CN116859245A - Method and device for identifying abnormal battery cells, server and storage medium - Google Patents

Abstract

The application relates to the technical field of battery management, in particular to a method and a device for identifying abnormal battery cells, a server and a storage medium, wherein the method comprises the following steps: acquiring charging data of multiple charging and discharging cycles of each battery monomer in the battery pack; identifying a first charging time when each battery cell in the charging data reaches a reference voltage, and calculating charging deviation time of each battery cell relative to the reference cell according to the first charging time and a second charging time of the reference cell of the battery pack; fitting the charging deviation time of each battery cell in each charging and discharging cycle to obtain a curve of the charging deviation time changing along with the number of charging and discharging cycles, and identifying abnormal battery cells with abnormal self-discharging in each battery cell according to the fitting slope of the curve. Therefore, the problems that in the prior art, a complex model needs to be built and additional calculation is carried out to determine the battery with abnormal self-discharge of the battery, and the false alarm rate is high when the battery is applied to real vehicle data are solved.

Description

Method and device for identifying abnormal battery cells, server and storage medium

Technical Field

The application relates to the technical field of battery management, in particular to a method and a device for identifying abnormal battery cells, a server and a storage medium.

Background

With the rapid development and popularization of electric vehicles, it is becoming more and more important to ensure the safety of the electric vehicles by using power batteries as core components of the electric vehicles. The accidents such as fire and even explosion of the electric automobile caused by the thermal runaway of the power battery are easy to cause casualties and property loss, and are important points and difficulties in the research of the safety problem of the electric automobile at present.

One of the main causes of thermal runaway in power cells is internal battery short circuits. The internal short circuit causes the internal formation of a loop in the battery and continuously consumes electric quantity, and the external appearance is that the electric quantity of the battery is abnormally reduced, namely, the self-discharge is abnormal. As the severity of internal short circuits increases, the self-discharge of the battery increases abnormally, the amount of heat generated increases, and eventually, thermal runaway of the battery is caused.

The related art discloses a method and a device for detecting micro short circuit of a battery, which are specifically implemented by calculating internal short circuit leakage current of the battery unit according to a charging capacity difference value of the battery unit relative to a reference unit at the end time of two adjacent charging processes, and further estimating an internal short circuit resistance value through average voltage at the end time of two charging processes. And finally, comparing the leakage current and the internal short circuit resistance with corresponding threshold values to judge whether an internal short circuit exists. However, this method has the following problems: 1. only two charging processes are adopted for judgment, and the battery is easy to be influenced by voltage/current data jump, partial data loss and the like when the battery is applied to real vehicle data, and the normal battery is misjudged as an internal short-circuit battery; 2. the reference monomer is fixed to be the highest voltage monomer after each charging, the reference monomer is firstly needed to be judged before each calculation, and the calculated result meter is contrastive when the reference monomer is changed; 3. the calculation process is complex, and a plurality of parameters such as reference charging time, reference charging capacity, average voltage, internal short circuit resistance and the like need to be calculated. Another related art discloses a method for detecting an internal short circuit of a lithium ion battery cell, which includes obtaining a relationship among OCV (Open circuit voltage, open-circuit voltage), capacity Q and SOC (State of Charge) as input through a preliminary test, constructing an equivalent circuit model to compare voltage, temperature and change rate thereof in a charging and discharging process of the battery cell, and determining an internal short circuit fault of the battery cell when a threshold value is exceeded. However, this method has the following problems: 1. a large number of battery monomers are required to be tested in advance, and accurate battery parameters are obtained as comparison input; 2. the model construction calculation is complex, the model parameters are required to be identified repeatedly, and the model construction method is difficult to adapt to complex and changeable real vehicle working conditions.

Disclosure of Invention

The invention aims to provide an abnormal battery monomer identification method, which aims to solve the problems that in the prior art, a complex model is required to be established and additional calculation is required to be carried out to determine a battery with abnormal self-discharge of the battery, and the false alarm rate is high when the battery is applied to real vehicle data; the second purpose is to provide a recognition device of abnormal battery cell; a third object is to provide a server; a fourth object is to provide a computer-readable storage medium.

In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:

a method for identifying abnormal battery cells, the method being applied to a server, wherein the method comprises the steps of: acquiring charging data of multiple charging and discharging cycles of each battery monomer in the battery pack; identifying a first charging time when each battery cell in the charging data reaches a reference voltage, and calculating charging deviation time of each battery cell relative to the reference cell according to the first charging time and a second charging time of the reference cell of the battery pack; fitting the charging deviation time of each battery cell in each charging and discharging cycle to obtain a curve of the charging deviation time changing along with the number of charging and discharging cycles, and identifying abnormal battery cells with abnormal self-discharging in each battery cell according to the fitting slope of the curve.

According to the technical means, the embodiment of the application can judge whether the battery is abnormal in self-discharge or not by acquiring the charging data of multiple charging and discharging cycles of each battery cell in the battery pack and utilizing the charging deviation time fitting change slope in multiple cycles, so that misjudgment caused by data jump when only two charging processes are adopted is avoided, the self-discharge judgment is directly carried out by adopting the charging deviation time change slope, the test calibration is not needed in advance, a complex model is built or additional complex operation is carried out, and the calculation amount and the complexity of the self-discharge abnormal battery judgment are reduced.

Further, the identifying abnormal battery cells having self-discharge abnormality in each battery cell according to the fitted slope of the curve includes: judging whether the fitting slope is larger than a first slope threshold value or not; and if the fit slope is larger than the first slope threshold, judging that the battery cell is an abnormal battery cell with self-discharge abnormality.

According to the technical means, the embodiment of the application can directly judge whether the battery cell is an abnormal battery cell with self-discharge abnormality by using the fitting slope and the first slope threshold value, does not need to carry out additional complex calculation, and reduces the calculated amount.

Further, the identifying abnormal battery cells having self-discharge abnormality in each battery cell according to the fitted slope of the curve includes: judging whether the fit slope is smaller than a second slope threshold value or not; and if the fit slope is smaller than the second slope threshold, judging that the reference cell is an abnormal cell with self-discharge abnormality.

According to the technical means, the embodiment of the application can directly judge whether the battery cell is an abnormal battery cell with self-discharge abnormality by using the fitting slope and the second slope threshold, does not need to carry out additional complex calculation, and reduces the calculated amount.

Further, the fitting the charging deviation time of each battery cell in each charging and discharging cycle to obtain a curve of the charging deviation time varying with the number of charging and discharging cycles, includes: selecting a charging cycle time window of each charging monomer; determining a target cycle number interval of each charging monomer according to the charging cycle number window; fitting the charging deviation time of each charging and discharging cycle in the target cycle number interval to obtain a curve of the charging deviation time changing along with the charging and discharging cycle number.

According to the technical means, the embodiment of the application can determine the target cycle number interval of each charging monomer according to the charging cycle number window, fit the charging deviation time of each charging and discharging cycle in the target cycle number interval, and obtain the curve of the charging deviation time changing along with the charging and discharging cycle number, and be used for determining the following fitting slope according to the curve.

Further, the identifying the first charging time when each battery cell in the charging data reaches the reference voltage includes: taking any battery monomer in all battery monomers of the battery pack as a reference monomer; extracting a voltage change curve of the reference single body in the charging data, and selecting the voltage which can be reached by all the battery single bodies as the reference voltage according to the voltage change curve; and acquiring a first charging moment when each battery cell in any voltage interval reaches the reference voltage.

According to the technical means, any battery cell in the battery pack can be selected as the reference cell, extra judgment before calculation is avoided, the reference voltage is determined according to the voltage change curve in the charging process, the selection of the reference voltage is not unique, and a plurality of reference voltages can be selected for calculation.

Further, the selecting, according to the voltage change curve, the voltage that can be reached by all the battery cells as the reference voltage includes: acquiring charging current of the reference monomer in each charging voltage interval; extracting all charging voltage intervals of the reference single body in the voltage change curve according to the charging current; and selecting the voltage which can be reached by all the battery cells in the charging voltage interval as the reference voltage.

According to the technical means, according to the embodiment of the application, all charging voltage intervals of the reference single body in the voltage change curve can be extracted according to the charging current of the reference single body in each charging voltage interval, and the voltages which can be reached by all the battery single bodies in the charging voltage intervals are selected as the reference voltages.

Further, the selecting the voltage that can be reached by all the battery cells in the charging voltage interval as the reference voltage includes: acquiring the selection sequence of all charging voltage intervals; sequentially selecting the reference voltages in charging voltage intervals according to the selection sequence, wherein in any charging voltage interval, selecting the median voltage of the charging voltage interval when selecting for the first time, taking the median voltage as the reference voltage if all the battery cells can reach the median voltage, otherwise, selecting the interval lower limit voltage of the charging voltage interval and the average voltage of the median voltage; and if all the battery cells can reach the average voltage, taking the average voltage as the reference voltage, otherwise, selecting the reference voltage in the next charging voltage interval.

According to the technical means, the embodiment of the application can select the reference voltage according to the selection sequence of the charging voltage intervals, and fully considers the influence of the polarized internal resistance on the reference voltage during inter-step current switching.

Further, the obtaining the first charging time when each battery cell in the arbitrary voltage interval reaches the reference voltage includes: identifying a charging current of a charging voltage interval; and recording the charging time when the battery cell reaches the reference voltage and is the same as the charging current of the charging voltage interval.

According to the technical means, the embodiment of the application can record the first charging time when each battery cell reaches the reference voltage in any charging voltage interval so as to be used for calculating the charging deviation time later.

Further, before acquiring the charge data of multiple charge-discharge cycles of each battery cell in the battery pack, the method further comprises: acquiring charge and discharge data of a battery pack; extracting the actual times of charge and discharge cycles in the charge and discharge data; and if the actual times are greater than the preset times, acquiring charging data of multiple charging and discharging cycles of each battery cell in the battery pack, otherwise, not identifying the abnormal battery cell.

According to the technical means, the embodiment of the application can acquire the charging data of the repeated charging and discharging cycles of each battery cell in the battery pack when the number of the charging and discharging cycles is larger than the preset number, otherwise, the abnormal battery cells are not identified, and the erroneous judgment caused by data jump when the self-discharging abnormal judgment is carried out only by adopting a few charging and discharging processes is avoided.

Further, after identifying the abnormal battery cell in which the self-discharge abnormality exists in each battery cell according to the fitted slope of the curve, the method further comprises: generating preset reminding information; and sending the preset reminding information to a preset terminal so as to carry out self-discharge abnormal reminding according to the preset reminding information.

According to the technical means, after the battery monomer with the self-discharge abnormality is identified, the embodiment of the application can remind a user to timely process the battery monomer, so that the phenomenon of thermal runaway of the power battery is avoided.

Further, after identifying the abnormal battery cell in which the self-discharge abnormality exists in each battery cell according to the fitted slope of the curve, the method further comprises: identifying the identity of the abnormal battery cell; and sending the identification to a preset terminal so as to locate the abnormal battery cells in the battery pack according to the identification.

According to the technical means, after the battery cell with the self-discharge abnormality is identified, the identification of the abnormal battery cell can be identified, so that a user can position the abnormal battery cell in the battery pack according to the identification and replace the abnormal battery cell in time.

An apparatus for identifying abnormal battery cells, the apparatus being applied to a server, wherein the apparatus comprises: the acquisition module is used for acquiring charging data of multiple charging and discharging cycles of each battery cell in the battery pack; the calculation module is used for identifying a first charging time when each battery cell in the charging data reaches a reference voltage, and calculating charging deviation time of each battery cell relative to the reference cell according to the first charging time and a second charging time of the reference cell of the battery pack; the identification module is used for fitting the charging deviation time of each battery cell in each charging and discharging cycle to obtain a curve of the charging deviation time changing along with the number of charging and discharging cycles, and identifying abnormal battery cells with self-discharging abnormality in each battery cell according to the fitting slope of the curve.

Further, the identification module is further to: judging whether the fitting slope is larger than a first slope threshold value or not; and if the fit slope is larger than the first slope threshold, judging that the battery cell is an abnormal battery cell with self-discharge abnormality.

Further, the identification module is further to: judging whether the fit slope is smaller than a second slope threshold value or not; and if the fit slope is smaller than the second slope threshold, judging that the reference cell is an abnormal cell with self-discharge abnormality.

Further, the identification module is further to: selecting a charging cycle time window of each charging monomer; determining a target cycle number interval of each charging monomer according to the charging cycle number window; fitting the charging deviation time of each charging and discharging cycle in the target cycle number interval to obtain a curve of the charging deviation time changing along with the charging and discharging cycle number.

Further, the computing module is further to: taking any battery monomer in all battery monomers of the battery pack as a reference monomer; extracting a voltage change curve of the reference single body in the charging data, and selecting the voltage which can be reached by all the battery single bodies as the reference voltage according to the voltage change curve; and acquiring a first charging time when each battery cell in any charging voltage interval reaches the reference voltage.

Further, the computing module is further to: acquiring charging current of the reference monomer in each charging voltage interval; extracting all charging voltage intervals of the reference single body in the voltage change curve according to the charging current; and selecting the voltage which can be reached by all the battery cells in the charging voltage interval as the reference voltage.

Further, the computing module is further to: acquiring the selection sequence of all charging voltage intervals; sequentially selecting the reference voltages in charging voltage intervals according to the selection sequence, wherein in any charging voltage interval, selecting the median voltage of the charging voltage interval when selecting for the first time, taking the median voltage as the reference voltage if all the battery cells can reach the median voltage, otherwise, selecting the interval lower limit voltage of the charging voltage interval and the average voltage of the median voltage; and if all the battery cells can reach the average voltage, taking the average voltage as the reference voltage, otherwise, selecting the reference voltage in the next charging voltage interval.

Further, the computing module is further to: identifying a charging current of a charging voltage interval; and recording the charging time when the battery cell reaches the reference voltage and is the same as the charging current of the charging voltage interval.

Further, the device for identifying abnormal battery cells further comprises: the extraction module is used for acquiring charge and discharge data of the battery pack before acquiring charge data of multiple charge and discharge cycles of each battery cell in the battery pack; and extracting the actual times of charge and discharge cycles in the charge and discharge data, acquiring the charge data of the charge and discharge cycles of each battery cell in the battery pack, and otherwise, not identifying the abnormal battery cell.

Further, the device for identifying abnormal battery cells further comprises: the reminding module is used for generating preset reminding information after identifying abnormal battery cells with abnormal self-discharge in each battery cell according to the fitting slope of the curve; and sending the preset reminding information to a preset terminal so as to carry out self-discharge abnormal reminding according to the preset reminding information.

Further, the device for identifying abnormal battery cells further comprises: the sending module is used for identifying the identification of the abnormal battery cell after identifying the abnormal battery cell with the self-discharge abnormality in each battery cell according to the fitting slope of the curve; and sending the identification to a preset terminal so as to locate the abnormal battery cells in the battery pack according to the identification.

A server, comprising: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the identification method of the abnormal battery cell as described in the embodiment.

A computer-readable storage medium having stored thereon a computer program that is executed by a processor for implementing the method of identifying abnormal cells as described in the above embodiments.

The application has the beneficial effects that:

(1) According to the embodiment of the application, the charging data of multiple charging and discharging cycles of each battery monomer in the battery pack can be obtained, whether the battery is abnormal in self-discharging or not can be judged by utilizing the charging deviation time fitting change slope in multiple cycles, the misjudgment caused by data jump in the process of only adopting twice charging is avoided, the self-discharging judgment is directly carried out by adopting the charging deviation time change slope, the test calibration is not required, a complex model is built or additional complex operation is not required, and the calculation amount and the complexity of the self-discharging abnormal battery judgment are reduced.

(2) The embodiment of the application can directly judge whether the battery cell is an abnormal battery cell with self-discharge abnormality by utilizing the fitting slope and the first slope threshold value, does not need to carry out additional complex calculation, and reduces the calculated amount.

(3) The embodiment of the application can directly judge whether the battery cell is an abnormal battery cell with self-discharge abnormality by utilizing the fitting slope and the two slope threshold values, does not need to carry out additional complex calculation, and reduces the calculated amount.

(4) According to the embodiment of the application, the target cycle number interval of each charging monomer can be determined according to the charging cycle number window, the charging deviation time of each charging and discharging cycle in the target cycle number interval is fitted, and the curve of the charging deviation time changing along with the charging and discharging cycle number is obtained and is used for the subsequent determination according to the fitting slope of the curve.

(5) According to the embodiment of the application, any battery cell in the battery pack can be selected as the reference cell, so that additional judgment before calculation is avoided, the reference voltage is determined according to the voltage change curve in the charging process, the selection of the reference voltage is not unique, and a plurality of reference voltages can be selected for calculation.

(6) According to the embodiment of the application, all charging voltage intervals of the reference single body in the voltage change curve can be extracted according to the charging current of the reference single body in each charging voltage interval, and the voltage which can be reached by all the battery single bodies in the charging voltage intervals is selected as the reference voltage.

(7) According to the embodiment of the application, the reference voltage can be selected according to the selection sequence of the charging voltage intervals, and the influence of the polarized internal resistance on the reference voltage during inter-step current switching is fully considered.

(8) The embodiment of the application can record the first charging time when each battery cell reaches the reference voltage in any voltage interval so as to be used for calculating the charging deviation time later.

(9) According to the embodiment of the application, when the number of charge and discharge cycles is greater than the preset number, the charge data of the charge and discharge cycles of each battery cell in the battery pack can be obtained, otherwise, the abnormal battery cells are not identified, and the erroneous judgment caused by data jump when the self-discharge abnormal judgment is carried out by only adopting a few charge and discharge processes is avoided.

(10) According to the embodiment of the application, after the battery monomer with the self-discharge abnormality is identified, the user can be reminded to timely process, and the phenomenon of thermal runaway of the power battery is avoided.

(11) The embodiment of the application can identify the identification of the abnormal battery cell after identifying the battery cell with the self-discharge abnormality, so that a user can position the abnormal battery cell in the battery pack according to the identification and replace the abnormal battery cell in time.

Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.

Drawings

Fig. 1 is a flow chart of a method for identifying an abnormal battery cell according to an embodiment of the present application;

FIG. 2 is a graph showing variation of charge bias time with cycle number according to an embodiment of the present application;

FIG. 3 is a plot of charge bias time-varying slope values provided by an embodiment of the present application;

FIG. 4 is a flowchart of a method for identifying abnormal battery cells according to an embodiment of the present application;

fig. 5 is a schematic diagram of an apparatus for identifying abnormal battery cells according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application.

Detailed Description

Further advantages and effects of the present application will become readily apparent to those skilled in the art from the disclosure herein, by referring to the accompanying drawings and the preferred embodiments. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.

It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present application by way of illustration, and only the components related to the present application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.

Specifically, fig. 1 is a schematic flow chart of a method for identifying an abnormal battery cell according to an embodiment of the present application.

As shown in fig. 1, the method for identifying the abnormal battery cell includes the following steps:

In step S101, charge data of a plurality of charge-discharge cycles of each battery cell in the battery pack is acquired.

The embodiment of the application can extract the charging data of each charging and discharging cycle of each battery cell in the battery pack based on the running data of the real vehicle in a long time period, wherein the real vehicle running data refer to the voltage curves of each battery cell in the charging and running discharging processes of the vehicle stored in the cloud, and the battery pack of the embodiment of the application takes a ternary battery as an example.

In the embodiment of the application, before acquiring the charging data of multiple charging and discharging cycles of each battery cell in the battery pack, the method further comprises the following steps: acquiring charge and discharge data of a battery pack; extracting the actual times of charge and discharge cycles in the charge and discharge data; if the actual times are greater than the preset times, charging data of multiple charging and discharging cycles of each battery cell in the battery pack are obtained, and otherwise, abnormal battery cells are not identified.

Wherein, the single charge and discharge of the battery cell is considered as one charge and discharge cycle. The preset number of times may be set according to specific situations, and is not particularly limited, and may be set to 6 times or 7 times, for example.

It can be understood that the embodiment of the application can extract the actual times in the charge and discharge data, and acquire the charge data of the charge and discharge cycles of each battery cell in the battery pack when the charge and discharge cycle times are greater than the preset times, otherwise, the abnormal battery cells are not identified, and the misjudgment caused by data jump when the self-discharge abnormal judgment is carried out only by adopting a few charge and discharge processes is avoided.

For example, taking the preset number of times N as 7 as an example, when the number of charge and discharge cycles is less than N, no self-discharge abnormality determination is performed, and in each charge and discharge cycle, only the charge process data is extracted for self-discharge abnormality battery determination.

In step S102, a first charging time when each battery cell in the charging data reaches the reference voltage is identified, and charging deviation time of each battery cell relative to the reference cell is calculated according to the first charging time and a second charging time of the reference cell of the battery pack.

It can be understood that according to the embodiment of the application, the charging deviation time of each battery cell relative to the reference cell can be calculated according to the first charging time when each battery cell reaches the reference voltage and the second charging time of the reference cell of the battery pack, so that the abnormal battery cell with the self-discharge abnormality can be identified by using the charging deviation time later.

The method for calculating the charging deviation time of the reference monomer can be as follows:

wherein,,a first charging time when the reference monomer reaches the reference voltage in the kth charging cycle; />For the second charging time when the rest of the battery cells in the kth charging cycle reach the reference voltage, i=1, 2, …, n total of n battery cells; The relative charge bias time of the remaining battery cells in the kth charge cycle. The relative charging deviation time is positive, which indicates that the battery monomer reaches the reference voltage before the reference monomer, and the electric quantity of the battery is higher; the relative charging deviation time is negative, the surface battery cell reaches the reference voltage after the reference cell, and the battery electric quantity is lower.

In an embodiment of the present application, identifying a first charging time when each battery cell in charging data reaches a reference voltage includes: taking any battery monomer in all battery monomers of the battery pack as a reference monomer; extracting a voltage change curve of a reference single body in the charging data, and selecting the voltage which can be reached by all the battery single bodies as the reference voltage according to the voltage change curve; and acquiring a first charging time when each battery cell in any charging voltage interval reaches the reference voltage.

The reference single cell is any single cell randomly selected from all battery single cells in the battery pack, and the reference voltage is the voltage which can be reached by all battery single cells in the same charging step current in the real vehicle charging process.

It can be understood that in the embodiment of the application, each battery of the battery pack but any battery cell with a weight is taken as a reference cell, a voltage change curve of the reference cell in charging data is extracted, a reference voltage is determined according to the voltage change curve in the charging process, and a first charging time when each battery cell in any charging voltage interval reaches the reference voltage is further obtained.

In the embodiment of the application, the voltage which can be achieved by all the battery cells is selected as the reference voltage according to the voltage change curve, and the method comprises the following steps: acquiring charging current of a reference monomer in each charging voltage interval; extracting all charging voltage intervals of a reference monomer in a voltage change curve according to the charging current; and selecting the voltage which can be reached by all the battery cells in the charging voltage interval as a reference voltage.

It can be understood that in the embodiment of the application, according to the reference cell, all charging voltage intervals of the reference cell in the charging current extraction voltage variation curve of each charging voltage are selected, and the voltage which can be reached by all batteries in the charging voltage intervals is selected as the reference voltage.

Due to the step charging strategy in the real vehicle charging process, the voltage is in different voltage intervals V _x ，V _y ]Different charging currents I are adopted _x The reference voltage should be within the voltage interval V _x ，V _y ]The selection is made in the range and the reference voltage can be reached by the reference monomer in the charging process as much as possible. In addition, the selection of the reference voltage is not unique, and a plurality of reference voltages can be selected for calculation.

In the embodiment of the application, the voltage which can be reached by all the battery cells is selected as the reference voltage in the charging voltage interval, which comprises the following steps: acquiring the selection sequence of all charging voltage intervals; selecting reference voltages in charging voltage intervals in sequence according to a selection sequence, wherein in any charging voltage interval, selecting the median voltage of the charging voltage interval when selecting for the first time, taking the median voltage as the reference voltage if all the battery cells can reach the median voltage, otherwise, selecting the interval lower limit voltage of the charging voltage interval and the average voltage of the median voltage; if all the battery cells can reach the average voltage, the average voltage is used as the reference voltage, otherwise, the reference voltage is selected in the next charging voltage interval.

It can be understood that the embodiment of the application can select the reference voltages according to the selection sequence of the charging voltage intervals, when the reference voltages are selected for the first time, the median voltage of the charging voltage intervals is selected, if the battery cells can reach the median voltage, the median voltage is used as the reference voltage, otherwise, the lower limit voltage of the intervals of the charging voltage intervals and the average voltage of the median voltage are selected, if all the battery cells can reach the average voltage, the average voltage is used as the reference voltage, otherwise, the reference voltage is selected in the next charging voltage interval.

Specifically, as the real vehicle adopts a ladder charging strategy, the selection of the reference voltage is selected according to different ladder sections and charging currents in the charging process, and the specific selection method of the reference voltage is as follows:

taking the charge step section as an example, the charge step section is divided into three stages, wherein the battery voltage sections are respectively [ V1, V2), [ V2, V3), [ V3, V4), and the currents in each corresponding charge section are I1, I2 and I3. When the battery cells are abnormal in self-discharge, the charging deviation time among the cells is longer along with the rise of the battery voltage in the charging process. In order to make the self-discharge abnormality easier to identify in the charging ladder under the same current, firstly, reference voltage selection is carried out in the highest voltage interval [ V3, V4 ], voltage drop exists during the inter-ladder current switching, the reference voltage needs to be selected in the range of (V3, V4) to avoid the influence of polarized internal resistance when changing current, and all battery monomers can reach the reference voltage.

Further, obtaining a first charging time when each battery cell in any voltage interval reaches a reference voltage includes: identifying a charging current of a charging voltage interval; the charging time when the battery cell reaches the reference voltage and is the same as the charging current in the charging voltage interval is recorded.

It can be understood that in the embodiment of the application, any battery cell can be selected as a reference cell, a voltage change curve of the reference cell in charging data is extracted, a reference voltage is determined according to the voltage change curve, and in each charging process, the charging time when each battery cell reaches the reference voltage and is the same as the charging current in the voltage interval is recorded as the first charging time.

In step S103, a curve of the charge deviation time of each battery cell according to the charge cycle times is obtained by fitting the charge deviation time of each battery cell in each charge and discharge cycle, and abnormal battery cells having abnormal self-discharge in each battery cell are identified according to the fit slope of the curve.

It can be understood that according to the embodiment of the application, a curve of the change of the relative charge deviation time along with the charge-discharge cycle times can be obtained according to the calculation result of each charge cycle, a cycle time window is selected, the relative charge deviation time change slope is fitted, whether the self-discharge abnormality exists in each battery monomer is judged according to the relative charge deviation time fit slope value, the self-discharge judgment is directly carried out by adopting the relative charge deviation time change slope in a long period, the test calibration, the establishment of a complex model or the additional complex operation are not needed in advance, and the calculation amount and the complexity of the self-discharge abnormality battery judgment are reduced.

In the embodiment of the application, a curve of the variation of the charging deviation time along with the number of charging and discharging cycles is obtained by fitting the charging deviation time of each battery monomer in each charging and discharging cycle, and the curve comprises the following steps: selecting a charging cycle time window of each charging monomer; determining a target cycle number interval of each charging monomer according to the charging cycle number window; fitting the charging deviation time of each charging and discharging cycle in the target cycle number interval to obtain a curve of the charging deviation time changing along with the charging and discharging cycle number.

The specific fitting process of the specific charging deviation time change slope is as follows: and selecting a charging cycle time window W, and fitting the relative charging deviation time by adopting a shape of y=mx+b on a cycle time interval [ k-W, k ] to obtain a slope m of the relative charging deviation time changing along with the cycle time.

It should be noted that, as shown in fig. 2, the curve of the variation of the relative charge deviation time of the self-discharging abnormal cell with the cycle number is significantly different from that of the normal cell.

In the embodiment of the application, identifying abnormal battery cells with abnormal self-discharge in each battery cell according to the fitting slope of a curve comprises the following steps: judging whether the fitting slope is larger than a first slope threshold value or not; and if the fitting slope is larger than the first slope threshold, judging that the battery cell is an abnormal battery cell with the self-discharge abnormality.

Wherein the first slope threshold may be set to a positive threshold k+.

It can be appreciated that in the embodiment of the present application, when the fitting slope value of the battery cell is positive and greater than the first slope threshold, the battery cell is determined to be a self-discharge abnormal cell, as shown in fig. 3.

In the embodiment of the application, identifying abnormal battery cells with abnormal self-discharge in each battery cell according to the fitting slope of a curve comprises the following steps: judging whether the fitting slope is smaller than a second slope threshold value or not; if the fit slope is smaller than the second slope threshold, the reference cell is judged to be an abnormal cell with self-discharge abnormality.

Wherein the second slope threshold may be set to a negative threshold K-.

It can be appreciated that when the fitting slope value of the battery cell is negative and smaller than the second slope threshold, the embodiment of the application can determine that the battery cell is a self-discharge abnormal cell.

In the embodiment of the present application, after identifying an abnormal battery cell in which a self-discharge abnormality exists in each battery cell according to the fitted slope of the curve, the method further includes: generating preset reminding information; and sending preset reminding information to a preset terminal so as to carry out self-discharge abnormal reminding according to the preset reminding information.

The preset reminding information can be set to prompt that the battery monomer with abnormal self-discharge exists in the battery monomer, and prompt a user that the power battery has the danger of thermal runaway. The preset terminal can be a central control display screen of the vehicle.

It can be appreciated that after the battery monomer with the self-discharge abnormality is identified, the embodiment of the application can remind the user to timely process the battery monomer, so that the phenomenon of thermal runaway of the power battery is avoided.

In the embodiment of the present application, after identifying an abnormal battery cell in which a self-discharge abnormality exists in each battery cell according to the fitted slope of the curve, the method further includes: identifying the identity of the abnormal battery cell; and sending the identification to a preset terminal so as to position the abnormal battery cells in the battery pack according to the identification.

It can be understood that after the battery cells with abnormal self-discharge in the battery pack are identified, the embodiment of the application can identify the identification of the abnormal battery cells, so that a user can position the abnormal battery cells in the battery pack according to the identification and replace the abnormal battery cells in time.

The method for identifying abnormal battery cells according to the embodiment of the present application is described below by way of a specific embodiment, as shown in fig. 4, and includes the following steps:

Step 1: and extracting a charging process in each charging and discharging cycle based on the operation data of the real vehicle in a long period of time.

The real vehicle operation data refer to voltage curves of each battery cell in the vehicle charging and driving discharging process stored in the cloud, and the battery cells in the operation data are charged and discharged once to be regarded as a charging and discharging cycle. The long period of time is generally considered that the number of charge and discharge cycles is equal to or greater than N, and the range of values adopted here is, but not limited to, n=7, and when the number of charge and discharge cycles is less than N, no self-discharge abnormality judgment is made. In each charge-discharge cycle, only the charge process data is extracted for self-discharge abnormal battery judgment.

Step 2: and selecting a battery cell as a reference cell, and determining a reference voltage according to a voltage change curve in the charging process.

The reference cell may be any cell during charge and discharge. Due to the ladder charging strategy in the real vehicle charging process, the charging system is different in the following stepsVoltage interval V _x ，V _y ]Different charging currents I are adopted _x . The reference voltage should be within the voltage interval V _x ，V _y ]Is selected within the range and enables as many reference cells as possible to reach the reference voltage during charging. In addition, the selection of the reference voltage is not unique, and a plurality of reference voltages can be selected for calculation.

Step 3: in each charging process, the charging time when each battery cell reaches the reference voltage is recorded.

For real vehicle data charged by adopting the step current, only the charging time when each battery cell reaches the reference voltage and is the same as the charging current in the voltage interval.

Step 4: and calculating the charging deviation time of each battery cell relative to the reference cell according to the charging time of each battery cell.

The calculation process of the charging deviation time of the reference monomer comprises the following steps:

in the method, in the process of the invention,a first charging time when the reference monomer reaches the reference voltage in the kth charging cycle; />For the second charging time when the rest of the battery cells in the kth charging cycle reach the reference voltage, i=1, 2, …, n total of n battery cells;the relative charge bias time of the remaining battery cells in the kth charge cycle. The relative charging deviation time is positive, which indicates that the battery monomer reaches the reference voltage before the reference monomer, and the electric quantity of the battery is higher; the relative charge deviation time is negative, the surface battery cell reaches the reference voltage after the reference cell, and the battery cell is powered onThe battery power is lower.

Step 5: and obtaining a curve of the change of the relative charge deviation time along with the charge and discharge cycle times according to the calculation result of each charge cycle, selecting a cycle time window, and fitting the change slope of the relative charge deviation time.

As shown in fig. 2, the relative charge deviation time of the self-discharging abnormal cell is significantly different from that of the normal cell. The specific fitting process of the relative charging deviation time change slope is as follows: selecting a charging cycle time window W; and fitting the relative charging deviation time by adopting the shape of y=mx+b in the cycle number interval [ k-W, k ] to obtain the slope m of the relative charging deviation time changing along with the cycle number.

Step 6: and judging whether each single battery has self-discharge abnormality according to the relative charge deviation time fitting slope value.

The method for judging whether the self-discharge is abnormal or not according to the relative charging deviation time fitting slope value comprises the following steps: if the fitting slope value of the battery cell is positive and greater than a preset positive threshold value K+, determining that the battery cell is a self-discharge abnormal cell, as shown in FIG. 3; and if the fitting slope value of the battery monomer is negative and smaller than a preset negative threshold K-, judging that the reference monomer is a self-discharge abnormal monomer.

According to the method for identifying the abnormal battery cells, provided by the embodiment of the application, whether the battery is abnormal in self-discharge or not can be judged by acquiring the charging data of multiple charging and discharging cycles of each battery cell in the battery pack and utilizing the charging deviation time fitting change slope in multiple cycles, so that misjudgment caused by data jump in the process of only adopting twice charging is avoided, the self-discharge judgment is directly carried out by adopting the charging deviation time change slope, test calibration, complex model establishment or additional complex operation is not required, the calculated amount and complexity of the self-discharge abnormal battery judgment are reduced, the reference cell can be selected at will, and the additional judgment before calculation is avoided.

Next, an apparatus for identifying abnormal battery cells according to an embodiment of the present application will be described with reference to the accompanying drawings.

Fig. 5 is a block diagram schematically illustrating an apparatus for identifying abnormal battery cells according to an embodiment of the present application.

As shown in fig. 5, the abnormal cell identification apparatus 10 includes: an acquisition module 100, a calculation module 200 and an identification module 300.

The acquiring module 100 is configured to acquire charging data of multiple charging and discharging cycles of each battery cell in the battery pack; the calculating module 200 is configured to identify a first charging time when each battery cell in the charging data reaches a reference voltage, and calculate a charging deviation time of each battery cell relative to the reference cell according to the first charging time and a second charging time of the reference cell of the battery pack; the identifying module 300 is configured to fit a curve of the charge deviation time of each battery cell according to the charge cycle times of each charge and discharge cycle, and identify an abnormal battery cell with abnormal self-discharge in each battery cell according to a fit slope of the curve.

In an embodiment of the present application, the identification module 300 is further configured to: judging whether the fitting slope is larger than a first slope threshold value or not; and if the fitting slope is larger than the first slope threshold, judging that the battery cell is an abnormal battery cell with the self-discharge abnormality.

In an embodiment of the present application, the identification module 300 is further configured to: judging whether the fitting slope is smaller than a second slope threshold value or not; if the fit slope is smaller than the second slope threshold, the reference cell is judged to be an abnormal cell with self-discharge abnormality.

In an embodiment of the present application, the identification module 300 is further configured to: selecting a charging cycle time window of each charging monomer; determining a target cycle number interval of each charging monomer according to the charging cycle number window; fitting the charging deviation time of each charging and discharging cycle in the target cycle number interval to obtain a curve of the charging deviation time changing along with the charging and discharging cycle number.

In an embodiment of the present application, the computing module 200 is further configured to: taking any battery monomer in all battery monomers of the battery pack as a reference monomer; extracting a voltage change curve of a reference single body in the charging data, and selecting the voltage which can be reached by all the battery single bodies as the reference voltage according to the voltage change curve; and acquiring a first charging time when each battery cell in any charging voltage interval reaches the reference voltage.

In an embodiment of the present application, the computing module 200 is further configured to: acquiring charging current of a reference monomer in each charging voltage interval; extracting all charging voltage intervals of a reference monomer in a voltage change curve according to the charging current; and selecting the voltage which can be reached by all the battery cells in the charging voltage interval as a reference voltage.

In an embodiment of the present application, the computing module 200 is further configured to: acquiring the selection sequence of all charging voltage intervals; selecting reference voltages in charging voltage intervals in sequence according to a selection sequence, wherein in any charging voltage interval, selecting the median voltage of the charging voltage interval when selecting for the first time, taking the median voltage as the reference voltage if all the battery cells can reach the median voltage, otherwise, selecting the interval lower limit voltage of the charging voltage interval and the average voltage of the median voltage; if all the battery cells can reach the average voltage, the average voltage is used as the reference voltage, otherwise, the reference voltage is selected in the next charging voltage interval.

In an embodiment of the present application, the computing module 200 is further configured to: identifying a charging current of a charging voltage interval; the charging time when the battery cell reaches the reference voltage and is the same as the charging current in the charging voltage interval is recorded.

In the embodiment of the present application, the apparatus 10 of the embodiment of the present application further includes: and an extraction module.

The extraction module is used for acquiring charge and discharge data of the battery pack before acquiring charge data of multiple charge and discharge cycles of each battery cell in the battery pack; extracting the actual times of charge and discharge cycles in the charge and discharge data; if the actual times are greater than the preset times, charging data of multiple charging and discharging cycles of each battery cell in the battery pack are obtained, and otherwise, abnormal battery cells are not identified.

In the embodiment of the present application, the apparatus 10 of the embodiment of the present application further includes: and a reminding module.

The reminding module is used for generating preset reminding information after identifying abnormal battery monomers with abnormal self-discharge in each battery monomer according to the fitting slope of the curve; and sending preset reminding information to a preset terminal so as to carry out self-discharge abnormal reminding according to the preset reminding information.

In the embodiment of the present application, the apparatus 10 of the embodiment of the present application further includes: and a transmitting module.

The sending module is used for identifying the identification of the abnormal battery cell after identifying the abnormal battery cell with the self-discharge abnormality in each battery cell according to the fitting slope of the curve; and sending the identification to a preset terminal so as to position the abnormal battery cells in the battery pack according to the identification.

It should be noted that the foregoing explanation of the embodiment of the method for identifying an abnormal battery cell is also applicable to the device for identifying an abnormal battery cell of this embodiment, and will not be repeated here.

According to the identification device for the abnormal battery cells, provided by the embodiment of the application, whether the battery is abnormal in self-discharge or not can be judged by acquiring the charging data of multiple charging and discharging cycles of each battery cell in the battery pack and utilizing the charging deviation time fitting change slope in multiple cycles, so that misjudgment caused by data jump in the process of only adopting twice charging is avoided, the self-discharge judgment is directly carried out by adopting the charging deviation time change slope, test calibration, complex model establishment or additional complex operation is not required, the calculated amount and complexity of the self-discharge abnormal battery judgment are reduced, the reference cell can be selected at will, and the additional judgment before calculation is avoided.

Fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application. The server may include:

a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.

The processor 602 implements the method for identifying abnormal battery cells provided in the above-described embodiment when executing a program.

Further, the server further includes:

a communication interface 603 for communication between the memory 601 and the processor 602.

A memory 601 for storing a computer program executable on the processor 602.

The memory 601 may include a high-speed RAM (Random Access Memory ) memory, and may also include a nonvolatile memory, such as at least one disk memory.

If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component, external device interconnect) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.

Alternatively, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may perform communication with each other through internal interfaces.

The processor 602 may be a CPU (Central Processing Unit ) or ASIC (Application Specific Integrated Circuit, application specific integrated circuit) or one or more integrated circuits configured to implement embodiments of the present application.

The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the method for identifying abnormal battery cells as above.

In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.

It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable gate arrays, field programmable gate arrays, and the like.

Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.

While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (14)

1. A method for identifying abnormal battery cells, wherein the method is applied to a server, and wherein the method comprises the following steps:

acquiring charging data of multiple charging and discharging cycles of each battery monomer in the battery pack;

identifying a first charging time when each battery cell in the charging data reaches a reference voltage, and calculating charging deviation time of each battery cell relative to the reference cell according to the first charging time and a second charging time of the reference cell of the battery pack;

Fitting the charging deviation time of each battery cell in each charging and discharging cycle to obtain a curve of the charging deviation time changing along with the number of charging and discharging cycles, and identifying abnormal battery cells with abnormal self-discharging in each battery cell according to the fitting slope of the curve.

2. The method for identifying abnormal cells according to claim 1, wherein the step of identifying abnormal cells having self-discharge abnormality in each cell according to the fitted slope of the curve comprises:

judging whether the fitting slope is larger than a first slope threshold value or not;

and if the fit slope is larger than the first slope threshold, judging that the battery cell is an abnormal battery cell with self-discharge abnormality.

3. The method for identifying abnormal cells according to claim 1 or 2, wherein the step of identifying abnormal cells in which self-discharge abnormality exists in the respective cells according to the fitted slope of the curve comprises:

judging whether the fit slope is smaller than a second slope threshold value or not;

and if the fit slope is smaller than the second slope threshold, judging that the reference cell is an abnormal cell with self-discharge abnormality.

4. The method for identifying abnormal battery cells according to claim 1, wherein said fitting the charge deviation time of each battery cell at each charge-discharge cycle to obtain a curve of the charge deviation time as a function of the number of charge-discharge cycles, comprises:

selecting a charging cycle time window of each charging monomer;

determining a target cycle number interval of each charging monomer according to the charging cycle number window;

fitting the charging deviation time of each charging and discharging cycle in the target cycle number interval to obtain a curve of the charging deviation time changing along with the charging and discharging cycle number.

5. The method for identifying abnormal battery cells according to claim 1, wherein the identifying the first charging time when each battery cell in the charging data reaches a reference voltage includes:

taking any battery monomer in all battery monomers of the battery pack as a reference monomer;

extracting a voltage change curve of the reference single body in the charging data, and selecting the voltage which can be reached by all the battery single bodies as the reference voltage according to the voltage change curve;

and acquiring a first charging time when each battery cell in any charging voltage interval reaches the reference voltage.

6. The method for identifying abnormal battery cells according to claim 5, wherein the selecting, according to the voltage change curve, a voltage that can be reached by all battery cells as the reference voltage includes:

acquiring charging current of the reference monomer in each charging voltage interval;

extracting all charging voltage intervals of the reference single body in the voltage change curve according to the charging current;

and selecting the voltage which can be reached by all the battery cells in the charging voltage interval as the reference voltage.

7. The method for identifying abnormal battery cells according to claim 6, wherein the selecting the voltage that can be reached by all battery cells in the charging voltage interval as the reference voltage includes:

acquiring the selection sequence of all charging voltage intervals;

sequentially selecting the reference voltages in charging voltage intervals according to the selection sequence, wherein in any charging voltage interval, selecting the median voltage of the charging voltage interval when selecting for the first time, taking the median voltage as the reference voltage if all the battery cells can reach the median voltage, otherwise, selecting the interval lower limit voltage of the charging voltage interval and the average voltage of the median voltage; and if all the battery cells can reach the average voltage, taking the average voltage as the reference voltage, otherwise, selecting the reference voltage in the next charging voltage interval.

8. The method for identifying abnormal battery cells according to claim 5, wherein the obtaining the first charging time when each battery cell reaches the reference voltage in the arbitrary voltage interval comprises:

identifying a charging current of a charging voltage interval;

and recording the charging time when the battery cell reaches the reference voltage and is the same as the charging current of the charging voltage interval.

9. The method for identifying abnormal cells according to claim 1, further comprising, before acquiring charge data of a plurality of charge-discharge cycles of each cell in the battery pack:

acquiring charge and discharge data of a battery pack;

extracting the actual times of charge and discharge cycles in the charge and discharge data;

and if the actual times are greater than the preset times, acquiring charging data of multiple charging and discharging cycles of each battery cell in the battery pack, otherwise, not identifying the abnormal battery cell.

10. The method for identifying abnormal cells according to claim 1, further comprising, after identifying abnormal cells in which self-discharge abnormality exists in the respective cells according to a fitted slope of the curve:

generating preset reminding information;

And sending the preset reminding information to a preset terminal so as to carry out self-discharge abnormal reminding according to the preset reminding information.

11. The method for identifying abnormal cells according to claim 1, further comprising, after identifying abnormal cells in which self-discharge abnormality exists in the respective cells according to a fitted slope of the curve:

identifying the identity of the abnormal battery cell;

and sending the identification to a preset terminal so as to locate the abnormal battery cells in the battery pack according to the identification.

12. An apparatus for identifying abnormal cells, wherein the apparatus is applied to a server, and wherein the apparatus comprises:

the acquisition module is used for acquiring charging data of multiple charging and discharging cycles of each battery cell in the battery pack;

the calculation module is used for identifying a first charging time when each battery cell in the charging data reaches a reference voltage, and calculating charging deviation time of each battery cell relative to the reference cell according to the first charging time and a second charging time of the reference cell of the battery pack;

the identification module is used for fitting the charging deviation time of each battery cell in each charging and discharging cycle to obtain a curve of the charging deviation time changing along with the number of charging and discharging cycles, and identifying abnormal battery cells with self-discharging abnormality in each battery cell according to the fitting slope of the curve.

13. A server, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of identifying an abnormal cell according to any one of claims 1-11.

14. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for realizing the method of identifying an abnormal cell according to any one of claims 1 to 11.