Lithium iron phosphate battery self-discharge detection method, electronic device and readable storage medium

Abstract

Embodiments of the present disclosure provide a lithium iron phosphate battery self-discharge detection method, an electronic device and a readable storage medium. The method comprises: sorting the obtained battery data; the battery data at least includes the highest voltage and the lowest voltage; when it is judged that each battery data in a sliding window of a set length meets a preset condition, calculating the sum of the voltage difference of the highest voltage and the lowest voltage corresponding to each battery data in the sliding window; wherein the sliding window includes N battery data, the sliding window moves smoothly along the sorted battery data, and N is an integer greater than zero; in the case of judging that the sum of the voltage difference is greater than a set voltage difference threshold, it is determined that the battery self-discharge performance is abnormal. According to the embodiments of the present disclosure, the self-discharge condition of the lithium iron phosphate battery can be quickly and reliably detected during driving.

Description

Technical Field

[0001] This disclosure relates to the field of battery testing technology, and more specifically, to a method for detecting the self-discharge of a lithium iron phosphate battery, an electronic device, and a readable storage medium. Background Technology

[0002] Power batteries typically consist of multiple individual cells connected in series or parallel. Each individual cell must possess good performance consistency to ensure the battery pack's performance, cycle life, and safety. Key indicators for measuring battery consistency include capacity, self-discharge, and internal resistance. Among these, severe self-discharge increases the inconsistency between individual cells, thereby affecting the battery's usable capacity.

[0003] Traditional methods for detecting the self-discharge of lithium iron phosphate batteries are usually based on detecting leakage current during the development of individual cells, which cannot detect the self-discharge of the battery during driving. Therefore, it is necessary to propose a new method for detecting the self-discharge of lithium iron phosphate batteries. Summary of the Invention

[0004] One objective of this disclosure is to provide a method for detecting the self-discharge of lithium iron phosphate batteries, which can quickly and reliably detect the self-discharge status of the battery during driving.

[0005] According to a first aspect of the present disclosure, a method for detecting the self-discharge of a lithium iron phosphate battery is provided, comprising:

[0006] The acquired battery data is sorted; the battery data includes at least the highest voltage and the lowest voltage.

[0007] When each battery data within a sliding window of a set length satisfies a preset condition, the sum of the voltage differences between the highest and lowest voltages corresponding to each battery data within the sliding window is calculated; wherein, the sliding window includes N battery data, and the sliding window moves smoothly along the sorted battery data, where N is an integer greater than zero;

[0008] If the sum of the voltage differences is greater than a set voltage difference threshold, the battery's self-discharge performance is determined to be abnormal.

[0009] Optionally, the battery data may further include: battery identifier, battery data acquisition time, state of charge / discharge, state of charge, and minimum temperature.

[0010] Optionally, the preset conditions include:

[0011] The maximum voltage is greater than the set maximum voltage;

[0012] The charging / discharging state is the discharging state;

[0013] The minimum temperature is greater than the set temperature; and,

[0014] The battery data acquisition time is less than the set interval time.

[0015] Optionally, before sorting the acquired battery data, the method further includes:

[0016] Obtain a training sample set, which includes multiple training samples, each of which has a corresponding real fault label;

[0017] The training samples are input into the parameter optimization algorithm model, the training parameters are optimized and calculated, and the predicted fault label is output; wherein, the training parameters include the length corresponding to the sliding window, the set voltage difference threshold, and the set maximum voltage;

[0018] The true fault label and the predicted fault label are calculated according to the set optimization loss function. If the calculation result does not meet the set error threshold, the training parameters are adjusted, and the operation of inputting the training samples into the parameter optimization algorithm model for calculation is executed.

[0019] If the calculation results meet the set error threshold, the optimized training parameters are output.

[0020] Optionally, calculating the sum of voltage differences corresponding to each battery data point within the sliding window includes:

[0021] Calculate the voltage difference between the highest voltage and the lowest voltage for each of the battery data;

[0022] The sum of all the voltage differences is calculated by summing them.

[0023] Optionally, before sorting the acquired battery data, the method further includes:

[0024] The battery data is subjected to data cleaning processing; wherein the data cleaning processing includes at least: processing to remove null values, processing to remove outlier voltage values, and processing to remove battery data acquired at the same time.

[0025] Optionally, sorting the acquired battery data includes:

[0026] The battery data is sorted in ascending order of acquisition time.

[0027] Optionally, after calculating the sum of voltage differences corresponding to each battery data point within the sliding window, the method further includes:

[0028] If the sum of the voltage differences is less than or equal to the set voltage difference threshold, the battery self-discharge performance is determined to be normal.

[0029] According to a second aspect of the present disclosure, an electronic device is provided, including a memory and a processor, the memory being used to store executable instructions; the processor being used to execute, under the control of the instructions, the lithium iron phosphate battery self-discharge detection method according to any one of the first aspects of the present disclosure.

[0030] According to a third aspect of the present disclosure, a readable storage medium is provided storing a computer program that, when executed by a processor, implements the lithium iron phosphate battery self-discharge detection method as described in any one of the first aspects of the present disclosure.

[0031] One beneficial effect of this disclosure is that, by sorting the acquired battery data, which includes at least the highest and lowest voltages, and when each battery data within a sliding window of a set length satisfies a preset condition, the sum of the voltage differences between the highest and lowest voltages corresponding to each battery data within the sliding window is calculated; wherein the sliding window includes N battery data, and the sliding window moves smoothly along the sorted battery data, where N is a positive integer; and when the sum of the voltage differences is greater than a set voltage difference threshold, the battery self-discharge performance is determined to be abnormal. The lithium iron phosphate battery self-discharge detection method according to embodiments of this disclosure can be deployed in a vehicle-side or cloud-based Battery Management System (BMS) for self-discharge anomaly detection. It does not require additional hardware, nor does it rely on prior knowledge such as electrochemical models and SOC-OCV correspondence tables, nor does it require electrochemical parameter calibration. Instead, it is based on analyzing battery data acquired during driving, enabling rapid and reliable detection of the self-discharge status of lithium iron phosphate batteries during driving. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the invention and, together with their description, serve to explain the principles of the invention.

[0033] Figure 1 This is a schematic block diagram of the hardware configuration of an exemplary electronic device;

[0034] Figure 2 This is a schematic flowchart of a self-discharge detection method for lithium iron phosphate batteries according to an embodiment of the present disclosure;

[0035] Figure 3 This is a schematic diagram of the hardware structure of an exemplary electronic device. Detailed Implementation

[0036] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0037] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0038] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0039] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0040] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0041] <Hardware Configuration>

[0042] like Figure 1 As shown, an electronic device 1000 that can apply the self-discharge detection method for lithium iron phosphate batteries disclosed herein may include a processor 1100, a memory 1200, a communication device 1300, a display device 1400, an input device 1500, etc.

[0043] Processor 1100 may be a mobile processor. Memory 1200 includes, for example, ROM (Read-Only Memory), RAM (Random Access Memory), and non-volatile memory such as a hard disk. Communication device 1300 may be capable of wired or wireless communication. Communication device 1300 may include short-range communication devices, such as any device performing short-range wireless communication based on short-range wireless communication protocols such as Hilink, WiFi (IEEE 802.11), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, and LiFi. Communication device 1300 may also include long-range communication devices, such as any device performing WLAN, GPRS, or 2G / 3G / 4G / 5G long-range communication. Display device 1400 is, for example, an LCD screen or a touch screen. Input device 1500 may include, for example, a touch screen or a keyboard.

[0044] In this embodiment, the memory 1200 of the electronic device 1000 is used to store instructions that control the processor 1100 to operate in order to implement the self-discharge detection method for lithium iron phosphate batteries. Those skilled in the art can design instructions based on the scheme disclosed herein. How the instructions control the processor to operate is well known in the art and will not be described in detail here.

[0045] Despite Figure 1 The present disclosure illustrates multiple devices of electronic device 1000; however, this disclosure may refer only to some of these devices. For example, terminal device 1000 may refer only to memory 1200 and processor 1100, communication device 1300 and display device 1400.

[0046] It should be understood that, despite Figure 1 Only one electronic device 1000 is shown, but this does not mean that the number of electronic devices 1000 is limited.

[0047] <Method Implementation>

[0048] Figure 2 This is a schematic flowchart of a self-discharge detection method for lithium iron phosphate batteries according to an embodiment of the present disclosure.

[0049] like Figure 2 As shown, the self-discharge detection method for lithium iron phosphate batteries in this embodiment can be specifically derived from, as follows: Figure 1 The electronic device 1000 shown is performing this action.

[0050] Specifically, the self-discharge detection method for lithium iron phosphate batteries in this embodiment may include the following steps 2100 to 2300:

[0051] Step 2100: Sort the acquired battery data; the battery data includes at least the highest voltage and the lowest voltage.

[0052] The electronic device 1000 can obtain real-time battery data uploaded by the lithium iron phosphate battery at set time intervals during driving from a cloud server. This battery data may further include: battery identifier, battery data acquisition time, charge / discharge state, state of charge, and minimum temperature.

[0053] When sorting the acquired battery data, the electronic device 1000 can sort the data in ascending order of acquisition time.

[0054] In one embodiment, in order to remove invalid battery data, the electronic device 1000 may perform data cleaning processing on the battery data before sorting the acquired battery data; wherein, the data cleaning processing includes at least: processing to remove null values, processing to remove outlier voltage values, and processing to remove battery data acquired at the same time.

[0055] After the data cleaning process is completed, the battery data is sorted in ascending order of acquisition time.

[0056] Step 2200: When it is determined that each of the battery data within a sliding window of a set length meets the preset conditions, calculate the sum of the voltage differences between the highest and lowest voltages corresponding to each of the battery data within the sliding window; wherein, the sliding window includes N of the battery data, and the sliding window moves smoothly along the sorted battery data, where N is an integer greater than zero.

[0057] In this step, the preset conditions include: the highest voltage is greater than a set maximum voltage; the charging / discharging state is a discharging state; the lowest temperature is greater than a set temperature; and the battery data acquisition time is less than a set interval time. Each piece of battery data within the sliding window must meet the above preset conditions.

[0058] It is understood that if any of the battery data in the sliding window does not meet the preset conditions, then there is no need to execute this step 2200. After moving the sliding window, the battery data in the sliding window is judged again to see if it meets the preset conditions. If the preset conditions are met, this step 2200 is executed.

[0059] When calculating the sum of the voltage differences between the highest and lowest voltages corresponding to each battery data within the sliding window, the electronic device 1000 may first calculate the voltage difference between the highest and lowest voltages of each battery data; then sum all the voltage differences to obtain the sum of the voltage differences.

[0060] In one embodiment, to ensure that the battery data is from a non-low-temperature environment and to avoid interference with the detection results due to high internal resistance and high voltage difference at low temperatures, the set temperature Para_Temp can be 15°C. To ensure no frame drops, the set interval time Para_Time can be set to 45 seconds. The set maximum voltage, the length of the sliding window, and the set voltage difference can be obtained by training the training samples using a parameter optimization algorithm model.

[0061] Specifically, before step 2100 above, a training sample set can be obtained first. Each sample in the training sample set has a real fault label label_real. The main training parameters include the length of the sliding window Len_Window, the voltage difference threshold Para_DV, and the maximum voltage Para_Vmax.

[0062] The training sample input parameter optimization algorithm model is used to optimize the training parameters and output a predicted fault label, label_pred. Then, the true fault label and the predicted fault label are calculated according to the set optimization loss function. If the calculation result does not meet the set error threshold, the training parameters are adjusted, and the operation of optimizing the training sample input parameter algorithm model is executed again. If the calculation result meets the set error threshold, the optimized training parameters are output.

[0063] For example, the true fault label `label_real` and the predicted fault label `label_pred` can be calculated according to the set optimization loss function 1-Phi(label_real, label_pred). Here, Phi is the correlation coefficient between the true fault label `label_real` and the predicted fault label `label_pred`. The true fault label `label_real` can include true fault labels and true non-fault labels. For example, if the true fault label is 1, then the true non-fault label is 0; if the true fault label is 0, then the true non-fault label is 1. Similarly, the predicted fault label `label_pred` can include predicted fault labels and predicted non-fault labels. For example, if the predicted fault label is 1, then the predicted non-fault label can be 0; if the predicted fault label is 0, then the predicted non-fault label can be 1.

[0064] In this embodiment, the parameter optimization algorithm model used can be a genetic algorithm.

[0065] In one example, in the training parameter combination obtained by the genetic algorithm, the maximum voltage Para_Vmax can be set to 3.2V, the length of the sliding window Len_Window can be set to 27 frames, and the voltage difference Para_DV can be set to 0.304V.

[0066] In other words, for each battery data point within a 27-frame sliding window, the system checks whether the highest voltage is greater than 3.2V, whether the charge / discharge state is in a discharge state, whether the lowest temperature is greater than 15℃, and whether the battery data acquisition time is less than 45 seconds. If all the above checks are positive, the system calculates the voltage difference between the highest and lowest voltages for each battery data point, and then sums all the voltage differences to obtain the sum of the voltage differences.

[0067] Step 2300: If the sum of the voltage differences is greater than a set voltage difference threshold, the battery self-discharge performance is determined to be abnormal.

[0068] In this step, the set voltage difference threshold is the product of the set voltage difference Para_DV and the length of the sliding window Len_Window. If the sum of the voltage differences is greater than the set voltage difference threshold, according to the battery's SOC-OCV (State Of Charge-Open Circuit Voltage) curve, the set voltage difference must be caused by the lowest voltage Vmin entering the end of discharge. At this time, the battery's highest voltage Vmax is still maintained at a high level, such as 3.2V. This indicates that the imbalance between the battery's lowest voltage Vmin and highest voltage Vmax is very serious, indicating that the battery self-discharge is serious and there is a safety hazard. When the electronic device 1000 determines that the battery's self-discharge performance is abnormal, it can issue an alarm message.

[0069] Optionally, the alarm notification information may include at least one of the following: audible alarm, notification message alarm. No specific limitations are imposed here.

[0070] It is understood that if the sum of the voltage differences is less than or equal to the set voltage difference threshold, the electronic device 1000 can determine that the battery self-discharge performance is normal.

[0071] According to the lithium iron phosphate battery self-discharge detection method of this embodiment, the acquired battery data is sorted; the battery data includes at least the highest voltage and the lowest voltage; when each battery data within a sliding window of a set length meets a preset condition, the sum of the voltage differences between the highest and lowest voltages corresponding to each battery data within the sliding window is calculated; wherein, the sliding window includes N battery data, and the sliding window moves smoothly along the sorted battery data, where N is a positive integer; if the sum of the voltage differences is greater than a set voltage difference threshold, the battery self-discharge performance is determined to be abnormal. The lithium iron phosphate battery self-discharge detection method of this embodiment can be deployed on a vehicle-side or cloud-based battery management system (BMS) for self-discharge anomaly detection. It does not require additional hardware, nor does it rely on prior knowledge such as electrochemical models and SOC-OCV correspondence tables, nor does it require electrochemical parameter calibration. Instead, it quickly and reliably detects the self-discharge status of lithium iron phosphate batteries by analyzing battery data acquired during driving.

[0072] <Electronic Device Examples>

[0073] In this embodiment, an electronic device 3000 is also provided.

[0074] like Figure 3As shown, the electronic device 3000 may include a processor 3100 and a memory 3200, the memory 3200 being used to store executable instructions; the processor 3100 being used to operate the electronic device 3000 to execute instructions according to the above. Figure 2 The embodiment describes a method for detecting the self-discharge of lithium iron phosphate batteries.

[0075] <Media Example>

[0076] This disclosure provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the lithium iron phosphate battery self-discharge detection method provided in any of the foregoing embodiments.

[0077] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0078] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0079] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0080] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0081] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0082] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or another programmable lithium iron phosphate battery self-discharge detection device to produce a machine such that, when executed by the processor of the computer or other programmable lithium iron phosphate battery self-discharge detection device, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, a programmable lithium iron phosphate battery self-discharge detection device, and / or other equipment to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0083] Computer-readable program instructions may also be loaded onto a computer, other programmable lithium iron phosphate battery self-discharge detection device, or other equipment to cause a series of operational steps to be executed on the computer, other programmable lithium iron phosphate battery self-discharge detection device, or other equipment to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable lithium iron phosphate battery self-discharge detection device, or other equipment to perform the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are equivalent.

[0085] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for detecting the self-discharge of a lithium iron phosphate battery, characterized in that, include: The acquired battery data is sorted; the battery data includes at least the highest voltage and the lowest voltage. When each battery data within a sliding window of a set length satisfies a preset condition, the sum of the voltage differences between the highest and lowest voltages corresponding to each battery data within the sliding window is calculated; wherein, the sliding window includes N battery data, and the sliding window moves smoothly along the sorted battery data, where N is an integer greater than zero; If the sum of the voltage differences is greater than a set voltage difference threshold, the battery self-discharge performance is determined to be abnormal. The battery data also includes: battery identifier, battery data acquisition time, charge / discharge state, state of charge, and minimum temperature; The preset conditions include: The maximum voltage is greater than the set maximum voltage; The charging / discharging state is the discharging state; The minimum temperature is greater than the set temperature; and, The battery data acquisition time is less than the set interval time; Before sorting the acquired battery data, the method further includes: Obtain a training sample set, which includes multiple training samples, each of which has a corresponding real fault label; The training samples are input into the parameter optimization algorithm model, the training parameters are optimized and calculated, and the predicted fault label is output; wherein, the training parameters include the length corresponding to the sliding window, the set voltage difference threshold, and the set maximum voltage; The true fault label and the predicted fault label are calculated according to the set optimization loss function. If the calculation result does not meet the set error threshold, the training parameters are adjusted, and the operation of inputting the training samples into the parameter optimization algorithm model for calculation is executed. If the calculation results meet the set error threshold, the optimized training parameters are output.

2. The method according to claim 1, characterized in that, Calculating the sum of voltage differences corresponding to each battery data point within the sliding window includes: Calculate the voltage difference between the highest voltage and the lowest voltage for each of the battery data; The sum of all the voltage differences is calculated by summing them.

3. The method according to claim 1, characterized in that, Before sorting the acquired battery data, the method further includes: The battery data is subjected to data cleaning processing; wherein the data cleaning processing includes at least: processing to remove null values, processing to remove outlier voltage values, and processing to remove battery data acquired at the same time.

4. The method according to claim 1, characterized in that, The sorting of the acquired battery data includes: The battery data is sorted in ascending order of acquisition time.

5. The method according to claim 1, characterized in that, After calculating the sum of voltage differences corresponding to each battery data point within the sliding window, the method further includes: If the sum of the voltage differences is less than or equal to the set voltage difference threshold, the battery self-discharge performance is determined to be normal.

6. An electronic device comprising a memory and a processor, the memory being used to store executable instructions; the processor being used to execute, under the control of the instructions, the self-discharge detection method for lithium iron phosphate batteries according to any one of claims 1 to 5.

7. A readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the self-discharge detection method for lithium iron phosphate batteries as described in any one of claims 1 to 5.

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