Method and device for predicting health state of battery

Abstract

The embodiment of the invention provides a battery health state prediction method and device, and belongs to the technical field of battery safety, and the prediction method comprises the steps: collecting a corresponding discharge relaxation curve when a to-be-detected battery is discharged to a preset charge state; determining a relaxation degree according to the discharge relaxation curve, wherein the relaxation degree is a difference value between a discharge voltage corresponding to a preset sampling time in the discharge relaxation curve and an open-circuit voltage and an ohm voltage drop; and substituting the relaxation degree into a prediction model, and determining the health state of the to-be-detected battery. According to the method and the device for predicting the health state of the battery, the health state of the battery is estimated according to the relaxation degree of the battery to be detected, and interference of open-circuit voltage and ohm voltage drop is eliminated in the process of calculating the relaxation degree; in the process of establishing the prediction model, the optimal prediction model and the optimal charge state are determined by evaluating a plurality of initial prediction models, the prediction precision is improved, and the method is suitable for most battery health state estimation scenes.

Description

Technical Field

[0001] The present invention relates to the field of battery safety technology, and in particular to a method and device for predicting a battery health state. Background Art

[0002] With the rapid development of new energy and lithium battery technology, lithium batteries are widely used in various fields, such as power and energy storage. As the battery is used, it is aging and the battery health status SOH will gradually decrease. When the battery health status SOH is too small, the battery needs to be replaced to ensure the normal use of the energy supply equipment. Therefore, a method for evaluating the battery health status SOH is needed to monitor the battery health status.

[0003] The existing prediction method establishes a prediction model based on the relaxation voltage curve to predict the battery SOH. However, the battery health status SOH predicted by the existing method has a large error. Summary of the invention

[0004] The inventor of the present invention has found through research that the main reason why the battery health status SOH predicted by the existing method has a large error is that the interference of open circuit voltage and ohmic voltage drop is not considered. Based on the research results, an embodiment of the present invention provides a method for predicting the battery health status, and the prediction method includes:

[0005] Collect the corresponding discharge relaxation curve when the battery under test is discharged to a preset state of charge;

[0006] Determine the relaxation degree according to the discharge relaxation curve, wherein the relaxation degree is the difference between the discharge voltage corresponding to the preset sampling time in the discharge relaxation curve and the open circuit voltage and the ohmic voltage drop;

[0007] The relaxation degree is brought into the prediction model to determine the health state of the battery to be tested.

[0008] Optionally, when the battery to be tested is discharged to a preset state of charge, the corresponding discharge relaxation curve includes:

[0009] The battery to be tested is charged and discharged, and the rated capacity of the battery to be tested is determined according to the collected discharge capacity;

[0010] Determine the target discharge capacity according to the preset state of charge and rated capacity;

[0011] After the battery to be tested is fully charged, it starts to discharge until the discharge capacity of the battery to be tested reaches the target discharge capacity, and the corresponding discharge relaxation curve is collected.

[0012] Optionally, the battery to be tested is charged and discharged, and determining the rated capacity of the battery to be tested according to the collected discharge capacity includes:

[0013] Preset a first charging rate and a first discharging rate;

[0014] The battery to be tested is charged according to a preset first charging rate and discharged according to a first discharging rate, the discharge capacity of the battery to be tested during the discharging process is collected, and the rated capacity of the battery is determined according to the discharge capacity.

[0015] Optionally, after the battery to be tested is fully charged, discharge is started so that the discharge capacity of the battery to be tested reaches a target discharge capacity, and collecting the corresponding discharge relaxation curve includes:

[0016] Preset the second charging rate, the second discharging rate, the sampling frequency, and the discharging stop time;

[0017] After the battery to be tested is fully charged at the second charging rate, it is discharged at the second discharge rate; after the discharge capacity of the battery to be tested reaches the target discharge capacity, the battery to be tested stops discharging until a preset stop discharge time, samples the voltage at a preset sampling frequency, and determines a discharge relaxation curve based on the collected voltage data.

[0018] Optionally, the preset state of charge and the preset model are obtained in the following manner:

[0019] Selecting a plurality of sample batteries, wherein the sample batteries and the battery to be tested belong to the same batch;

[0020] Setting a set of multiple preset states of charge;

[0021] For any state of charge in the set: calculating the corresponding relaxation degree when sample batteries in different health states are discharged to the state of charge, establishing training data according to the relaxation degree and the corresponding different health states, standardizing and denoising the training data, and training the training data using a machine learning algorithm to obtain an initial prediction model corresponding to the state of charge;

[0022] The initial prediction models corresponding to different states of charge are evaluated, and the initial prediction model with the smallest error is set as the final prediction model, and the corresponding state of charge is set as the preset state of charge.

[0023] On the other hand, an embodiment of the present invention provides a device for predicting a battery health state, the device comprising:

[0024] Data acquisition module: used to collect the corresponding discharge relaxation curve when the battery under test is discharged to a preset state of charge;

[0025] Data processing module: used to determine the relaxation degree according to the discharge relaxation curve, wherein the relaxation degree is the difference between the discharge voltage corresponding to the preset sampling time in the discharge relaxation curve and the open circuit voltage and the ohmic voltage drop;

[0026] Data output module: used to output the health status of the battery to be tested.

[0027] Optionally, when the battery to be tested is discharged to a preset state of charge, the corresponding discharge relaxation curve includes:

[0028] The battery to be tested is charged and discharged, and the rated capacity of the battery to be tested is determined according to the collected discharge capacity;

[0029] Determine the target discharge capacity according to the preset state of charge and rated capacity;

[0030] After the battery to be tested is fully charged, it starts to discharge until the discharge capacity of the battery to be tested reaches the target discharge capacity, and the corresponding discharge relaxation curve is collected.

[0031] Optionally, the battery to be tested is charged and discharged, and determining the rated capacity of the battery to be tested according to the collected discharge capacity includes:

[0032] Preset a first charging rate and a first discharging rate;

[0033] The battery to be tested is charged according to a preset first charging rate and discharged according to a first discharging rate, the discharge capacity of the battery to be tested during the discharging process is collected, and the rated capacity of the battery is determined according to the discharge capacity.

[0034] Optionally, after the battery to be tested is fully charged, discharge is started so that the discharge capacity of the battery to be tested reaches a target discharge capacity, and collecting the corresponding discharge relaxation curve includes:

[0035] Preset the second charging rate, the second discharging rate, the sampling frequency, and the discharging stop time;

[0036] After the battery to be tested is fully charged at the second charging rate, it is discharged at the second discharge rate; after the discharge capacity of the battery to be tested reaches the target discharge capacity, the battery to be tested stops discharging until a preset stop discharge time, samples the voltage at a preset sampling frequency, and determines a discharge relaxation curve based on the collected voltage data.

[0037] Optionally, the preset state of charge and the preset model are obtained in the following manner:

[0038] Selecting a plurality of sample batteries, wherein the sample batteries and the battery to be tested belong to the same batch;

[0039] Setting a set of multiple preset states of charge;

[0040] For any state of charge in the set: calculating the corresponding relaxation degree when sample batteries in different health states are discharged to the state of charge, establishing training data according to the relaxation degree and the corresponding different health states, standardizing and denoising the training data, and training the training data using a machine learning algorithm to obtain an initial prediction model corresponding to the state of charge;

[0041] The initial prediction models corresponding to different states of charge are evaluated, and the initial prediction model with the smallest error is set as the final prediction model, and the corresponding state of charge is set as the preset state of charge.

[0042] Through the above technical scheme, the present invention provides a method and device for predicting the health state of a battery, which estimates the health state of the battery according to the relaxation degree of the battery to be tested, and eliminates the interference of open circuit voltage and ohmic voltage drop in the process of calculating the relaxation degree; in the process of establishing a prediction model, a preferred prediction model and preferred state of charge are determined by evaluating multiple initial prediction models, thereby improving the prediction accuracy and being suitable for most battery health state estimation scenarios.

[0043] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0045] Figure 1 is a flow chart of the prediction method provided by the present invention;

[0046] Figure 2 is the discharge relaxation voltage-time curve;

[0047] Figure 3 It is a battery health status-relaxation degree curve;

[0048] Figure 4 It is a schematic diagram of the structure of the prediction device provided by the present invention. DETAILED DESCRIPTION

[0049] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0050] Figure 1 is a flow chart of the prediction method provided by the present invention; Figure 2 is the discharge relaxation voltage-time curve; Figure 3 It is a battery health status-relaxation degree curve.

[0051] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting a battery health state, the method comprising:

[0052] S101, collecting a discharge relaxation curve corresponding to the battery under test when it is discharged to a preset state of charge;

[0053] First, the battery to be tested is charged and discharged, and the rated capacity of the battery to be tested is determined according to the collected discharge capacity.

[0054] Furthermore, a first charging rate and a first discharging rate are preset; the first charging rate is preferably 0.5C, and the first discharging rate is preferably 0.5C; wherein the 0.5C rate is a discharge strategy for discharging the battery from 100% to 0% within 2 hours.

[0055] For example, the battery to be tested is charged at a rate of 0.5C and discharged at a rate of 0.5C, the discharge capacity of the battery to be tested during the discharge process is collected, and the rated capacity of the battery is determined based on the discharge capacity. It should be noted that the battery to be tested can be charged and discharged multiple times, and the average value of the recorded discharge capacity is used as the rated capacity of the battery to be tested.

[0056] Then, the target discharge capacity is determined according to the preset state of charge and the rated capacity;

[0057] Wherein, the preset state of charge is preferably 65%;

[0058] Finally, after the battery to be tested is fully charged, discharge is started so that the discharge capacity of the battery to be tested reaches the target discharge capacity, and the corresponding discharge relaxation curve is collected;

[0059] Furthermore, a second charge rate, a second discharge rate, a sampling frequency, and a discharge stop time are preset; wherein, the second charge rate is preferably 0.5C, and the second discharge rate is preferably 0.5C; the discharge stop time can be one of 3 hours, 6 hours, 9 hours, 12 hours, 15 hours, 18 hours, and 24 hours, and the discharge stop time is preferably 6 hours; the sampling frequency is preferably 20fs, that is, the voltage is sampled every 0.05 seconds.

[0060] For example, after the battery to be tested is fully charged at a rate of 0.5C, it starts to discharge at a rate of 0.5C; after the discharge capacity of the battery to be tested reaches the target discharge capacity, that is, when the state of charge of the battery to be tested is 65%, the battery to be tested starts to stop discharging, and the stop discharge time is 6 hours. During this period, the voltage is sampled every 0.05 seconds, and a discharge relaxation curve is established based on the collected voltage data. The discharge relaxation curve is as follows: Figure 2As shown, the horizontal axis represents time and the vertical axis represents voltage.

[0061] S102, determining a relaxation degree according to the discharge relaxation curve;

[0062] The degree of relaxation is calculated using the following formula:

[0063] ΔU relax =|U1-U2-U3|

[0064] Where ΔU relax is the relaxation degree, U1 is the voltage value corresponding to the discharge relaxation curve at the preset sampling time, U2 is the open circuit voltage after stabilization, and U3 is the ohmic voltage drop;

[0065] It should be noted that the preset sampling time is preferably 0.1s, that is, 1 is the voltage corresponding to 0.1s after the end of discharge in the discharge relaxation curve; the stabilized open circuit voltage is the voltage value corresponding to the discharge relaxation curve when the battery stops discharging time is close to or equal to the preset stop discharge time, for example, the preset stop discharge time is 6 hours. When the test battery stops discharging for 6 hours, the voltage value in the corresponding discharge relaxation curve is the open circuit voltage; the ohmic voltage drop is the difference between the voltage at 0.1s after stopping discharge in the discharge relaxation curve and the discharge voltage.

[0066] Combination Figure 2 The calculation formula for calculating the degree of relaxation is explained in detail, such as Figure 2 As shown, the voltage at point A is the voltage at the end of discharge, the voltage at point B is the voltage 0.1s after the end of discharge, and the voltage at point C is the open circuit voltage. Among them, U1 is the voltage at point A, U2 is the voltage at point C, and U3 is the difference between the voltages at points A and B.

[0067] S103, bringing the relaxation degree into a prediction model to determine the health state of the battery to be tested.

[0068] Furthermore, the preferred method of presetting the state of charge and the process of establishing the preset model are introduced:

[0069] Select multiple sample batteries, where the sample batteries and the battery to be tested belong to the same batch of batteries and have the same production process, production materials, and regulatory models as the battery to be tested. Under normal temperature, charge and discharge the sample batteries multiple times at a rate of 1.5C to rapidly age the sample batteries. Determine the health status of the sample batteries based on the remaining capacity and initial capacity of the sample batteries.

[0070] A set of multiple preset state of charge is set. For example, the state of charge set includes: 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%, 40%;

[0071] For any state of charge in the set: calculate the corresponding relaxation degree when the sample batteries in different health states are discharged to the state of charge, establish training data according to the relaxation degree and the corresponding different health states, and visualize part of the training data, such as Figure 3 As shown, there is a linear relationship between the battery health state and the relaxation degree, where the horizontal axis represents the battery health state and the vertical axis represents the relaxation degree.

[0072] The training data is standardized and denoised, and a machine learning algorithm is used to train the training data to obtain an initial prediction model corresponding to the state of charge.

[0073] For example, the following linear regression model can be used as the initial prediction model corresponding to the state of charge:

[0074] y=k*x+b

[0075] Among them, y is the battery health state, x is the relaxation degree corresponding to the health state, k and b are the model parameters that need to be solved;

[0076] The least squares method can be used to solve the parameters k and b.

[0077]

[0078]

[0079] Among them, x i is the relaxation degree of the i-th sample battery, y i is the health status corresponding to the i-th sample battery, is the average relaxation degree of the sample battery, is the average health status of the sample batteries.

[0080] The initial prediction models corresponding to different states of charge are evaluated, and the initial prediction model with the smallest error is set as the final prediction model, and the corresponding state of charge is set as a preset state of charge, wherein the preset state of charge is preferably 65%.

[0081] Figure 4 It is a schematic diagram of the structure of the prediction device provided by the present invention. Figure 4 As shown, the device comprises:

[0082] Data acquisition module: used to collect the corresponding discharge relaxation curve when the battery under test is discharged to a preset state of charge;

[0083] Data processing module: used to determine the relaxation degree according to the discharge relaxation curve, wherein the relaxation degree is the difference between the discharge voltage corresponding to the preset sampling time in the discharge relaxation curve and the open circuit voltage and the ohmic voltage drop;

[0084] Data output module: used to output the health status of the battery to be tested.

[0085] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0089] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0090] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0091] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0092] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0093] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting battery health status, characterized in that: The prediction method comprises: Collect the corresponding discharge relaxation curve when the battery under test is discharged to a preset state of charge; Determine the relaxation degree according to the discharge relaxation curve, wherein the relaxation degree is the difference between the discharge voltage corresponding to the preset sampling time in the discharge relaxation curve and the open circuit voltage and the ohmic voltage drop; The relaxation degree is brought into the prediction model to determine the health state of the battery to be tested.

2. The method according to claim 1, characterized in that When the battery to be tested is discharged to a preset state of charge, the corresponding discharge relaxation curve includes: The battery to be tested is charged and discharged, and the rated capacity of the battery to be tested is determined according to the collected discharge capacity; Determine the target discharge capacity according to the preset state of charge and rated capacity; After the battery to be tested is fully charged, it starts to discharge until the discharge capacity of the battery to be tested reaches the target discharge capacity, and the corresponding discharge relaxation curve is collected.

3. The method according to claim 2, characterized in that The battery to be tested is charged and discharged, and the rated capacity of the battery to be tested is determined according to the collected discharge capacity, including: Preset a first charging rate and a first discharging rate; The battery to be tested is charged according to a preset first charging rate and discharged according to a first discharging rate, the discharge capacity of the battery to be tested during the discharging process is collected, and the rated capacity of the battery is determined according to the discharge capacity.

4. The method according to claim 2, characterized in that: After the battery to be tested is fully charged, it starts to discharge so that the discharge capacity of the battery to be tested reaches the target discharge capacity, and collecting the corresponding discharge relaxation curve includes: Preset the second charging rate, the second discharging rate, the sampling frequency, and the discharging stop time; After the battery to be tested is fully charged at the second charging rate, it is discharged at the second discharge rate; after the discharge capacity of the battery to be tested reaches the target discharge capacity, the battery to be tested stops discharging until a preset stop discharge time, samples the voltage at a preset sampling frequency, and determines a discharge relaxation curve based on the collected voltage data.

5. The method according to any one of claims 1 to 4, characterized in that: The preset state of charge and the preset model are obtained in the following manner: Selecting a plurality of sample batteries, wherein the sample batteries and the battery to be tested belong to the same batch; Setting a set of multiple preset states of charge; For any state of charge in the set: calculating the corresponding relaxation degree when sample batteries in different health states are discharged to the state of charge, establishing training data according to the relaxation degree and the corresponding different health states, standardizing and denoising the training data, and training the training data using a machine learning algorithm to obtain an initial prediction model corresponding to the state of charge; The initial prediction models corresponding to different states of charge are evaluated, and the initial prediction model with the smallest error is set as the final prediction model, and the corresponding state of charge is set as the preset state of charge.

6. A battery health status prediction device, characterized in that: The device includes: Data acquisition module: used to collect the corresponding discharge relaxation curve when the battery under test is discharged to a preset state of charge; Data processing module: used to determine the relaxation degree according to the discharge relaxation curve, wherein the relaxation degree is the difference between the discharge voltage corresponding to the preset sampling time in the discharge relaxation curve and the open circuit voltage and the ohmic voltage drop; Data output module: used to output the health status of the battery to be tested.

7. The device according to claim 6, characterized in that When the battery to be tested is discharged to a preset state of charge, the corresponding discharge relaxation curve includes: The battery to be tested is charged and discharged, and the rated capacity of the battery to be tested is determined according to the collected discharge capacity; Determine the target discharge capacity according to the preset state of charge and rated capacity; After the battery to be tested is fully charged, it starts to discharge until the discharge capacity of the battery to be tested reaches the target discharge capacity, and the corresponding discharge relaxation curve is collected.

8. The device according to claim 7, characterized in that The battery to be tested is charged and discharged, and the rated capacity of the battery to be tested is determined according to the collected discharge capacity, including: Preset a first charging rate and a first discharging rate; The battery to be tested is charged according to a preset first charging rate and discharged according to a first discharging rate, the discharge capacity of the battery to be tested during the discharging process is collected, and the rated capacity of the battery is determined according to the discharge capacity.

9. The device according to claim 7, characterized in that After the battery to be tested is fully charged, it starts to discharge so that the discharge capacity of the battery to be tested reaches the target discharge capacity, and collecting the corresponding discharge relaxation curve includes: Preset the second charging rate, the second discharging rate, the sampling frequency, and the discharging stop time; After the battery to be tested is fully charged at the second charging rate, it is discharged at the second discharge rate; after the discharge capacity of the battery to be tested reaches the target discharge capacity, the battery to be tested stops discharging until a preset stop discharge time, samples the voltage at a preset sampling frequency, and determines a discharge relaxation curve based on the collected voltage data.

10. The device according to any one of claims 6 to 9, characterized in that: The preset state of charge and the preset model are obtained in the following manner: Selecting a plurality of sample batteries, wherein the sample batteries and the battery to be tested belong to the same batch; Setting a set of multiple preset states of charge; For any state of charge in the set: calculating the corresponding relaxation degree when sample batteries in different health states are discharged to the state of charge, establishing training data according to the relaxation degree and the corresponding different health states, standardizing and denoising the training data, and training the training data using a machine learning algorithm to obtain an initial prediction model corresponding to the state of charge; The initial prediction models corresponding to different states of charge are evaluated, and the initial prediction model with the smallest error is set as the final prediction model, and the corresponding state of charge is set as the preset state of charge.

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