable trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measured terminal voltage, therefore, the first voltage error shows an increasing trend.

[0093] Another example, during the charging process of the battery, if the estimated SOC is falsely low and the OCV corresponding to the true SOC has entered the non-plateau region (for example, the true SOC is greater than 60%), the first measured terminal voltages corresponding to multiple sampling time points will increase as the true SOC increases, and show a relatively fast increasing trend; while the OCV corresponding to the estimated SOC is still in the plateau region (for example, the estimated SOC is between 30% and 60%), the first model terminal voltages corresponding to multiple sampling time points change little as the estimated SOC increases, showing a relatively stable trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measured terminal voltage, therefore, the first voltage error shows a decreasing trend.

[0094] Also, during the discharge process of the battery, if the estimated SOC is falsely low and the OCV corresponding to the true SOC has entered the plateau region (for example, the true SOC is greater than 30% and less than 60%), the first measured terminal voltages corresponding to multiple sampling time points change little with the decrease of the true SOC and show a relatively stable trend; while the OCV corresponding to the estimated SOC is still in the non-plateau region (for example, the estimated SOC is greater than 60%), the first model terminal voltages corresponding to multiple sampling time points will decrease with the decrease of the estimated SOC and show a relatively fast decreasing trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measured terminal voltage, the first voltage error shows a decreasing trend.

[0095] Again, during the charging process of the battery, if the estimated SOC is falsely high and the OCV corresponding to the true SOC is in the plateau region (for example, the true SOC is greater than 30% and less than 60%), the first measured terminal voltages corresponding to multiple sampling time points change little with the increase of the true SOC and show a relatively stable trend; while the OCV corresponding to the estimated SOC has entered the non-plateau region (for example, the estimated SOC is greater than 60%), the first model terminal voltages corresponding to multiple sampling time points will increase with the increase of the estimated SOC and show a relatively fast increasing trend; since the first voltage error is determined based on the difference between the first model terminal voltage and the first measured terminal voltage, the first voltage error shows an increasing trend.

[0096] It can be understood that when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, it indicates that the estimated SOC and the true SOC do not simultaneously lie in the plateau region or the non-plateau region of the OCV, that is, there is a deviation between the estimated SOC and the true SOC, and calibration is required.

[0097] In some embodiments, multiple sampling time points may correspond to multiple state-of-charge intervals, and when the first voltage errors show a monotonic change trend in these multiple state-of-charge intervals, the estimated state of charge of the battery is calibrated.

[0098] For example, when the first voltage errors corresponding to multiple consecutive state-of-charge intervals show a monotonic increasing or decreasing trend, it can be determined that there is a deviation between the estimated SOC and the true SOC corresponding to these consecutive state-of-charge intervals, and the estimated SOC is calibrated. Among them, the interval ranges of each state-of-charge interval in these multiple consecutive state-of-charge intervals may be different.

[0099] Exemplarily, the estimated SOC can be calibrated when the first voltage errors corresponding to the first interval where the estimated SOC is between 27% and 25%, the second interval where the estimated SOC is between 24% and 18%, and the third interval where the estimated SOC is between 17% and 10% show an increasing trend.

[0100] Exemplarily, 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 errors of each SOC interval segment show a monotonic change trend, the estimated SOC is calibrated.

[0101] In the embodiments of the present application, first, the first measured 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 by using the equivalent circuit model of the battery based on the estimated state of charge of the battery at the sampling time point; then, based on the first measured terminal voltage and the corresponding first model terminal voltage corresponding to the sampling time point, 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 correspondence 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 OCV of the battery with respect to the SOC varies in different SOC interval ranges, and when there is a deviation between the estimated state of charge of the battery and the true SOC, the estimated state of charge of the battery and the true SOC may be in two SOC interval ranges with different rates of change of the OCV with respect to the SOC respectively. In this way, there will be a trend that the deviation between the estimated state of charge of the battery and the true SOC changes monotonically with time. Therefore, by determining the change trend of the first voltage errors corresponding to multiple sampling time points, it is possible to more accurately determine whether there is a deviation between the estimated state of charge of the battery and the true SOC, and thus more accurately identify whether the estimated state of charge of the battery needs to be calibrated. In this way, when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, by calibrating the current state of charge of the battery, the opportunity for SOC correction can be increased, the correction effect of SOC can be improved, thereby reducing the problem of overvoltage or undervoltage of the battery caused by the SOC deviation, and further improving the battery performance.

[0102] In some embodiments, the estimated state of charge of the battery is determined by using a target estimation model. The calibration of the estimated state of charge of the battery in step S103 described above may include the following steps S201 to step S202:

[0103] Step S201: Adjust the model parameters of the target estimation model to obtain a target estimation model with adjusted parameters.

[0104] Here, the target estimation model with adjusted parameters is used to reduce the deviation between the estimated SOC and the true SOC.

[0105] In some embodiments, since the methods for estimating the state of charge are different, the target estimation models may be different.

[0106] For example, the target estimation model may include an open-circuit voltage model for estimating the state of charge according to the open-circuit voltage method.

[0107] For another example, the target estimation model may include a current integration model for estimating the state of charge according to the current integration method.

[0108] For still another example, the target estimation model may include a Kalman filter model for estimating the state of charge according to the Kalman filter method.

[0109] In some embodiments, the target estimation model may include at least one of a neural network model, a recursive model, a convergence model, etc.

[0110] The model parameters of the target estimation model may include at least one of, but not limited to, coefficients, weights, number of iterations, etc.

[0111] In some embodiments, the model parameters of the target estimation model may be adjusted according to the model parameters obtained in the pre-test, or the model parameters of the target estimation model may be adjusted according to empirical values. The embodiments of the present application do not limit this.

[0112] Step S202: Use the target estimation model with adjusted parameters to estimate the state of charge of the battery, and obtain a calibrated estimated state of charge.

[0113] Here, using the target estimation model with adjusted parameters to estimate the state of charge of the battery can reduce the deviation between the estimated state of charge and the true state of charge.

[0114] In some embodiments, the deviation between the estimated state of charge and the true state of charge can be reduced by making the estimated state of charge converge to the true state of charge.

[0115] In the embodiments of the present application, by adjusting the model parameters of the target estimation model for determining the estimated state of charge of the battery, a target estimation model with adjusted parameters is obtained; and the target estimation model with adjusted parameters is used to estimate the state of charge of the battery, and a calibrated estimated state of charge is obtained. 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.

[0116] In some embodiments, the target estimation model is used to perform a weighting process on the state prediction quantity and the state observation quantity of the battery to obtain the estimated state of charge of the battery. The model parameters include the first weight coefficient of the state prediction quantity and the second weight coefficient of the state observation quantity. Adjusting the model parameters of the target estimation model in step S201 may include at least one of the following steps S301 to step S302:

[0117] Step S301: Decrease the first weight coefficient.

[0118] Here, the state prediction quantity may be a variable obtained by predicting the battery state. For example, the battery state may include SOC, and the state observation quantity may be the unweighted estimated SOC obtained by predicting the SOC.

[0119] The state observation quantity may be a variable obtained by observing the battery. For example, the state observation quantity may include voltage.

[0120] Since the first weight coefficient is the weight coefficient of the state prediction quantity, therefore, when the first weight coefficient is decreased, the influence degree of the state prediction quantity on the estimated state of charge can be reduced, that is, the estimated state of charge is made closer to the true state of charge corresponding to the state observation quantity.

[0121] 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; among them, the predicted value, the observed value, and the state estimation value may respectively correspond to the state prediction quantity, the state observation quantity, and the estimated state of charge in the embodiments of the present application.

[0122] When initializing the Kalman filter algorithm, it is necessary to determine the state equation and the observation equation of the battery, as well as the initial state and the variance matrix of the battery. In the prediction step, the state and the variance matrix at the next moment are predicted according to the state equation of the battery and the control quantities (such as current, temperature, etc.); in the update step, the state and the variance matrix of the system are updated according to the observed value and the predicted value. By continuously repeating these two steps of prediction and update, the Kalman filter algorithm can update the state estimation value in real time and process a system with noise.

[0123] In some embodiments, the method for estimating the SOC by using the Kalman filter model through measuring the current and voltage of the battery may include the following steps S3011 to step S3014:

[0124] Step S3011: Determine the state transition equation and the observation equation.

[0125] Among them, the state transition equation of the battery can correspond to the above-mentioned state equation of the battery, which is used to describe the variation law of the battery state (such as SOC, internal resistance, etc.) over time, while the observation equation is used to describe the relationship between the observed value (such as voltage) and the battery state.

[0126] Exemplarily, the state transition equation of the battery can be seen in Formula (4):

[0127] (4);

[0128] Among them, k can correspond to the next moment, k can correspond to the current moment, x k is the state variable (such as SOC), u k is the control quantity, ω k is the process noise, A and B are the coefficient matrices.

[0129] Exemplarily, the observation equation of the battery can be seen in Formula (5):

[0130] (5);

[0131] Among them, y k is the observation output (such as OCV), v k is the measurement noise, C is the coefficient matrix.

[0132] Step S3012, Initialization.

[0133] Among them, during the initialization process, it is necessary to set the initial SOC and the initial variance matrix of the battery, and the initial SOC can be determined by methods such as the open circuit voltage method.

[0134] Step S3013, Prediction.

[0135] Among them, the battery state (such as SOC) at the next moment can be predicted according to the state equation of the battery and the control quantity (such as current, temperature).

[0136] Exemplarily, the process of predicting the battery state can be seen in Formula (6):

[0137] (6);

[0138] The process of the prior estimation error covariance P k can be seen in Formula (7):

[0139] (7);

[0140] Wherein, is the a priori estimated error covariance, Q is the process noise covariance matrix, A T is the matrix A transpose matrix.

[0141] Step S3014, Update.

[0142] Wherein, the observed value and predicted value of the voltage at the current moment can be used to update the SOC estimated value and variance matrix of the battery. In this process, the Kalman gain matrix can be used to adjust the weights of the predicted value and the observed value to obtain a more accurate SOC estimate.

[0143] Exemplarily, the determination process of the Kalman gain matrix K can refer to formula (8):

[0144] (8);

[0145] Wherein, the coefficient matrix C can be used as the state observation matrix, and , C T is the matrix C transpose matrix, G is the measurement noise covariance matrix;

[0146] Based on the Kalman gain matrix K to update the battery state the process can refer to formula (9):

[0147] (9);

[0148] Based on the Kalman gain matrix K to update the error covariance P k the process can refer to formula (10):

[0149] (10);

[0150] Wherein, I is the identity matrix, P k is the a posteriori estimated error covariance.

[0151] Exemplarily, can correspond to the state prediction quantity in the calibration method of the above state of charge at the next moment. It can be determined based on formula (9) that 1 -KC It can correspond to the first weight coefficient in the above-mentioned method for calibrating the state of charge, and can reduce the first weight coefficient, and further make (i.e., the state prediction value) have a reduced weight, making the state estimation value closer to the true state.

[0152] In some embodiments, it can be determined based on formula (8) that K increases as C increases. Therefore, by increasing C or K , KC can be increased, thereby making the first weight coefficient 1 - KC decrease.

[0153] Step S302: Increase the second weight coefficient.

[0154] Here, since the second weight coefficient is the weight coefficient of the state observation value, therefore, when the first weight coefficient is increased, the influence degree of the state observation value on the estimated state of charge can be improved, that is, the estimated state of charge is closer to the true state of charge corresponding to the state observation value.

[0155] Exemplarily, in the process of estimating the SOC through the above-mentioned Kalman filter model, it can be determined based on formula (9) that K can correspond to the second weight coefficient in the above-mentioned method for calibrating the state of charge. By increasing K , the second weight coefficient can be increased, and further make y k (i.e., the observation value) have an increased weight, making the state estimation value closer to the true state corresponding to the observation value.

[0156] In some embodiments, it can be determined based on formula (8) that K increases as C increases. Therefore, by increasing C , the second weight coefficient K can be increased.

[0157] In some embodiments, it can be achieved by increasing C the rate of change used to describe the degree of change of OCV with SOC in C , so that

[0158]

[0158] It can be understood that when the first weight coefficient is decreased and the second weight coefficient is increased at the same time, the influence degree of the state prediction value on the estimated state of charge can be reduced while further increasing the influence degree of the state observation value on the estimated state of charge, making the estimated state of charge closer to the true state of charge corresponding to the state observation value.

[0159] In the embodiments of the present application, by reducing the first weight coefficient of the state prediction quantity for determining the predicted state of charge in the target estimation model and / or increasing the second weight coefficient of the state observation quantity, it is possible to make the predicted state of charge closer to the true state of charge corresponding to the state observation quantity, thereby improving the accuracy of battery SOC calibration.

[0160] In some embodiments, the above step S103 may include the following steps S401 to S402:

[0161] Step S401: Obtain the current predicted state of charge of the battery.

[0162] Step S402: Calibrate the current predicted state of charge of the battery when the current predicted state of charge of the battery is within a preset state of charge interval; the rate of change of the open-circuit voltage of the battery with respect to the state of charge within the preset state of charge interval is greater than the rate-of-change threshold.

[0163] Here, when the rate of change of the open-circuit voltage with respect to the state of charge exceeds the rate-of-change threshold, it can indicate that the change of the OCV of the battery with respect to the SOC is relatively obvious. Considering that the first model terminal voltage is determined based on the state of charge of the battery, in the case where the change of the OCV with respect to the SOC is not obvious, the accuracy of the first model terminal voltage determined based on the OCV will be reduced, and further reduce 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. Therefore, when the current predicted state of charge of the battery is within the preset state of charge interval, the current predicted state of charge of the battery is calibrated.

[0164] Exemplarily, when the rate of change of the open-circuit voltage of the battery with respect to the state of charge exceeds the rate-of-change 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%, SOC greater than 95%, etc.

[0165] In an embodiment of the present application, when the change rate of the open-circuit voltage corresponding to the currently estimated state of charge (SOC) of the battery with respect to the SOC is greater than a change rate threshold, the currently estimated SOC of the battery is calibrated. In this way, considering that when the change rate of the open-circuit voltage of the battery with respect to the SOC exceeds the change rate threshold, the change of the open-circuit voltage with respect to the SOC is usually more obvious (for example, the OCV corresponding to the SOC of the battery is in a non-platform region). In this case, the change trend of the error between the model terminal voltage and the measured 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, calibrating the SOC when the change rate of the open-circuit voltage corresponding to the currently estimated SOC of the battery is greater than the change rate threshold can reduce the situation of mis-calibration caused by inaccurate identification of the change trend of the first voltage error, and further improve the accuracy and referenceability of the calibration result.

[0166] In some embodiments, the above method for calibrating the state of charge may further include the following step S501:

[0167] Step S501: For each sampling time point, using the equivalent circuit model of the battery, 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, determine the first model terminal voltage corresponding to the sampling time point.

[0168] Here, the first operating condition index can be used to characterize the working environment and / or working state of the battery under the first measured terminal voltage, and can be the operating condition index collected when measuring the first measured terminal voltage; wherein, the operating condition index may include, but is not limited to, at least one of current, current rate, temperature, humidity, etc.

[0169] In some embodiments, the first measured terminal voltage of the battery and the first operating condition index corresponding to the first measured terminal voltage can be obtained during the use of the battery; or the first measured terminal voltage of the battery and the first operating condition index corresponding to the first measured terminal voltage can be obtained when the battery is in a non-use state (for example, a stationary state).

[0170] In some embodiments, the first measured terminal voltage of the battery and the first operating condition index corresponding to the first measured terminal voltage can be obtained in real time; or the first measured terminal voltage of the battery and the first operating condition index corresponding to the first measured terminal voltage can be obtained regularly based on a target time interval.

[0171] The first model terminal voltage can be the terminal voltage of the battery estimated based on the model parameters of the equivalent circuit model and the estimated state of charge of the battery under the first operating condition index.

[0172] In some embodiments, for each sampling point, based on the equivalent circuit model of the battery and the estimated state of charge of the battery, the first model terminal voltage corresponding to the battery under the first operating condition index can be determined.

[0173] It can be understood that since both the first measured terminal voltage and the corresponding first model terminal voltage are under the first operating condition, therefore, the first voltage error determined based on the first measured 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 actually measured battery terminal voltage under the first operating condition index.

[0174] In some embodiments, based on the first measured terminal voltage and the first model terminal voltage corresponding to each sampling time point, the first voltage error under the first operating condition index corresponding to each sampling time point can be determined.

[0175] In the embodiments of the present application, for each sampling time point, using the equivalent circuit model of the battery, 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, the first model terminal voltage corresponding to the sampling time point is determined. In this way, by combining the estimated state of charge of the battery and the operating condition index, a more accurate first model terminal voltage can be determined, thereby a more accurate first voltage error can be obtained, and further, the situation where the estimated SOC needs to be calibrated can be more accurately identified.

[0176] In some embodiments, the above method for calibrating the state of charge may further include the following step S601:

[0177] Step S601: Calibrate the estimated state of charge of the battery when the first voltage error corresponding to at least one sampling time point exceeds the reference voltage error range;

[0178] Wherein, the reference voltage error range characterizes 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.

[0179] Here, the target credibility condition may be a condition characterizing that the estimated state of charge of the battery is within a credible interval.

[0180] In some embodiments, it can be determined that the estimated state of charge meets the target credibility condition when the absolute value of the difference between the estimated state of charge of the battery and the corresponding true state of charge is less than the absolute value threshold.

[0181] For example, when the estimated state of charge of the battery is 28% and the true state of charge is 25%, the absolute value of the difference between the estimated state of charge and the corresponding true state of charge is 3%, which is less than the absolute value threshold of 5%, then the estimated state of charge meets the target credibility condition.

[0182] When the estimated state of charge of the battery meets the target credibility condition, it can represent 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.

[0183] 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 this sampling time point is not credible and needs to be calibrated.

[0184] For example, when the OCV corresponding to any one of the true SOC and the estimated SOC is in the plateau region and the OCV corresponding to the other is in the non-plateau region, 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 this sampling time point is not credible and needs to be calibrated.

[0185] Another example is when the OCV corresponding to any one of the true SOC and the estimated SOC is in the non-plateau region and the OCV corresponding to the other is in the plateau region, 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 this sampling time point is not credible and needs to be calibrated.

[0186] Also, when the OCVs corresponding to the true SOC and the estimated SOC are both in the non-plateau region, 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 this sampling time point is not credible and needs to be calibrated.

[0187] It can be understood that since the change of OCV with SOC is relatively slow when it is in the plateau region, when the OCVs corresponding to the true SOC and the estimated SOC are both in the plateau region, the first voltage error is small and it is difficult to exceed the reference voltage error range. Therefore, when the OCVs corresponding to the true SOC and the estimated SOC are both in the plateau region, there is no need to calibrate the estimated SOC.

[0188] Exemplarily, the reference voltage error range can include but is not limited to [-30mV, 30mV], [-25mV, 25mV], [-20mV, 20mV], etc.

[0189] In the embodiments 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 credibility condition, by determining whether the first voltage error corresponding to the estimated SOC exceeds the reference voltage error range, it can be determined whether the estimated SOC meets the target credibility condition, and thus the situation where the estimated SOC needs to be calibrated can be identified more accurately.

[0190] In some embodiments, the method for calibrating the state of charge may further include the following steps S701 to S704:

[0191] Step S701: Obtain at least one second measured terminal voltage of the battery during the target test process, and the corresponding second operating condition index and measured state of charge for each second measured terminal voltage.

[0192] Here, the target test process may include the usage test process of the electrical device.

[0193] In some embodiments, the usage test of the electrical device may be pre - performed to collect the operation data of the electrical device in different scenarios; the operation data may include, but is not limited to, current, current rate, voltage, temperature, SOC, etc.

[0194] In some embodiments, during the target test process, the battery may be in a used state or a non - used state.

[0195] The second operating condition index can be used to characterize the working environment and / or working state of the battery at the second measured terminal voltage, and can be the operating condition index collected when measuring the second measured terminal voltage.

[0196] The measured state of charge can be the state of charge of the battery collected when measuring the second measured terminal voltage.

[0197] Step S702: For each second measured 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 index.

[0198] Here, the second model terminal voltage can be the battery terminal voltage estimated based on the model parameters of the equivalent circuit model and the state of charge of the battery under the second operating condition index.

[0199] Step S703: Based on each second measured terminal voltage and the corresponding second model terminal voltage, determine the second voltage error of each second model terminal voltage.

[0200] Exemplarily, the first voltage error E’The process can be seen in Equation (11):

[0201] (11);

[0202] Wherein, V t3 is the second model terminal voltage, V t4 is the second measurement terminal voltage.

[0203] Step S704: Determine the reference voltage error range based on the second voltage errors of each second model terminal voltage.

[0204] Here, by screening the second voltage errors, the second operating condition indicators corresponding to the second voltage errors within the first voltage error range can be determined as candidate operating conditions, and the first voltage error range can be determined as the candidate voltage error range corresponding to the candidate operating conditions.

[0205] Exemplarily, the second operating condition indicators corresponding to each second voltage error when at least one second voltage error is in [-30mV, 30mV] can be sorted out as candidate operating conditions. For example, when the second voltage error is in [-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 can be that the minimum temperature is greater than 30 degrees.

[0206] In some embodiments, the in-vehicle test can be carried out on a vehicle using a battery to collect the operation data of the vehicle in different scenarios such as high-speed conditions and urban conditions; based on the operation data corresponding to each scenario, determine the model terminal voltage determined based on the equivalent circuit model in each scenario; based on the measurement terminal voltage corresponding to each scenario, determine the voltage error of the model terminal voltage in each scenario; based on the operation data corresponding to each scenario, determine the operating conditions where the voltage error of the model terminal voltage is within the preset threshold range as candidate operating conditions; determine the range of the voltage error of the model terminal voltage corresponding to the operation data under the candidate operating conditions as the candidate voltage error range corresponding to the candidate operating conditions; and further determine the reference voltage error range based on the candidate voltage error range.

[0207] In the embodiments of the present application, at least one second measured terminal voltage collected during the target test process of the battery, as well as the corresponding second operating condition index and measured state of charge for each second measured terminal voltage are utilized; for each second measured 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 errors of each second model terminal voltage, a 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 credible based on the reference voltage error range.

[0208] In some embodiments, the above step S101 may include the following steps S801 to S802:

[0209] Step S801: Collect the first measured terminal voltages corresponding to multiple sampling time points within the target state of charge change interval of the battery.

[0210] Step S802: For each sampling time point, obtain the first model terminal voltage corresponding to the first measured terminal voltage.

[0211] The above step S102 may include the following steps S803 to S805:

[0212] Step S803: Calculate the average value of the multiple first measured terminal voltages corresponding to multiple sampling time points to obtain the average measured terminal voltage.

[0213] Step S804: Calculate the average value of the multiple first model terminal voltages corresponding to the multiple first measured terminal voltages to obtain the average model terminal voltage.

[0214] Step S805: Determine the difference between the average model terminal voltage and the average measured terminal voltage as the first voltage error.

[0215] 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.

[0216] For example, when 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 interval in which the SOC changes from 15% to 24%, that is, a 9% change interval.

[0217] Another example, when the SOC of the battery changes from 15% to 24% during or after use, the target state of charge change interval may include each 3% change interval during the process in which the SOC changes from 15% to 24%, and may include at least one of interval segments such as 15% to 18%, 16% to 19%, 18% to 21%, and 21% to 24%.

[0218] In some embodiments, the first measurement terminal voltage may be collected multiple times at sampling time points with the same or different time intervals.

[0219] It can be understood that the " V t1 – V t2 " in the above formula (3) may be replaced by the first voltage error obtained based on the difference between the average model terminal voltage and the average measurement terminal voltage in step S805.

[0220] In the embodiments of the present application, the first measurement terminal voltages corresponding to different moments are collected multiple times at multiple sampling time points within the target state of charge change interval, and the first measurement terminal voltages corresponding to each of the first measurement terminal voltages are also collected; and the averages of 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 calculated; 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 averages 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 referenceability of the first voltage error can be improved.

[0221] As the battery is used, the SOC deviation will gradually accumulate. If the long-term accumulated SOC deviation is not corrected, it will cause undesirable phenomena such as overvoltage or undervoltage of the battery.

[0222] In the related art, the SOC estimation techniques of batteries cover various methods such as the open-circuit voltage method, the ampere-hour integration method, the internal resistance measurement method, the Kalman filter method, and the neural network algorithm. Although these methods have their respective 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.

[0223] Lithium iron phosphate (LFP) batteries have become an ideal power source 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 open-circuit voltage does not change significantly with the state of charge when it is in the plateau region, and it is difficult to accurately estimate the state of charge when the open-circuit voltage is in the plateau region. The current Kalman filter algorithm cannot correct the SOC in the voltage plateau interval or may cause incorrect correction, affecting the accuracy of correcting the state of charge of the battery.

[0224] On this basis, an embodiment of the present application provides a method for calibrating the state of charge. Taking the scenario of calibrating the state of charge of a lithium iron phosphate battery as an example, the method for calibrating the state of charge provided by the embodiment of the present application is described. As Figure 3 shown, the method for calibrating the state of charge includes the following steps S1001 to S1006:

[0225] Step S1001: Establish a model for the lithium iron phosphate battery.

[0226] Here, an open-loop model is established for the lithium iron phosphate battery.

[0227] In implementation, the model may include a first-order RC equivalent circuit model.

[0228] Step S1002: Through the OCV test experiment and the pulsed current test experiment, and using an optimization algorithm, complete the off-line identification process of the parameters.

[0229] Here, the parameters for completing the off-line identification process may correspond to the model parameters of the equivalent circuit model in the method for calibrating the state of charge described above.

[0230] Step S1003: Determine a target estimation model based on the Kalman filter algorithm.

[0231] Here, the method for estimating the SOC using the target estimation model may refer to the above steps S3011 to S3014.

[0232] Step S1004: Determine the reference voltage error range.

[0233] In some embodiments, candidate operating condition conditions with a second voltage error within the candidate voltage error range may be screened out by trying various combinations of operating conditions; based on the first operating condition index of the battery collected at the sampling time point, the reference voltage error range is determined from the candidate voltage error range.

[0234] Exemplarily, the current I of the battery in scenarios such as high-speed operating conditions and urban operating conditions can be collected through the on-vehicle test of the vehicle using the battery t0 to I tn 、voltage U t0 to U tn 、temperature T0 to T n and SOC0 to SOC nand other data; using parameters such as current, temperature, and SOC as the input of the model in step S1001, and taking the calculated terminal voltage of the model as the output. The calculation process can refer to formula (1), where the calculated terminal voltage of the model can correspond to the second terminal voltage in the above-mentioned state of charge calibration method; calculate the second voltage error within each 3% SOC change interval, where the second voltage error is determined based on the difference between the average value of the second measured terminal voltages collected at multiple moments and the average value of the second model terminal voltages corresponding to each second measured terminal voltage; and count the second operating condition indicators within the 3% SOC change interval, such as the average current rate of the 3% SOC change segment, the minimum temperature of the 3% SOC change segment, etc.; by combining multiple second operating condition indicators with the second voltage error within [-30 mV, 30 mV], candidate operating condition conditions are screened out, such as the average current rate is less than 0.5C and the minimum temperature is greater than 30 degrees, and [-30 mV, 30 mV] is used as the candidate voltage error range corresponding to the candidate operating condition conditions; further, when the first operating condition indicator corresponding to the first voltage error is that the average current rate is less than 0.5C and the minimum temperature is greater than 30 degrees, [-30 mV, 30 mV] is used as the reference voltage error range.

[0235] Step S1005: During the operation of the battery, determine the first voltage errors corresponding to multiple sampling time points; and when the first voltage errors meet the target error condition, adjust the model parameters of the target estimation model.

[0236] Here, the first voltage errors meeting the target error condition may include, but are not limited to, at least one of the following: the first voltage errors showing a monotonic change trend continuously for multiple times, the first voltage errors exceeding the reference voltage error range, etc.

[0237] Exemplarily, during the operation of the battery, starting from the estimated SOC of n%, calculate the average measured terminal voltage and the average model terminal voltage within each z% (such as 2%, 3%) SOC change interval to obtain the first voltage error within each z% SOC change interval, and at the same time determine the average current rate and the minimum temperature corresponding to each z% SOC change interval;

[0238] When the average current rate corresponding to each z% SOC change interval is less than 0.5C and the minimum temperature is greater than 30 degrees, it can be determined that the first voltage error within the z% SOC change interval should be within [-30 mV, 30 mV], that is, within the reference voltage error range.

[0239] When the first voltage errors corresponding to consecutive h z% SOC change intervals continuously increase or decrease, and / or are greater than 30 mV or less than -30 mV, adjust the state observation matrix C in the Kalman filter algorithm.

[0240] Step S1006: Estimate the state of charge of the battery by using the target estimation model with adjusted parameters, and obtain the calibrated estimated state of charge.

[0241] Here, the target estimation model based on the Kalman filter algorithm can converge the estimated SOC towards the true SOC in each subsequent iteration process after parameter adjustment.

[0242] In some embodiments, when it is determined that the estimated SOC needs to be calibrated, the number of target multiples is amplified to increase the state observation matrix C, so as to increase the weight of the state observation quantity and decrease the weight of the state prediction quantity, thereby converging the estimated SOC towards the true SOC and achieving the purpose of correcting the estimated SOC.

[0243] Exemplarily, the number of target multiples can be a preset value or an empirical value. For example, 10 times, and the embodiments of the present application do not make a limitation in this regard.

[0244] In the embodiments of the present application, during the operation of the battery, when the first voltage error satisfies 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 using the target estimation model with adjusted parameters. In this way, on the one hand, when there are deviations such as the current charge state of the battery being too high or too low, the state of charge of the battery can be calibrated, reducing the problems of overvoltage or undervoltage of the battery caused by the SOC deviation, and further improving the device performance of the electrical device supported by the battery; on the other hand, when estimating the SOC based on the Kalman filter algorithm, a higher convergence effect can be achieved.

[0245] Exemplarily, as Figure 4 shown, Figure 4 the abscissa in it can represent time, and the ordinate can represent the value corresponding to the SOC; at time t0, the estimated SOC is 80%, and the true SOC is 70%, and the estimated SOC is 10% higher than the true SOC; when the estimated SOC is 32%, the true SOC is already less than 30%. At this time, the OCV corresponding to the estimated SOC is in the plateau region, and the OCV corresponding to the true SOC is in the non-plateau region; 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; visibly, starting from time t2, the estimated SOC and the true SOC tend to be close.

[0246] The embodiments of the present application provide a calibration device for the state of charge, as Figure 5 shown. The calibration device 30 for the state of charge includes:

[0247] An acquisition module 31, configured to acquire a first measured terminal voltage corresponding to the battery at multiple 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 by using an equivalent circuit model of the battery based on an estimated state of charge of the battery at the sampling time point;

[0248] A determination module 32, configured to determine a first voltage error corresponding to the sampling time point based on the first measured terminal voltage and the corresponding first model terminal voltage corresponding to the sampling time point;

[0249] A calibration module 33, configured to calibrate the estimated state of charge of the battery when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend.

[0250] In some embodiments, the estimated state of charge of the battery is determined by using a target estimation model, and the calibration module may further be configured to: adjust model parameters of the target estimation model to obtain a target estimation model with adjusted parameters; use the target estimation model with adjusted parameters to estimate the state of charge of the battery to obtain a calibrated estimated state of charge.

[0251] In some embodiments, the target estimation model is configured to perform a weighted processing on a state prediction quantity and a 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 may further be configured to perform at least one of the following: reduce the first weight coefficient; increase the second weight coefficient.

[0252] In some embodiments, the acquisition module may further be configured to: acquire the current estimated state of charge of the battery;

[0253] The calibration module may further be configured 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 interval; an open-circuit voltage change rate of the battery within the preset state of charge interval with respect to the state of charge is greater than a change rate threshold.

[0254] In some embodiments, the determination module may further be configured to: for each sampling time point, use the equivalent circuit model of the battery 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 a first operating condition index of the battery collected at the sampling time point.

[0255] In some embodiments, the calibration module may further be configured to: calibrate the estimated state of charge of the battery when a 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 an error range of a model terminal voltage determined based on the equivalent circuit model when the estimated state of charge of the battery meets a target credibility condition.

[0256] In some embodiments, the obtaining module may further be configured to: obtain at least one second measured terminal voltage of the battery during a target test process, and a second operating condition index and a measured state of charge corresponding to each of the second measured terminal voltages.

[0257] The determining module may further be configured to: for each of the second measured terminal voltages, determine a second model terminal voltage of the battery under the corresponding second operating condition index based on the equivalent circuit model and the corresponding measured state of charge; determine a second voltage error of each of the second model terminal voltages based on each of the second measured terminal voltages and the corresponding second model terminal voltage; and determine the reference voltage error range based on the second voltage errors of the second model terminal voltages.

[0258] In some embodiments, the obtaining module may further be configured to: collect the first measured terminal voltages corresponding to a plurality of the sampling time points within a target state of charge change interval of the battery; and for each of the sampling time points, obtain the first model terminal voltage corresponding to the first measured terminal voltage.

[0259] The determining module may further be configured to: calculate an average measured terminal voltage by averaging the plurality of first measured terminal voltages corresponding to the plurality of sampling time points; calculate an average model terminal voltage by averaging the plurality of first model terminal voltages corresponding to the plurality of first measured terminal voltages; and determine a difference between the average model terminal voltage and the average measured terminal voltage as the first voltage error.

[0260] An embodiment of the present application provides a computer program, including computer-readable code, when the computer-readable code runs in a computer device, a processor in the computer device executes to implement some or all of the steps in the above method for calibrating the state of charge.

[0261] An embodiment of the present application provides a computer program product, including a computer program or instruction, when the computer program or instruction is executed by a processor, some or all of the steps in the above method for calibrating the state of charge are implemented.

[0262] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be executed by a processor to implement the above-mentioned state of charge calibration method.

[0263] An embodiment of the present application provides a battery system. As Figure 6 shown, the battery system 40 includes at least one battery 41 and a battery management system 42, and the battery management system 42 is used to implement some or all of the steps in the above-mentioned state of charge calibration method.

[0264] An embodiment of the present application provides an electrical device. As Figure 7 shown, the electrical device 50 includes the battery system 40 in the above-mentioned embodiment of the application.

[0265] Here, the electrical device may include, but is not limited to, automobiles, airplanes, electric bicycles, electric motorcycles, electric speedboats, ships, etc.

[0266] In the description of the present application, the descriptions with reference to terms such as "in one embodiment", "in some embodiments", "in other embodiments", "in still other embodiments", or "exemplary" etc. mean that the specific features, structures, materials or characteristics described in connection 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 expressions of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine different embodiments or examples described in the present application and the features of different embodiments or examples.

[0267] The above are only exemplary embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application are all included in the protection scope of the present application.

Claims

1. A method for calibrating the state of charge, characterized in that The calibration method includes: Obtaining the first measured terminal voltage corresponding to the battery at multiple sampling time points, and the 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 by using the equivalent circuit model of the battery based on the estimated state of charge of the battery at the sampling time point; Based on the first measured terminal voltage and the corresponding first model terminal voltage corresponding to the sampling time point, determining the first voltage error corresponding to the sampling time point; When the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, calibrating the estimated state of charge of the battery; Wherein, when the first voltage errors corresponding to multiple sampling time points show a monotonic change trend, calibrating the estimated state of charge of the battery includes one of the following: During the discharge process of the battery, when the current estimated state of charge of the battery is within a preset state of charge interval and the first voltage errors corresponding to multiple sampling time points show a monotonic increasing trend, or when the current estimated state of charge of the battery is not within the preset state of charge interval and the first voltage errors corresponding to multiple sampling time points show a monotonic decreasing trend, calibrating the estimated state of charge of the battery; the open circuit voltage of the battery within the preset state of charge interval has a rate of change with respect to the state of charge greater than a change rate threshold; During the charging process of the battery, when the current estimated state of charge of the battery is not within the preset state of charge interval and the first voltage errors corresponding to multiple sampling time points show a monotonic increasing trend, or when the current estimated state of charge of the battery is within the preset state of charge interval and the first voltage errors corresponding to multiple sampling time points show a monotonic decreasing trend, calibrating the estimated state of charge of the battery.

2. The method for calibrating the state of charge according to claim 1, wherein The estimated state of charge of the battery is determined by using a target estimation model; Calibrating the estimated state of charge of the battery includes: Adjusting the model parameters of the target estimation model to obtain a target estimation model with adjusted parameters; Using the target estimation model with adjusted parameters 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; Adjusting the model parameters of the target estimation model includes at least one of the following: Reducing the first weight coefficient; Increasing 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, Calibrating the estimated state of charge of the battery includes: Obtaining the current estimated state of charge of the battery; When the currently estimated state of charge of the battery is within a preset state of charge range, calibrate the currently estimated state of charge of the battery; the rate of change of the open-circuit voltage of the battery within the preset state of charge range with respect to the state of charge 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 includes: For each of the sampling time points, using the equivalent circuit model of the battery, 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, determine the first model terminal voltage corresponding to 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 includes: When the first voltage error corresponding to at least one of the sampling time points exceeds a reference voltage error range, calibrate the estimated state of charge of the battery; 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 credibility condition.

7. The method for calibrating the state of charge according to claim 6, characterized in that, The method further includes: Obtain at least one second measured terminal voltage of the battery during a target test process, and the second operating condition index and the measured state of charge corresponding to each of the second measured terminal voltages; For each of the second measured terminal voltages, 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 index; Based on each of the second measured terminal voltages and the corresponding second model terminal voltages, determine the second voltage error of each of the second model terminal voltages; Based on the second voltage errors of the second model terminal voltages, determine the reference voltage error range.

8. A state of charge calibration device, characterized in that The calibration device includes: An acquisition module, configured to acquire the first measured terminal voltage corresponding to the battery at a plurality of sampling time points, and the 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 the estimated state of charge of the battery at the sampling time point using the equivalent circuit model of the battery; A determination module, configured to determine the first voltage error corresponding to the sampling time point based on the first measured terminal voltage corresponding to the sampling time point and the corresponding first model terminal voltage; A calibration module, configured to calibrate the estimated state of charge of the battery when the first voltage errors corresponding to a plurality of the sampling time points show a monotonic change trend; wherein the calibration module is further configured to perform one of the following: During the discharge process of the battery, when the currently estimated state of charge of the battery is within a preset state of charge range and the first voltage errors corresponding to a plurality of the sampling time points show a monotonic increasing trend, or when the currently estimated state of charge of the battery is not within the preset state of charge range and the first voltage errors corresponding to a plurality of the sampling time points show a monotonic decreasing trend, calibrate the estimated state of charge of the battery; the rate of change of the open-circuit voltage of the battery within the preset state of charge range with respect to the state of charge is greater than a rate-of-change threshold; During the charging process of the battery, when the currently estimated state of charge of the battery is not within the preset state of charge interval and the first voltage errors corresponding to multiple sampling time points show a monotonically increasing trend, or when the currently estimated state of charge of the battery is within the preset state of charge interval and the first voltage errors corresponding to multiple sampling time points show a monotonically decreasing trend, the estimated state of charge of the battery is calibrated.

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

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, it implements the steps in the method according to any one of claims 1 to 7.

11. A battery system, characterized in that, It includes 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 device, characterized in that, It includes the battery system according to claim 11.

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