Battery life degradation curve acquisition method and device, intelligent equipment and storage medium

Abstract

The invention relates to the technical field of energy storage batteries, in particular to a battery life degradation curve acquisition method and device, intelligent equipment and a storage medium, and aims to solve the technical problem of how to acquire a life degradation curve of a full life cycle of a battery in a short time. In order to achieve the purpose, the method comprises the steps of controlling a normal-temperature starting point discharge energy value of accelerated aging of a parallel sample based on a high-stress acceleration condition, controlling the parallel sample to perform cyclic hanging measurement to a normal-temperature end point discharge energy value based on a normal-temperature cyclic condition, and obtaining a normal-temperature discharge energy value sequence of a normal-temperature cyclic energy retention rate interval corresponding to the parallel sample, and acquiring a battery life degradation curve based on all normal-temperature discharge energy value sequences. Through the method provided by the invention, the actually measured life degradation curve of the whole life cycle of the battery under the normal-temperature working condition can be quickly obtained, and the life degradation curve can better reflect the actual trend of the battery in the later period, so that powerful data support can be provided for verifying whether the project life index is reached or not.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage batteries, and in particular to a method, apparatus, intelligent device, and storage medium for obtaining a battery life degradation curve. Background Art

[0002] The energy storage industry is currently experiencing rapid growth. Iron-lithium batteries are rapidly increasing their use in the energy storage sector due to their outstanding advantages, such as low cost, long life, safety, and reliability. Furthermore, with technological advancements, the lifespan of iron-lithium batteries for energy storage has significantly increased. Some companies have already claimed to have developed batteries with lifespans of 12,000 cycles or even higher, achieving the same lifespan as both solar power and energy storage.

[0003] However, this claimed lifespan is mainly predicted based on a small amount of measured data, and it is difficult to guarantee whether the measured trend of the battery in the later stage can match the predicted value well. If a long-life battery is measured to the end of its lifespan (for example: 12,000 cycles, energy retention rate of 70%), it often takes more than 5 years, which is almost impossible to achieve given the limited development cycle of battery development projects. Therefore, it is necessary to develop an accelerated testing method that can obtain the life degradation curve of the battery throughout its life cycle in a shorter period of time, thereby providing strong support for verifying whether the project life indicators are achieved.

[0004] Accordingly, the art needs a new technical solution that can accelerate the acquisition of the life degradation curve of the battery throughout its life cycle to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects, the present application is proposed to solve or at least partially solve the technical problem of how to obtain the life degradation curve of the battery throughout its life cycle in a relatively short time.

[0006] In a first aspect, a method for obtaining a battery life degradation curve is provided, the method comprising: Based on the preset high-stress acceleration conditions, each parallel sample is controlled to undergo cyclic hanging testing, so that each parallel sample is accelerated to age to its corresponding normal temperature starting point discharge energy value, wherein the normal temperature starting point discharge energy value is determined based on a preset normal temperature cycle energy retention rate range corresponding to each parallel sample; Based on the normal temperature cycle conditions corresponding to the normal temperature cycle life indicator, controlling each parallel sample to undergo cycle testing until reaching the corresponding normal temperature endpoint discharge energy value, wherein the normal temperature endpoint discharge energy value is determined based on the normal temperature cycle energy retention rate range; During the cyclic hanging test, the number of normal temperature cycles of each parallel sample and the normal temperature discharge energy value corresponding to each normal temperature cycle number are obtained to obtain a normal temperature discharge energy value sequence of the normal temperature cycle energy retention rate interval corresponding to each parallel sample; The battery life degradation curve is obtained based on the normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate interval.

[0007] In one technical solution of the above-mentioned method for obtaining a battery life degradation curve, the method further includes: determining a first energy retention rate value based on a high stress acceleration coefficient and a battery degradation trend curve, wherein the high stress acceleration coefficient is determined based on the high stress acceleration condition; All the normal temperature cycle energy retention rate intervals are determined based on the first energy retention rate value, a second energy retention rate value corresponding to the battery life end threshold value, and a preset energy retention rate interval step size.

[0008] In one technical solution of the above-mentioned method for obtaining a battery life degradation curve, “determining a first energy retention rate value based on a high stress acceleration factor and a battery degradation trend curve” includes: Determining a high stress cycle life index corresponding to the high stress acceleration condition based on the high stress acceleration coefficient and the normal temperature cycle life index; Determining the life span of the high stress battery under the high stress acceleration condition based on the high stress cycle life index; The first energy retention rate value is determined based on the high-stress battery life and the battery degradation trend curve.

[0009] In one technical solution of the above-mentioned method for obtaining a battery life degradation curve, “obtaining the battery life degradation curve based on the normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate range” includes: According to the order of the right endpoints of the normal temperature cycle energy retention rate intervals from large to small, the normal temperature discharge energy value sequences are sequentially spliced to obtain the normal temperature discharge energy value sequence for the entire life cycle; The battery life degradation curve is obtained based on the full life cycle normal temperature discharge energy value sequence.

[0010] In one technical solution of the above-mentioned method for obtaining a battery life degradation curve, “obtaining the battery life degradation curve based on the full life cycle normal temperature discharge energy value sequence” includes: The serial number corresponding to each item in the full life cycle normal temperature discharge energy value sequence is used as the abscissa of the battery life degradation curve; Each item in the full life cycle normal temperature discharge energy value sequence is normalized and used as the vertical coordinate of the battery life degradation curve.

[0011] In one technical solution of the above-mentioned method for obtaining a battery life degradation curve, the method further includes: Determining the normal temperature starting point discharge energy value corresponding to the normal temperature cycle energy retention rate interval based on the initial true energy value of the battery and the right endpoint of each normal temperature cycle energy retention rate interval; Based on the initial real energy value of the battery and the left endpoint of each normal temperature cycle energy retention rate interval, the normal temperature endpoint discharge energy value corresponding to the normal temperature cycle energy retention rate interval is determined.

[0012] In one technical solution of the above-mentioned method for obtaining the battery life degradation curve, the high stress acceleration condition includes: at least one of a temperature condition, a rate condition and a DOD condition.

[0013] In a second aspect, a device for obtaining a battery life degradation curve is provided, the device comprising: A cyclic hang test module, wherein the cyclic hang test module is configured to perform the following operations: Based on the preset high-stress acceleration conditions, each parallel sample is controlled to undergo cyclic hanging testing, so that each parallel sample is accelerated to age to its corresponding normal temperature starting point discharge energy value, wherein the normal temperature starting point discharge energy value is determined based on a preset normal temperature cycle energy retention rate range corresponding to each parallel sample; Based on the normal temperature cycle conditions corresponding to the normal temperature cycle life indicator, controlling each parallel sample to undergo cycle testing until reaching the corresponding normal temperature endpoint discharge energy value, wherein the normal temperature endpoint discharge energy value is determined based on the normal temperature cycle energy retention rate range; A data acquisition module is configured to perform the following operations: during the cyclic hanging test, obtain the number of normal temperature cycles of each parallel sample and the normal temperature discharge energy value corresponding to each normal temperature cycle number, and obtain a normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate interval corresponding to each parallel sample; A data processing module is configured to perform the following operations: based on the normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate interval, obtain the battery life degradation curve.

[0014] In a third aspect, a smart device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned method for obtaining the battery life degradation curve is implemented.

[0015] In a fourth aspect, a storage medium stores a plurality of program codes, wherein the computer program, when executed by the at least one processor, implements the method described in any one of the technical solutions of the above-mentioned method for obtaining a battery life degradation curve.

[0016] One or more of the above-mentioned technical solutions of the present application have at least one or more of the following beneficial effects: the life degradation curve of the battery measured under normal temperature conditions over its entire life cycle can be quickly obtained, and since the life degradation curve is entirely based on measured data, the life degradation curve can better reflect the actual trend of the battery in the later stage, and can provide strong data support for verifying whether the project life indicators of battery development have been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The disclosure of this application will become more easily understood with reference to the accompanying drawings. Those skilled in the art will readily appreciate that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0018] Figure 1 This is a flowchart of the main steps of a method for obtaining a battery life degradation curve according to an embodiment of the present application.

[0019] Figure 2 It is a flow chart of the main steps of a method for determining the normal temperature cycle energy retention rate interval according to an embodiment of the present application.

[0020] Figure 3 is an example diagram of a battery degradation trend curve according to an embodiment of the present application.

[0021] Figure 4 This is an example diagram of all high-stress cycle ERR intervals, normal temperature cycle ERR intervals, and the normal temperature starting point discharge energy values and normal temperature end point discharge energy values corresponding to each normal temperature cycle ERR interval according to an embodiment of the present application.

[0022] Figure 5 This is an example diagram of energy value-cycle number corresponding to a normal temperature discharge energy value sequence according to an embodiment of the present application.

[0023] Figure 6 This is an example diagram of an energy value-cycle number curve and a life degradation curve of a battery throughout its life cycle according to an embodiment of the present application.

[0024] Figure 7 This is a main structural block diagram of a device for acquiring a battery life degradation curve according to another embodiment of the present application.

[0025] Figure 8 It is a main structural block diagram of a smart device according to another embodiment of the present application. DETAILED DESCRIPTION

[0026] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the scope of protection of the present application.

[0027] In the description of this application, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "a" and "the" may also include the plural forms.

[0028] See attached Figure 1 , Figure 1 The figure is a flow chart of the main steps of a method for obtaining a battery life degradation curve according to an embodiment of the present application. The method for obtaining a battery life degradation curve in the embodiment of the present application includes: Step S101: Based on the preset high stress acceleration conditions, control each parallel sample to perform cyclic hanging test, so that each parallel sample is accelerated to age to its corresponding normal temperature starting point discharge energy value Step S102: Based on the normal temperature cycle conditions corresponding to the normal temperature cycle life index, control each parallel sample to perform cycle testing to the corresponding normal temperature endpoint discharge energy value; Step S103: During the cyclic test, the number of normal temperature cycles of each parallel sample and the normal temperature discharge energy value corresponding to each normal temperature cycle number are obtained to obtain a sequence of normal temperature discharge energy values in the normal temperature cycle energy retention interval corresponding to each parallel sample; Step S104: obtaining a battery life degradation curve based on a normal temperature discharge energy value sequence in a normal temperature cycle energy retention rate interval.

[0029] The idea of the technical solution of this application is: first, under high-stress acceleration conditions, multiple sample batteries are accelerated to age to their corresponding preset energy retention rates (Energy Retention Rate, ERR); then, under normal temperature cycling conditions, each sample battery that has completed accelerated aging is subjected to cycle testing (cyclic charge and discharge testing) within its corresponding normal temperature cycle energy retention rate range (normal temperature cycle ERR range); and then the normal temperature cycle test data of all normal temperature cycle ERR ranges are integrated to quickly obtain the full life cycle degradation curve of the battery measured under normal temperature conditions.

[0030] Before executing step S101, each normal temperature cycle ERR interval for normal temperature cycle test must be determined first. Figure 2 , Figure 2 1 is a flow chart of the main steps of a method for determining the ERR interval of a normal temperature cycle according to an embodiment of the present application. The method for determining the ERR interval of a normal temperature cycle in the embodiment of the present application includes: Step S201: determining a high stress cycle life index corresponding to a high stress acceleration condition based on a high stress acceleration coefficient and a normal temperature cycle life index; Step S202: determining the life span of the high stress battery under high stress acceleration conditions based on the high stress cycle life index; Step S203: determining a first energy retention rate value based on the high-stress battery life and the battery degradation trend curve; Step S204: determining all normal temperature cycle energy retention intervals based on the first energy retention value, the second energy retention value corresponding to the battery life end threshold, and the preset energy retention interval step size.

[0031] In step S201, the high stress acceleration condition includes at least one of temperature acceleration, rate acceleration, and DOD acceleration. Based on the high stress acceleration condition, a high stress acceleration coefficient relative to a normal temperature working condition (normal temperature cycle condition) can be determined.

[0032] As an example, based on historical experience, the degradation trend of iron-lithium batteries (Energy Retention Rate, ERR) can be expressed as 1-a*t z Here, z represents the number of cycles, and a and t represent set parameters. Those skilled in the art may determine the values of a and t for different operating conditions based on the battery's operating temperature, rate, and DOD. Typically, z is 0.7, and a can be set between [0.0004, 0.001] based on the normal temperature cycle life indicator.

[0033] In the embodiment of the present application, the nominal energy value of the A-type iron-lithium battery is 900Wh, and its initial true energy value is 911Wh. Under normal temperature conditions (25℃_0.5P / 0.5P_0-100% SOC, that is, the normal temperature cycle conditions are: the ambient temperature condition is 25℃, the rate condition is 0.5P, and the DOD condition is 0-100% SOC), when the end of life (End of Life, EOL) corresponding to the end of life threshold of the A-type iron-lithium battery is set to 70% ERR, its normal temperature cycle life index is 12,000 cycles.

[0034] For type A lithium iron battery, a is 0.0004, which can be obtained as follows Figure 3 The degradation trend curve shown is shown in FIG. 1 , wherein the horizontal axis represents the number of cycles (cycles) and the vertical axis represents the energy retention rate (Energy Retention / %).

[0035] In step S201 , as an example, the preset high stress acceleration condition is 60° C._1P / 1P_0-100% SOC, that is, the ambient temperature condition is 60° C., the rate condition is 1P, and the DOD condition is 0-100% SOC.

[0036] The high-stress acceleration factor can be derived based on a small amount of measured data. Specifically, the measured termination threshold is set at 95% ERR. Two groups of batteries are charged and discharged under normal temperature cycling conditions and high-stress acceleration conditions. The first cycle number (normal temperature cycling conditions) and the second cycle number (high-stress acceleration conditions) when the ERR value of the two groups of batteries reaches 95% are recorded. The ratio of the first cycle number to the second cycle number is the high-stress acceleration factor.

[0037] In the embodiment of the present application, the high stress acceleration coefficient obtained based on a small amount of measured data is 3, the normal temperature cycle life index is 12000 cycles, and the high stress cycle life index corresponding to the high stress acceleration condition is determined to be 12000 / 3=4000 cycles.

[0038] In step S202, the high-stress battery life is estimated after 4000 cycles based on a high-stress cycling strategy under a preset high-stress acceleration condition. As an example, the high-stress cycling strategy is: 60°C_1P / 1P_0-100% SOC, with a rest time of 20 minutes at both ends of the charge and discharge cycles.

[0039] The time required for a model A lithium iron battery to complete 4000 cycles is approximately 1 year, so it can be determined that the high stress battery life under high stress acceleration conditions is equal to 360 days.

[0040] In step S203, the time required to complete each cycle is first estimated based on the normal temperature cycling strategy corresponding to the normal temperature cycling conditions. As an example, the warm cycling strategy is: 25°C_0.5P / 0.5P_0-100% SOC, and the rest time at both ends of the charge and discharge is 20 minutes each.

[0041] At this time, the time required for the type A iron-lithium battery to complete one cycle is about 4.8 hours, which corresponds to the high-stress battery life obtained in step S202. Under the normal temperature cycle strategy, 1,800 cycles can be completed in 360 days.

[0042] Combine Figure 3 The battery degradation trend curve shown is used to determine the ERR value corresponding to 1800 cycles under normal temperature cycling conditions, which is the first energy retention rate value (first ERR value). As an example, the first ERR value in the embodiment of the present application is 92%.

[0043] In step S204, a second energy retention rate value (second ERR value) is first obtained based on the battery's end-of-life threshold, and the second ERR value is less than the first ERR value; then, based on the preset energy retention rate interval step (ERR interval step), the interval formed by the first ERR value and the second ERR value is divided into multiple continuous sub-ERR intervals; multiple continuous sub-ERR intervals and the ERR interval from 100% ERR to the first ERR value together constitute the entire normal temperature cycle ERR interval of this application.

[0044] As an example, the first ERR value is 92%, the second ERR value corresponding to the end-of-life threshold is 72%, and the ERR interval step is 2% ERR. At this time, according to the step size of 2%, the [72%, 92%] interval can be divided into 11 consecutive sub-ERR intervals ([72%, 74%], [74%, 76%], ..., [90%, 92%]), plus the ERR interval of [92%, 100%], a total of 12 numerically continuous normal temperature cycle ERR intervals are obtained.

[0045] The normal temperature starting discharge energy value corresponding to the normal temperature cycle ERR interval can be determined based on the battery's initial true energy value and the right endpoint of each normal temperature cycle ERR interval. The normal temperature ending discharge energy value corresponding to the normal temperature cycle ERR interval can be determined based on the battery's initial true energy value and the left endpoint of each normal temperature cycle ERR interval. The normal temperature starting discharge energy value also serves as the stopping condition for high-stress accelerated aging.

[0046] like Figure 4 As shown, Figure 4 This is an example diagram of all high-stress cycle ERR intervals, normal temperature cycle ERR intervals, and the normal temperature starting point discharge energy values and normal temperature end point discharge energy values corresponding to each normal temperature cycle ERR interval in one embodiment of the present application.

[0047] In the embodiment of the present application, there are 12 continuous constant temperature cycle ERR intervals. Accordingly, 12 parallel samples are required. The serial numbers of the parallel samples are related to the corresponding constant temperature cycle ERR interval ranges. Figure 4 shown.

[0048] Parallel sample numbered 1 (parallel sample 1) has only the normal temperature cycling ERR range of [92% to 100%], meaning that parallel sample 1 does not require cycling under high-stress accelerated conditions. Parallel samples numbered 2 through 11 (parallel samples 2 through 12) have both a corresponding normal temperature cycling ERR range and a corresponding high-stress cycling energy retention range (high-stress cycling ERR range). The high-stress cycling ERR range for each parallel sample ranges from 100% to the right endpoint of the corresponding normal temperature cycling ERR range.

[0049] Next return Figure 1 The method for obtaining the battery life degradation curve is described in detail. In step S101, based on the high-stress cycling strategy corresponding to the high-stress acceleration conditions (60°C_1P / 1P_0-100% SOC, with a rest time of 20 minutes at both ends of charge and discharge), parallel samples 2 to 12 are controlled and cycled to observe accelerated aging to the corresponding normal temperature starting point discharge energy value.

[0050] As an example, the parallel sample 2 is accelerated to age at the normal temperature cycle ERR interval [90%, 92%], and the corresponding normal temperature starting point discharge energy value is 838Wh; the parallel sample 3 is accelerated to age at the normal temperature cycle ERR interval [88%, 90%], and the corresponding normal temperature starting point discharge energy value is 820Wh; the parallel sample 12 is accelerated to age at the normal temperature cycle ERR interval [70%, 72%], and the corresponding normal temperature starting point discharge energy value is 656Wh.

[0051] In step S102, for parallel sample 1 and each parallel sample that has completed step S101, based on the normal temperature cycling strategy corresponding to the normal temperature cycling conditions (25°C_0.5P / 0.5P_0-100% SOC, with a rest time of 20 minutes at both ends of charge and discharge), each parallel sample is controlled to perform a cycle test until the normal temperature endpoint discharge energy value corresponding to each parallel sample is reached.

[0052] As an example, the normal temperature endpoint discharge energy value corresponding to the cycle test of parallel sample 1 to the normal temperature cycle ERR interval [92%, 100%] is 838Wh; the normal temperature endpoint discharge energy value corresponding to the cycle test of parallel sample 2 to the normal temperature cycle ERR interval [90%, 92%] is 820Wh; the normal temperature endpoint discharge energy value corresponding to the cycle test of parallel sample 12 to the normal temperature cycle ERR interval [70%, 72%] is 638Wh.

[0053] It should be noted that testing can begin simultaneously for all parallel samples. This means that control sample 1 can be tested simultaneously under normal temperature cycling conditions, while control samples 2 through 12 can be tested simultaneously under high stress acceleration conditions. Furthermore, after any parallel sample completes the high stress acceleration cycle, it can immediately begin testing in its corresponding normal temperature cycle ERR interval.

[0054] In step S103, during the cyclic hanging test process, the number of normal temperature cycles of each parallel sample is recorded, and the discharge energy value (normal temperature discharge energy value) corresponding to each normal temperature cycle number is obtained through the DC power meter to obtain the normal temperature discharge energy value sequence of all parallel samples in the normal temperature cycle ERR interval.

[0055] The sequence of normal temperature discharge energy values corresponding to each parallel sample can be expressed as {Wn(1), Wn(2), …, Wn(m), …, Wn(Nn)}, where n represents the serial number of the parallel sample. There are Nn items of data in parallel sample n. Wn(m) is the mth item in the sequence of normal temperature discharge energy values corresponding to parallel sample n, that is, the normal temperature discharge energy value with serial number m, and 1≤m≤Nn.

[0056] As an example, the normal temperature discharge energy value sequence corresponding to parallel sample 1 is {W1(1), W1(2),…, W1(m),…, W1(N1)}, and parallel sample 1 has a total of N1 data items; the normal temperature discharge energy value sequence corresponding to parallel sample 2 is {W2(1), W2(2),…, W2(m),…, W2(N2)}, and parallel sample 2 has a total of N2 data items; the normal temperature discharge energy value sequence corresponding to parallel sample 12 is {W12(1), W12(2),…, W12(m),…, W12(N12)}, and parallel sample 12 has a total of N12 data items.

[0057] like Figure 5 As shown, Figure 5 (a) is an example diagram of energy value-cycle number corresponding to the normal temperature discharge energy value sequence of parallel sample 1. Figure 5 (b) is an example diagram of energy value-cycle number corresponding to the normal temperature discharge energy value sequence corresponding to parallel sample 2. Figure 5 (c) is an example diagram of energy value-cycle number corresponding to the normal temperature discharge energy value sequence corresponding to parallel sample 12, where the horizontal axis is the cycle number (cycle) and the vertical axis is the discharge energy value (Energy / Wh).

[0058] In step S104, first, the normal temperature discharge energy value sequences are sequentially spliced according to the order of the right endpoints of the normal temperature cycle ERR intervals from large to small to obtain a normal temperature discharge energy value sequence for the entire life cycle.

[0059] In the embodiment of the present application, the right endpoints of the normal temperature cycle ERR intervals corresponding to parallel samples 1 to parallel samples 12 decrease successively, and accordingly, the normal temperature discharge energy value sequences corresponding to parallel samples 1 to parallel samples 12 are spliced in sequence to obtain the normal temperature discharge energy value sequence of the entire life cycle {W1(1), W1(2),…, W1(m),…, W1(N1), W2(1), W2(2),…, W2(m),…, W2(N2),…, W12(1), W12(2),…, W12(m),…, W12(N12)}.

[0060] In the normal temperature discharge energy value sequence for the entire life cycle, there are a total of N1+N2+…+N12 data items, among which the first data item (W2(1)) of the normal temperature discharge energy value sequence of parallel sample 2 becomes the N1+1th data item of the normal temperature discharge energy value sequence for the entire life cycle; the first data item (W3(1)) of the normal temperature discharge energy value sequence of parallel sample 3 becomes the N1+N2+1th data item of the normal temperature discharge energy value sequence for the entire life cycle, and so on, the first data item (W12(1)) of the normal temperature discharge energy value sequence of parallel sample 12 becomes the N1+N2+…+N11+1th data item of the normal temperature discharge energy value sequence for the entire life cycle.

[0061] The energy value-cycle number curve corresponding to the normal temperature discharge energy value sequence of the whole life cycle is as follows Figure 6 (a) shows the cycle number on the horizontal axis and the discharge energy value on the vertical axis (Energy / Wh). After normalizing each item in the full life cycle normal temperature discharge energy value sequence based on the initial true energy value of the battery, we can get the following: Figure 6 (b) shows a battery life degradation curve represented by energy retention rate, where the horizontal axis is the number of cycles (cycle) and the vertical axis is the energy retention rate (Energy Retention / %).

[0062] It should be noted that those skilled in the art can set the values of parameters such as the high-stress acceleration condition, the battery's end-of-life threshold, the measured end-of-life threshold, and the ERR interval step size according to actual conditions. For example, for power batteries, the end-of-life threshold can be set to 80% ERR, and for energy storage batteries, the end-of-life threshold can be set to 70% ERR; the measured end-of-life threshold of 95% ERR can be set to 96% ERR; the high-stress acceleration condition can be set to 55°C_1.5P / 1.5P_0-100% SOC, or the high-stress acceleration condition can be set to 55°C_1.0P / 1.0P_20% SOC-100% SOC; the ERR interval step size can be set to 1% ERR, etc. Those skilled in the art will understand that, without departing from the technical solution of the present application, the adjustment of these preset parameters shall constitute an equivalent technical solution and shall therefore fall within the scope of protection of the present application.

[0063] It should be pointed out that, considering that the method of the present application includes a variety of actual measurement data, such as temperature, time, discharge energy value, etc., the terms "for", "to", "equal to", etc. described in the present application that represent equal relationships can indicate that the target value and the measured value are exactly the same; or it can indicate that the target value and the measured value are approximately equal within the allowable deviation range.

[0064] In the embodiment of the present application, parallel samples 1 to 12 are set to start the cycle test synchronously. According to the high stress cycle strategy and the normal temperature cycle strategy, the longest cycle test time is parallel sample 12, which takes about 1.6 years, of which about 0.9 years under high stress accelerated conditions (refer to about 4000 cycles corresponding to 72% ERR) and about 0.6 years under normal temperature cycle conditions (refer to Figure 5 (c), approximately 1100 cycles). If a model A lithium iron battery is cycled from 100% ERR to 70% ERR (end-of-life threshold) based on a room temperature cycling strategy, it would take approximately 6 years (12,000 cycles).

[0065] From the above, it can be seen that the method of the present application can quickly obtain the life degradation curve of the battery measured under normal temperature conditions for the entire life cycle, and since the life degradation curve is entirely based on measured data, the life degradation curve can better reflect the actual trend of the battery in the later stage, and can provide strong data support for verifying whether the project life indicators of battery development have been achieved.

[0066] Furthermore, the present application also provides a device for obtaining a battery life degradation curve.

[0067] See Figure 7 , Figure 7 FIG. 1 is a main structural block diagram of a device for obtaining a battery life degradation curve according to another embodiment of the present application. Figure 7As shown, the battery life degradation curve acquisition device 7 in this embodiment includes a cyclic testing module 71 , a data acquisition module 72 and a data processing module 73 .

[0068] The cyclic testing module 71 is configured to perform the following operations: based on preset high-stress acceleration conditions, control each parallel sample to undergo cyclic testing, so that each parallel sample is accelerated to age to its corresponding normal temperature starting point discharge energy value, wherein the normal temperature starting point discharge energy value is determined based on a preset normal temperature cycle energy retention rate range that corresponds one-to-one to each parallel sample.

[0069] The cycle test module 71 is also configured to perform the following operations: based on the normal temperature cycle conditions corresponding to the normal temperature cycle life index, control each parallel sample to perform cycle test to the corresponding normal temperature endpoint discharge energy value, wherein the normal temperature endpoint discharge energy value is determined based on the normal temperature cycle energy retention rate range.

[0070] The data acquisition module 72 is configured to perform the following operations: during the cyclic hanging test process, obtain the number of normal temperature cycles of each parallel sample and the normal temperature discharge energy value corresponding to each normal temperature cycle number, and obtain the normal temperature discharge energy value sequence of the normal temperature cycle energy retention rate interval corresponding to each parallel sample.

[0071] The data processing module 73 is configured to perform the following operations: based on the normal temperature discharge energy value sequence of all normal temperature cycle energy retention rate intervals, obtain a battery life degradation curve.

[0072] Furthermore, the present application also provides a smart device.

[0073] In an embodiment of a smart device according to the present application, the smart device may include at least one processor; and a memory in communication with the at least one processor; wherein the memory stores a computer program, and when the computer program is executed by the at least one processor, the method for obtaining a battery life degradation curve described in any of the above embodiments is implemented. Figure 8 , Figure 8 exemplarily shows that the smart device 8 includes a memory 81 and a processor 82, and the memory 81 and the processor 82 are communicatively connected via a bus.

[0074] Furthermore, the present application also provides a storage medium.

[0075] In a storage medium embodiment according to the present application, the storage medium can be configured to store a program for executing the battery life degradation curve acquisition method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned battery life degradation curve acquisition method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The storage medium can be a storage device formed by various electronic devices. As an example, the storage medium in the embodiment of the present application is a non-transitory storage medium.

[0076] It should be noted that the method of this application is also applicable to non-iron lithium batteries. When non-iron lithium batteries can also undergo accelerated testing under high-stress acceleration conditions and the normal temperature cycle ERR range can be obtained based on the energy degradation trend curve determined theoretically or empirically, the life degradation curve of non-iron lithium batteries can also be obtained based on the method of this application.

[0077] It should be pointed out that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of the present application, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application, and therefore will also fall within the scope of protection of this application.

[0078] It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium can include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0079] Thus far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present application.

Claims

1. A method for obtaining a battery life degradation curve, characterized in that: The method comprises: Based on the preset high-stress acceleration conditions, each parallel sample is controlled to undergo cyclic hanging testing, so that each parallel sample is accelerated to age to its corresponding normal temperature starting point discharge energy value, wherein the normal temperature starting point discharge energy value is determined based on a preset normal temperature cycle energy retention rate range corresponding to each parallel sample; Based on the normal temperature cycle conditions corresponding to the normal temperature cycle life indicator, controlling each parallel sample to undergo cycle testing until reaching the corresponding normal temperature endpoint discharge energy value, wherein the normal temperature endpoint discharge energy value is determined based on the normal temperature cycle energy retention rate range; During the cyclic hanging test, the number of normal temperature cycles of each parallel sample and the normal temperature discharge energy value corresponding to each normal temperature cycle number are obtained to obtain a normal temperature discharge energy value sequence of the normal temperature cycle energy retention rate interval corresponding to each parallel sample; The battery life degradation curve is obtained based on the normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate interval.

2. The method for obtaining a battery life degradation curve according to claim 1, wherein: The method further comprises: determining a first energy retention rate value based on a high stress acceleration coefficient and a battery degradation trend curve, wherein the high stress acceleration coefficient is determined based on the high stress acceleration condition; All the normal temperature cycle energy retention rate intervals are determined based on the first energy retention rate value, a second energy retention rate value corresponding to the battery life end threshold value, and a preset energy retention rate interval step size.

3. The method for obtaining a battery life degradation curve according to claim 2, wherein: “Determining a first energy retention rate value based on a high stress acceleration factor and a battery degradation trend curve” includes: Determining a high stress cycle life index corresponding to the high stress acceleration condition based on the high stress acceleration coefficient and the normal temperature cycle life index; Determining the life span of the high stress battery under the high stress acceleration condition based on the high stress cycle life index; The first energy retention rate value is determined based on the high-stress battery life and the battery degradation trend curve.

4. The method for obtaining a battery life degradation curve according to claim 1, wherein: “Obtaining the battery life degradation curve based on the normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate interval” includes: According to the order of the right endpoints of the normal temperature cycle energy retention rate intervals from large to small, the normal temperature discharge energy value sequences are sequentially spliced to obtain the normal temperature discharge energy value sequence for the entire life cycle; The battery life degradation curve is obtained based on the full life cycle normal temperature discharge energy value sequence.

5. The method for obtaining a battery life degradation curve according to claim 4, wherein: “Obtaining the battery life degradation curve based on the full life cycle normal temperature discharge energy value sequence” includes: The serial number corresponding to each item in the full life cycle normal temperature discharge energy value sequence is used as the abscissa of the battery life degradation curve; Each item in the full life cycle normal temperature discharge energy value sequence is normalized and used as the vertical coordinate of the battery life degradation curve.

6. The method for obtaining a battery life degradation curve according to claim 1, wherein: The method further comprises: Determining the normal temperature starting point discharge energy value corresponding to the normal temperature cycle energy retention rate interval based on the initial true energy value of the battery and the right endpoint of each normal temperature cycle energy retention rate interval; Based on the initial real energy value of the battery and the left endpoint of each normal temperature cycle energy retention rate interval, the normal temperature endpoint discharge energy value corresponding to the normal temperature cycle energy retention rate interval is determined.

7. The method for obtaining a battery life degradation curve according to claim 1, wherein: The high stress acceleration condition includes at least one of a temperature condition, a rate condition and a DOD condition.

8. A device for obtaining a battery life degradation curve, characterized in that: The device comprises: A cyclic hang test module, wherein the cyclic hang test module is configured to perform the following operations: Based on the preset high-stress acceleration conditions, each parallel sample is controlled to undergo cyclic hanging testing, so that each parallel sample is accelerated to age to its corresponding normal temperature starting point discharge energy value, wherein the normal temperature starting point discharge energy value is determined based on a preset normal temperature cycle energy retention rate range corresponding to each parallel sample; Based on the normal temperature cycle conditions corresponding to the normal temperature cycle life indicator, controlling each parallel sample to undergo cycle testing until reaching the corresponding normal temperature endpoint discharge energy value, wherein the normal temperature endpoint discharge energy value is determined based on the normal temperature cycle energy retention rate range; A data acquisition module is configured to perform the following operations: during the cyclic hanging test, obtain the number of normal temperature cycles of each parallel sample and the normal temperature discharge energy value corresponding to each normal temperature cycle number, and obtain a normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate interval corresponding to each parallel sample; A data processing module is configured to perform the following operations: based on the normal temperature discharge energy value sequence in the normal temperature cycle energy retention rate interval, obtain the battery life degradation curve.

9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores a computer program, and when the computer program is executed by the at least one processor, the method for obtaining a battery life degradation curve according to any one of claims 1 to 7 is implemented.

10. A storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the battery life degradation curve acquisition method according to any one of claims 1 to 7.

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