Health state determination method and device, storage medium and electronic device

Abstract

The invention discloses a state of health determination method and device, a storage medium and an electronic device, and the method comprises the steps: determining a first internal resistance value of a to-be-tested battery at any internal resistance calculation point, and obtaining a first dynamic condition feature of any internal resistance calculation point, the to-be-tested battery being a battery in an aging state; and according to the first dynamic condition characteristics and an initial internal resistance prediction model, predicting a third internal resistance value of the to-be-tested battery in an unaged state, the initial internal resistance prediction model being a model which is fitted based on an initial internal resistance characteristic library and expresses a mapping relation between the dynamic condition characteristics and unaged internal resistance of the battery. The initial internal resistance feature library comprises a plurality of initial internal resistance features of the reference battery, each initial internal resistance feature comprises a second internal resistance value and a second dynamic condition feature of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state; and determining a target health state of the to-be-tested battery under the first dynamic condition according to the first internal resistance value and the third internal resistance value.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and more specifically, to a method and device for determining a health status, a storage medium, and an electronic device. Background Art

[0002] Battery state of health (SOH) assessment is one of the core functions in a battery management system, which aims to provide a quantitative indicator of the battery's current performance compared with its performance in a brand new state (i.e., unaged state).

[0003] In the prior art, SOH based on the definition of internal resistance is usually used to calculate SOH. The core principle is to use the quotient of the aged internal resistance at a certain internal resistance calculation point and the brand new (unaged state, that is, SOH = 100%) internal resistance as a quantitative indicator of the aging state, and map the indicator to the SOH value. In practice, a method is usually used to calculate the aged internal resistance of the battery at a certain calculation point, and the brand new internal resistance under the current conditions is obtained by querying a table, which is obtained through various experiments in an offline environment.

[0004] However, whether it is the aged internal resistance or the new internal resistance of the battery, its value will be affected by a variety of dynamic conditions, such as the battery's state of charge (SOC), temperature, previous operating conditions, and internal resistance type. In the prior art, usually only the impact of temperature and SOC on the aged or new internal resistance value is considered, and the previous operating conditions and internal resistance type are often ignored, resulting in the calculated or found aged or new internal resistance cannot accurately reflect the health of the battery. The aging state quantitative index calculated based on the aged and new internal resistance cannot be accurately mapped to SOH, so the accuracy of the calculated SOH cannot be ensured.

[0005] With regard to the problem of low accuracy of the battery health status calculated based on the internal resistance value in the related art, no effective solution has been proposed so far.

[0006] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0007] The embodiments of the present application provide a method and device for determining a health status, a storage medium, and an electronic device to at least solve the problem of low accuracy of the battery health status calculated based on internal resistance in the related art.

[0008] According to one aspect of an embodiment of the present application, a method for determining a health state is provided, comprising: determining a first internal resistance value of a battery to be tested at any internal resistance calculation point, and obtaining a first dynamic condition feature at any internal resistance calculation point, wherein the battery to be tested is a battery in an aged state; predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition feature and an initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that is fitted based on an initial internal resistance feature library and expresses a mapping relationship between a battery dynamic condition feature and an unaged internal resistance, the initial internal resistance feature library comprising: a plurality of initial internal resistance features of a reference battery, each initial internal resistance feature comprising: a second internal resistance value and a second dynamic condition feature of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state; determining a target health state of the battery to be tested under the first dynamic condition according to the first internal resistance value and the third internal resistance value.

[0009] In an exemplary embodiment, predicting the third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model includes: inputting the first dynamic condition characteristic into the initial internal resistance prediction model so that the initial internal resistance prediction model outputs the third internal resistance value, wherein the initial internal resistance prediction model is a model trained by a machine learning algorithm using the second dynamic condition characteristic in the initial internal resistance characteristic library as input and the second internal resistance value in the initial internal resistance characteristic library as a target value.

[0010] In an exemplary embodiment, before predicting the third internal resistance value of the battery to be tested in an unaged state based on the first dynamic condition characteristics and the initial internal resistance prediction model, the method also includes: determining the second dynamic condition characteristics and the second internal resistance value of the reference battery at each internal resistance calculation point; splicing the second dynamic condition characteristics and the second internal resistance value of the reference battery at each internal resistance calculation point to generate a single initial internal resistance characteristic of the reference battery at each internal resistance calculation point; and constructing the initial internal resistance characteristic library based on the initial internal resistance characteristics of multiple internal resistance calculation points.

[0011] In an exemplary embodiment, determining the target health state of the battery to be tested under a first dynamic condition based on the first internal resistance value and the third internal resistance value includes: calculating the difference between the first internal resistance value and the third internal resistance value; calculating the quotient of the difference and the third internal resistance value, and determining the quotient as the internal resistance increase rate corresponding to the battery to be tested; determining a preset scaling factor, and determining the target health state based on the internal resistance increase rate and the preset scaling factor.

[0012] In an exemplary embodiment, determining a preset scaling factor, and determining the target health state based on the internal resistance increase rate and the preset scaling factor, includes: determining the performance life end point of the battery to be tested; determining the preset scaling factor according to a first formula, wherein the first formula is: preset scaling factor = 1 / (1-EOL), EOL is the performance life end point; determining the target health state according to a second formula, wherein the second formula is: target health state = 100% - (preset scaling factor × internal resistance increase rate).

[0013] In an exemplary embodiment, after determining the target health state of the battery to be tested under the first dynamic condition based on the first internal resistance value and the third internal resistance value, the method further includes: obtaining the third dynamic condition characteristics of the reference battery at multiple preset internal resistance calculation points; predicting the fourth internal resistance value of the reference battery in an unaged state under each third dynamic condition based on the third dynamic condition characteristics and the initial internal resistance prediction model; constructing an unaged internal resistance value storage table based on the third dynamic condition characteristics and the fourth internal resistance value; and determining the internal resistance value of the battery to be tested in an unaged state under any dynamic condition based on the unaged internal resistance value storage table.

[0014] In an exemplary embodiment, obtaining the first dynamic condition characteristic of any internal resistance calculation point includes: determining the pre-operating condition characteristic of the battery to be tested at any internal resistance calculation point according to an exponential moving average algorithm, wherein the pre-operating condition characteristic includes: the current change characteristic and the ambient temperature change characteristic of the battery to be tested at any internal resistance calculation point; and, obtaining the single cell difference characteristic corresponding to the battery to be tested, and obtaining the internal resistance type characteristic of the battery to be tested at any internal resistance calculation point; determining the single cell difference characteristic, the pre-operating condition characteristic and the internal resistance type characteristic as the first dynamic condition characteristic of the battery to be tested at any internal resistance calculation point.

[0015] According to another aspect of an embodiment of the present application, a device for determining a health state is also provided, including: a first determination module, used to determine a first internal resistance value of a battery to be tested at any internal resistance calculation point, and obtain a first dynamic condition feature of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state; a prediction module, used to predict a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition feature and an initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model fitted based on an initial internal resistance feature library, expressing a mapping relationship between the dynamic condition feature of the battery and the unaged internal resistance, the initial internal resistance feature library including: multiple initial internal resistance features of a reference battery, each initial internal resistance feature including: a second internal resistance value and a second dynamic condition feature of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state; a second determination module, used to determine the target health state of the battery to be tested under the first dynamic condition according to the first internal resistance value and the third internal resistance value.

[0016] According to another aspect of the embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned health status determination method when running.

[0017] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the health status determination method through the computer program.

[0018] According to another aspect of the embodiment of the present application, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned health status determination method is implemented.

[0019] Through the embodiment of the present application, the first internal resistance value of the battery to be tested in an aging state at any internal resistance calculation point is determined, and the first dynamic condition feature of the battery to be tested at any internal resistance calculation point is obtained; the third internal resistance value of the battery to be tested in an unaged state is predicted according to the first dynamic condition feature and the initial internal resistance prediction model of the mapping relationship between the dynamic condition feature of the battery fitted based on the initial internal resistance feature library and the unaged internal resistance, wherein the initial internal resistance feature library includes: multiple initial internal resistance features of a reference battery in an unaged state, each initial internal resistance feature includes: the second internal resistance value of the reference battery at any internal resistance calculation point and the second dynamic condition feature; the target health state of the battery to be tested under the first dynamic condition is determined according to the first internal resistance value and the third internal resistance value. That is, after determining the first internal resistance value and the first dynamic condition feature of the battery to be tested in an aging state, the embodiment of the present application can predict the third internal resistance value of the battery to be tested in an unaged state based on the first dynamic condition feature and the initial internal resistance feature library, and then determine the target health state of the battery to be tested under the first dynamic condition according to the first internal resistance value and the third internal resistance value. That is, the embodiment of the present application predicts the new internal resistance of the battery to be tested in the aging state under the current dynamic conditions through the initial internal resistance prediction model. Then the dynamic conditions of the unaged internal resistance and the aged internal resistance are aligned, so that the increase in internal resistance is only related to the aging state and the new internal resistance is more accurate, thereby calculating a more accurate SOH. Through the embodiment of the present application, the problem of low accuracy of the battery health status calculated based on the internal resistance in the related art can be solved, thereby improving the accuracy of calculating the battery health status. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The exemplary embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0021] Figure 1 It is a hardware structure block diagram of a computer terminal of the method for determining the health status of an embodiment of the present application;

[0022] Figure 2 is a flow chart of a method for determining a health status according to an embodiment of the present application;

[0023] Figure 3 is a flow chart of a method for calculating SOH based on prediction of non-aging internal resistance under any working condition according to an optional embodiment of the present application;

[0024] Figure 4 It is a structural block diagram of a device for determining a health status according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal of the method for determining the health status of an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 (processor 102 may include but is not limited to a microprocessor (Microprocessor Unit, referred to as MPU) or a programmable logic device (Programmable logic device, referred to as PLD)) and a memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for determining the health status in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] In order to solve the above problems, a method for determining a health status is provided in this embodiment, which is applied to Figure 1 Computer terminal in Figure 2 is a flow chart of a method for determining a health status according to an embodiment of the present application, the flow includes steps S202 to S206:

[0031] Step S202, determining a first internal resistance value of the battery to be tested at any internal resistance calculation point, and obtaining a first dynamic condition characteristic of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state;

[0032] The first dynamic condition characteristics of the battery to be tested may include: single cell difference characteristics of the battery to be tested, pre-operating condition characteristics of the battery to be tested, and internal resistance type characteristics of the battery to be tested.

[0033] The single cell difference characteristics of the battery to be tested may include: the nominal (rated) capacity, calibrated capacity, lower cut-off voltage, upper cut-off voltage, rated voltage, etc. of the battery to be tested.

[0034] The above internal resistance calculation point may be a SOH calculation point.

[0035] Step S204, predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that is fitted based on an initial internal resistance feature library and expresses a mapping relationship between the dynamic condition characteristic of the battery and the unaged internal resistance, the initial internal resistance feature library comprising: a plurality of initial internal resistance characteristics of a reference battery, each initial internal resistance characteristic comprising: a second internal resistance value and a second dynamic condition characteristic of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state;

[0036] The above-mentioned initial internal resistance prediction model is a prediction model trained based on the cycle data of the reference battery in the unaged state, the initial internal resistance feature is a brand new internal resistance feature of the reference battery in the unaged state, and the initial internal resistance feature library is an internal resistance feature library constructed based on the brand new internal resistance feature of the reference battery in the unaged state. In other words, "initial" means the unaged state, that is, the state of the battery at SOH = 100%.

[0037] Step S206 , determining a target health state of the battery to be tested under a first dynamic condition according to the first internal resistance value and the third internal resistance value.

[0038] The target state of health (SOH) of the battery under test is an indicator used to describe the current performance of the battery compared to the performance when it was brand new. SOH is usually given as a percentage, reflecting the degree of battery aging and the remaining service life.

[0039] After determining the first internal resistance value and the first dynamic condition characteristic of the battery to be tested in the aging state, the embodiment of the present application can predict the third internal resistance value of the battery to be tested in the unaged state based on the first dynamic condition characteristic and the initial internal resistance characteristic library, and then determine the target health state of the battery to be tested under the first dynamic condition according to the first internal resistance value and the third internal resistance value. That is, the embodiment of the present application predicts the internal resistance of the brand new unaged state corresponding to the dynamic condition characteristic of the battery to be tested in the aging state through the initial internal resistance prediction model, and then aligns the dynamic conditions of the unaged internal resistance and the aged internal resistance, so that the increase in internal resistance is only related to the aging state, thereby calculating a more accurate SOH. Through the embodiment of the present application, the problem of low accuracy of the battery health state calculated based on the internal resistance value in the related art can be solved, thereby improving the accuracy of calculating the battery health state.

[0040] Optionally, the above-mentioned step S204 predicts the third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model, including: inputting the first dynamic condition characteristic into the initial internal resistance prediction model so that the initial internal resistance prediction model outputs the third internal resistance value, wherein the initial internal resistance prediction model is a model trained by a machine learning algorithm, using the second dynamic condition characteristic in the initial internal resistance characteristic library as input and the second internal resistance value in the initial internal resistance characteristic library as a target value.

[0041] It is understandable that the third internal resistance value can be predicted according to the first dynamic condition characteristic and the initial internal resistance prediction model, wherein:

[0042] In order to ensure the prediction ability of the initial internal resistance prediction model, the initial internal resistance prediction model can be trained using battery data corresponding to a reference battery in an unaged state (ie, a brand new state, SOH=100%) when constructing the initial internal resistance feature library.

[0043] It is understandable that the embodiment of the present application needs to use a reference battery in a brand new state to build an initial internal resistance feature library. However, the battery testing process is very time-consuming and labor-intensive, and the cost of directly using a brand new battery for a dedicated test of the method of the embodiment of the present application is too high (if the battery is charged and discharged using different set working conditions, and the internal resistance calculation points are set to generate dynamic condition characteristics, more resources are required).

[0044] In actual applications, both battery manufacturers and vehicle manufacturers need to use a large number of new, unaged batteries for various types of tests. Therefore, the initial internal resistance feature library can be constructed based on these existing test data, thus avoiding high dedicated test costs.

[0045] These existing test data are used as the basis for training the initial internal resistance prediction model, so that the initial internal resistance prediction model can predict the new unaged battery internal resistance corresponding to the dynamic condition characteristics of the battery under test in the aged state, so as to align the dynamic conditions of the unaged internal resistance and the aged internal resistance, so that the increase in internal resistance is only related to aging, thereby calculating a more accurate SOH.

[0046] That is to say, the initial internal resistance prediction model is a model trained based on the initial internal resistance feature library. The specific training method is: using the second dynamic condition feature in the initial internal resistance feature library as input, and the second internal resistance value in the initial internal resistance feature library as the target value, and then training the initial internal resistance prediction model through a machine learning algorithm. Among them, the second dynamic condition feature may include: the single cell difference feature of the reference battery in an unaged state, the pre-operating condition feature of the reference battery at multiple internal resistance calculation points, and the internal resistance type feature of the reference battery at multiple internal resistance calculation points. The second internal resistance value is the internal resistance value of the reference battery in an unaged state.

[0047] That is to say, the initial internal resistance feature library = n × (second dynamic condition feature, second internal resistance value); wherein, the second dynamic condition feature = monomer difference feature + pre-operating condition feature + internal resistance type feature, and both the pre-operating condition feature and the internal resistance type feature vary with the internal resistance calculation point.

[0048] Since the initial internal resistance prediction model is trained with the second internal resistance value as the target, in an embodiment of the present application, the first dynamic condition characteristics of the battery to be tested can be input into the initial internal resistance prediction model, and then the initial internal resistance prediction model can output the third internal resistance value of the battery to be tested in an unaged state.

[0049] Optionally, before predicting the third internal resistance value of the battery to be tested in an unaged state based on the first dynamic condition characteristics and the initial internal resistance prediction model, it is also necessary to construct an initial internal resistance characteristic library, specifically: determining the second dynamic condition characteristics and the second internal resistance value of the reference battery at each internal resistance calculation point; splicing the second dynamic condition characteristics and the second internal resistance value of the reference battery at each internal resistance calculation point to generate a single initial internal resistance characteristic of the reference battery at each internal resistance calculation point; and constructing the initial internal resistance characteristic library based on the initial internal resistance characteristics of multiple internal resistance calculation points.

[0050] It is understandable that the initial internal resistance feature library can be constructed using the second dynamic condition feature and the second internal resistance value corresponding to the reference battery in an unaged state. The initial internal resistance feature library in this embodiment of the application serves to train the initial internal resistance prediction model.

[0051] Specific:

[0052] The second dynamic condition characteristic and the second internal resistance value of the reference battery at each internal resistance calculation point can be determined and spliced, and the spliced ​​characteristic is a single initial internal resistance characteristic of the reference battery; and then the initial internal resistance characteristic library is constructed according to the initial internal resistance characteristics of multiple internal resistance calculation points.

[0053] In an alternative embodiment, Figure 3is a flow chart of a method for calculating SOH based on prediction of non-aging internal resistance under any working condition according to an optional embodiment of the present application, such as Figure 3 As shown, Figure 3 Step S301 in the above method defines the technical solution for constructing the initial internal resistance feature library, which specifically includes: step S301-1, step S301-2, step S301-3 and step S301-4:

[0054] Step S301 - 1 : Determine a second dynamic condition characteristic of a reference battery.

[0055] The second dynamic condition feature of the reference battery is a feature record for the reference battery at different internal resistance calculation points. It can include three sub-features, namely, the single cell difference feature of the reference battery, the pre-operating condition feature of the reference battery at each internal resistance calculation point, and the internal resistance type feature of the reference battery at each internal resistance calculation point.

[0056] The single cell difference characteristics of the reference battery are similar to those of the battery to be tested, and may include, for example, the nominal capacity and rated voltage of the reference battery. The single cell difference characteristics do not change with changes in the pre-operating conditions and internal resistance types.

[0057] Step S301-2, obtaining a second internal resistance value, wherein the second internal resistance value is the internal resistance value of the reference battery in an unaged state at any internal resistance calculation point. The unaged internal resistance of the battery corresponding to the internal resistance calculation point under different dynamic conditions is different, therefore, what is obtained here is the unaged internal resistance of the reference battery under the dynamic conditions corresponding to the current internal resistance calculation point.

[0058] Step S301 - 3 , concatenating the second dynamic condition feature and the second internal resistance value of each internal resistance calculation point to generate a single initial internal resistance feature of each internal resistance calculation point.

[0059] It is understandable that, since different second dynamic condition characteristics and different second internal resistance values ​​corresponding to the reference battery can be obtained at different internal resistance calculation points, it is necessary to splice different second dynamic condition characteristics and different second internal resistance values ​​to obtain different initial internal resistance characteristics corresponding to the reference battery at different internal resistance calculation points, and multiple initial internal resistance characteristics can constitute an initial internal resistance characteristic library.

[0060] The second dynamic condition feature is combined with the second internal resistance value to form a complete initial internal resistance feature of the reference battery. The initial internal resistance feature includes the basic characteristics of the reference battery, the dynamic change record of the previous working condition, and the internal resistance value of the reference battery in a new state, providing comprehensive data support for training the initial internal resistance prediction model.

[0061] Step S301 - 4 , constructing an initial internal resistance feature library.

[0062] The initial internal resistance feature library includes: initial internal resistance features of all internal resistance calculation points.

[0063] After constructing the initial internal resistance feature library, the initial internal resistance prediction model can be trained based on the initial internal resistance feature library. Specifically: Figure 3 Step S302 in defines a technical solution for training the initial internal resistance prediction model, including taking the dynamic condition features in the initial internal resistance feature library as input data, taking the second internal resistance value in the initial internal resistance feature library as the target value, fitting the target relationship model to learn the mapping relationship between the dynamic condition features of the battery and the new internal resistance value of the battery in an unaged state, specifically:

[0064] Step S302, constructing an initial internal resistance prediction model: using the second dynamic condition feature in the initial internal resistance feature library as input, and using the second internal resistance value in the initial internal resistance feature library as a target, to construct an initial internal resistance prediction model. This allows the initial internal resistance prediction model to learn how to predict the internal resistance value in a brand new state from the given dynamic condition feature, that is, to establish a mapping relationship between the dynamic condition feature and the unaged internal resistance value.

[0065] It is understandable that the initial internal resistance prediction model usually refers to a certain machine learning model, such as a neural network, which can learn a prediction mechanism by fitting the relationship between the new dynamic condition characteristics in the training data set and the unaged internal resistance value, so that the trained initial internal resistance prediction model can predict the corresponding unaged internal resistance value when facing new dynamic condition characteristics.

[0066] Then, the first dynamic condition feature is input into the initial internal resistance prediction model to obtain the third internal resistance value of the battery to be tested in a non-aging state.

[0067] Optionally, after determining the third internal resistance value, the target health state of the battery to be tested under the first dynamic condition can be determined according to the first internal resistance value and the third internal resistance value, including: calculating the difference between the first internal resistance value and the third internal resistance value; calculating the quotient of the difference and the third internal resistance value, and determining the quotient as the internal resistance increase rate corresponding to the battery to be tested; determining a preset scaling factor, and determining the target health state according to the internal resistance increase rate and the preset scaling factor.

[0068] Among them, determining a preset scaling factor, and determining the target health state according to the internal resistance increase rate and the preset scaling factor, includes: determining the performance life end point of the battery to be tested; determining the preset scaling factor according to a first formula, wherein the first formula is: preset scaling factor = 1 / (1-EOL), EOL is the performance life end point; determining the target health state according to a second formula, wherein the second formula is: target health state = 100% - (preset scaling factor × internal resistance increase rate).

[0069] It is understood that the method for determining the target health state of the battery to be tested is:

[0070] (1) Calculate the internal resistance increase rate: internal resistance increase rate = (first internal resistance value - third internal resistance value) / third internal resistance value.

[0071] For example, if the difference between the first internal resistance value and the third internal resistance value is 1 ohm, and the third internal resistance value in the unaged state is 10 ohms, then the internal resistance increase rate is 10%.

[0072] (2) Determine a preset scaling factor: The preset scaling factor is determined based on the end of life (EOL) of the battery to be tested. EOL is used to indicate that the battery performance has dropped to a level that cannot meet normal working requirements, for example: the battery's fully charged capacity drops to 80% of the battery's rated capacity.

[0073] The calculation formula of the preset scaling factor is: preset scaling factor = 1 / (1-EOL) (i.e., the first formula of the present application). When the EOL is 80%, the preset scaling factor = 1 / (1-0.8) = 5. The function of the preset scaling factor is to map the internal resistance increase rate to the percentage change of SOH, so as to ensure that the calculation result of SOH is more reasonable.

[0074] (3) Calculate the target state of health: Use the internal resistance increase rate and the preset scaling factor to determine the target state of health (SOH) of the battery to be tested. The calculation formula for the target state of health is: target state of health = 100% - (preset scaling factor × internal resistance increase rate). For example, if the internal resistance increase rate is 10% and the preset scaling factor is 5, then the target state of health = 100% - (5 × 10%) = 50%. Among them, the embodiment of the present application defines the attenuation state according to EOL. That is, the purpose of introducing the scaling factor is to define the SOH according to the actual application scenario.

[0075] In an alternative embodiment, Figure 3 Step S303 in the above is a step of determining the target health state of the battery to be tested, specifically:

[0076] Step S303, calculating the target health status.

[0077] Calculate the first internal resistance value of the aged battery under a certain dynamic condition, and then input this dynamic condition feature into the initial internal resistance prediction model so that the model can predict the third internal resistance value of the reference battery when it is not aged. According to these two internal resistance values ​​and the SOH expression defined by the internal resistance, the corresponding SOH value can be calculated.

[0078] Optionally, after determining the target health state of the battery to be tested under the first dynamic condition according to the first internal resistance value and the third internal resistance value, the method further includes: obtaining the third dynamic condition characteristics of the reference battery at multiple preset internal resistance calculation points; predicting the fourth internal resistance value of the reference battery in an unaged state under each third dynamic condition according to the third dynamic condition characteristics and the initial internal resistance prediction model; constructing an unaged internal resistance value storage table according to the third dynamic condition characteristics and the fourth internal resistance value; and determining the internal resistance value of the battery to be tested in an unaged state under any dynamic condition according to the unaged internal resistance value storage table.

[0079] It can be understood that in addition to predicting the third internal resistance value of the battery to be tested in an unaged state under a certain dynamic condition through the initial internal resistance prediction model, an internal resistance value storage table for indicating the relationship between the dynamic condition and the unaged internal resistance value can also be established, and then the internal resistance value storage table can be looked up and interpolated to obtain the third internal resistance value of the battery to be tested in an unaged state under a certain dynamic condition.

[0080] That is to say, in some low-computing-power online scenarios, complex initial internal resistance prediction models may be difficult to run directly. At this time, different dynamic conditions can be set in an offline high-computing-power environment, and the various designed third dynamic condition characteristics can be input into the initial internal resistance prediction model respectively, and then the fourth internal resistance value of the battery to be tested in the unaged state under different dynamic conditions can be predicted. Finally, a storage table of the third dynamic condition characteristics and the fourth internal resistance value is constructed, and the table is looked up and interpolated according to the real-time working conditions when applied online to obtain the corresponding unaged internal resistance value.

[0081] Among them, the dynamic conditions record the internal resistance type of the previous working condition and the current internal resistance calculation point, which is expressed as characteristic values: monomer difference characteristics, previous working condition characteristics, and internal resistance type. The above-mentioned third dynamic condition characteristic can be = the second dynamic condition characteristic + an additionally designed dynamic condition characteristic that does not exist in the second dynamic characteristic. (That is, assuming that the current cumulative increment of the second dynamic condition characteristic includes 10A and 20A, then the dynamic condition characteristic of the current cumulative increment of 15A can be designed as the third dynamic condition characteristic, and the same applies to the remaining characteristics. Except for the monomer difference characteristic, this remains consistent in the second and third dynamic condition characteristics, because whether it is the second or third dynamic condition characteristic, it essentially records the real-time status of a certain monomer).

[0082] Through the above technical solution, before calculating the SOH of the battery to be tested under any dynamic condition, it can be determined whether this dynamic condition has been analyzed through the internal resistance value storage table, that is, check whether the internal resistance value storage table contains the third dynamic condition characteristic of any dynamic condition and the unaged internal resistance value corresponding to the third dynamic condition characteristic under any dynamic condition.

[0083] That is, it is possible to determine whether the dynamic condition characteristics in the internal resistance value storage table completely match the dynamic condition to be calculated. If so, the unaged internal resistance value corresponding to the dynamic condition to be calculated can be directly determined through the internal resistance value storage table.

[0084] On the contrary, if it does not exist, an interpolation algorithm is needed to calculate the corresponding new internal resistance value.

[0085] The principle of interpolation is: assuming that the input feature is (x, y) and the target value is z, if two sets of features and their target values ​​are known: a(1, 2|5) and b(5, 10|8), when the target value of the input feature (3, 6) needs to be predicted, the interpolation algorithm needs to be used to estimate the target value of the input feature (3, 6) based on the features and target values ​​of a and b. It can be understood that the above method is a method of inferring a target value between data values ​​through existing data.

[0086] The interpolation algorithms used in the embodiments of the present application include but are not limited to: linear interpolation, spline interpolation, kernel function interpolation, etc.

[0087] In addition, a simpler and less complex relational model can be used to fit the relationship between the dynamic condition characteristics and the SOH value predicted by the complex relational model, and then the simple relational model can be applied online to determine the different SOH values ​​corresponding to each dynamic condition. The difference between this method and the method of fitting based on the initial internal resistance feature library using a simpler and less complex relational model is that the complex relational model (i.e., the initial internal resistance prediction model) is more generalizable, and for various dynamic conditions that are not in the initial internal resistance feature library, the SOH value predicted by the complex relational model is closer to the actual SOH value under the dynamic condition, while the simple relational model can be fitted based on these data while maintaining a low computational complexity while further improving the prediction accuracy and reducing errors.

[0088] Optionally, the obtaining of the first dynamic condition characteristic of any internal resistance calculation point in an embodiment of the present application includes: determining the pre-operating condition characteristics of the battery to be tested at any internal resistance calculation point according to an exponential moving average algorithm, wherein the pre-operating condition characteristics include: current change characteristics and ambient temperature change characteristics of the battery to be tested at any internal resistance calculation point; and, obtaining the single cell difference characteristics corresponding to the battery to be tested, and obtaining the internal resistance type characteristics of the battery to be tested at any internal resistance calculation point; determining the single cell difference characteristics, the pre-operating condition characteristics and the internal resistance type characteristics as the first dynamic condition characteristics of the battery to be tested at any internal resistance calculation point.

[0089] It is understandable that the internal resistance value of the battery is closely related to the dynamic conditions, and the dynamic condition characteristics include: single cell difference characteristics, internal resistance type characteristics and pre-operating condition characteristics.

[0090] Among them, the internal resistance value of the battery is closely related to the previous cycle condition, but it is not realistic to directly store the previous cycle condition, and its calculation and storage costs are too high. In order to achieve the recording of the previous operating condition characteristics while reducing the calculation complexity and storage cost, the embodiment of the present application defines a technical solution for determining the previous operating condition characteristics in the first dynamic condition characteristics through an exponential moving average algorithm. Among them, the exponential moving average algorithm (Exponential Moving Average, referred to as EMA): EMA can achieve weighted previous historical sequences through the cumulative weight α, and will give higher weights to recent data points as a whole, while earlier data points will be given lower weights. That is, EMA is essentially a recursive form, which can achieve weighted accumulation and reduce calculation complexity. The specific formula is: EMA t =α×x t +(1-α)×EMA t-1 .

[0091] Among them, EMA t-1 is the exponential moving average result of the previous cumulative recursive calculation point; EMAt is the exponential moving average result of the current cumulative recursive calculation point (i.e., the cumulative statistic, which can be the current change characteristics (including the cumulative current increment and the cumulative current negative increment) and the ambient temperature change characteristics (including the cumulative ambient temperature increment and the cumulative ambient temperature negative increment) in the embodiment of the present application; xt is the incremental data between the current cumulative recursive calculation point and the previous cumulative recursive calculation point, which can be the current change characteristics and the ambient temperature change characteristics between the current cumulative recursive calculation point and the previous cumulative recursive calculation point.

[0092] It is understandable that the current cumulative recursive calculation point may increase or decrease compared with the previous cumulative recursive calculation point. First, the increment type is determined. If it increases, the cumulative increment is updated; if it decreases, the cumulative decrease is updated.

[0093] In the embodiment of the present application, the increment is a positive value and the decrease is a negative value.

[0094] Cumulative recursive calculation points refer to calculation points set periodically or under specific conditions during the use of the battery, and are usually used to collect and update the battery's pre-condition characteristics. The cumulative recursive calculation point and the internal resistance calculation point can be at the same time, that is, the pre-condition characteristics are updated while calculating the internal resistance value; or they can be at different times, for example: the pre-condition characteristics may continue to accumulate during the use of the battery, while the internal resistance value is the result of calculation in a specific period or under conditions.

[0095] It can be understood that the current change characteristics and the ambient temperature change characteristics can be calculated by the above exponential moving average. Among them, the current change characteristics include: cumulative current increment and cumulative current negative increment, and the ambient temperature change characteristics include: cumulative ambient temperature increment and cumulative ambient temperature negative increment. The following is a specific description of the meaning of the cumulative current increment, the cumulative current negative increment, the cumulative ambient temperature increment, and the cumulative ambient temperature negative increment.

[0096] The cumulative current increment refers to the cumulative change of the battery's current increase over a period of time; while the cumulative negative current increment refers to the cumulative change of the battery's current decrease over a period of time. The EMA algorithm can be used to obtain the cumulative change of the battery's cumulative current increment / negative increment over a period of time before the current internal resistance calculation point at a lower calculation and storage cost.

[0097] Similarly, the cumulative ambient temperature increment refers to the cumulative change in the ambient temperature corresponding to the battery over a period of time; while the cumulative negative ambient temperature increment refers to the cumulative change in the ambient temperature corresponding to the battery over a period of time. The EMA algorithm can also be used to obtain the cumulative change in the battery's cumulative ambient temperature increment / negative increment over a period of time before the current internal resistance calculation point at a lower calculation and storage cost.

[0098] Among them, the corresponding cumulative current increment (negative increment) under different pre-conditions is different. The cumulative current increment (negative increment) can be used to represent the current change characteristics. By recording two cumulative variables, the operating condition sequence data containing hundreds or thousands of data points can be identified from the side.

[0099] In summary, the method for determining the pre-condition characteristics in the embodiment of the present application is: determine the cumulative ambient temperature increment (negative increment) and the cumulative current increment (negative increment) by an exponential moving average algorithm. The cumulative ambient temperature increment (negative increment) can be combined or used alone as a feature to reflect the dynamic change trend of the ambient temperature. This dynamic change trend is the ambient temperature change characteristic. That is, the ambient temperature change characteristic can be the direct value of the EMA cumulative ambient temperature increment and the ambient temperature negative increment, or it can be a function thereof, such as: their ratio, difference or average value, etc., which depends on the specific application requirements. Similarly, the cumulative current increment (negative increment) can be combined or used alone as a feature to reflect the dynamic change trend of the battery current. This dynamic change trend is the current change characteristic. That is, the current change characteristic can be the direct value of the EMA cumulative current increment and the negative increment, or it can be a function thereof, such as: their ratio, difference or average value, etc., which also depends on the specific application requirements.

[0100] In addition, the above cumulative amount only records the pre-change conditions of the internal resistance calculation point, but different starting points will get different change end states after the same working condition, and they correspond to different change processes. Therefore, it is necessary to record the starting state of the pre-condition. In order to reduce the number of features, the SOC of the battery at the internal resistance calculation point can be used to record the end state of the pre-condition change, which is equivalent to recording the starting state of the pre-condition. Since the SOC is estimated by the algorithm in practice, there may be estimation errors, so the terminal voltage can be used to identify the true SOC of the battery to avoid estimation errors. For the convenience of description, the terminal voltage is placed in the internal resistance type feature for explanation.

[0101] In addition, the embodiments of the present application also need to determine the internal resistance type characteristics of the battery to be tested. It should be noted that the principle of calculating SOH based on the internal resistance of the battery is that the internal resistance and the aging state of the battery can correspond one to one. However, in actual applications, the factors related to the calculated internal resistance include: the pre-conditions of the internal resistance calculation point, terminal voltage, temperature, current, rate, sampling period (can also be multiplied with the rate and combined into one feature), and aging state. If the influence of factors other than the aging state on the internal resistance value is not excluded, then the internal resistance value and the aging state cannot correspond one to one, and thus the accurate SOH cannot be calculated.

[0102] For example: If the influence of any factors other than the aging state on the calculation of the internal resistance value is not excluded (these factors are not distinguished in the features): Assume that there are two batteries of the same type, A and B (the former is 20% SOH and the latter is 80% SOH), both of which calculate the first internal resistance value at a certain internal resistance calculation point. If the ambient temperature of A is 45 degrees and that of B is 10 degrees, the first internal resistance values ​​calculated for A and B are the same. Then when using feature matching or model fitting to calculate SOH, the SOH of A and B are both fuzzy and centered at 50% (the model does not know that temperature will affect the calculation of the battery internal resistance, and the model will assume that under the same conditions of other factors, the internal resistance calculated at different temperatures is the same, and their aging states are the same), but the predicted value of 50% SOH is far from the actual SOH values ​​of A and B.

[0103] If the temperature is recorded as a feature, the effect of temperature on the internal resistance calculation will be considered during the matching or fitting calculation. Even if the internal resistance is the same at different temperatures, it represents different aging states, and the unaged internal resistance is also different at different temperatures. The calculated SOH will not have a vague center value, but an accurate and close to the real SOH value.

[0104] In the embodiment of the present application, it is assumed that the first internal resistance value calculated by the battery at a certain internal resistance calculation point = W0×R0+W1×R1+W2×R2 (the above formula is only for illustration, and the actual relationship may be more complicated). Among them, R1, R2, and R0 are used to indicate the internal resistance values ​​of different types of internal resistances, and W0, W1, and W2 are used to indicate different coefficients corresponding to different types of internal resistances.

[0105] When a certain current rate is applied to the battery at a certain moment, the internal resistance of the battery will change over time. The W0, W1, and W2 coefficients at each moment may be different, which means that the type of internal resistance calculated will change over time. Existing research has difficulty in accurately finding out the changing rules of W0, W1, and W2 with current rate and cycle time, and it is difficult to rely on these coefficients to represent the type of internal resistance.

[0106] But it is clear that the internal resistance types calculated under different current magnifications and sampling periods are different. Therefore, the current magnification and sampling period can be used as features to indirectly identify the type of calculated internal resistance. In form, they can be separated into two features, or multiplied and combined into one feature.

[0107] Therefore, the current multiplication factor and the sampling period can be used as features separately or combined into one feature as an internal resistance type feature.

[0108] That is, the internal resistance type characteristics of the battery to be tested can be determined by the following methods: determining the current multiplier of the battery to be tested at each internal resistance calculation point, and determining the sampling period corresponding to each internal resistance calculation point of the battery to be tested; determining the internal resistance type characteristics of the battery to be tested based on the current multiplier and the sampling period.

[0109] Among them, the internal resistance type characteristics may also include: terminal voltage, temperature, current value, etc. at the current internal resistance calculation point. Since the charging and discharging internal resistance of the battery to be tested is different, the current and rate need to be distinguished between positive and negative.

[0110] In this embodiment, a device for determining a health state is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0111] Figure 4 : is a structural block diagram of a device for determining a health status according to an embodiment of the present application, the device comprising:

[0112] A first determination module 42 is used to determine a first internal resistance value of the battery to be tested at any internal resistance calculation point, and obtain a first dynamic condition characteristic of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state;

[0113] A prediction module 44, configured to predict a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and an initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that is fitted based on an initial internal resistance feature library and expresses a mapping relationship between a battery dynamic condition characteristic and an unaged internal resistance, wherein the initial internal resistance feature library comprises: a plurality of initial internal resistance characteristics of a reference battery, each initial internal resistance characteristic comprising: a second internal resistance value and a second dynamic condition characteristic of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state;

[0114] The second determination module 46 is configured to determine a target health state of the battery under a first dynamic condition according to the first internal resistance value and the third internal resistance value.

[0115] Through the above-mentioned device, after determining the first internal resistance value and the first dynamic condition characteristic of the battery to be tested in the aging state, the third internal resistance value of the battery to be tested in the unaged state can be predicted based on the first dynamic condition characteristic and the initial internal resistance characteristic library, and then the target health state of the battery to be tested under the first dynamic condition is determined according to the first internal resistance value and the third internal resistance value. That is, the embodiment of the present application predicts the internal resistance of the brand new unaged state corresponding to the dynamic condition characteristic of the battery to be tested in the aging state through the initial internal resistance prediction model, and then aligns the dynamic conditions of the unaged internal resistance and the aged internal resistance, so that the increase in internal resistance is only related to the aging state, thereby calculating a more accurate SOH. Through the embodiment of the present application, the problem of low accuracy of the battery health state calculated based on the internal resistance in the related art can be solved, thereby improving the accuracy of the battery health state.

[0116] In an exemplary embodiment, the prediction module 44 is also used to input the first dynamic condition feature into the initial internal resistance prediction model so that the initial internal resistance prediction model outputs the third internal resistance value, wherein the initial internal resistance prediction model is a model trained by a machine learning algorithm using the second dynamic condition feature in the initial internal resistance feature library as input and the second internal resistance value in the initial internal resistance feature library as a target value.

[0117] In an exemplary embodiment, the prediction module 44 is also used to determine the second dynamic condition characteristics and the second internal resistance value of the reference battery at each internal resistance calculation point; splice the second dynamic condition characteristics and the second internal resistance value of the reference battery at each internal resistance calculation point to generate a single initial internal resistance characteristic of the reference battery at each internal resistance calculation point; and construct the initial internal resistance characteristic library based on the initial internal resistance characteristics of multiple internal resistance calculation points.

[0118] In an exemplary embodiment, the second prediction module 46 is further used to calculate the difference between the first internal resistance value and the third internal resistance value; calculate the quotient of the difference and the third internal resistance value, and determine the quotient as the internal resistance increase rate corresponding to the battery to be tested; determine a preset scaling factor, and determine the target health state based on the internal resistance increase rate and the preset scaling factor.

[0119] In an exemplary embodiment, the second prediction module 46 is also used to determine the end point of the performance life of the battery to be tested; determine the preset scaling factor according to a first formula, wherein the first formula is: preset scaling factor = 1 / (1-EOL), EOL is the end point of the performance life; determine the target health state according to a second formula, wherein the second formula is: target health state = 100% - (preset scaling factor × internal resistance increase rate).

[0120] In an exemplary embodiment, the second prediction module 46 is further used to obtain the third dynamic condition characteristics of the reference battery at multiple preset internal resistance calculation points; predict the fourth internal resistance value of the reference battery in an unaged state under each third dynamic condition according to the third dynamic condition characteristics and the initial internal resistance prediction model; construct an unaged internal resistance value storage table according to the third dynamic condition characteristics and the fourth internal resistance value; and determine the internal resistance value of the battery to be tested in an unaged state under any dynamic condition according to the unaged internal resistance value storage table.

[0121] In an exemplary embodiment, the first determination module 42 is further used to determine the pre-operating condition characteristics of the battery to be tested at any internal resistance calculation point according to an exponential moving average algorithm, wherein the pre-operating condition characteristics include: current change characteristics and ambient temperature change characteristics of the battery to be tested at any internal resistance calculation point; and, obtain the single cell difference characteristics corresponding to the battery to be tested, and obtain the internal resistance type characteristics of the battery to be tested at any internal resistance calculation point; determine the single cell difference characteristics, the pre-operating condition characteristics and the internal resistance type characteristics as the first dynamic condition characteristics of the battery to be tested at any internal resistance calculation point.

[0122] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0123] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0124] S1, determining a first internal resistance value of a battery to be tested at any internal resistance calculation point, and obtaining a first dynamic condition characteristic of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state;

[0125] S2, predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that expresses a mapping relationship between the dynamic condition characteristic of the battery and the unaged internal resistance based on an initial internal resistance feature library, the initial internal resistance feature library comprising: a plurality of initial internal resistance characteristics of a reference battery, each initial internal resistance characteristic comprising: a second internal resistance value and a second dynamic condition characteristic of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state;

[0126] S3: Determine a target health state of the battery to be tested under a first dynamic condition according to the first internal resistance value and the third internal resistance value.

[0127] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0128] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0129] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0130] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0131] S1, determining a first internal resistance value of a battery to be tested at any internal resistance calculation point, and obtaining a first dynamic condition characteristic of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state;

[0132] S2, predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that expresses a mapping relationship between the dynamic condition characteristic of the battery and the unaged internal resistance based on an initial internal resistance feature library, the initial internal resistance feature library comprising: a plurality of initial internal resistance characteristics of a reference battery, each initial internal resistance characteristic comprising: a second internal resistance value and a second dynamic condition characteristic of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state;

[0133] S3: Determine a target health state of the battery to be tested under a first dynamic condition according to the first internal resistance value and the third internal resistance value.

[0134] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0135] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0136] Optionally, in this embodiment, the computer program product may be configured to perform the following steps through a computer program:

[0137] S1, determining a first internal resistance value of a battery to be tested at any internal resistance calculation point, and obtaining a first dynamic condition characteristic of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state;

[0138] S2, predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that expresses a mapping relationship between the dynamic condition characteristic of the battery and the unaged internal resistance based on an initial internal resistance feature library, the initial internal resistance feature library comprising: a plurality of initial internal resistance characteristics of a reference battery, each initial internal resistance characteristic comprising: a second internal resistance value and a second dynamic condition characteristic of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state;

[0139] S3: Determine a target health state of the battery to be tested under a first dynamic condition according to the first internal resistance value and the third internal resistance value.

[0140] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0141] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0142] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining a health status, characterized in that: include: Determine a first internal resistance value of a battery to be tested at any internal resistance calculation point, and obtain a first dynamic condition characteristic of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state; Predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that is fitted based on an initial internal resistance feature library and expresses a mapping relationship between the dynamic condition characteristic of the battery and the unaged internal resistance, the initial internal resistance feature library comprising: a plurality of initial internal resistance characteristics of a reference battery, each initial internal resistance characteristic comprising: a second internal resistance value and a second dynamic condition characteristic of the reference battery at any internal resistance calculation point, the reference battery being a battery in an unaged state; The target health state of the battery to be tested under the first dynamic condition is determined according to the first internal resistance value and the third internal resistance value.

2. The method according to claim 1, characterized in that Predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model includes: The first dynamic condition feature is input into the initial internal resistance prediction model so that the initial internal resistance prediction model outputs the third internal resistance value, wherein the initial internal resistance prediction model is a model trained by a machine learning algorithm, using the second dynamic condition feature in the initial internal resistance feature library as input and the second internal resistance value in the initial internal resistance feature library as a target value.

3. The method according to claim 1, characterized in that Before predicting the third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and the initial internal resistance prediction model, the method further includes: Determine the second dynamic condition characteristic and the second internal resistance value of the reference battery at each internal resistance calculation point; splicing the second dynamic condition characteristic of the reference battery at each internal resistance calculation point and the second internal resistance value to generate a single initial internal resistance characteristic of the reference battery at each internal resistance calculation point; The initial internal resistance feature library is constructed according to the initial internal resistance features of a plurality of internal resistance calculation points.

4. The method according to claim 1, characterized in that: Determining a target health state of the battery to be tested under a first dynamic condition according to the first internal resistance value and the third internal resistance value includes: Calculating a difference between the first internal resistance value and the third internal resistance value; Calculating a quotient of the difference and the third internal resistance value, and determining the quotient as an internal resistance increase rate corresponding to the battery to be tested; A preset scaling factor is determined, and the target health state is determined according to the internal resistance increase rate and the preset scaling factor.

5. The method according to claim 4, characterized in that Determining a preset scaling factor, and determining the target health state according to the internal resistance increase rate and the preset scaling factor, comprises: Determining the end point of the performance life of the battery under test; Determining the preset scaling factor according to a first formula, wherein the first formula is: preset scaling factor=1 / (1-EOL), and EOL is the end of the performance life; The target health state is determined according to a second formula, wherein the second formula is: target health state=100%-(preset scaling factor×internal resistance increase rate).

6. The method according to claim 1, characterized in that After determining the target health state of the battery to be tested under the first dynamic condition according to the first internal resistance value and the third internal resistance value, the method further includes: Acquiring a third dynamic condition characteristic of the reference battery at a plurality of preset internal resistance calculation points; Predicting a fourth internal resistance value of the reference battery in an unaged state under each third dynamic condition according to the third dynamic condition characteristic and the initial internal resistance prediction model; Constructing a non-aging internal resistance value storage table according to the third dynamic condition characteristic and the fourth internal resistance value; The internal resistance value of the battery to be tested in an unaged state under any dynamic condition is determined according to the unaged internal resistance value storage table.

7. The method according to claim 1, characterized in that Obtaining the first dynamic condition characteristic of any internal resistance calculation point includes: Determine the pre-operating condition characteristics of the battery under test at any internal resistance calculation point according to an exponential moving average algorithm, wherein the pre-operating condition characteristics include: current change characteristics and ambient temperature change characteristics of the battery under test at any internal resistance calculation point; and Obtaining a single cell difference characteristic corresponding to the battery to be tested, and obtaining an internal resistance type characteristic of the battery to be tested at any internal resistance calculation point; The single cell difference feature, the pre-operating condition feature and the internal resistance type feature are determined as the first dynamic condition feature of the battery to be tested at any internal resistance calculation point.

8. A device for determining health status, characterized in that: include: A first determination module is used to determine a first internal resistance value of a battery to be tested at any internal resistance calculation point, and obtain a first dynamic condition characteristic of any internal resistance calculation point, wherein the battery to be tested is a battery in an aging state; A prediction module, used for predicting a third internal resistance value of the battery to be tested in an unaged state according to the first dynamic condition characteristic and an initial internal resistance prediction model, wherein the initial internal resistance prediction model is a model that expresses a mapping relationship between the dynamic condition characteristic of the battery and the unaged internal resistance based on an initial internal resistance feature library, the initial internal resistance feature library comprising: a plurality of initial internal resistance characteristics of a reference battery, each initial internal resistance characteristic comprising: a second internal resistance value and a second dynamic condition characteristic of the reference battery at any internal resistance calculation point, and the reference battery is a battery in an unaged state; The second determination module is configured to determine a target health state of the battery to be tested under a first dynamic condition according to the first internal resistance value and the third internal resistance value.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

Images