CN117347890A - Method and device for calibrating state of charge - Google Patents

Abstract

The application provides a method and a device for calibrating a state of charge, wherein the method comprises the steps of detecting expansion force of an operation process of an energy storage battery, wherein the operation process comprises a charging process and a discharging process; according to the expansion force and the corresponding state of charge, determining an expansion force extreme point and the state of charge corresponding to the expansion force extreme point in the operation process of the energy storage battery; and carrying out charge state calibration on the energy storage battery based on the expansion force extreme point and the corresponding charge state.

Description

Method and device for calibrating state of charge

Technical Field

The present invention relates to the field of energy storage battery technologies, and in particular, to a method and an apparatus for calibrating a state of charge.

Background

The accurate estimation of the state of charge (SOC) and the health degree (SOH) of the energy storage battery can ensure the safe and reliable operation of the energy storage system, optimize the energy storage battery system and provide basis for energy management, safety management and the like of the energy storage system. Currently, the detection method of the SOC of the energy storage battery mainly comprises an ampere-hour integration method and an open-circuit voltage method.

Lithium iron phosphate batteries are a type of energy storage battery that is currently in common use. The lithium iron phosphate battery has the characteristic of voltage plateau, and the open circuit voltage of the lithium iron phosphate battery is not obvious along with the change of the SOC in the plateau. This characteristic results in an error in detecting the SOC of the lithium iron phosphate battery based on the above method.

At present, the calibration is generally performed based on the SOC of the energy storage battery in a full charge state or a full discharge state, however, the energy storage battery may not be in the full charge state or the full discharge state for a long time in actual use, which is not beneficial to calibrating the detection error of the SOC accumulated in the operation process of the energy storage battery.

Disclosure of Invention

In view of the above-described shortcomings of the prior art, the present invention provides a method and apparatus for calibrating state of charge.

A first aspect of the present application provides a method of calibrating a state of charge, comprising:

detecting expansion force of an operation process of the energy storage battery, wherein the operation process comprises a charging process and a discharging process;

determining an expansion force extreme point and a state of charge corresponding to the expansion force extreme point in the operation process of the energy storage battery according to the expansion force and the corresponding state of charge;

and carrying out charge state calibration on the energy storage battery based on the expansion force extreme point and the corresponding charge state.

The step of detecting the expansion force corresponds to step S101 of the first embodiment;

a step of determining the expansion force extreme point and the state of charge corresponding to the expansion force extreme point, which corresponds to the mapping relationship between steps S102 and S103, S103 in the first embodiment, and corresponds to the expansion force extreme point and one expression form of the corresponding state of charge;

The step of performing the state of charge calibration corresponds to steps S104 to S106 of the first embodiment.

In some alternative embodiments, after detecting the expansion force of the energy storage battery during operation, the method further comprises:

obtaining a target expansion force statistical characteristic of the energy storage battery according to the expansion force;

and detecting the battery health of the energy storage battery according to the target expansion force statistical characteristic and a preset first corresponding relation, wherein the first corresponding relation is the corresponding relation between the target expansion force statistical characteristic and the battery health.

The specific implementation manner of this embodiment may refer to the implementation manner of step S803 of the second embodiment, and the ratio between the updated maximum available capacity obtained in S803 and the maximum available capacity of the fresh energy storage battery is equal to the detected battery health of the energy storage battery in this embodiment.

In some optional embodiments, after detecting the battery health of the energy storage battery according to the target expansion force statistical feature and the preset first correspondence, the method further includes:

and calibrating the state of charge of the energy storage battery according to the battery health of the energy storage battery.

For a specific implementation manner of this embodiment, see step S804 of the second embodiment.

In some optional embodiments, the process of obtaining the first correspondence includes:

detecting various expansion force statistical characteristics and corresponding battery health degrees of the energy storage battery in the full charge and discharge process;

screening out target expansion force statistical features from the multiple expansion force statistical features according to the expansion force statistical features and the correlation degree of the battery health degree;

a first correspondence is determined based on the target swelling force statistics and the battery health.

In this embodiment, the process of detecting the statistical expansion force characteristic and the corresponding battery health degree, and further screening the target expansion force characteristic corresponds to step S801 of the second embodiment, and the step of determining the first correspondence corresponds to step S802 of the second embodiment.

In some optional embodiments, after detecting the battery health of the energy storage battery according to the target expansion force statistical feature and the preset first correspondence, the method further includes:

and calibrating the state of charge corresponding to the expansion force extreme point according to the battery health of the energy storage battery.

For a specific implementation manner of this embodiment, reference may be made to step S105 of the second embodiment, regarding the content of the method for calibrating the mapping relationship.

In some optional embodiments, before determining the expansion force extreme point and the state of charge corresponding to the expansion force extreme point in the operation process of the energy storage battery according to the expansion force, the method further includes:

and carrying out data preprocessing on the expansion force and the charge state, wherein the data preprocessing comprises at least one of repeated value redundancy removal, disordered value reordering, abnormal value rejection, missing value filling and noise reduction filtering smoothing.

For a specific implementation manner of this embodiment, reference may be made to the content of the data preprocessing in step S101 of the first embodiment.

A second aspect of the present application provides an apparatus for calibrating state of charge, comprising:

the detection unit is used for detecting the expansion force of the operation process of the energy storage battery, wherein the operation process comprises a charging process and a discharging process;

the determining unit is used for determining an expansion force extreme point and a charge state corresponding to the expansion force extreme point in the operation process of the energy storage battery according to the expansion force and the corresponding charge state;

and the calibration unit is used for calibrating the state of charge of the energy storage battery based on the expansion force extreme point and the corresponding state of charge.

In some alternative embodiments, the apparatus further comprises:

An obtaining unit, configured to obtain a target expansion force statistical feature of the energy storage battery according to the expansion force;

the detection unit is used for detecting the battery health of the energy storage battery according to the target expansion force statistical characteristic and a preset first corresponding relation, wherein the first corresponding relation is the corresponding relation between the target expansion force statistical characteristic and the battery health.

In some alternative embodiments, the calibration unit is further configured to:

and calibrating the state of charge of the energy storage battery according to the battery health of the energy storage battery.

Optionally, the apparatus further comprises a construction unit for:

detecting various expansion force statistical characteristics and corresponding health degrees of the energy storage battery in the full charge and discharge process;

screening out target expansion force statistical features from the multiple expansion force statistical features according to the expansion force statistical features and the correlation degree of the battery health degree;

a first correspondence is determined based on the target swelling force statistics and the battery health.

The method and the device for calibrating the state of charge have the beneficial effects that:

according to the method, the expansion force characteristic in the operation process of the energy storage battery is combined, and some expansion force extreme points and corresponding states of charge in the operation process are determined, so that when the SOC of the energy storage battery is detected, even if the energy storage battery is not in a full charge or full discharge state for a long time, the states of charge can be calibrated according to the expansion force extreme points detected in the process and the corresponding SOCs, and therefore detection errors of the SOCs of the energy storage battery can be eliminated in time, and accuracy of SOC detection results of the energy storage battery is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for calibrating state of charge according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a wired and wireless energy storage battery management system according to an embodiment of the present application;

fig. 3 is a block diagram of a BMU for wired communication according to an embodiment of the present application;

fig. 4 is a block diagram of a BMU for wireless communication according to an embodiment of the present application;

fig. 5 is a schematic diagram of a correspondence relationship between an open-circuit voltage and an expansion force of an energy storage battery under a complete charge-discharge cycle according to an embodiment of the present application;

fig. 6 is a schematic diagram of a correspondence relationship between maximum and minimum values of expansion force and state of charge of an energy storage battery under different healthiess according to an embodiment of the present disclosure;

Fig. 7 is a schematic diagram of expansion force curves of different charge and discharge cycles of an energy storage battery according to an embodiment of the present disclosure;

FIG. 8 is a flow chart of another method of calibrating state of charge provided by embodiments of the present application;

fig. 9 is a graph showing a relationship between SOH and expansion force corresponding to the end of full charge of the energy storage battery under different temperature conditions according to the embodiment of the present application;

FIG. 10 is a flow chart of yet another method of calibrating state of charge provided by embodiments of the present application;

fig. 11 is a schematic structural diagram of a device for calibrating a state of charge according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

Referring to fig. 1, a flowchart of a method for calibrating a state of charge according to the present embodiment is provided, and the method may include the following steps.

S101, acquiring multidimensional sensor data of the energy storage battery during charge and discharge cycles under different working conditions, and preprocessing the data.

The method provided in any embodiment of the present application may be any electronic device that has data processing capability and is capable of communicating with a battery management unit, and for convenience of explanation, the execution subject will be referred to as a calibration device hereinafter. The battery management unit can collect data of the energy storage battery during operation through the sensor.

The working condition of the energy storage battery can be represented by a group of working condition parameters, and the working condition parameters can be various and are not limited. For example, the power, depth of discharge (Depth ofDischarge, DOD) and temperature may be used to represent the operating condition of the energy storage cell in this embodiment.

When executing S101, a plurality of groups of different working condition parameters corresponding to various working conditions can be determined, and the numerical value of at least one working condition parameter is different between every two groups of different working condition parameters. For example, a first set of operating parameters corresponding to a first operating condition may be determined to be a multiplying power x1, a depth of discharge x2, and a temperature x3, and a second set of operating parameters corresponding to a second operating condition may be determined to be a multiplying power y1, a depth of discharge y2, and a temperature y2.

And under each working condition, carrying out repeated full charge and discharge cycle tests on the energy storage battery respectively, and detecting through a plurality of sensors in the test process to obtain the multidimensional sensor data.

By combining the above examples, multiple full charge and discharge cycle tests can be performed on the energy storage battery according to a first set of working condition parameters to obtain multi-dimensional sensor data under the first working condition, and multiple full charge and discharge cycle tests can be performed on the energy storage battery according to a second set of working condition parameters to obtain multi-dimensional sensor data under the second working condition.

The specific test method of the full charge-discharge cycle test in this embodiment is not limited, and for example, the above test may be performed by a cross test.

The energy storage battery used for the test can be one energy storage battery, namely, under each working condition, only one energy storage battery is tested; and the test system can also be a plurality of energy storage batteries, namely a plurality of energy storage batteries can be tested simultaneously under each working condition.

The multiple energy storage batteries are used for testing, and the interference of individual differences among different energy storage batteries on the detected multidimensional sensor data can be eliminated by averaging the data of the multiple energy storage batteries and deleting the maximum value and/or the minimum value, so that the accuracy of the subsequent steps is ensured.

When the test is carried out, the energy storage batteries which are the same in model and in the same production batch as the energy storage batteries to be calibrated can be selected for the test, so that the difference between the energy storage batteries in different models and/or different production batches can be eliminated, the obtained multidimensional sensor data can be ensured to accurately reflect the characteristics of the energy storage batteries to be calibrated, and the accuracy of the subsequent calibration step is improved.

The multi-dimensional sensor data refer to data of the energy storage battery in multiple dimensions, which are acquired in the test process, and the type of the acquired multi-dimensional sensor data is not limited in the embodiment, so long as parameters such as expansion force characteristics required by subsequent steps, charge state of the energy storage battery and the like can be determined based on the multi-dimensional sensor data.

The multidimensional sensor data can comprise current data, voltage data, temperature data and expansion force data of the energy storage battery, which are detected in real time in the test process.

The current data and the voltage data can be detected by a current meter and a voltage meter which are preset at specific positions on the circuit, the temperature data can be detected by a temperature sensor arranged on the surface of the energy storage battery, and the expansion force data can be detected by a pressure sensor arranged on the energy storage battery. The pressure sensor may be disposed at different positions of the energy storage battery, which is not limited in this embodiment.

The pressure sensor may be disposed on the cell surface of the energy storage cell or intermediate the two winding cores inside the energy storage cell, for example. For the former arrangement, the pressure sensor may be wired to the BMU, and for the latter arrangement, the pressure sensor may be wireless connected to the BMU.

During the test, the multidimensional sensor data of each energy storage cell can be collected by a battery management system (BatteryManagement System, BMS) as shown in fig. 2.

The BMS may be a system level management unit (system-level battery management system, SBMS), BMU and cluster level management system (Cluster Management Unit, CMU). There may be a plurality of BMUs, one for each energy storage cell.

The BMU can provide functions such as electric core voltage acquisition, temperature acquisition, expansion force acquisition and balance control, and the CMU can provide functions such as current acquisition, total voltage acquisition, protection processing, data processing and switching device control.

The BMU and the CMU are in communication connection, and when a plurality of CMUs exist, the CMUs can be also in communication connection.

The BMS may be implemented on a wired basis or on a wireless basis.

In the case of a wired implementation, the communication connection between the BMU and the CMU is a daisy chain communication connection, and the communication connection between the CMUs is a controller area network (ControllerAreaNetwork, CAN) bus connection. The communication connection between the respective modules in the BMS can be referred to in this case as fig. 3.

Under the condition of wireless mode realization, the communication connection mode between the BMU and the CMU is wireless local area network WiFi connection, and the communication connection mode between the CMUs is wireless local area network WiFi connection. Referring to fig. 4, in this case, each BMU may be configured with a wireless BMS chip for accessing WiFi, so as to implement a wireless communication connection.

In this embodiment, the BMU is connected to each sensor on the energy storage battery through the I/O interface, and through the I/O interface, the BMU may collect the multidimensional sensor data detected by the sensor, and report the collected multidimensional sensor data to the CMU.

After the CMU obtains the multidimensional sensor data, the data may be further reported to the calibration device (e.g., to an upper computer such as a computer connected by communication), so that the calibration device may execute the method of the present embodiment; alternatively, the CMU itself may act as a calibration device, performing the method of the present embodiment based on multi-dimensional sensor data.

The data preprocessing may include any one of repeated value de-redundancy, disordered value reordering, outlier rejection, missing value padding, noise reduction filtering smoothing, and the like.

Repeating the value to remove redundancy means that multiple groups of data which are repeatedly reported due to communication errors and the like are identified from the received multidimensional sensor data, then the repeated multiple groups of data are deleted, and only one group is reserved.

The out-of-order value reordering refers to detecting a plurality of groups of data with wrong ordering in the received multi-dimensional sensor data, and then reordering the groups of data from new ordering, for example, the sensor detects one group of multi-dimensional sensor data at the time t1, and detects another group of multi-dimensional sensor data at the time t2, and then reporting the data to the calibration equipment, wherein the data is arranged between the two groups of data, and then the out-of-order value reordering can be performed on the two groups of data.

Outlier rejection refers to detecting and deleting sensor data that significantly exceeds an expected range, for example, the expansion force in a set of multidimensional sensor data is 20 kilograms force (Kgf) that is significantly less than the expected expansion force range, and identifying the set of multidimensional sensor data as outliers and rejecting.

The missing value filling refers to finding out missing multi-dimensional sensor data, and filling the missing data set in a data fitting mode based on other sensor data before and after the missing data. For example, a group of data at time t0 may be missing, and the data at time t0 may be padded by a data fitting method based on the data at time t1 before time t0 and the data at time t2 after time t 0.

The missing value filling may fill in the removed abnormal value after the abnormal value is removed.

Noise reduction and smoothing refers to the processing of filtering the received multidimensional sensor data as a signal.

For a specific implementation manner of the above data preprocessing operation, reference may be made to related technical literature, and details are not repeated here.

Data preprocessing is an optional step, and in some embodiments, subsequent steps may be performed directly from the acquired multidimensional sensor data without data preprocessing.

S102, obtaining expansion force characteristics in a battery charge-discharge cycle.

Expansion force characteristics, including but not limited to maximum, minimum, initial, and end values of expansion force in a charge-discharge cycle.

The definition of the minimum value is that the first derivative of the expansion force at the value is equal to 0, and the second derivative is greater than 0; the maximum value is defined as the first derivative of the expansion force at this value being equal to 0 and the second derivative being less than 0; the initial value is defined as the expansion force value at the moment when the SOC is equal to 0 during the charge-discharge cycle; the end value is defined as the expansion force value at the time when the SOC is equal to 100% during the charge-discharge cycle.

When executing S102, the calibration device may read real-time expansion force data of an energy storage battery during a charge-discharge cycle, process the data by a data fitting method, obtain a function (or a change curve) of expansion force with time during the charge-discharge cycle, and determine each expansion force characteristic of the energy storage battery during the charge-discharge cycle based on the function (or the curve).

For example, the calibration device may obtain an expansion force curve as shown in fig. 5 according to real-time expansion force data of a certain energy storage battery during a certain charge and discharge cycle, and further determine, based on the curve, an expansion force maximum value ch_1 and an expansion force minimum value ch_2 during the charge and discharge cycle, and an expansion force minimum value dis_1 and an expansion force maximum value dis_2 during the discharge.

S103, establishing a mapping relation between the expansion force characteristic and the SOC.

In step S103, the calibration device may first obtain the state of charge SOC of the energy storage battery in real time during the charge-discharge cycle. The calibration device can calculate and obtain the real-time SOC of the energy storage battery in the charge-discharge cycle process through a related algorithm according to the energy storage current data and/or the voltage data in the multidimensional sensor data.

Based on the real-time SOC during the charge-discharge cycle, the calibration device is able to determine the SOC of the energy storage battery each time the aforementioned expansion force characteristics are detected, from which several expansion force characteristic-SOC combinations can be determined.

For example, in the n1 st charge/discharge cycle of an energy storage battery, the expansion force minimum value P1 is detected at time t1, and the state of charge at time t1 is SOC1, so that a combination (P1, SOC 1) of the n1 st charge/discharge cycle can be obtained.

By means of the method, for any working condition, the calibration device can determine a plurality of expansion force characteristic-SOC combinations under the working condition by analyzing expansion force characteristics of each energy storage battery under the working condition in each charge and discharge cycle and corresponding SOCs when the expansion force characteristics are detected.

The calibration device may then perform a data fit based on the plurality of expansion force feature-SOC combinations under the operating conditions to obtain a mapping between the expansion force features and the SOC, which may be represented in a variety of forms, including, but not limited to, functional expressions, curves, data sheets, and the like. The fitting method can be any data fitting method in the related art, and is not limited.

For example, the calibration device may determine a combination of the maximum expansion force during charging ch_1 and SOC under the first operating condition, for example, (ch1=330kgf, soc=30.52%), (ch1=380kgf, soc= 31.52%) and so on, and then perform data fitting on the combination to obtain the mapping relationship between ch_1 and SOC under the first operating condition.

It should be noted that, the processing procedures from S102 to S103 are processing procedures performed on the multidimensional sensor data of different working conditions, and the mapping relationship determined in S103 may include mapping relationships under various different working conditions.

In combination with the foregoing examples, after the calibration device obtains the multidimensional sensor data of different working conditions, on one hand, the multidimensional sensor data under the first working condition is processed through S102 to S103 to obtain a mapping relationship corresponding to the first working condition; on the other hand, the multidimensional sensor data under the second working condition is processed through the processes from S102 to S103, and the mapping relation corresponding to the second working condition is obtained.

S104, detecting the expansion force of the energy storage battery in actual operation.

The energy storage battery in step S104 refers to an energy storage battery that needs to calibrate the SOC.

The mode of detecting the expansion force of the energy storage battery in actual operation is consistent with the mode of acquiring the multidimensional sensor data, only a pressure sensor is required to be arranged on the energy storage battery, and then the expansion force data provided by the pressure sensor is acquired in real time through the BMU, so that the expansion force detection method is not repeated.

S105, determining a charge state calibration point based on the expansion force.

In performing S105, the calibration device may first determine whether the aforementioned expansion force characteristics are currently detected based on the real-time detected expansion force of the energy storage battery.

Specifically, the calibration device may calculate, based on the expansion force measured in real time, a first derivative value and a second derivative value of the expansion force that change over time in real time, determine, based on the first derivative value and the second derivative value, in combination with the definition of the expansion force characteristics described above, whether a maximum expansion force value or a minimum expansion force value is currently detected, and further determine, based on whether the energy storage battery is currently in a charged state or a discharged state, whether any one of the following four expansion force characteristics is detected:

expansion force maximum value ch_1 and expansion force minimum value ch_2 at the time of charging, expansion force minimum value dis_1 and expansion force maximum value dis_2 at the time of discharging.

When any expansion force characteristic is determined to be detected, the mapping relation is called according to the expansion force characteristic, the state of charge corresponding to the expansion force characteristic is determined, and the determined state of charge is the state of charge calibration point in S105.

For example, the calibration device determines, according to the real-time expansion force of the energy storage battery, a current detected expansion force minimum value ch_2, where the corresponding value is ch2=360 Kgf, and then invokes the mapping relationship between the current detected expansion force minimum value ch_2=360 Kgf and the state of charge determined in S103, and calculates the corresponding state of charge based on the mapping relationship, to obtain, for example, soc=60.8%.

It should be noted that the mapping relationship used for calculation in S105 is a mapping relationship corresponding to the current working condition of the energy storage battery.

For example, in S103, a mapping relationship between the minimum value of the expansion force and the state of charge during charging corresponding to the first working condition and a mapping relationship between the minimum value of the expansion force and the state of charge during charging corresponding to the second working condition are determined respectively, while in S104, the energy storage battery is under the first working condition, and the calibration device may execute S105 by using the mapping relationship corresponding to the first working condition, so as to obtain a corresponding calibration point of the state of charge.

Optionally, if the predetermined working conditions in S101 are not the same as the working conditions where the energy storage battery is currently located, the working condition where the parameters of each working condition are closest to the working conditions where the energy storage battery is currently located may be selected, and the state of charge calibration point is calculated based on the mapping relation of the selected working conditions.

And S106, calibrating the charge state based on the charge state calibration point.

In step S106, the calibration device may first detect the real-time state of charge of the battery by using the ampere-hour integration method according to the current of the battery during operation, and at the same time, the calibration device detects the expansion force of the battery in real time, and if it is detected that the current expansion force is at a certain expansion force extreme point, the detection result of the ampere-hour integration method can be calibrated according to the corresponding state of charge calibration point.

The principle of ampere-hour integration can be expressed by the following formula.

Wherein, SOC1 represents the real-time state of charge obtained by an ampere-hour integration method, SOC0 represents the initial state of charge, a is a preset coefficient representing charge-discharge efficiency, I represents the charge-discharge current, cn represents the maximum available capacity of the battery, and t represents the time from the initial state of charge to the charge-discharge. The initial state of charge may be detected by an open circuit voltage method according to an open circuit voltage value of the battery, or may be set to 100% or 0% when the battery is in a full charge state or a full discharge state, or may be detected by other means, without limitation.

When detecting the SOC value in real time according to the ampere-hour integration method, the SOC obtained by the ampere-hour integration method may be inaccurate due to the influence of various errors (such as current sampling precision and initial SOC error), and the method provided by the embodiment may calibrate these errors by using the state of charge calibration point.

The calibration may be performed in the following manner:

when the calibration equipment determines that the current battery is at a certain expansion force extreme point, according to the current state of the battery, a charge state calibration point corresponding to the expansion force extreme point is found, then the SOC value of the charge state calibration point is compared with the SOC value detected by an ampere-hour integration method, and if the SOC value and the SOC value are inconsistent, the SOC value is determined as the calibrated SOC value.

Wherein the current state of the battery may be one of a charged state and a discharged state.

For example, the calibration device detects that the current SOC value is 31% according to the ampere-hour integration method, and detects the maximum expansion force point of the battery currently in the charged state, that is, ch_1, and further determines that the SOC value corresponding to ch_1 is 30.75% according to the mapping relationship between the expansion force characteristic and the SOC established previously, where the calibration device may calibrate the detection result of the ampere-hour integration method by using 30.75% of the SOC value determined according to the maximum expansion force value, in other words, the calibration device may determine 30.75% as the calibrated SOC value and output the SOC value.

In some alternative embodiments, the calibration device may also calibrate the state of charge-open circuit voltage curve from the state of charge calibration points in the following manner.

After determining a state of charge calibration point, the calibration device may first obtain the open circuit voltage of the energy storage battery at this time (i.e. when the expansion force characteristic in S105 is detected) (which may be referred to as the calibration open circuit voltage), then the calibration device detects whether the SOC corresponding to the calibration open circuit voltage and the state of charge calibration point are consistent in the current state of charge-open circuit voltage curve, if so, calibration is not required, and if not, the state of charge-open circuit voltage curve is corrected according to the calibration open circuit voltage and the state of charge calibration point.

The correction method may be to re-fit a state of charge-open circuit voltage curve to obtain a calibrated state of charge-open circuit voltage curve, where the state of charge corresponding to the calibrated curve at the calibrated open circuit voltage is equal to the state of charge calibration point.

For example, the state of charge calibration point is soc=70%, assuming that the calibration open circuit voltage at this time is ocv=3.35V, the state of charge at the ocv=3.35V of the current state of charge-open circuit voltage curve is found to be soc=63% by comparison, and is inconsistent with the state of charge calibration point, so the calibration device corrects the curve based on the calibration open circuit voltage ocv=3.35V, the state of charge calibration point soc=70%, and the state of charge at the ocv=3.35V of the corrected curve is soc=70%.

The above-mentioned state of charge-open circuit voltage curve is merely an example of a correspondence relationship between the state of charge and the open circuit voltage, and in other alternative embodiments, the corresponding relationship may be calibrated by a functional expression, a table or other data form, which is not limited to the above-mentioned curve.

S107, the state of charge is output.

In step S107, if the battery is at the expansion force extreme point, the calibration device outputs the state of charge calibrated in accordance with step S106, and if the battery is not at the expansion force extreme point, the calibration device outputs the state of charge detected in accordance with the ampere-hour integration method.

The beneficial effects of this embodiment lie in:

as can be seen from the expansion force curve shown in fig. 5, the expansion force characteristic mainly occurs in the process of charging and discharging the energy storage battery, so by the method of the embodiment, the calibration device can calibrate the state of charge of the energy storage battery by using the detected expansion force characteristic without the need of the energy storage battery in a full charge state or a full discharge state.

Example two

Referring to fig. 8, a flowchart of a method for calibrating a state of charge according to the present embodiment is provided, and the method may include the following steps.

S101, acquiring multidimensional sensor data of the energy storage battery during charge and discharge cycles under different working conditions, and preprocessing the data. S102, obtaining expansion force characteristics in a battery charge-discharge cycle.

S801, calculating a correlation coefficient between the expansion force statistical characteristic and the battery health degree to determine a target expansion force statistical characteristic with high correlation degree.

In step S801, the calibration device first determines the expansion force statistics of each energy storage cell during each full charge-discharge cycle, and the maximum available capacity of the cell at the end of this cycle, based on the obtained multidimensional sensor data.

Based on the maximum available capacity of the energy storage battery, the calibration device may determine the battery health of the energy storage battery, and in general, after a full charge-discharge cycle is completed, the calibration device may determine the ratio of the maximum available capacity of the energy storage battery at that time to the maximum available capacity of a fresh energy storage battery of the same type as the battery health of the energy storage battery at that time. The fresh energy storage battery is understood to be an energy storage battery which has not been used after production.

The maximum available capacity of the energy storage battery after each full charge-discharge cycle can be detected by the calibration device from current data in the multi-dimensional sensor data. Specifically, the calibration device may obtain real-time current data of the energy storage battery during the discharging process from soc=100% to soc=0 in this cycle, integrate the current data of this process with time, and then obtain the accumulated amount of charge discharged from the energy storage battery during this discharging process, and determine the accumulated amount of charge discharged as the maximum available capacity of the energy storage battery at this time.

By collecting multidimensional sensor data of a plurality of energy storage cells during a plurality of full charge and discharge cycles, the calibration device is able to obtain a swelling force statistic feature and a battery health SOH corresponding to each full charge and discharge cycle, i.e. to obtain a plurality of data combinations of swelling force statistic features and battery health, such as (swelling force statistic features x1, SOH 1), (swelling force statistic features x2, SOH 2) etc.

There are many kinds of statistical characteristics of the expansion force, and the embodiment is not limited to a specific kind. As an example, the expansion force statistical feature determined in S801 may include:

the difference or ratio between the maximum value and the minimum value of the expansion force (denoted as a first difference or a first ratio), the difference or ratio between the maximum value and the minimum value of the expansion force (denoted as a second difference or a second ratio), and the difference or ratio between the expansion force corresponding to the charging end time and the expansion force corresponding to the charging start time (denoted as a third difference or a third ratio).

The first ratio may be a ratio of a maximum value to a minimum value in a charging process or a ratio of a maximum value to a minimum value in a discharging process. The first difference is the same.

The definition of the maximum and minimum values of the expansion force refers to the first embodiment, the maximum value of the expansion force refers to the maximum value of all expansion force data measured in a full charge and discharge cycle, and the minimum value of the expansion force corresponds to the minimum value of the expansion force.

For each expansion force statistic, the calibration device may obtain a plurality of such expansion force statistic and battery health data combinations by analyzing the data of each energy storage battery over a plurality of cycles, e.g. by analyzing the data, a plurality of data combinations corresponding to a first ratio, e.g. (first ratio x1, SOH 1), (first ratio x2, SOH 2), etc., and a plurality of data combinations corresponding to a second difference, e.g. (second difference y1, SOH 3), (second difference y2, SOH 4), etc., may be obtained.

After obtaining these data combinations, the calibration device may determine, for each expansion force statistic, a correlation coefficient between such expansion force statistic and the battery health, based on the data combination corresponding to such expansion force statistic.

The correlation coefficient can be calculated based on any algorithm for calculating the correlation coefficient in the related technical field, and the specific calculation process is not limited in this embodiment. For example, the correlation coefficient may be a pearson correlation coefficient, and a specific algorithm of the pearson correlation coefficient may be referred to the literature of the related art, which is not described herein.

Then, the calibration device may select one of the expansion force statistics as the target expansion force statistics according to the magnitude of the correlation coefficient of each expansion force statistics, and the selection may be performed by selecting, as the target expansion force statistics, the expansion force statistics in which the correlation coefficient is the largest.

For example, if the calculated correlation coefficient of the first ratio is 0.5, the correlation coefficient of the second difference is 0.6, and the correlation coefficient of the third ratio is 0.8, then the third ratio may be selected as the target expansion force statistic feature.

S802, processing the target expansion force statistical characteristic and the battery health degree based on a neural network algorithm to obtain a first corresponding relation between the target expansion force statistical characteristic and the battery health degree.

In S802, the calibration device may input the plurality of data combinations of the target expansion force statistical features determined in S801 into a neural network algorithm, and obtain the first correspondence by means of neural network training. The specific process can be referred to the literature about neural network model training in the related art, and will not be described in detail.

The neural network algorithm in the embodiment may be any neural network algorithm in the related art, and is not limited. By way of example, the neural network algorithm may be a Long Short term memory network (Long Short-TermMemorynetworks, LSTM) algorithm.

For example, assuming that the first ratio is determined as the target expansion force statistic feature, in S802, the calibration device may combine a plurality of data of the first ratio obtained previously, for example, (first ratio x1, SOH 1), (first ratio x2, SOH 2) or the like, into a neural network algorithm, thereby obtaining a first correspondence between the first ratio and the battery health.

It should be noted that, using the neural network algorithm to establish the first correspondence is an alternative scheme of the present embodiment, and in other alternative embodiments, the data combination obtained in S801 may also be analyzed by other methods to obtain the first correspondence, for example, a plurality of data combinations obtained by processing with the data fitting algorithm may be used to obtain the first correspondence.

The first correspondence may have various expression forms, and is not limited, and exemplary, the first correspondence may be expressed as any one of a data table, an algorithm model, a function expression, and a curve.

Referring to fig. 9, a corresponding relationship curve between the expansion force of the battery and the health of the battery in the fully charged state provided in this embodiment can be seen that the corresponding relationship curve is different according to different temperatures.

Therefore, the process of establishing the first correspondence from S801 to S802 may be a process implemented separately for the multidimensional sensor data of each working condition, considering the influence of different working conditions on the characteristics of the energy storage battery. That is, for each working condition preset in S101, the calibration device processes the multidimensional sensor data corresponding to the working condition according to the process, so as to establish a first corresponding relationship corresponding to the working condition.

In combination with the foregoing example, the calibration device may process the multidimensional sensor data corresponding to the first working condition according to the process from S801 to S802, thereby establishing a first corresponding relationship corresponding to the first working condition, and similarly, process the multidimensional sensor data corresponding to the second working condition according to the process, thereby obtaining the first corresponding relationship corresponding to the second working condition.

S103, establishing a mapping relation between the expansion force characteristic and the SOC.

S104, detecting the expansion force of the energy storage battery in actual operation.

And S803, updating the maximum available capacity of the energy storage battery according to the target expansion force statistical characteristics in actual operation, and obtaining the updated maximum available capacity.

The energy storage battery in step S803 refers to an energy storage battery that needs to calibrate the SOC.

The target expansion force statistics of the energy storage battery during actual operation may be determined based on the expansion force during actual operation in S104.

The target expansion force is the first ratio, and the calibration device detects the maximum expansion force value and the minimum expansion force value according to the real-time expansion force in the actual running process of the energy storage battery, and when the calibration device detects the maximum expansion force value and the minimum expansion force value successively, the ratio of the maximum expansion force value and the minimum expansion force value can be calculated to obtain the first ratio at the moment.

After the target expansion force statistical feature is detected, the calibration equipment can calculate and obtain the current battery health degree of the energy storage battery corresponding to the target expansion force statistical feature by utilizing the first corresponding relation, and then calculate and obtain the updated maximum available capacity according to the battery health degree and the pre-recorded maximum available capacity of the fresh battery.

For example, the calibration device detects that the first ratio is 0.7 at a certain moment in the actual running process of a certain energy storage battery, when the first ratio is determined to be 0.7 after calculation based on the first corresponding relation, the corresponding battery health SOH is 70%, and then the calibration device multiplies the current SOH70% by the pre-recorded maximum available capacity Cmax of the fresh battery to obtain the current updated maximum available capacity of the energy storage battery as 0.7Cmax.

The first correspondence relationship used in S803 is a first correspondence relationship corresponding to the current working condition of the energy storage battery. Specific how to determine the first corresponding relationship corresponding to the current working condition of the energy storage battery can refer to the content of the mapping relationship corresponding to the current working condition determined in step S105 in the first embodiment, which is not described in detail.

S804, updating the ampere-hour integration algorithm according to the updated maximum available capacity.

The ampere-hour integration algorithm is an algorithm for detecting the state of charge of the energy storage battery in the related battery technology, and the specific implementation manner of the ampere-hour integration algorithm can be referred to the related technical literature and is not repeated, and the principle is that the state of charge of the energy storage battery at a certain moment can be calculated based on the integral value of current which is cut to the moment and is in time when the energy storage battery actually operates and the maximum available capacity of the energy storage battery.

The maximum available capacity in the ampere-hour integration algorithm is generally defaulted to the maximum available capacity when the detected energy storage battery is not used, namely defaulted to the aforementioned Qmax, however, as the battery is used, the actual maximum available capacity obviously gradually decreases on the basis, which leads to inaccurate charge state detected by the ampere-hour integration algorithm.

Thus, in S804, the updating of the ampere-hour integration algorithm by the calibration device may include:

the maximum available capacity currently used by the ampere-hour integration algorithm is replaced with the updated maximum available capacity obtained in S803.

S105, determining a charge state calibration point based on the expansion force.

Fig. 6 and fig. 7 are schematic diagrams of the corresponding relationship between the maximum and minimum values of the expansion force and the state of charge of the energy storage battery provided in the embodiment of the present application under different health degrees SOH, and fig. 7 is a schematic diagram of the expansion force curve of the energy storage battery provided in the embodiment of the present application under different charge and discharge cycles. It can be seen that as the battery ages (in particular, the battery health decreases), its swelling force during charge and discharge cycles, as well as the state of charge corresponding to the swelling force characteristics, change.

Therefore, in order to improve the accuracy of the calibration method of the present embodiment in consideration of the influence of battery aging, after the calibration apparatus determines the battery health through S803, the calibration apparatus may calibrate the mapping relationship between the expansion force characteristics and the state of charge according to the battery health first, and then execute S105 according to the calibrated mapping relationship.

The method for calibrating the mapping relation can be as follows:

firstly, determining a third corresponding relation of the state of charge corresponding to each expansion force characteristic along with the change of the battery health degree based on the obtained multidimensional sensor data in the repeated full charge and discharge cycle process;

the third correspondence may be represented by a curve as shown in fig. 6, and it can be seen that fig. 6 includes two curves, one corresponding to ch_2, where the corresponding relationship of the state of charge corresponding to ch_2 changes with the health degree of the battery, and the other corresponding to ch_1, where the corresponding relationship of the state of charge corresponding to ch_1 changes with the health degree of the battery; then, after determining the current battery health of the energy storage battery, the state of charge corresponding to the current battery health can be determined based on the third corresponding relation, and calibration is performed based on the mapping relation between the expansion force characteristics corresponding to the state of charge and the state of charge.

For example, the mapping relation before calibration maps the currently detected maximum expansion force ch1=330 Kgf in the discharging process to 34% of the state of charge, and according to the third corresponding relation, it is determined that the state of charge corresponding to ch1 of the energy storage battery should be 30.52% under the current battery health, so that the mapping relation is calibrated, and the mapping relation after calibration maps ch1=330 Kgf to 30.52% of the state of charge.

The above calibration method is merely an example, and in other embodiments, the mapping relationship may be calibrated by other methods, so long as the influence of battery aging on the expansion force of the battery and the state of charge corresponding to the expansion force extremum can be considered, and the method is not limited.

The above calibration method can also be performed separately for different conditions. For the first working condition, a third corresponding relation under the working condition is established according to the multidimensional sensor data under the first working condition, and the mapping relation of the working condition is calibrated by using the third corresponding relation of the working condition; and aiming at the second working condition, establishing a third corresponding relation under the working condition according to the multidimensional sensor data under the second working condition, and calibrating the mapping relation of the working condition by utilizing the third corresponding relation of the working condition.

The process of executing S105 based on the calibrated mapping relationship is consistent with the process of executing S105 based on the mapping relationship in the first embodiment, and will not be described again.

And S106, calibrating the charge state based on the charge state calibration point.

S107, the state of charge is output.

The beneficial effects of this embodiment lie in:

on one hand, the scheme combines current data and expansion force data in a full charge and discharge cycle test to establish a first corresponding relation between the target expansion force statistical characteristic and the battery health degree, so that the detection of the battery health degree is realized based on the expansion force of the energy storage battery.

On the other hand, the method obtains the battery health degree of the energy storage battery during actual operation according to the expansion force of the energy storage battery during actual operation, further obtains the actual maximum available capacity (equivalent to the updated maximum available capacity of S803), and updates the safe time integral algorithm of the energy storage battery by utilizing the actual maximum available capacity, so that the accuracy of the state of charge detected based on the safe time integral algorithm is improved.

Example III

According to the method for calibrating a state of charge provided in the first and second embodiments, the embodiment of the present application further provides a method for calibrating a state of charge, please refer to fig. 10, which is a flowchart of the method, and the method may include the following steps.

S1001, detecting expansion force of an operation process of the energy storage battery, wherein the operation process comprises a charging process and a discharging process.

S1002, determining an expansion force extreme point and a state of charge corresponding to the expansion force extreme point in the operation process of the energy storage battery according to the expansion force and the corresponding state of charge.

And S1003, calibrating the charge state of the energy storage battery based on the expansion force extreme point and the corresponding charge state.

According to the method for calibrating the state of charge provided in the embodiment of the present application, the embodiment of the present application further provides a device for calibrating the state of charge, please refer to fig. 11, which is a schematic structural diagram of the device, and the device may include the following units.

A detection unit 1101 for detecting an expansion force of an operation process of the energy storage battery, the operation process including a charging process and a discharging process;

the determining unit 1102 is configured to determine an expansion force extreme point and a state of charge corresponding to the expansion force extreme point in the operation process of the energy storage battery according to the expansion force and the corresponding state of charge;

the calibration unit 1103 is configured to calibrate the state of charge of the energy storage battery based on the expansion force extreme point and the corresponding state of charge.

The beneficial effects of this embodiment lie in:

according to the method, the expansion force characteristic in the operation process of the energy storage battery is combined, and some expansion force extreme points and corresponding states of charge in the operation process are determined, so that when the SOC of the energy storage battery is detected, even if the energy storage battery is not in a full charge or full discharge state for a long time, the states of charge can be calibrated according to the expansion force extreme points detected in the process and the corresponding SOCs, and therefore detection errors of the SOCs of the energy storage battery can be eliminated in time, and accuracy of SOC detection results of the energy storage battery is improved.

Optionally, the apparatus further comprises:

an obtaining unit 1104 for obtaining a target expansion force statistic feature of the energy storage battery according to the expansion force;

the detecting unit 1105 is configured to detect a battery health of the energy storage battery according to the target expansion force statistical feature and a preset first corresponding relationship, where the first corresponding relationship is a corresponding relationship between the target expansion force statistical feature and the battery health.

Optionally, the calibration unit 1103 is further configured to:

and calibrating the state of charge of the energy storage battery according to the battery health of the energy storage battery.

Optionally, the apparatus further comprises a construction unit 1106 for:

detecting various expansion force statistical characteristics and corresponding health degrees of the energy storage battery in the full charge and discharge process;

screening out target expansion force statistical features from a plurality of expansion force statistical features according to the correlation between the expansion force statistical features and the health degree;

a first correspondence is determined based on the target expansion force statistics and the health.

Optionally, the calibration unit 1103 is further configured to:

and calibrating the state of charge corresponding to the expansion force extreme point according to the battery health of the energy storage battery.

Optionally, the determining unit 1102 is further configured to:

and carrying out data preprocessing on the expansion force and the charge state, wherein the data preprocessing comprises at least one of repeated value redundancy elimination, disordered value reordering, abnormal value elimination, missing value filling and noise reduction filtering smoothing.

The specific working principle and the beneficial effects of the device for calibrating the state of charge in this embodiment can be referred to the relevant steps of the method for calibrating the state of charge provided in any embodiment of the present application, which are not described herein again.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.

Those skilled in the art can make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method of calibrating state of charge, comprising:

detecting expansion force of an operation process of the energy storage battery, wherein the operation process comprises a charging process and a discharging process;

determining an expansion force extreme point and a state of charge corresponding to the expansion force extreme point in the operation process of the energy storage battery according to the expansion force and the corresponding state of charge;

and carrying out charge state calibration on the energy storage battery based on the expansion force extreme point and the corresponding charge state.

2. The method of claim 1, further comprising, after detecting the expansion force of the energy storage cell during operation:

obtaining a target expansion force statistical characteristic of the energy storage battery according to the expansion force;

And detecting the battery health of the energy storage battery according to the target expansion force statistical characteristic and a preset first corresponding relation, wherein the first corresponding relation is the corresponding relation between the target expansion force statistical characteristic and the battery health.

3. The method according to claim 2, wherein after detecting the battery health of the energy storage battery according to the target expansion force statistical feature and a preset first correspondence, further comprising:

and calibrating the state of charge of the energy storage battery according to the battery health of the energy storage battery.

4. The method of claim 2, wherein obtaining the first correspondence comprises:

detecting various expansion force statistical characteristics and corresponding battery health degrees of the energy storage battery in the full charge and discharge process;

screening out target expansion force statistical features from the multiple expansion force statistical features according to the expansion force statistical features and the correlation degree of the battery health degree;

a first correspondence is determined based on the target swelling force statistics and the battery health.

5. The method according to claim 2, wherein after detecting the battery health of the energy storage battery according to the target expansion force statistical feature and a preset first correspondence, further comprising:

And calibrating the state of charge corresponding to the expansion force extreme point according to the battery health of the energy storage battery.

6. The method according to claim 1, wherein before determining an expansion force extreme point of the energy storage battery operation process and a state of charge corresponding to the expansion force extreme point according to the expansion force, further comprises:

and carrying out data preprocessing on the expansion force and the charge state, wherein the data preprocessing comprises at least one of repeated value redundancy removal, disordered value reordering, abnormal value rejection, missing value filling and noise reduction filtering smoothing.

7. An apparatus for calibrating state of charge, comprising:

the detection unit is used for detecting the expansion force of the operation process of the energy storage battery, wherein the operation process comprises a charging process and a discharging process;

the determining unit is used for determining an expansion force extreme point and a charge state corresponding to the expansion force extreme point in the operation process of the energy storage battery according to the expansion force and the corresponding charge state;

and the calibration unit is used for calibrating the state of charge of the energy storage battery based on the expansion force extreme point and the corresponding state of charge.

8. The apparatus of claim 7, wherein the apparatus further comprises:

an obtaining unit, configured to obtain a target expansion force statistical feature of the energy storage battery according to the expansion force;

the detection unit is used for detecting the battery health of the energy storage battery according to the target expansion force statistical characteristic and a preset first corresponding relation, wherein the first corresponding relation is the corresponding relation between the target expansion force statistical characteristic and the battery health.

9. The apparatus of claim 8, wherein the calibration unit is further to:

and calibrating the state of charge of the energy storage battery according to the battery health of the energy storage battery.

10. The apparatus of claim 8, further comprising a construction unit for:

detecting various expansion force statistical characteristics and corresponding health degrees of the energy storage battery in the full charge and discharge process;

screening out target expansion force statistical features from the multiple expansion force statistical features according to the expansion force statistical features and the correlation degree of the battery health degree;

a first correspondence is determined based on the target swelling force statistics and the battery health.