Online estimation method and system for state of charge errors

Abstract

An online estimation method and system for state of charge (SOC) errors. The method comprises: acquiring an initial SOC error value at a power-on moment of a battery management system, and updating the initial SOC error value by means of an OCV table lookup algorithm when a correction condition is satisfied; calculating a real-time SOC error of a battery by means of an ampere-hour integration algorithm; detecting a real-time calculation process of the ampere-hour integration algorithm, and when a Rint model correction condition is triggered, correcting the real-time SOC error by means of a Rint model algorithm; and when the battery reaches a preset condition, zeroing out the SOC error and then performing updating by means of the ampere-hour integration algorithm until a power-off moment of the battery management system is reached, saving a corresponding real-time SOC error and outputting same. A theoretical basis for SOC error estimation is provided to realize online SOC error estimation; algorithms having higher precision are selected during estimation, so as to reduce SOC errors; and the calculation of a current SOC error can be calibrated, such that the method and system are adapted to scenarios involving different battery packs and different aging conditions.

Description

A state of charge error online estimation method and system

[0001] Cross-reference to Related Applications

[0002] This application claims the benefit of Chinese Patent Application No. 202410530131.4, filed April 29, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] The present application relates to the technical field of batteries, in particular to a state of charge error online estimation method and system. BACKGROUND

[0004] At present, in the development of the battery management system (BMS) of an electric vehicle, the state of charge SOC represents the remaining capacity, which represents the ratio of the remaining capacity of a battery after being used for a period of time or a long period of time without use to the capacity of the fully charged state of the battery, and is commonly expressed in percentage. The battery SOC cannot be directly measured, but can only be estimated by parameters such as the battery terminal voltage, charging and discharging current, and internal resistance. These parameters are also affected by various uncertain factors such as battery aging, ambient temperature changes, and vehicle driving states, and there are various deviations in the estimation method, parameter collection, and other aspects. In CN109581242A “State of Charge SOC Error Estimation Method and System”, an error estimation method and system for the state of charge SOC are proposed: the scheme considers various factors affecting the SOC estimation error and proposes a calculation model for algorithm error; the errors corresponding to all error sources determined are introduced into the algorithm to obtain the error of the SOC; although the scheme considers various sources of error comprehensively, it fails to analyze the actual engineering application specifically, resulting in low SOC error estimation accuracy. Therefore, the prior art needs to be improved.

[0005] SUMMARY

[0006] The present application provides a state of charge error online estimation method and system, which solves the problem of low SOC error estimation accuracy of the battery management system in the prior art.

[0007] To solve the above technical problems, the first aspect of the present application provides a state of charge error online estimation method, comprising:

[0008] An initial value of the state of charge error corresponding to the power-on time of the battery on the battery management system is obtained, and when it is detected that the OCV correction condition is met this time, the initial value of the state of charge error is updated by an OCV lookup table algorithm, and the OCV correction condition is that the battery is stationary for a first preset time;

[0009] updating the state of charge error of the battery in real time by the ampere-hour integration algorithm and the initial value of the state of charge error, to obtain a real-time state of charge error;

[0010] correcting the real-time state of charge error by the Rint model algorithm when it is detected that the updating process of the state of charge error of the battery triggers a Rint model correction condition;

[0011] when the battery reaches a preset condition, clearing the real-time state of charge error and taking it as the initial value of the state of charge error, so as to update the state of charge error of the battery in real time by the ampere-hour integration algorithm until the power-off time of the battery management system is reached, saving the real-time state of charge error corresponding to the power-off time of the battery management system and outputting.

[0012] Further, the Rint model correction condition is that when the updating process of the state of charge error of the battery reaches a second preset time, a first state of charge error is less than a second state of charge error; wherein the first state of charge error is obtained by calculating the state of charge error of the battery by the ampere-hour integration algorithm and the initial value of the state of charge error; and the second state of charge error is obtained by calculating the state of charge error of the battery by the Rint model algorithm.

[0013] Further, the preset condition includes a first preset condition and a second preset condition; wherein the first preset condition is that the battery is in a full charge state; the second preset condition is that the battery is in an empty state; when the battery reaches the first preset condition, the battery completes a charging process; when the battery reaches the second preset condition, the battery completes a discharging process; and the battery reaching a preset condition specifically means that the battery reaches the first preset condition or the second preset condition.

[0014] Further, before the real-time state of charge error corresponding to the power-off time of the battery management system is saved and outputted until the power-off time of the battery management system is reached, the method further includes:

[0015] detecting the updating process of the state of charge error of the battery, and calibrating the real-time state of charge error when the calibration algorithm is triggered.

[0016] Further, the calibration algorithm includes an OCV lookup table calibration algorithm, an ampere-hour integration calibration algorithm and a Rint model calibration algorithm; wherein,

[0017] The calibration conditions corresponding to the OCV lookup table calibration algorithm include a first calibration condition, a second calibration condition, and a third calibration condition; the first calibration condition is that the initial error of the state of charge is calculated by the OCV lookup table algorithm; the second calibration condition is that the battery completes one charging or discharging process; and the third calibration condition is that the battery again completes one charging or discharging process, and the real-time error of the state of charge in the process is calculated only by the ampere-hour integral algorithm.

[0018] The ampere-hour integral calibration algorithm includes a fourth calibration condition, a fifth calibration condition, and a sixth calibration condition; the fourth calibration condition is that the battery completes one charging or discharging process; the fifth calibration condition is that the battery again completes one charging or discharging process, and the completed process is the same as that in the fourth calibration condition; and the sixth calibration condition is that the battery again completes one charging or discharging process, and the completed process is different from that in the fourth calibration condition.

[0019] The Rint model calibration algorithm includes a seventh calibration condition, an eighth calibration condition, and a ninth calibration condition; the seventh calibration condition is that the battery completes one charging or discharging process corresponding to the seventh calibration condition; the eighth calibration condition is that, in the charging or discharging process corresponding to the seventh calibration condition, the second error of the state of charge calculated by the Rint model algorithm is taken as the real-time second error of the state of charge; and the ninth calibration condition is that the battery again completes one charging or discharging process.

[0020] Further, the updating process of the error of the state of charge of the battery is detected, and when the calibration algorithm is triggered, the real-time error of the state of charge is calibrated, including:

[0021] When the updating process of the error of the state of charge of the battery meets the first calibration condition, the second calibration condition, and the third calibration condition at the same time, the OCV lookup table calibration algorithm is triggered, and the real-time error of the state of charge is calibrated by the following formula: e(OVC0)=e1-Q1 / Q2*e2

[0022] In the formula, e(OVC0) is the real-time error of the state of charge after calibration by the OCV lookup table calibration algorithm; e1 and Q1 are respectively the real-time error of the state of charge in the process corresponding to the second calibration condition and the discharge capacity of the battery; and e2 and Q2 are respectively the real-time error of the state of charge in the process corresponding to the second calibration condition and the discharge capacity of the battery.

[0023] Further, the updating process of the error of the state of charge of the battery is detected, and when the calibration algorithm is triggered, the real-time error of the state of charge is calibrated, including:

[0024] When the updating process of the error of the state of charge of the battery meets the fourth calibration condition, the fifth calibration condition, and no other calibration algorithm is triggered, the ampere-hour integral calibration algorithm is triggered, and an average error is obtained by counting and according to the discharge capacity of the battery in the process corresponding to the fourth calibration condition and the fifth calibration condition; the average error is the error of the average charging of 10 AH of the battery;

[0025] The average error is recalibrated, and the calibrated average error is taken as the real-time state of charge error calibrated by the ampere-hour integral calibration algorithm.

[0026] Further, the updating process of the error of the state of charge of the battery is detected, and when a calibration algorithm is triggered, the real-time state of charge error is calibrated, comprising:

[0027] When the updating process of the error of the state of charge of the battery meets the fourth calibration condition, the sixth calibration condition, the discharge capacity of the battery in the process corresponding to the fourth calibration condition and the sixth calibration condition is less than a preset capacity, and no other calibration algorithm is triggered, the ampere-hour integral calibration algorithm is triggered, and the calculation error of the capacity aging SOH of the battery is taken as the real-time state of charge error for calibration, and the calibration result obtained is taken as the real-time state of charge error calibrated by the ampere-hour integral calibration algorithm.

[0028] Further, the updating process of the error of the state of charge of the battery is detected, and when a calibration algorithm is triggered, the real-time state of charge error is calibrated, comprising:

[0029] When the updating process of the error of the state of charge of the battery meets the seventh calibration condition, the eighth calibration condition, the ninth calibration condition, the discharge capacity of the battery in the process corresponding to the seventh calibration condition, the eighth calibration condition and the ninth calibration condition is less than the preset capacity, and no other calibration algorithm is triggered, the Rint model calibration algorithm is triggered, and the real-time state of charge error is calibrated by the following formula: e(Rint) = e1 - Q1 / Q2 * e2

[0030] In the formula, e(Rint) is the real-time state of charge error calibrated by the Rint model calibration algorithm; e1 and Q1 are respectively the real-time state of charge error in the process corresponding to the seventh calibration condition and the discharge capacity of the battery; e2 and Q2 are respectively the real-time state of charge error in the process corresponding to the ninth calibration condition and the discharge capacity of the battery.

[0031] Further, an initial value of the error of the state of charge corresponding to the power-on time of the battery on the battery management system is obtained, comprising:

[0032] determining the state of the battery when the battery management system is powered on, if it is detected that the current power-on does not meet the OCV correction condition, the state of charge error stored in the memory of the battery management system is taken as the initial value of the state of charge error.

[0033] Further, the calculation expression of the ampere-hour integral algorithm is:

[0034] In the formula, SOC0 is the initial value of SOC; η is the charging and discharging efficiency; I is the charging and discharging current; t is the integral period; N is the number of integral periods starting from SOC0; SOH is the capacity aging state; Q rate is the rated capacity of the battery.

[0035] Further, the real-time state of charge error is corrected by the Rint model algorithm, comprising:

[0036] The minimum value is selected from the first state of charge error and the second state of charge error as the real-time state of charge error at the current time, and the algorithm corresponding to the selected error is used to continue calculating the state of charge error of the battery until the battery reaches the preset condition.

[0037] Further, when the battery is in a full charge state, the maximum single cell voltage of the battery is greater than or equal to the full charge voltage, and the charging current of the battery is less than or equal to a first preset current and lasts for a third preset time.

[0038] Further, when the battery is in a discharge state, the minimum single cell voltage of the battery is less than or equal to the discharge cutoff voltage, and the discharging current of the battery is less than or equal to a second preset current and lasts for a fourth preset time

[0039] The second aspect of the present application provides a state of charge error online estimation system, comprising:

[0040] The data acquisition module is configured to acquire an initial value of the state of charge error corresponding to the power-on time of the battery management system, and update the initial value of the state of charge error by the OCV lookup table algorithm when it is detected that the current power-on meets the OCV correction condition, wherein the OCV correction condition is that the battery is stationary for a first preset time.

[0041] The error updating module is configured to update the state of charge error of the battery in real time by the ampere-hour integral algorithm and the initial value of the state of charge error to obtain a real-time state of charge error.

[0042] The error correction module is configured to correct the real-time state of charge error by the Rint model algorithm when it is detected that the updating process of the state of charge error of the battery triggers the Rint model correction condition.

[0043] The iterative update module is used to clear the real-time state of charge error to zero and use it as the initial value of the state of charge error when the battery reaches the preset conditions, so as to update the state of charge error of the battery in real time through the ampere-hour integration algorithm until the power-off time of the battery management system is reached, save the real-time state of charge error corresponding to the power-off time of the battery management system and output it.

[0044] Compared with the prior art, the beneficial effects of the embodiments of this application are as follows:

[0045] This application provides an online estimation method and system for state of charge (SCC) error. The method includes: obtaining an initial SCC error value corresponding to the power-on time of the battery management system; updating the initial SCC error value using an OCV lookup table algorithm when the power-on condition is detected to be met, wherein the OCV correction condition is that the battery has been stationary for a first preset time; updating the SCC error of the battery in real time using an ampere-hour integral algorithm and the initial SCC error value to obtain a real-time SCC error; correcting the real-time SCC error using a Rint model algorithm when the update process triggers a Rint model correction condition; clearing the real-time SCC error to zero and using it as the initial SCC error value when the battery reaches the preset condition, so as to update the SCC error of the battery in real time using the ampere-hour integral algorithm until the power-off time of the battery management system is reached; saving and outputting the real-time SCC error corresponding to the power-off time of the battery management system. This application provides a theoretical basis for SOC error estimation and enables online SOC error estimation; it selects a higher precision algorithm during estimation to reduce SOC error; and it can calibrate the calculation of the current SOC error to match different battery packs and different aging conditions. Attached Figure Description

[0046] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 is a flowchart of an online estimation method for state of charge error provided in a certain embodiment of this application;

[0048] Figure 2 is an error diagram corresponding to positive and negative COV bias according to a certain embodiment of this application;

[0049] Figure 3 shows the SOH provided in a certain embodiment of this application. m SOH errorA relationship diagram with SOC error;

[0050] Fig. 4 is a Rint battery model diagram provided by an embodiment of the present application;

[0051] Fig. 5 is a device diagram of a state of charge error online estimation system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings and embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0053] It should be understood that the step numbers used herein are only for the convenience of description, and are not limited to the execution sequence of the steps.

[0054] It should be understood that the terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0055] The terms "comprise" and "include" indicate the presence of described features, whole, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0056] The term "and / or" means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0057] In an embodiment, as shown in Fig. 1, the first aspect of the present application provides a state of charge error online estimation method, comprising:

[0058] S1, obtaining a state of charge error initial value corresponding to a power-on time of a battery on a battery management system, and updating the state of charge error initial value by OCV lookup table algorithm when it is detected that the present power-on satisfies OCV (Open Circuit Voltage) correction condition, the OCV correction condition being that the battery is rested for a first preset time;

[0059] Specifically, when the battery management system is powered on, the state of the battery is determined, if it does not meet the OCV correction condition, the state of charge error stored in the memory of the battery management system is read as the initial value of the state of charge error; if it has been stationary for a first predetermined time, it meets the OCV correction condition, and the corresponding relationship between OCV and SOC is used to estimate the SOC of the battery by table lookup; wherein the first predetermined time is determined according to the actual situation, and no specific numerical value is limited. Assuming that there is a good linear relationship between the OCV curve between samples, the expression is as follows: SOC ref =f(OCV)

[0060] In the formula, f(OCV) is the OCV table calculation result using linear interpolation algorithm; SOC ref is the true value of the SOC reference.

[0061] Due to the voltage sampling error, the SOC obtained by table lookup also has an error. Assuming that the current battery meets the static time for OCV correction, the open circuit voltage sampling value is OCV m , the open circuit voltage true value is OCV0, and the error between them is Verror, then OCV0=OCV m -V error

[0062] At this time, the estimation error of SOC error can be expressed as: SOC error =f(OCV m )-f(OCV m -V error )

[0063] Next, taking the sampling accuracy of a certain sampling chip as an example, the voltage sampling accuracy is shown in Table 1. It is known that a certain battery has an OCV range of 2.966V-4.092V corresponding to 0-100% SOC, as shown in Table 2. Within the range of OCV, the corresponding voltage sampling accuracy is ±3.9mV, as shown in Table 1:

[0064] The OCV-SOC table of the battery (Table 2) is as follows:

[0065] The voltage sampling accuracy is brought into the estimation error formula of SOC, and the calculation can obtain the errors when the voltage is positive and negative, respectively: SOC error+ and SOC error- : SOC error+ =f(OCV m )-f(OCV m -3.9mV) SOC error- =f(OCV m) - f(OCV m + 3.9 mV)

[0066] Assuming that the sampling voltage of the current open circuit voltage is the OCV voltage corresponding to Table 2, the errors corresponding to the positive and negative biases of the COV are shown in FIG. 2. To protect the battery, when charging, the SOC may be considered to be small to prevent overcharging; when discharging, the SOC may be considered to be large to prevent overdischarging. Therefore, the positive and negative bias errors of the SOC need to be considered separately for different use scenarios in actual use. However, in fact, the OCV-SOC curve is affected by the temperature T and the battery aging SOH. Therefore, in order to reduce the estimation error, the OCV curve formula can be optimized: SOC ref_T,SOH = f(OCV, T, SOH)

[0067] In the formula, f(OCV, T, SOH) is the OCV lookup table calculation result considering temperature and aging; SOC ref_T,SOH is the true value of the SOC reference considering the influence of temperature and aging.

[0068] At this time, the estimation error of the SOC SOC error can be expressed as: SOC error = f(OCV m , T, SOH) - f(OCV m -V error , T, SOH)

[0069] Because the influence of temperature on the OCV-SOC curve is small, to simplify the calculation, only the influence of the SOH error on the calculation result can be considered: SOC error = f(OCV m , T, SOH m ) - f(OCV m -V error , T, SOH m -SOH error )

[0070] In the formula, SOH m is the calculated value of SOH; SOH error is the error corresponding to the calculated value of SOH. Since the SOH error has two directions of positive and negative, it needs to be considered together with the positive and negative deviations of the OCV. There are four arrangements as shown in Table 3, and the maximum value is taken as the large error, and the minimum value is taken as the small error:

[0071] Whether the battery has been rested for a first preset time in the application, the state of charge error of the battery is initialized by different methods, which ensures the accuracy of the initial value and facilitates the accurate calculation of the real-time state of charge error of the battery subsequently.

[0072] S2, updating the state of charge error of the battery in real time by the ampere-hour integration algorithm and the initial state of charge error, to obtain a real-time state of charge error;

[0073] Specifically, the ampere-hour integration algorithm is mainly applicable to a working condition where the SOC initial value is known, and the SOC estimation has good accuracy when the current sensor has high accuracy. The calculation expression is:

[0074] In the formula, SOC0 is the SOC initial value; η is the charging and discharging efficiency; I is the charging and discharging current; t is the integration period; N is the number of integration periods starting from SOC0; SOH is the capacity aging state; Q rate is the rated capacity of the battery.

[0075] The rated capacity of the battery can be directly measured at the factory by default, which is a standard reference value, and the error is not considered. The error of the charging and discharging efficiency is generally above 99%, and for the purpose of simplifying the calculation, the error is not considered here, and the default value is set to 100%. The integration period is determined by the Timer function of the MCU, and the error is small, so the error is not considered here. Therefore, when the error of Qrate, η and t is not considered, the SOC error of the ampere-hour integration algorithm is related to the error of the initial value, the SOH error and the current error: SOC0= SOC 0m -SOC 0error SOH=SOH m -SOH error I(k)=I(k) m -I(k) error

[0076] In the formula, SOC0, SOH and I(k) are the true values corresponding to the SOC initial value, the capacity aging and the sampling current, respectively; SOC 0m , SOH m , I(k) m are the corresponding calculation values, and SOC 0error , SOH error , I(k) error are the corresponding errors.

[0077] Without considering the error of Qrate, η and t, the calculation value of the current SOC SOC cal is:

[0078] Subtracting the error, the true value is obtained:

[0079] The above two formulas can be arranged as follows:

[0080] Therefore, the SOC error calculated by the ampere-hour integration method can be divided into three parts. For convenience of the following description, A-B+C is used; A is the initial SOC error, when SOH errror and I(k) error is 1, the SOC error is equal to the initial SOC error according to the above formula; B is the middle part of the above formula, when SOC 0errror and I(k) error is 0, the SOC error is related to the current integration amount, the calculated SOH SOH m and the SOH error SOH error C is the third part of the above formula, when the initial error of SOC and the estimation error of SOH are 0, the estimation error of SOC is mainly related to the error of the current.

[0081] In B, the error of SOC is proportional to the current calculated cumulative integral; when the battery starts from full charge to empty, the maximum value of the SOC error can be calculated, which satisfies:

[0082] At this time, the error of SOC can be expressed as:

[0083] The SOH m in the above formula, the relationship between SOH error and the SOC error is shown in FIG. 3. When the estimation error of SOH is constant, the error of SOC will gradually increase with the increase of SOH aging. For the ampere-hour integration algorithm, to control the SOC error within 3%, the estimation accuracy of SOH needs to be <±3%.

[0084] In C, to evaluate the influence of current integration on the SOC error, a standard current meter is needed to calibrate the current sampling chip: the corresponding current error is obtained by using the current detected by the current Hall sensor to look up the table, and the error is integrated into C to calculate the SOC error caused by the current sampling error. A simpler method is to import a specific working condition, and the battery is charged and discharged in a certain interval for 50 times, and then the battery is emptied, and the SOC error at the end of the working condition is calculated. Since in the overcharge cycle, the charge capacity ≈ discharge capacity. Therefore, the charge capacity and the SOC error corresponding to 50 cycles can be counted to obtain the error e_chg10Ah corresponding to 10AH (calibration). At this time, the SOC error caused by the current error can be expressed as: SOC error = SOC error (A)-SOC error (B)+SOC error (C)

[0085] SOC initial value, SOH, the size of the current error in two directions, in the case of uncertainty A, B, C three parts of the error direction, SOC error can be written as the absolute value of the three, that is: SOC error = ± (|SOC error (A) | + |SOC error (B) | + |SOC error (C) |).

[0086] S3, when the update process of the error of the state of charge of the battery is detected to trigger the Rint model correction condition, the real-time state of charge error is corrected by the Rint model algorithm;

[0087] Wherein, the Rint model correction condition is that when the update process reaches the second preset time, the first state of charge error is less than the second state of charge error; wherein, the first state of charge error is calculated by the ampere-hour integral algorithm and the initial value of the state of charge error of the battery; the second state of charge error is calculated by the Rint model algorithm.

[0088] Specifically, the Rint model algorithm is a simplified battery model, as shown in Figure 4, which estimates SOC by estimating OCV under dynamic conditions by using the relationship between internal resistance and voltage. Because the Rint model does not consider the influence of voltage polarization, it is generally applicable to conditions above 10℃, and small current (generally <0.1C) for a period of time (OCV can be referred to as the static time). As shown in Figure 4, taking the discharge direction as an example: OCV m = V m + I m × R 0m

[0089] In the formula, V m , I m , R 0m are the sampling voltage, sampling current, and equivalent ohmic resistance, respectively; OCV m is the calculated open circuit voltage. Among them, considering the aging of the internal resistance, R 0m can be written as the following expression: R 0m = R 0_BOL × SOHR m

[0090] In the formula, R 0_BOL and SOHR m represent the internal resistance at BOL and its corresponding internal resistance aging state, respectively, and the SOC estimation value SOC cal is calculated: SOC cal = f (OCV m ) = f (Vm +I m ×R 0_BOL ×SOHR m )

[0091] In the formula, R 0_BOL is the initial value of the internal resistance, which is related to temperature and SOC.

[0092] Considering the errors of V m , I m and SOHR m , the expression of the true SOC SOC real is SOC real = f(V m -V error +R 0_BOL ×(I m -I error )×(SOHR m -SOHR error ))

[0093] The SOC error can be obtained by subtracting the SOC estimated value SOC cal from the true SOC SOC real .

[0094] In the charging and discharging process of the battery, the ampere-hour integral algorithm is essentially running all the time, and only when the second preset time is reached in each charging and discharging process, the state of charge error calculated by the Rint model algorithm is compared with the state of charge error calculated by the ampere-hour integral algorithm at this time, and the smaller one is taken as the real-time state of charge error at the current time, and the algorithm corresponding to the comparison result is used to continue calculation until the end of the charging or discharging process of the battery. The application selects the algorithm with higher accuracy when estimating to reduce the SOC error.

[0095] S4, when the battery reaches a preset condition, the real-time state of charge error is cleared and taken as the initial value of the state of charge error, so as to update the state of charge error of the battery in real time by the ampere-hour integral algorithm until the power-off time of the battery management system is reached, the real-time state of charge error corresponding to the power-off time of the battery management system is saved and outputted;

[0096] In an embodiment, the preset condition includes a first preset condition and a second preset condition; wherein the first preset condition is that the battery is in a full charge state; the second preset condition is that the battery is in an empty state; when the battery reaches the first preset condition, the battery completes a charging process; when the battery reaches the second preset condition, the battery completes a discharging process; the battery reaching a preset condition specifically refers to the battery reaching the first preset condition or the second preset condition.

[0097] Specifically, when the battery is in a full charge state, the highest single cell voltage of the battery is greater than or equal to a full charge voltage, a charging current of the battery is less than or equal to a first preset current, and the battery lasts for a third preset time; when the battery is in an empty state, the lowest single cell voltage of the battery is less than or equal to a discharge cutoff voltage, a discharging current of the battery is less than or equal to a second preset current, and the battery lasts for a fourth preset time; wherein the full charge voltage and the first preset current and the third preset time in the charging cutoff stage are calibration values, and the discharge cutoff voltage and the second preset current and the fourth preset time in the discharging cutoff stage are calibration values, and the calibration values are determined according to actual conditions, and no specific numerical value is limited here.

[0098] The application divides the power-on and power-off process of the battery management system into the cycle charging and discharging process of the battery. In each charging and discharging process of the battery, the state of charge error is updated in real time by the ampere-hour integral algorithm, and a higher precision algorithm is selected during estimation to reduce the SOC error. The charging and discharging process of the battery is detected, and when the Rint model correction condition is met, the real-time state of charge error of the battery is corrected by the Rint model algorithm. When the calibration algorithm is triggered, the real-time state of charge error of the battery is calibrated by a suitable calibration algorithm to improve the accuracy of online estimation of the SOC error and realize matching of different battery packs and different aging conditions. When the battery is fully charged or empty, the real-time state of charge error is cleared and the next discharging or charging process of the battery is performed, realizing accurate estimation of the SOC error in the whole life cycle of the battery.

[0099] In an embodiment, before saving the real-time state of charge error corresponding to the power-off time of the battery management system and outputting before the power-off time of the battery management system is reached, the application further comprises: detecting the updating process of the state of charge error of the battery, and calibrating the real-time state of charge error when the calibration algorithm is triggered, wherein the calibration algorithm comprises an OCV lookup table calibration algorithm, an ampere-hour integral calibration algorithm and a Rint model calibration algorithm; wherein,

[0100] The calibration conditions corresponding to the OCV lookup table calibration algorithm comprise a first calibration condition, a second calibration condition and a third calibration condition; the first calibration condition is that the initial value of the state of charge error is calculated by the OCV lookup table algorithm; the second calibration condition is that the battery completes a charging or discharging process; and the third calibration condition is that the battery again completes a charging or discharging process, and the real-time state of charge error of the process is calculated only by the ampere-hour integral algorithm.

[0101] In an embodiment, detecting the updating process of the state of charge error of the battery and calibrating the real-time state of charge error when the calibration algorithm is triggered comprises:

[0102] OCV table calibration algorithm is triggered when the updating process of the state of charge error of the battery meets the first calibration condition, the second calibration condition and the third calibration condition simultaneously, and the real-time state of charge error is calibrated by the following formula: e(OVC0) = e1 - Q1 / Q2*e2

[0103] In the formula, e(OVC0) is the real-time state of charge error calibrated by the OCV table calibration algorithm; e1 and Q1 are respectively the real-time state of charge error and the discharge capacity of the battery corresponding to the process of the second calibration condition; e2 and Q2 are respectively the real-time state of charge error and the discharge capacity of the battery corresponding to the process of the third calibration condition.

[0104] Specifically, when the SOC initial value error and the current collection error are not considered, the estimation error of the SOC is proportional to the ampere-hour integral capacity. To simplify the calculation, the current sampling error can be ignored when the throughput (capacity integral in one current direction) between two full charges or emptying is less than 2 times the rated capacity. The present application uses e(OVC0) to replace the theoretically calculated value (table value) under the temperature and SOH condition at OCV0, so as to calibrate the error calculation of the SOC. Because OCV0 may not be completely consistent with the table OCV value, the present application takes the point closest to OCV0 in the table OCV to improve the accuracy of the SOC estimation.

[0105] The ampere-hour integral calibration algorithm includes a fourth calibration condition, a fifth calibration condition and a sixth calibration condition; the fourth calibration condition is that the battery completes one charging or discharging process; the fifth calibration condition is that the battery again completes one charging or discharging process, and the completed process is the same as the process completed in the fourth calibration condition; and the sixth calibration condition is that the battery again completes one charging or discharging process, and the completed process is different from the process completed in the fourth calibration condition.

[0106] In an embodiment, the updating process of the state of charge error of the battery is detected, and the real-time state of charge error is calibrated when the calibration algorithm is triggered, including:

[0107] The ampere-hour integral calibration algorithm is triggered when the updating process of the state of charge error of the battery meets the fourth calibration condition and the fifth calibration condition, and no other calibration algorithm is triggered, and the average error is obtained by counting and according to the discharge capacity of the battery corresponding to the processes of the fourth calibration condition and the fifth calibration condition; the average error is the error of the battery when the average charge is 10 AH.

[0108] The average error is re-calibrated, and the calibrated average error is taken as the real-time state of charge error calibrated by the ampere-hour integral calibration algorithm.

[0109] Specifically, the current working condition meets the fourth calibration condition and the fifth calibration condition, and no other SOC calibration mode is triggered during this period. At this time, the SOC error is approximately equal to the integral error of the current, and the integral amount during this period is counted to obtain the error of the average charging 10AH. Thus, e_chg10Ah is recalibrated. To reduce the influence of accidental errors, the throughput requirement can be increased, for example, more than 10 times the rated capacity.

[0110] The throughput here refers to the charging and discharging capacity of the battery, which is generally used to count the cycle times of the battery. Since the charging capacity and the discharging capacity of the battery are approximately equal, one direction capacity can be used for counting in actual use. For example, the discharging direction capacity is used for counting. When the discharging capacity reaches 1 times the rated capacity, it can be considered that the battery has undergone an equivalent full charge and slow discharge cycle.

[0111] In an embodiment, the updating process of the state of charge error of the battery is detected, and the real-time state of charge error is calibrated when the calibration algorithm is triggered, further comprising:

[0112] When the updating process of the state of charge error of the battery meets the fourth calibration condition, the sixth calibration condition, and the battery discharge capacity corresponding to the fourth calibration condition and the sixth calibration condition is less than a preset capacity, and no other calibration algorithm is triggered, the ampere-hour integral calibration algorithm is triggered, and the calculation error of the capacity aging SOH of the battery is calibrated as the real-time state of charge error. The calibration result obtained is taken as the real-time state of charge error calibrated by the ampere-hour integral calibration algorithm.

[0113] Specifically, the current working condition meets the fourth calibration condition and the sixth calibration condition, and the total capacity throughput during this period is less than 2 times the rated capacity, and no other SOC calibration mode is triggered. At this time, the SOC error is approximately equal to the calculation error of the capacity aging SOH. The estimation error of SOH at this time can be calibrated, so as to calibrate the error caused by the SOH error in the ampere-hour integral algorithm.

[0114] The Rint model calibration algorithm includes a seventh calibration condition, an eighth calibration condition, and a ninth calibration condition. The seventh calibration condition is that the battery completes a charging or discharging process corresponding to the seventh calibration condition. The eighth calibration condition is that the second state of charge error calculated by the Rint model algorithm is taken as the real-time second state of charge error during the charging or discharging process corresponding to the seventh calibration condition. The ninth calibration condition is that the battery completes a charging or discharging process again.

[0115] In an embodiment, the updating process of the state of charge error of the battery is detected, and the real-time state of charge error is calibrated when the calibration algorithm is triggered, comprising:

[0116] When the update process of the error of the state of charge of the battery meets the seventh calibration condition, the eighth calibration condition, the ninth calibration condition, and the discharge capacity of the battery corresponding to the seventh calibration condition, the eighth calibration condition, and the ninth calibration condition is less than the preset capacity, and other calibration algorithms are not triggered, the Rint model calibration algorithm is triggered, and the real-time error of the state of charge is calibrated by the following formula: e(Rint) = e1 - Q1 / Q2*e2

[0117] In the formula, e(Rint) is the real-time error of the state of charge calibrated by the Rint model calibration algorithm; e1 and Q1 are the real-time error of the state of charge and the discharge capacity of the battery corresponding to the seventh calibration condition, respectively; e2 and Q2 are the real-time error of the state of charge and the discharge capacity of the battery corresponding to the ninth calibration condition, respectively.

[0118] Specifically, when the seventh calibration condition, the eighth calibration condition, and the ninth calibration condition are met, and other SOC calibration methods have not been triggered, the total capacity throughput is less than 2 times the rated capacity during this period. At this time, the SOC error is approximately the SOC error estimated by the Rint model; the errors of voltage sampling and current sampling are considered comprehensively, and the error of the SOHR is calibrated, so that the SOC error calculated by the Rint model is more accurately evaluated.

[0119] The embodiment of the application is based on the problem that the SOC error estimation accuracy of the battery in the battery management system is not high, and an online state of charge error estimation method is designed, which realizes the online state of charge error estimation method and system provided by the application. The method comprises: obtaining an initial state of charge error value corresponding to the power-on time of the battery on the battery management system, and updating the initial state of charge error value through an OCV lookup table algorithm when it is detected that the current power-on satisfies the OCV correction condition, wherein the OCV correction condition is that the battery is stationary for a first preset time; the state of charge error of the battery is updated in real time through the ampere-hour integral algorithm and the initial state of charge error value, and a real-time state of charge error is obtained; when it is detected that the update process triggers the Rint model correction condition, the real-time state of charge error is corrected through the Rint model algorithm; when the battery reaches a preset condition, the real-time state of charge error is cleared and used as the initial state of charge error value, so that the state of charge error of the battery is updated in real time through the ampere-hour integral algorithm until the power-off time of the battery management system is reached, the real-time state of charge error corresponding to the power-off time of the battery management system is saved and output. The technical scheme provides a theoretical basis for SOC error estimation, realizes online SOC error estimation, selects a higher precision algorithm during estimation to reduce SOC error, and can calibrate the calculation of the current SOC error to realize matching of different battery packs and different aging conditions.

[0120] It should be noted that although each step in the above flowchart is displayed in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders.

[0121] In another embodiment, as shown in FIG. 5, the second aspect of the application provides an online state of charge error estimation system, comprising:

[0122] The data acquisition module 10 is configured to obtain an initial state of charge error value corresponding to the power-on time of the battery on the battery management system, and update the initial state of charge error value through an OCV lookup table algorithm when it is detected that the current power-on satisfies the OCV correction condition, wherein the OCV correction condition is that the battery is stationary for a first preset time.

[0123] The error update module 20 is configured to update the state of charge error of the battery in real time through the ampere-hour integral algorithm and the initial state of charge error value, and obtain a real-time state of charge error.

[0124] an error correction module 30, configured to correct the real-time state-of-charge error by using an Rint model algorithm when it is detected that an update process triggers an Rint model correction condition of the state-of-charge error of the battery;

[0125] an iterative update module 40, configured to clear the real-time state-of-charge error and take it as the initial value of the state-of-charge error when the battery reaches a preset condition, so as to update the state-of-charge error of the battery in real time by using the ampere-hour integration algorithm until the power-off time of the battery management system is reached, save the real-time state-of-charge error corresponding to the power-off time of the battery management system, and output the real-time state-of-charge error.

[0126] It should be noted that the above-mentioned various modules in the state-of-charge error online estimation system can be realized by software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the state-of-charge error online estimation system in hardware form, or can be stored in the memory in the state-of-charge error online estimation system in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned various modules. For the specific limitations of the state-of-charge error online estimation system, refer to the limitations of the state-of-charge error online estimation method, which have the same functions and effects, and will not be described here.

[0127] In summary, the present application provides a state-of-charge error online estimation method and system, wherein the method comprises: obtaining an initial value of a state-of-charge error of a battery at a power-on time of a battery management system, and updating the initial value of the state-of-charge error by using an OCV lookup table algorithm when it is detected that the current power-on satisfies an OCV correction condition, the OCV correction condition being that the battery is rested for a first preset time; updating the state-of-charge error of the battery in real time by using an ampere-hour integration algorithm and the initial value of the state-of-charge error to obtain a real-time state-of-charge error; correcting the real-time state-of-charge error by using an Rint model algorithm when it is detected that an update process triggers an Rint model correction condition; clearing the real-time state-of-charge error and taking it as the initial value of the state-of-charge error when the battery reaches a preset condition, so as to update the state-of-charge error of the battery in real time by using the ampere-hour integration algorithm until the power-off time of the battery management system is reached, save the real-time state-of-charge error corresponding to the power-off time of the battery management system, and output the real-time state-of-charge error. The present application provides a theoretical basis for SOC error estimation, realizes online estimation of SOC error, selects a higher-precision algorithm during estimation to reduce the SOC error, and can calibrate the calculation of the current SOC error to realize matching of different battery packs and different aging conditions.

[0128] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0129] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method of estimating state-of-charge error on-line, wherein, The method comprises the following steps: acquiring an initial state-of-charge error corresponding to the time when the battery is powered on by a battery management system, and updating the initial state-of-charge error by an OCV lookup table algorithm when it is detected that the present power-on meets an OCV correction condition, the OCV correction condition being that the battery has been at rest for a first preset time; updating the state-of-charge error of the battery in real time by an ampere-hour integral algorithm and the initial state-of-charge error to obtain a real-time state-of-charge error; correcting the real-time state-of-charge error by a Rint model algorithm when it is detected that the updating process of the state-of-charge error of the battery triggers a Rint model correction condition; when the battery meets a preset condition, clearing the real-time state-of-charge error and taking it as the initial state-of-charge error, so as to update the state-of-charge error of the battery in real time by the ampere-hour integral algorithm until the power-off time of the battery management system is reached, saving the real-time state-of-charge error corresponding to the power-off time of the battery management system and outputting it.

2. The method of claim 1, wherein, The Rint model correction condition is that when the updating process of the state-of-charge error of the battery reaches a second preset time, a first state-of-charge error is greater than a second state-of-charge error; wherein the first state-of-charge error is obtained by calculating the state-of-charge error of the battery by the ampere-hour integral algorithm and the initial state-of-charge error; and the second state-of-charge error is obtained by calculating the state-of-charge error of the battery by the Rint model algorithm.

3. The method of claim 1, wherein, The preset condition comprises a first preset condition and a second preset condition; wherein the first preset condition is that the battery is in a full charge state; the second preset condition is that the battery is in an empty state; when the battery meets the first preset condition, the battery completes a charging process; when the battery meets the second preset condition, the battery completes a discharging process; and the battery meeting the preset condition specifically means that the battery meets the first preset condition or the second preset condition.

4. The method of claim 3, wherein, Before the real-time state-of-charge error corresponding to the power-off time of the battery management system is saved and outputted until the power-off time of the battery management system is reached, the method further comprises the following steps: detecting the updating process of the state-of-charge error of the battery, and calibrating the real-time state-of-charge error when the calibration algorithm is triggered.

5. A method of online estimation of state-of-charge error according to claim 4, wherein, The calibration algorithm comprises an OCV lookup table calibration algorithm, an ampere-hour integral calibration algorithm and a Rint model calibration algorithm; wherein, the calibration conditions corresponding to the OCV lookup table calibration algorithm comprise a first calibration condition, a second calibration condition and a third calibration condition; the first calibration condition is that the initial state-of-charge error is calculated by the OCV lookup table algorithm; the second calibration condition is that the battery completes a charging or discharging process; and the third calibration condition is that the battery completes a charging or discharging process again, and the real-time state-of-charge error of the process is calculated only by the ampere-hour integral algorithm. The ampere-hour integration calibration algorithm comprises a fourth calibration condition, a fifth calibration condition and a sixth calibration condition; the fourth calibration condition is that the battery completes one charging or discharging process; the fifth calibration condition is that the battery again completes one charging or discharging process, and the completed process is the same as that in the fourth calibration condition; and the sixth calibration condition is that the battery again completes one charging or discharging process, and the completed process is different from that in the fourth calibration condition. The Rint model calibration algorithm comprises a seventh calibration condition, an eighth calibration condition and a ninth calibration condition; the seventh calibration condition is that the battery completes one charging or discharging process; the eighth calibration condition is that, in the charging or discharging process corresponding to the seventh calibration condition, the second state-of-charge error calculated by the Rint model algorithm is taken as a real-time second state-of-charge error; and the ninth calibration condition is that the battery again completes one charging or discharging process.

6. A method of online estimation of state-of-charge error according to claim 5, wherein, The updating process of the state-of-charge error of the battery is detected, and the real-time state-of-charge error is calibrated when a calibration algorithm is triggered, comprising: When the updating process of the state-of-charge error of the battery meets the first calibration condition, the second calibration condition and the third calibration condition at the same time, the OCV lookup table calibration algorithm is triggered, and the real-time state-of-charge error is calibrated by the following formula: e(OVC0)=e1-Q1 / Q2*e2 In the formula, e(OVC0) is the real-time state-of-charge error calibrated by the OCV lookup table calibration algorithm; e1 and Q1 are respectively the real-time state-of-charge error of the process corresponding to the second calibration condition and the discharge capacity of the battery; and e2 and Q2 are respectively the real-time state-of-charge error of the process corresponding to the third calibration condition and the discharge capacity of the battery.

7. The method of online estimation of state-of-charge error according to claim 5, wherein, The updating process of the state-of-charge error of the battery is detected, and the real-time state-of-charge error is calibrated when a calibration algorithm is triggered, comprising: When the updating process of the state-of-charge error of the battery meets the fourth calibration condition, the fifth calibration condition and no other calibration algorithm is triggered at the same time, the ampere-hour integration calibration algorithm is triggered, and an average error is obtained by counting and according to the discharge capacity of the battery in the processes corresponding to the fourth calibration condition and the fifth calibration condition; the average error is the error of the battery when the battery is charged with an average of 10 AH; The average error is re-calibrated, and the calibrated average error is taken as the real-time state-of-charge error calibrated by the ampere-hour integration calibration algorithm.

8. The method of online estimation of state-of-charge error according to claim 5, wherein, The updating process of the state-of-charge error of the battery is detected, and the real-time state-of-charge error is calibrated when a calibration algorithm is triggered, comprising: When the updating process of the state-of-charge error of the battery meets the fourth calibration condition, the sixth calibration condition, and the discharge capacity of the battery corresponding to the fourth calibration condition and the sixth calibration condition is less than the preset capacity, and no other calibration algorithm is triggered, the ampere-hour integral calibration algorithm is triggered, the calculation error of the capacity aging SOH of the battery is calibrated as the real-time state-of-charge error, and the obtained calibration result is taken as the real-time state-of-charge error calibrated by the ampere-hour integral calibration algorithm.

9. The method of online estimation of state-of-charge error according to claim 5, wherein, The updating process of the state-of-charge error of the battery is detected, and when the calibration algorithm is triggered, the real-time state-of-charge error is calibrated, comprising: When the updating process of the state-of-charge error of the battery meets the seventh calibration condition, the eighth calibration condition, the ninth calibration condition, and the discharge capacity of the battery corresponding to the seventh calibration condition, the eighth calibration condition and the ninth calibration condition is less than the preset capacity, and no other calibration algorithm is triggered, the Rint model calibration algorithm is triggered, and the real-time state-of-charge error is calibrated by the following formula: e(Rint) = e1 - Q1 / Q2 * e2 In the formula, e(Rint) is the real-time state-of-charge error calibrated by the Rint model calibration algorithm; e1 and Q1 are the real-time state-of-charge error corresponding to the seventh calibration condition and the discharge capacity of the battery, respectively; e2 and Q2 are the real-time state-of-charge error corresponding to the ninth calibration condition and the discharge capacity of the battery, respectively.

10. The method of online estimation of state-of-charge error according to claim 1, wherein, The initial value of the state-of-charge error corresponding to the power-on time of the battery management system is obtained, comprising: The state of the battery at the power-on time of the battery management system is judged, and if it is detected that the present power-on does not meet the OCV correction condition, the state-of-charge error stored in the memory of the battery management system is taken as the initial value of the state-of-charge error.

11. The method of online estimation of state-of-charge error according to claim 1, wherein, The calculation expression of the ampere-hour integration algorithm is: In the formula, SOC0 is the initial value of SOC; η is the charge and discharge efficiency; I is the charge and discharge current; t is the integral period; N is the number of integral periods starting from SOC0; SOH is the state of capacity aging; Q rate is the battery rated capacity.

12. The method of online estimation of state-of-charge error according to claim 2, wherein, The real-time state-of-charge error is corrected by the Rint model algorithm, comprising: The value with the smallest value is selected from the first state-of-charge error and the second state-of-charge error as the real-time state-of-charge error at the current time, and the algorithm corresponding to the selected error is used to continue to calculate the state-of-charge error of the battery until the battery reaches the preset condition.

13. The method of online estimation of state-of-charge error according to claim 3, wherein, When the battery is in a full charge state, the highest single cell voltage of the battery is greater than or equal to the full charge voltage, the charge current of the battery is less than or equal to a first preset current, and lasts for a third preset time.

14. The method of online estimation of state-of-charge error according to claim 3, wherein, When the battery is in an empty state, the lowest single cell voltage of the battery is less than or equal to the discharge cutoff voltage, the discharge current of the battery is less than or equal to a second preset current, and lasts for a fourth preset time.

15. A state of charge error online estimation system, wherein, Comprising: The data acquisition module is configured to acquire an initial state-of-charge error corresponding to a power-on time of the battery management system, and update the initial state-of-charge error by an OCV lookup table algorithm when it is detected that the present power-on satisfies an OCV correction condition, wherein the OCV correction condition is that the battery is rested for a first preset time. The error updating module is configured to update the state-of-charge error of the battery in real time by an ampere-hour integral algorithm and the initial state-of-charge error, to obtain a real-time state-of-charge error. The error correction module is configured to correct the real-time state-of-charge error by a Rint model algorithm when it is detected that an updating process of the state-of-charge error of the battery triggers a Rint model correction condition. The iterative updating module is configured to clear the real-time state-of-charge error as the initial state-of-charge error when the battery reaches a preset condition, to update the state-of-charge error of the battery in real time by the ampere-hour integral algorithm, until a power-off time of the battery management system is reached, to save a real-time state-of-charge error corresponding to the power-off time of the battery management system and output.

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