Battery performance detection method, electronic equipment, storage medium and program product

Abstract

The invention discloses a battery performance detection method, electronic equipment, a storage medium and a program product, relates to the technical field of batteries, and is used for rapidly detecting battery performance. The battery performance detection method comprises the following steps: in a battery charging process, obtaining a differential voltage curve of a battery in a preset time period; and determining the performance of the battery based on trough characteristics in the differential voltage curve.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery performance detection method, electronic equipment, storage medium and program product. Background Art

[0002] The capacity inflection point of lithium-ion batteries marks the transition from slow decay to rapid decay of the battery's "capacity diving" stage. This transition poses a threat to the performance and safety of battery modules or battery packs because it leads to rapid capacity decline, increased inconsistency, intensified internal side reactions, increased gas production, increased expansion force, and temperature rise caused by increased internal resistance and reduced electrolyte. Therefore, in the sorting and use of cascade batteries, identifying and screening out diving batteries is crucial to maintaining battery performance and preventing safety accidents.

[0003] Most of the existing solutions for identifying battery capacity diving are relatively complicated and have long testing cycles, and are unable to quickly and accurately identify the battery capacity diving phenomenon. Summary of the invention

[0004] The embodiments of the present application provide a battery performance detection method, electronic device, storage medium and program product for quickly and accurately detecting battery performance, aiming to solve the problem of low efficiency and accuracy of battery performance detection in the prior art.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a battery performance detection method is provided, the method comprising: during battery charging, obtaining a differential voltage curve of the battery within a preset time period; and determining the battery performance based on a trough feature in the differential voltage curve.

[0007] The battery performance detection method provided in the embodiment of the present application obtains the differential voltage curve of the battery during the charging process, and then determines the performance of the battery through the trough characteristics of the differential voltage curve. Since the trough characteristics of the differential voltage curve are closely related to the characteristics of the battery, this method can more accurately and quickly evaluate the health status and performance of the battery, thereby improving the efficiency and accuracy of battery performance detection.

[0008] In some embodiments, the valley characteristic includes a differential voltage value of the valley.

[0009] In some embodiments, determining the performance of the battery based on the valley characteristics in the differential voltage curve includes: determining the performance of the battery based on the valley characteristics of the first valley in the differential voltage curve.

[0010] In some embodiments, when the valley characteristic includes the differential voltage value of the valley, the performance of the battery is determined based on the valley characteristic of the first valley in the differential voltage curve, including: when the differential voltage value of the first valley is greater than or equal to a preset threshold, determining that the battery has performance degradation.

[0011] In some embodiments, the preset threshold is determined based on differential voltage values ​​of a plurality of sample batteries that have experienced capacity decay, and the sample batteries are of the same model as the battery.

[0012] In some embodiments, the preset time period is a section in which the state of charge (SOC) value of the battery is less than or equal to the preset SOC value.

[0013] In some embodiments, during the battery charging process, a differential voltage curve of the battery within a preset time period is obtained, including: during the battery charging process, recording the voltage-capacity data of the battery within the preset time period; at least performing differential processing on the voltage-capacity data to obtain a differential voltage curve.

[0014] In some embodiments, at least performing differentiation processing on the voltage-capacity data to obtain a differential voltage curve includes: performing differentiation processing and smoothing processing on the voltage-capacity data to obtain a differential voltage curve.

[0015] In some embodiments, the method further includes: stopping charging when a first valley appears in the differential voltage curve.

[0016] In some embodiments, during the battery charging process, before obtaining the differential voltage curve of the battery within a preset time period, the method further includes: discharging the battery to a cut-off voltage.

[0017] In some embodiments, the battery is a single cell in a battery pack.

[0018] In some embodiments, before obtaining the differential voltage curve of the battery within a preset time period, the method further includes: charging the battery based on a small rate constant current.

[0019] In the second aspect, the present application provides a battery performance detection device, which can implement the battery performance detection method provided by the present application. The communication scheduling device includes: a processing module and an analysis module; the processing module is used to obtain the differential voltage curve of the battery within a preset time period during the battery charging process; the analysis module is used to determine the performance of the battery based on the trough characteristics in the differential voltage curve.

[0020] In a possible implementation manner, the valley feature includes a differential voltage value of the valley.

[0021] In a possible implementation, the analysis module is specifically configured to determine the performance of the battery based on the valley characteristics of the first valley in the differential voltage curve.

[0022] In a possible implementation, when the trough feature includes a differential voltage value of the trough, the analysis module is specifically configured to determine that the battery has a performance degradation phenomenon when the differential voltage value of the first trough is greater than or equal to a preset threshold.

[0023] In a possible implementation, the preset threshold is determined based on differential voltage values ​​of a plurality of sample batteries that have capacity decay, and the sample batteries are of the same model as the battery.

[0024] In a possible implementation, the preset time period is a section in which the state of charge (SOC) value of the battery is less than or equal to a preset SOC value.

[0025] In a possible implementation, the processing module is specifically used to record the voltage-capacity data of the battery within a preset time period during the battery charging process; at least perform differential processing on the voltage-capacity data to obtain a differential voltage curve.

[0026] In a possible implementation, the processing module is specifically used to perform differential processing and smoothing processing on the voltage-capacity data to obtain a differential voltage curve.

[0027] In a possible implementation, the communication scheduling device 400 further includes: a control module. The control module is configured to stop charging when the differential voltage curve has a first trough.

[0028] In a possible implementation, during the battery charging process, before obtaining the differential voltage curve of the battery within a preset time period, the control module is further configured to discharge the battery to a cut-off voltage.

[0029] In a possible implementation, the battery is a single cell in a battery pack.

[0030] In a possible implementation, before obtaining the differential voltage curve of the battery within a preset time period, the control module is also used to charge the battery based on a small rate constant current.

[0031] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory stores instructions executable by the processor; when the processor is configured to execute the instructions, the electronic device implements the method of the first aspect above.

[0032] In a fourth aspect, the present application provides a computer-readable storage medium, which includes: computer software instructions; when the computer software instructions are executed in an electronic device, the electronic device implements the method of the first aspect above.

[0033] In a fifth aspect, the present application provides a computer program product, which includes a computer program; when the computer program runs in an electronic device, the electronic device implements the method of the first aspect.

[0034] The beneficial effects of the second to fifth aspects mentioned above refer to the corresponding description of the first aspect and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A schematic diagram of the hardware architecture of a battery performance detection method provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a battery performance detection method provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of another battery performance detection method provided in an embodiment of the present application;

[0039] Figure 4 A flowchart of another battery performance detection method provided in an embodiment of the present application;

[0040] Figure 5 A flowchart of another battery performance detection method provided in an embodiment of the present application;

[0041] Figure 6 A flowchart of another battery performance detection method provided in an embodiment of the present application;

[0042] Figure 7 A schematic diagram of a differential voltage dV / dQ curve and its characteristic trough of a lithium iron phosphate-graphite system lithium ion battery provided in an embodiment of the present application;

[0043] Figure 8 A flowchart of another battery performance detection method provided in an embodiment of the present application;

[0044] Fig. 9 A schematic diagram of the cycle capacity attenuation and characteristic value Vmin change with the number of cycles of a lithium iron phosphate-graphite system lithium ion battery provided in the present application;

[0045] Fig.10A schematic diagram of the cycle capacity attenuation and characteristic value Vmin variation with the number of cycles of another lithium iron phosphate-graphite system lithium ion battery provided in the present application;

[0046] Fig.11 A schematic diagram of the cycle capacity attenuation and characteristic value Vmin variation with the number of cycles of another lithium iron phosphate-graphite system lithium ion battery provided in the present application;

[0047] Fig.12 A schematic diagram of determining an empirical critical value V0 for a lithium iron phosphate-graphite system lithium-ion battery provided in this application;

[0048] Fig.13 A schematic diagram of identifying a diving battery by using a characteristic value Vmin and an empirical critical value V0 for a lithium iron phosphate-graphite system lithium-ion battery provided in this application;

[0049] Fig.14 A flowchart of another battery performance detection method provided in an embodiment of the present application;

[0050] Fig.15 A schematic diagram of the structure of a battery performance detection device provided in an embodiment of the present application;

[0051] Fig.16 A schematic diagram of the structure of an electronic device provided in this application.

[0052] Reference numerals: collection device 101 , processing device 102 . DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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 are within the scope of protection of this application.

[0054] In the description of this application, it should be understood that the terms "upper", "lower", "left", "right", "front", "back", "inside", "outside", etc. indicate directions or positional relationships based on the directions or relative positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on this application. Unless otherwise specified, the above-mentioned directional description can be flexibly set in the process of actual application under the condition that the relative positional relationship shown in the accompanying drawings is met.

[0055] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0056] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection. It can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0057] In the present application, the terms "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, article or device including the element.

[0058] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0059] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0060] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0061] Lithium-ion batteries are widely used in various industries, and their safety issues have always been a focus of attention. The capacity inflection point of a lithium-ion battery is usually defined as the point at which the rate of capacity decay changes significantly during the charge and discharge cycle of the battery. Specifically, it marks the transition of the battery from the slow decay stage in the early stage of its life to the rapid decay stage in the later stage, which is also called "capacity diving". This transition is not sudden, but may occur gradually over multiple cycles, but a significant increase in the rate of capacity decay can usually be observed at the inflection point. Usually, we call batteries that have entered the rapid decay stage "diving batteries". For the cascade battery utilization industry, the battery is in the late stage of aging, and it is even more necessary to pay special attention to "diving batteries".

[0062] Whether in the sorting stage or the use stage of the second-life batteries, the diving batteries need to be identified and screened out. On the one hand, the presence of diving batteries will cause the capacity of the battery module or battery pack to drop rapidly and the inconsistency to become more serious. On the other hand, the internal side reactions of the diving batteries are more serious and the gas production is more. The resulting expansion force is greater and the expansion speed is faster, which in turn squeezes the surrounding batteries. In severe cases, it will cause the module to deform. At the same time, due to the increased internal resistance and lack of electrolyte, the temperature rise of the diving battery during the cycle operation is faster and the temperature is higher. These will bury serious safety hazards in the use of second-life batteries, so it is necessary to develop methods that can detect and identify diving batteries.

[0063] Among the existing methods for identifying diving batteries, one method associates the battery health status with the characteristic parameters of the dQ / dV curve, such as multi-peak area, multi-peak peak value, etc., to determine whether the battery has dived. However, the required dQ / dV curve needs to be obtained through a complete period of charging or discharging data, and must be tested at a small rate, resulting in a long test time (3-4 hours). Another method associates the battery diving point with characteristic parameters such as single-cycle or multi-cycle battery capacity, DC internal resistance, and average voltage. However, it is necessary to obtain the historical test data of the battery and judge the capacity inflection point by the change trend of the static parameters. It is difficult to apply it in practice for cascade batteries where historical data is difficult to obtain. There is also a method that uses a large amount of historical data for model training, and then inputs a period of cycle attenuation data into the model to obtain the predicted capacity trajectory, and then compares the actual capacity with the predicted capacity. If the difference between the two exceeds the set threshold, it is determined to be a diving battery. However, the accuracy of this type of model depends largely on the reliability of the training data, and the R&D cycle is long. In actual application, multiple cycles of charging and discharging data are required, and it cannot be applied to the rapid sorting of cascade batteries.

[0064] It can be seen that the existing technology mainly performs detection based on the historical complete charging and discharging data of the battery. However, due to the differences in the characteristics of the single cells in the battery pack, the charging and discharging endpoints of the series battery pack are limited by the minimum charging capacity and the minimum discharging capacity of the single cells. That is, when a single cell in the battery pack reaches the cut-off voltage during the charging or discharging process, the charging or discharging of the entire battery pack is also terminated accordingly. Therefore, it may not be possible to obtain complete charging and discharging data for different single cells in the battery pack, which increases the difficulty of identification. In addition, most of the existing technologies require a long development cycle and have the problem of low identification efficiency.

[0065] In response to the above technical problems, the present application proposes a battery performance detection method, the idea of ​​which is: during the battery charging process, obtain the differential voltage curve of the battery within a preset time period; based on the trough characteristics in the differential voltage curve, determine the battery performance. The battery performance detection method provided by the present application obtains the differential voltage curve of the battery during the charging process, and then determines the battery performance through the trough characteristics of the differential voltage curve. Since the trough characteristics of the differential voltage curve are closely related to the characteristics of the battery, this method can more accurately and quickly evaluate the health status and performance of the battery, thereby improving the efficiency and accuracy of battery performance detection.

[0066] Figure 1 A schematic diagram of the hardware architecture of a battery performance detection method provided in an embodiment of the present application. Figure 1 As shown, it includes: a collection device 101 and a processing device 102.

[0067] The acquisition device 101 is used to acquire voltage data and power data of the battery during the charging process, and send the voltage data and power data to the processing device 102 .

[0068] Exemplarily, the acquisition device 101 may be a sensor (eg, a voltage sensor, a power sensor), or other device capable of acquiring battery voltage data and power data, which is not limited in the present application.

[0069] The processing device 102 is used to obtain a differential voltage curve of the battery within a preset time period according to the voltage data and the power data of the battery during the charging process.

[0070] In some embodiments, the processing device 102 is further configured to determine the performance of the battery based on the valley characteristics in the differential voltage curve.

[0071] In some embodiments, the processing device 102 is further configured to determine whether the battery has a performance degradation phenomenon based on the differential voltage value of the first trough in the differential voltage curve.

[0072] Exemplarily, the processing device 102 may be a server cluster composed of multiple servers, or a single server, or a computer, or a processor or processing chip in a server or computer, etc. The embodiment of the present application does not limit the specific device form of the processing device 102.

[0073] It should be noted that Figure 1 This is just an exemplary framework diagram. Figure 1 The number of devices included in the Figure 1 In addition to the devices shown, other devices may also be included, which is not limited in the embodiments of the present application.

[0074] It should be noted that the application scenarios of the embodiments of the present disclosure are not limited. The system architecture and business scenarios described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. It is known to those skilled in the art that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0075] The battery performance detection method provided in the embodiment of the present application is described in detail below.

[0076] Figure 2 A schematic diagram of a battery performance detection method provided in an embodiment of the present application. Figure 2 As shown, including:

[0077] S101. During battery charging, a differential voltage curve of the battery within a preset time period is obtained.

[0078] In some embodiments, the differential voltage curve is a dV / dQ-Q curve, where V represents the real-time voltage of the battery during the charging process, and Q represents the real-time charge of the battery during the charging process.

[0079] In some embodiments, the battery is any single cell in a battery pack, and step S101 can be implemented by charging the battery pack and obtaining a differential voltage curve of any single cell in the battery pack within a preset time period.

[0080] Specifically, during the charging process of the battery pack, the voltage data of each single cell is collected, and the differential voltage dV / dQ curve of each single cell can be obtained by differentiating the single cell voltage and capacity. Exemplarily, the differential voltage curve of any single cell in the battery pack can be obtained in the following ways: the battery management system (BMS) uses high-precision sensors and a distributed measurement method to measure the real-time voltage and real-time power of each single cell in the charged battery pack.

[0081] In some embodiments, the charging process of the battery is a small rate constant current charging, for example, the charging rate range of the battery is set to 0.25C to 0.3C.

[0082] It is understandable that by charging the battery using a small rate constant current charging method, its electrical signal can reflect more information about the internal state of the battery, and can slow down the battery's attenuation rate, which helps maintain battery performance. At the same time, it can reduce the heat accumulation of the battery during the charging process and improve the safety of the battery.

[0083] In some embodiments, the preset time period is a section in which the battery state of charge (SOC) of the battery is less than or equal to a preset SOC value.

[0084] The battery state of charge (SOC) refers to the percentage of the battery's current remaining power, specifically the ratio of the battery's current stored power to the battery's rated capacity (expressed as a percentage).

[0085] In some embodiments, Figure 3 As shown, before the above step S101, the process further includes step S201: discharging the battery to a cut-off voltage.

[0086] The cut-off voltage refers to the voltage value at which the battery can no longer supply power when the voltage drops to a certain level during the discharge process of the battery.

[0087] In some embodiments, the battery is discharged at a constant current to a cut-off voltage.

[0088] It is understandable that the battery is discharged to the cut-off voltage before measurement, so that the differential voltage curve of the battery within a preset time period can be obtained during the battery charging process.

[0089] In some embodiments, during the charging process of the battery, a sampling interval of the real-time voltage data and the real-time capacity data of the battery is less than or equal to 10 seconds.

[0090] S102: Determine the performance of the battery based on the valley characteristics in the differential voltage curve.

[0091] In some embodiments, the performance of the battery includes the capacity of the battery.

[0092] In some embodiments, the valley feature in the differential voltage curve includes a differential voltage value of the valley.

[0093] It should be noted that according to experiments, the dV / dQ curve can reflect the internal state of the battery. For example, the dV / dQ curve can reflect the voltage fluctuation of the battery within the unit capacity range during the charging and discharging process. The trough of the dV / dQ-Q curve corresponds to the platform area of ​​the voltage-capacity curve, and the peak corresponds to the slope area between the two platforms, reflecting the phase change of the active material during the lithium insertion and extraction process. It is understandable that during the charging process, the internal state of the capacity diving battery is seriously deteriorated, especially the lithium insertion process on the negative electrode side is more difficult. Therefore, according to experimental findings (specific experimental data refer to the following, no further description is given here), in the low SOC section, the differential voltage value of the trough on the dV / dQ curve of the battery with severe capacity decay and the normal battery is quite different, which can be used to distinguish between batteries with capacity decay and normal batteries.

[0094] In some embodiments, the valley characteristics in the differential voltage curve may further include: the location of the valley and the depth of the valley.

[0095] It can be understood that the battery performance detection method provided in the present application obtains the differential voltage curve of the battery during the charging process, and then determines the performance of the battery through the trough characteristics of the differential voltage curve. Since the trough characteristics of the differential voltage curve are closely related to the characteristics of the battery, this method can more accurately and simply evaluate the health status and performance of the battery, thereby improving the efficiency of battery performance detection.

[0096] Figure 4 A flow chart of a battery performance detection method provided in an embodiment of the present application is shown as follows: Figure 4 As shown, the above step S101 can be specifically implemented as S1011 to S1012:

[0097] S1011. During the battery charging process, record the voltage-capacity data of the battery within a preset time period.

[0098] S1012, performing at least differential processing on the voltage-capacity data to obtain a differential voltage curve.

[0099] In a possible implementation, the above step S1012 can be implemented as follows: at least differentiate the voltage-capacity data to obtain the first-order differential derivative of the voltage-capacity (dV / dQ), and then obtain the differential voltage curve of the battery, that is, the dV / dQ-Q curve.

[0100] Another possible implementation method is that the above step S1012 can also be implemented as: performing differentiation and smoothing processing on the voltage-capacity data to obtain the first-order differential derivative of the voltage-capacity (dV / dQ), and then obtaining the differential voltage curve of the battery, that is, the dV / dQ-Q curve.

[0101] Exemplarily, the voltage-capacity curve is firstly first-order derived to obtain the differential voltage dV / dQ curve, and then the derivative result is smoothed by the Savitzky-Golay convolution smoothing method. Among them, the Savitzky-Golay convolution smoothing method is based on the least squares principle and realizes data smoothing by polynomial fitting. When applying the Savitzky-Golay convolution smoothing method, it is necessary to select two key parameters, window length and polynomial order, according to the sampling accuracy and sampling interval of the data set to ensure that the smoothed dV / dQ-Q differential voltage curve can effectively reflect the characteristics of the original data and significantly reduce noise interference, thereby providing an accurate and reliable data basis for subsequent analysis and research.

[0102] Exemplarily, the smoothing process may also include: local weighted regression scatter point smoothing, noise filtering, etc., which is not limited in the embodiments of the present application.

[0103] Figure 5 A flowchart of a battery performance detection method provided in an embodiment of the present application is as follows: Figure 5 As shown, the above step S102 can be specifically implemented as step S1021:

[0104] S1021: Determine battery performance based on the valley characteristics of the first valley in the differential voltage curve.

[0105] It should be noted that batteries with abnormal performance (such as capacity decay) usually have abnormal changes in characteristic troughs on the dV / dQ curve in the low SOC section. For example, the position of the characteristic trough may shift toward a higher or lower SOC, or the depth of the trough may change. These changes are closely related to processes such as loss of active materials and electrode side reactions inside the battery. Experiments have shown that in the low SOC section, if the differential voltage value, position, shape or depth of the trough of the dV / dQ curve is significantly different from the curve of a normal battery, it may indicate that the battery is at risk of diving. Normally, the first trough in the battery differential voltage curve will appear in the low SOC section of the battery, so consider determining the battery performance through the trough characteristics of the first trough in the differential voltage curve.

[0106] Further, such as Figure 6 As shown, in the case where the trough feature in the differential voltage curve includes the differential voltage value of the trough, step S102 can also be specifically implemented as step S1022:

[0107] S1022: When the differential voltage value of the first trough in the differential voltage curve is greater than or equal to a preset threshold, determine that the battery has a performance degradation phenomenon.

[0108] In some embodiments, the battery performance degradation phenomenon includes the battery capacity degradation phenomenon (also known as "capacity diving" or "diving battery").

[0109] Among them, the preset threshold is determined based on the differential voltage values ​​of multiple sample batteries of the same battery model as the above-mentioned battery and with capacity decay phenomenon. For example, the average value or maximum value of Vmin of multiple single cells of the same model can be used as the preset threshold of this test, that is, the critical value V0.

[0110] In some embodiments, since the first valley in the differential voltage curve of the battery usually appears in a low SOC section of the battery, the preset SOC value is the SOC value of the battery in the low SOC section. For example, the preset SOC value may be 25%.

[0111] In some embodiments, the preset SOC value is less than or equal to 25%.

[0112] It is understandable that since the first trough in the differential voltage curve of the battery usually appears in the low SOC section of the battery, there is no need to obtain the complete battery charging data when performing testing. Compared with the solution in the prior art that needs to obtain the charging and discharging data of the battery for multiple cycles, the problem that the charging and discharging endpoint of the battery pack is determined only by the single cell with the worst performance, and thus the complete charging and discharging data of other single cells cannot be obtained, can be ignored, thereby simplifying the detection method and improving the detection efficiency.

[0113] For example, Figure 7 A differential voltage dV / dQ curve and a characteristic trough diagram of a lithium iron phosphate-graphite system lithium ion battery provided in an embodiment of the present application. Figure 7 As shown in the figure, it is a differential voltage dV / dQ-Q curve obtained after differential smoothing of a lithium iron phosphate-graphite system secondary battery with a battery health state (SOH) of 80%, wherein the cross mark at the first trough is the characteristic trough to be identified, and the y-axis coordinate dV / dQ value corresponding to the trough is the differential voltage value of the above trough, recorded as the characteristic value Vmin.

[0114] Among them, the battery state of health (SOH) is an important parameter to measure the current health level of the battery. It reflects the battery's ability to store electrical energy relative to a new battery, usually expressed as a percentage. In this application, SOH is defined based on battery capacity decay, that is, the ratio of the battery's current capacity to the rated capacity.

[0115] In some embodiments, Figure 8 As shown, it also includes step S301:

[0116] S301. When the first trough appears on the differential voltage curve, stop charging.

[0117] It can be understood that, based on the above experimental results, the trough characteristics of the first trough of the differential voltage curve can be used as an important indicator to determine whether the battery is experiencing degradation. Therefore, charging can be stopped when the first trough appears in the differential voltage curve, thereby shortening the test time and improving identification efficiency.

[0118] To facilitate understanding of the battery performance detection method provided in the embodiment of the present application, the principles involved in the battery performance detection method provided in the embodiment of the present application are described below in combination with experimental data.

[0119] Experiment 1

[0120] Fig. 9 This is a schematic diagram of the cycle capacity decay and characteristic value Vmin change with the number of cycles of a lithium iron phosphate-graphite system lithium ion battery provided in this application. Fig. 9 As shown, the battery was subjected to a cycle aging experiment at room temperature starting from SOH=74%, and three 0.3C rate charge and discharge cycles were performed every 100 0.5C rate charge and discharge cycles. The charging voltage-capacity curve at 0.3C rate was taken to plot the differential voltage dV / dQ-Q curve, and the characteristic value Vmin was read to obtain the battery capacity and Vmin change trend with the number of cycles.

[0121] from Fig. 9 It can be seen that the battery capacity and the changing trend of the characteristic value Vmin are highly correlated. In the cycle process before 500 cycles, the battery capacity decayed relatively slowly, and at the same time, the Vmin value also showed a trend of slowly increasing. However, when the number of cycles exceeded 500, the battery capacity decay mode changed from linear to nonlinear, that is, the battery capacity dive phenomenon occurred. At this stage, the Vmin value also increased suddenly and the growth rate accelerated. This result shows that when the battery performance drops sharply, Vmin will change significantly, indicating that the battery may be about to experience or is experiencing a capacity dive, so the characteristic value Vmin can be used as an important indicator to identify the battery capacity dive phenomenon.

[0122] Experiment 2

[0123] Fig.10 This is another schematic diagram of the cycle capacity attenuation and characteristic value Vmin change with the number of cycles of a lithium iron phosphate-graphite system lithium ion battery provided in this application. Fig.10As shown, the battery was subjected to a cycle aging experiment at 45°C starting from SOH=100%, and three 0.25C rate charge and discharge cycles were performed every 100 1C rate charge and discharge cycles. The charging voltage-capacity curve at 0.25C rate was taken to plot the differential voltage dV / dQ-Q curve, and the characteristic value Vmin was read to obtain the battery capacity and Vmin change trend with the number of cycles.

[0124] from Fig.10 It can be seen that the changing trend of battery capacity and characteristic value Vmin is highly correlated. Before 1900 cycles, the decay process of battery capacity was relatively slow, and correspondingly, the characteristic value Vmin also showed a trend of gradual increase. After 1900 cycles, the decay mode of battery capacity changed from linear decay to nonlinear decay, that is, the battery capacity dive phenomenon occurred. At the same time, the characteristic value Vmin also increased suddenly, and the growth rate accelerated. This result shows that under high temperature conditions, the characteristic value Vmin can also effectively identify the battery capacity dive phenomenon.

[0125] Experiment 3

[0126] Fig.11 This is another schematic diagram of the cycle capacity attenuation and characteristic value Vmin change with the number of cycles of a lithium iron phosphate-graphite system lithium ion battery provided in this application. Fig.11 As shown, the battery is subjected to a cycle aging experiment at room temperature starting from SOH=100%, and three 0.25C rate charge and discharge cycles are performed every 100 1C rate charge and discharge cycles. The charging voltage-capacity curve at 0.25C rate is taken to plot the differential voltage dV / dQ-Q curve, and the characteristic value Vmin is read to obtain the changing trend of the battery capacity and the characteristic value Vmin with the number of cycles.

[0127] from Fig.11 It can be seen that the change trend of the battery capacity is highly consistent with the characteristic value Vmin, and during the cycle, no capacity diving phenomenon was observed in the battery. At the same time, the characteristic value Vmin always maintains a linear and slow increase trend, without a sudden increase or accelerated growth. This result shows that when the battery does not experience a capacity diving phenomenon, the linear and slow increase trend of Vmin indicates that the characteristic value Vmin will not misidentify the battery that does not experience a capacity diving phenomenon.

[0128] Experiment 4

[0129] Fig.12 A schematic diagram of determining the empirical critical value V0 of a lithium iron phosphate-graphite system lithium ion battery provided in this application. Fig.12As shown, the experiment involved 9 lithium iron phosphate-graphite system batteries with a rated capacity of 270Ah. All batteries were subjected to cycle aging experiments starting from SOH=100% at room temperature. In the experiment, three 0.25C charge and discharge cycles were performed every 100 0.5C rate charge and discharge cycles. The differential voltage dV / dQ-Q curve was plotted through the charging voltage-capacity curve at a 0.25C rate, and the characteristic value Vmin was read from it to obtain the battery capacity and Vmin change trend with the number of cycles.

[0130] from Fig.12 It can be seen that the corresponding capacity of each battery when the capacity dive occurs is different, but the corresponding characteristic value Vmin is in the same cycle area. This shows that when the electrochemical state inside the battery changes, the characteristic value Vmin can be used as a reliable indicator to identify the battery capacity dive phenomenon, and it is also consistent between different batteries of the same model. Therefore, for the same model of battery, the critical value V0 of the characteristic value Vmin of the same model when the capacity dive occurs can be determined based on the corresponding characteristic value Vmin when the capacity dive occurs in different single cells of the same model, as shown in Fig.12 The dashed line shown represents V0=0.001 in Experiment 4.

[0131] Depend on Fig.12 It can also be seen that when the battery capacity dives, the characteristic value Vmin increases faster. Therefore, it can be considered that when the characteristic value Vmin of the battery is greater than or equal to V0, the battery capacity dives.

[0132] Experiment 5

[0133] Fig.13 A schematic diagram of a lithium iron phosphate-graphite system lithium-ion battery provided in this application for identifying a diving battery through a characteristic value Vmin and an empirical critical value V0. Fig.13 As shown, there are more than 300 lithium iron phosphate-graphite system secondary batteries with a rated capacity of 270Ah, and the statistical results of identifying diving batteries by using Vmin and the critical value V0 obtained in Experiment 4.

[0134] from Fig.13 It can be seen that the characteristic value Vmin of a normal battery and a battery with a capacity dive is significantly different. Therefore, this identification method can clearly distinguish a battery with a capacity dive from a normal battery.

[0135] Based on the above experiments, it can be concluded that in the differential voltage curve (dV / dQ-Q curve) of the battery during charging, the trough characteristics of the first trough, that is, the differential voltage value of the first trough, also known as the characteristic value Vmin, is highly correlated with the battery's cycle capacity attenuation and is an important and reliable indicator for identifying the capacity diving phenomenon of the battery.

[0136] The battery performance detection method provided by the present application is introduced below through a complete embodiment. Fig.14 As shown, the method comprises the following steps:

[0137] S1. Determine a preset threshold.

[0138] The preset threshold is the same concept as the critical value V0 in the above Experiment 4.

[0139] Among them, the preset threshold is determined based on the differential voltage values ​​of multiple sample batteries that have the same model as the battery to be tested and have capacity decay. For example, the average or maximum value of the dV / dQ values ​​at the first trough in the differential voltage curve (dV / dQ-Q curve) of multiple single cells during the charging process can be used as the preset threshold for this detection.

[0140] S2. Discharge the battery to be tested at a constant current to the cut-off voltage.

[0141] Among them, a small rate constant current discharge method can be used to perform constant current discharge on the battery to be tested.

[0142] S3. Use a small rate constant current charging method to charge the battery under test.

[0143] Exemplarily, the charge rate may be set to 0.25C.

[0144] S4. During the charging process of the battery to be tested, the real-time voltage and real-time capacity of the battery to be tested within a preset time period are recorded.

[0145] The preset time period is a low SOC section of the battery to be tested. Exemplarily, the preset time period may be a section of 0% to 20% of the SOC.

[0146] S5. Based on the real-time voltage and real-time capacity of the battery to be tested, a differential voltage curve (dV / dQ-Q curve) of the battery to be tested is drawn.

[0147] S6. Obtain the dV / dQ value at the first trough of the dV / dQ-Q curve of the battery to be tested.

[0148] The dV / dQ value at the first trough is the characteristic value Vmin at the first trough mentioned above.

[0149] S7. Compare the dV / dQ value with a preset threshold value to determine whether the battery under test has performance degradation.

[0150] If the dV / dQ value is less than the preset threshold, it is determined that the battery under test has no performance degradation phenomenon;

[0151] If the dV / dQ value is greater than or equal to the preset threshold, it is determined that the battery to be tested does not experience performance degradation.

[0152] The battery performance detection method provided in the present application obtains the differential voltage curve of the battery during the charging process, and then determines the performance of the battery through the trough characteristics of the differential voltage curve. Since the trough characteristics of the differential voltage curve are closely related to the characteristics of the battery, this method can more accurately and simply evaluate the health status and performance of the battery, thereby improving the efficiency of battery performance detection.

[0153] It is understandable that the battery performance detection method provided in the present application realizes accurate evaluation of the battery health status and performance by analyzing the differential voltage curve of the battery to be tested during the charging process, especially the characteristic value Vmin of the first trough. Compared with the prior art, this method does not need to obtain multiple charge and discharge data of the battery cycle, which simplifies the detection process. At the same time, by setting a preset threshold and performing detection in the low SOC section, this method can quickly identify battery performance degradation, especially the battery capacity diving phenomenon, thereby improving detection efficiency.

[0154] It can be seen that the above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to achieve the above functions, the embodiment of the present application provides a hardware structure and / or software module corresponding to each function. It should be easily appreciated by those skilled in the art that, in combination with the modules and algorithm steps of each example described in the embodiment disclosed herein, the embodiment of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0155] The embodiment of the present application can divide the functional modules of the communication scheduling device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0156] Fig.15 This is a schematic diagram of the structure of a battery performance detection device provided in an embodiment of the present application, which can implement the battery performance detection method provided in the above method embodiment. Fig.15 As shown, the battery performance detection device 400 includes: a processing module 401 and an analysis module 402 .

[0157] The processing module 401 is used to obtain a differential voltage curve of the battery within a preset time period during the battery charging process;

[0158] The analysis module 402 is used to determine the performance of the battery based on the valley characteristics in the differential voltage curve.

[0159] In a possible implementation manner, the valley feature includes a differential voltage value of the valley.

[0160] In a possible implementation, the analysis module 402 is specifically configured to determine the performance of the battery based on the valley feature of the first valley in the differential voltage curve.

[0161] In a possible implementation, when the trough feature includes a differential voltage value of the trough, the analysis module 402 is specifically configured to determine that the battery has a performance degradation phenomenon when the differential voltage value of the first trough is greater than or equal to a preset threshold.

[0162] In a possible implementation, the preset threshold is determined based on differential voltage values ​​of a plurality of sample batteries that have capacity decay, and the sample batteries are of the same model as the battery.

[0163] In a possible implementation, the preset time period is a section in which the state of charge (SOC) value of the battery is less than or equal to a preset SOC value.

[0164] In a possible implementation, the processing module 401 is specifically configured to record the voltage-capacity data of the battery within a preset time period during the battery charging process; and at least perform differential processing on the voltage-capacity data to obtain a differential voltage curve.

[0165] In a possible implementation, the processing module 401 is specifically configured to perform differentiation and smoothing processing on the voltage-capacity data to obtain a differential voltage curve.

[0166] In a possible implementation, the communication scheduling device 400 further includes: a control module 403. The control module 403 is configured to stop charging when the differential voltage curve has a first trough.

[0167] In a possible implementation, during the battery charging process, before obtaining the differential voltage curve of the battery within a preset time period, the control module 403 is further configured to discharge the battery to a cut-off voltage.

[0168] In a possible implementation, the battery is a single cell in a battery pack.

[0169] In a possible implementation, before obtaining the differential voltage curve of the battery within a preset time period, the control module 403 is further configured to charge the battery based on a small rate constant current.

[0170] In the case of implementing the functions of the above-mentioned integrated modules in the form of hardware, the embodiment of the present invention provides a possible structural diagram of the electronic device involved in the above-mentioned embodiment. Fig.16 As shown, the electronic device 900 includes: a processor 902 , a communication interface 903 , and a bus 904 . Optionally, the electronic device 900 may further include a memory 901 .

[0171] The processor 902 may be a processor that implements or executes various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor 902 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the present application. The processor 902 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0172] The communication interface 903 is used to connect with other devices via a communication network, such as Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0173] The memory 901 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0174] As a possible implementation, the memory 901 may exist independently of the processor 902, and the memory 901 may be connected to the processor 902 via a bus 904 to store instructions or program codes. When the processor 902 calls and executes the instructions or program codes stored in the memory 901, the battery performance detection method provided in the embodiment of the present invention can be implemented.

[0175] In another possible implementation, the memory 901 may also be integrated with the processor 902 .

[0176] The bus 904 may be an extended industry standard architecture (EISA) bus, etc. The bus 904 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.16 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0177] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0178] The present application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be completed by computer program instructions to instruct related hardware, and the program can be stored in the above computer-readable storage medium. When the computer program instructions are executed on a computer, the computer executes the battery performance detection method described in any of the above embodiments.

[0179] Exemplarily, the above-mentioned computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks or magnetic tapes, etc.), optical disks (e.g., compact disks (CD), digital versatile disks (DVD), etc.), smart cards and flash memory devices (e.g., erasable programmable read-only memory (EPROM), cards, sticks or key drives, etc.). The various computer-readable storage media described in the present disclosure may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing and / or carrying instructions and / or data.

[0180] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program product is run on a computer, the computer is enabled to execute any one of the battery performance detection methods provided in the above embodiments.

[0181] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A battery performance detection method, characterized in that: The method comprises: During the battery charging process, obtaining a differential voltage curve of the battery within a preset time period; Based on the characteristics of the valleys in the differential voltage curve, the performance of the battery is determined.

2. The method according to claim 1, characterized in that The valley characteristic includes a differential voltage value of the valley.

3. The method according to claim 1, characterized in that The determining the performance of the battery based on the valley characteristics in the differential voltage curve comprises: The performance of the battery is determined based on the valley characteristics of the first valley in the differential voltage curve.

4. The method according to claim 3, characterized in that In the case where the valley feature includes a differential voltage value of a valley, determining the performance of the battery based on the valley feature of a first valley in the differential voltage curve includes: When the differential voltage value of the first trough is greater than or equal to a preset threshold, it is determined that the battery has a performance degradation phenomenon.

5. The method according to claim 4, characterized in that The preset threshold is determined based on differential voltage values ​​of a plurality of sample batteries that have a capacity decay phenomenon, and the sample batteries are of the same model as the battery.

6. The method according to claim 1, characterized in that The preset time period is a section in which the state of charge (SOC) value of the battery is less than or equal to a preset SOC value.

7. The method according to claim 1, characterized in that In the process of charging the battery, obtaining a differential voltage curve of the battery within a preset time period includes: During the charging process of the battery, recording the voltage-capacity data of the battery within the preset time period; At least the voltage-capacity data is differentiated to obtain the differential voltage curve.

8. The method according to claim 7, characterized in that The at least performing differential processing on the voltage-capacity data to obtain the differential voltage curve includes: The voltage-capacity data is differentiated and smoothed to obtain the differential voltage curve.

9. The method according to claim 1, characterized in that: The method further comprises: When the first trough appears on the differential voltage curve, charging is stopped.

10. The method according to claim 1, characterized in that In the process of charging the battery, before obtaining the differential voltage curve of the battery within a preset time period, the method further includes: The battery was discharged to a cut-off voltage.

11. The method according to claim 1, characterized in that: The battery is a single cell in a battery pack.

12. The method according to claim 1, characterized in that Before obtaining the differential voltage curve of the battery within a preset time period, the method further includes: The battery is charged based on a small rate constant current.

13. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is coupled to the memory; the memory is used to store computer instructions, and the computer instructions are loaded and executed by the processor to enable the computer device to implement the battery performance detection method as described in any one of claims 1 to 12.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes computer-executable instructions. When the computer-executable instructions are executed on a computer, the computer is enabled to execute the battery performance detection method according to any one of claims 1 to 12.

15. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is run on an electronic device, the electronic device is enabled to execute the battery performance detection method according to any one of claims 1 to 12.

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