Battery life prediction methods, devices and equipment

Abstract

This application provides a battery life prediction method, apparatus, and device. The method involves acquiring a battery to be processed and determining the corresponding evaluation conditions. Then, based on these evaluation conditions, a charge-discharge cycle test is performed on the battery to obtain its degradation rate. Based on the relationship curve between the number of cycles and the degradation rate, the battery life is determined. This method solves the problems of long testing cycles and low efficiency in existing battery life prediction methods, making it suitable for engineering applications. Furthermore, the embodiments of this application, based on the relationship curve between the number of cycles and the degradation rate, can predict battery life under different degradation rates, resulting in more comprehensive battery life prediction. Additionally, the embodiments of this application can also predict battery life under different evaluation conditions, meeting various application needs.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus and device for predicting battery life. Background Technology

[0002] As electric vehicles become more commercialized, a large number of automotive battery cells will be phased out in the next few years. While these power battery cells can no longer be used in electric vehicles, they can still be used in other applications with lower requirements for battery cells, such as energy storage systems.

[0003] In related technologies, when the capacity of a vehicle's power battery decays to a certain percentage of its initial capacity, such as below 80%, the significant reduction in driving range on a single charge signifies the end of the vehicle's onboard function. However, for energy storage applications with lower energy density requirements, power batteries still have considerable value. Through cascade utilization, not only can the performance of the power battery be fully utilized, but it also helps to reduce the battery's operating costs.

[0004] In existing technologies, the cascade utilization of retired batteries usually takes into account the remaining lifespan (number of cycles). However, predicting the remaining number of cycles is a complex process. Existing battery life prediction methods typically have long testing cycles, low efficiency, and are not suitable for engineering applications. Moreover, they cannot provide comprehensive predictions. Therefore, how to quickly and comprehensively predict battery lifespan has become a significant concern. Summary of the Invention

[0005] To address the problems existing in the prior art, this application provides a battery life prediction method, apparatus, and device.

[0006] In a first aspect, embodiments of this application provide a battery life prediction method, the method comprising:

[0007] Obtain the battery to be processed and determine the evaluation conditions corresponding to the battery to be processed;

[0008] According to the evaluation conditions, the battery to be treated is subjected to charge-discharge cycle test to obtain the degradation rate of the battery to be treated under the evaluation conditions.

[0009] Based on the type and model of the battery to be processed, obtain the relationship curve between the target number of cycles and the degradation rate;

[0010] Based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions, the lifespan of the battery to be treated is determined.

[0011] In one possible implementation, the step of performing a charge-discharge cycle test on the battery to be treated according to the evaluation conditions to obtain the degradation rate of the battery to be treated under the evaluation conditions includes:

[0012] Under the evaluation conditions, the battery to be treated is subjected to a preset number of charge-discharge cycle tests to determine the initial discharge capacity and the final discharge capacity of the battery to be treated.

[0013] Based on the initial discharge capacity and the final discharge capacity of the battery to be processed, calculate the percentage of the initial discharge capacity relative to the rated capacity of the battery (SOH) and the final SOH.

[0014] Based on the initial SOH and final SOH of the battery to be treated, and the preset number of times, the degradation rate of the battery to be treated under the evaluation conditions is obtained.

[0015] In one possible implementation, obtaining the degradation rate of the battery under the evaluation conditions based on the initial SOH and the final SOH of the battery to be treated, and the preset number of times, includes:

[0016] The capacitance percentage difference of the battery to be processed is determined based on the initial SOH and the final SOH of the battery to be processed.

[0017] Calculate the ratio of the capacitance percentage difference of the battery to be treated to the preset number of times, and use the ratio as the degradation rate of the battery to be treated under the evaluation conditions.

[0018] In one possible implementation, before obtaining the curve showing the relationship between the target number of cycles and the degradation rate based on the type and model of the battery to be processed, the method further includes:

[0019] Under preset experimental evaluation conditions, charge-discharge cycle tests were conducted on multiple different types and models of batteries to determine the discharge capacity of the multiple different types and models of batteries at different number of cycles.

[0020] Based on the discharge capacity of the various types and models of batteries at different cycle counts, calculate the SOH of the various types and models of batteries at different cycle counts;

[0021] Based on the State of Health (SOH) of the various types and models of batteries at different cycle counts, the degradation rate of the various types and models of batteries at different cycle counts is obtained, and based on the degradation rate of the various types and models of batteries at different cycle counts, the relationship curve between the cycle count and the degradation rate of the various types and models of batteries is determined.

[0022] In one possible implementation, obtaining the relationship curve between the target number of cycles and the degradation rate based on the type and model of the battery to be processed includes:

[0023] Obtain the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries;

[0024] Based on the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries, obtain the relationship curve between the target number of cycles and the degradation rate corresponding to the type and model of the battery to be processed.

[0025] In one possible implementation, determining the lifetime of the battery to be treated based on the relationship curve between the target number of cycles and the degradation rate, and the degradation rate of the battery under the evaluation conditions, includes:

[0026] Based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions, the number of cycles of the battery to be treated under the evaluation conditions is obtained.

[0027] The lifespan of the battery to be treated is determined based on the number of cycles performed under the evaluation conditions.

[0028] In one possible implementation, acquiring the battery to be processed includes:

[0029] Obtain pre-stored battery selection conditions, which are determined based on battery output.

[0030] According to the battery selection criteria, the battery to be processed is obtained from the retired batteries.

[0031] In one possible implementation, after acquiring the battery to be processed, the following is also included:

[0032] Determine whether the appearance of the battery to be processed meets the preset appearance requirements;

[0033] The determination of the evaluation conditions corresponding to the battery to be processed includes:

[0034] If the appearance of the battery to be processed meets the preset appearance requirements, then the evaluation conditions corresponding to the battery to be processed are determined.

[0035] Secondly, embodiments of this application provide a battery life prediction device, the device comprising:

[0036] A battery acquisition module is used to acquire the battery to be processed and determine the evaluation conditions corresponding to the battery to be processed.

[0037] The degradation rate acquisition module is used to perform charge-discharge cycle tests on the battery to be treated according to the evaluation conditions, and obtain the degradation rate of the battery to be treated under the evaluation conditions.

[0038] The relationship curve acquisition module is used to obtain the relationship curve between the target number of cycles and the decay rate based on the type and model of the battery to be processed;

[0039] The lifetime determination module is used to determine the lifetime of the battery to be treated based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions.

[0040] In one possible implementation, the attenuation rate obtaining module is specifically used for:

[0041] Under the evaluation conditions, the battery to be treated is subjected to a preset number of charge-discharge cycle tests to determine the initial discharge capacity and the final discharge capacity of the battery to be treated.

[0042] Calculate the initial SOH and final SOH of the battery to be processed based on the initial discharge capacity and the final discharge capacity of the battery to be processed.

[0043] Based on the initial SOH and final SOH of the battery to be treated, and the preset number of times, the degradation rate of the battery to be treated under the evaluation conditions is obtained.

[0044] In one possible implementation, the attenuation rate obtaining module is specifically used for:

[0045] The capacitance percentage difference of the battery to be processed is determined based on the initial SOH and the final SOH of the battery to be processed.

[0046] Calculate the ratio of the capacitance percentage difference of the battery to be treated to the preset number of times, and use the ratio as the degradation rate of the battery to be treated under the evaluation conditions.

[0047] In one possible implementation, a relationship curve determination module is also included, which is used to perform charge-discharge cycle tests on multiple different types and models of batteries under preset experimental evaluation conditions before the relationship curve obtaining module obtains the relationship curve between the target number of cycles and the decay rate according to the type and model of the battery to be processed, and to determine the discharge capacity of the multiple different types and models of batteries at different number of cycles.

[0048] Based on the discharge capacity of the various types and models of batteries at different cycle counts, calculate the SOH of the various types and models of batteries at different cycle counts;

[0049] Based on the State of Health (SOH) of the various types and models of batteries at different cycle counts, the degradation rate of the various types and models of batteries at different cycle counts is obtained, and based on the degradation rate of the various types and models of batteries at different cycle counts, the relationship curve between the cycle count and the degradation rate of the various types and models of batteries is determined.

[0050] In one possible implementation, the relationship curve acquisition module is specifically used for:

[0051] Obtain the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries;

[0052] Based on the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries, obtain the relationship curve between the target number of cycles and the degradation rate corresponding to the type and model of the battery to be processed.

[0053] In one possible implementation, the lifetime determination module is specifically used for:

[0054] Based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions, the number of cycles of the battery to be treated under the evaluation conditions is obtained.

[0055] The lifespan of the battery to be treated is determined based on the number of cycles performed under the evaluation conditions.

[0056] In one possible implementation, the battery acquisition module is specifically used for:

[0057] Obtain pre-stored battery selection conditions, which are determined based on battery output.

[0058] According to the battery selection criteria, the battery to be processed is obtained from the retired batteries.

[0059] In one possible implementation, the battery acquisition module is further configured to:

[0060] Determine whether the appearance of the battery to be processed meets the preset appearance requirements;

[0061] If the appearance of the battery to be processed meets the preset appearance requirements, then the evaluation conditions corresponding to the battery to be processed are determined.

[0062] Thirdly, embodiments of this application provide a battery life prediction device, comprising:

[0063] processor;

[0064] Memory; and

[0065] Computer programs;

[0066] The computer program is stored in the memory and configured to be executed by the processor, the computer program including instructions for performing the method as described in the first aspect.

[0067] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that causes a server to perform the method described in the first aspect.

[0068] Fifthly, embodiments of this application provide a computer program product, including computer instructions, which are executed by a processor according to the method described in the first aspect.

[0069] The battery life prediction method, apparatus, and device provided in this application provide a method for predicting battery life. This method acquires a battery to be processed and determines the corresponding evaluation conditions. Then, based on these evaluation conditions, it performs charge-discharge cycle tests on the battery to obtain its degradation rate. Based on the relationship curve between the number of cycles and the degradation rate, the lifespan of the battery to be processed is determined. This method solves the problems of long testing cycles and low efficiency in existing battery life prediction methods, making it suitable for engineering applications. Furthermore, based on the relationship curve between the number of cycles and the degradation rate, this application can predict battery life under different degradation rates, making battery life prediction more comprehensive. In addition, this application can also predict the lifespan of batteries under different evaluation conditions, meeting various application needs. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a schematic diagram of the battery life prediction system architecture provided in an embodiment of this application;

[0072] Figure 2 A schematic flowchart illustrating a battery life prediction method provided in an embodiment of this application;

[0073] Figure 3 A flowchart illustrating another battery life prediction method provided in this application embodiment;

[0074] Figure 4 A schematic diagram of the relationship between the number of cycles and SOH of a battery provided in an embodiment of this application;

[0075] Figure 5 This is a schematic diagram of the structure of a battery life prediction device provided in an embodiment of this application;

[0076] Figure 6 This is a schematic diagram of another battery life prediction device provided in an embodiment of this application;

[0077] Figure 7A A schematic diagram of the basic hardware architecture of a battery life prediction device provided in this application embodiment;

[0078] Figure 7B This is a schematic diagram of the basic hardware architecture of a battery life prediction device provided in an embodiment of this application. Detailed Implementation

[0079] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0080] The terms “first,” “second,” “third,” and “fourth,” etc. (if present), in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0081] The trend towards intelligent and electric vehicles (passenger cars, commercial vehicles, construction machinery vehicles, etc.) is becoming increasingly evident, and the penetration rate of new energy vehicles continues to rise. During use, batteries gradually age, leading to a decline in their lifespan and affecting vehicle performance, thus giving rise to the issue of battery retirement. How to reuse the remaining value of retired batteries is crucial to achieving a closed-loop system for green battery products.

[0082] In related technologies, retired batteries can be reused in a tiered manner based on their remaining value, such as in electric two-wheeled vehicles, communication base stations, and small energy storage devices. When judging the remaining value of batteries for tiered reuse, their remaining lifespan (number of cycles) in different application scenarios is a very critical indicator.

[0083] Currently, there are many methods for determining the number of battery cycles, but these methods suffer from long testing cycles, low efficiency, and are unsuitable for engineering applications. Furthermore, they cannot provide comprehensive predictions. For example, existing methods for determining the number of battery cycles involve conducting experimental tests on different battery models to calculate the average capacity decay rate of that model under different conditions. This allows for the calculation of the remaining lifespan based on the current usable capacity of the battery with an unknown lifespan. However, these methods require a large amount of test data, have long testing cycles, and can only calculate the average capacity decay rate, failing to provide a comprehensive prediction of battery lifespan.

[0084] To address the aforementioned issues, this application proposes a battery life prediction method. Based on the relationship curve between cycle number and degradation rate, it predicts battery life without requiring extensive testing, thus resolving the problems of long testing cycles and low efficiency in existing battery life prediction methods, making it suitable for engineering applications. Furthermore, based on the relationship curve between cycle number and degradation rate, this application can predict battery life at different degradation rates, resulting in a more comprehensive battery life prediction.

[0085] Optionally, the battery life prediction method provided in this application embodiment can be applied to, for example, Figure 1 The battery life prediction system shown. (As shown in the image) Figure 1 As shown, the system may include a receiving device 101, a processing device 102, and a display device 103.

[0086] In the specific implementation process, the receiving device 101 can be an input / output interface or a communication interface, and can be used for relevant battery information.

[0087] The processing device 102 can determine the battery to be processed based on the relevant battery information received by the receiving device 101. Furthermore, based on the relationship curve between the number of cycles and the degradation rate, it can predict the lifespan of the battery to be processed, solving the problem of long testing cycles and low efficiency in existing battery life prediction methods, making it suitable for engineering applications. Moreover, the embodiments of this application, based on the relationship curve between the number of cycles and the degradation rate, can predict battery lifespan at different degradation rates, improving the comprehensiveness of battery life prediction.

[0088] The display device 103 can be used to display the relationship curve between the number of cycles and the decay rate, the lifespan of the battery to be processed, etc.

[0089] The display device can also be a touch screen, used to receive user commands while displaying the above content, so as to achieve interaction with the user.

[0090] It should be understood that the above-mentioned processing device can be implemented by a processor reading instructions from memory and executing those instructions, or it can be implemented by a chip circuit.

[0091] The above system is only an example system. In specific implementation, it can be set up according to application requirements.

[0092] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the architecture of the battery life prediction system. In other feasible embodiments of this application, the above architecture may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of both.

[0093] Furthermore, the system architecture described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0094] The technical solutions of this application are described below using several embodiments as examples. The same or similar concepts or processes may not be repeated in some embodiments.

[0095] Figure 2 This is a flowchart illustrating a battery life prediction method provided in an embodiment of this application. The execution entity of this embodiment can be... Figure 1 The processing device in the process can be specifically executed based on the actual application scenario, and this application embodiment does not impose any particular restrictions on this. For example Figure 2 As shown, the battery life prediction method provided in this application embodiment may include the following steps:

[0096] S201: Obtain the battery to be processed and determine the evaluation conditions corresponding to the battery to be processed.

[0097] The batteries to be processed can be determined based on actual conditions. For example, the processing device can acquire pre-stored battery selection criteria, which are determined based on battery output. Then, based on these criteria, the device can select the batteries to be processed from retired batteries. For instance, the processing device can select batteries with higher mileage from retired logistics vehicles, batteries with higher usage frequency from retired light engineering machinery vehicles such as high-speed motors, or batteries with higher charge / discharge capacity from retired energy storage vehicles, etc.

[0098] Here, after acquiring the battery to be processed, the processing device can further determine whether the appearance of the battery to be processed meets the preset appearance requirements. If the appearance of the battery to be processed meets the preset appearance requirements, the processing device determines the evaluation conditions corresponding to the battery to be processed.

[0099] In this embodiment, the aforementioned preset appearance conditions can be determined based on actual conditions, such as no leakage. The processing device can acquire an image of the appearance of the battery to be processed, and determine whether the appearance of the battery meets the preset appearance requirements based on the image. Specifically, the processing device can input the appearance image into a preset judgment model, which is used to determine whether the appearance of the battery meets the preset appearance requirements. Based on the output of the judgment model, it can then determine whether the appearance of the battery meets the preset appearance requirements.

[0100] Furthermore, the aforementioned evaluation conditions can also be determined based on actual circumstances. The processing device can set different evaluation conditions according to different application scenarios; for example, in a scenario where retired batteries are used as energy storage batteries, evaluation condition one could be set. The processing device determines the evaluation conditions corresponding to the battery to be processed based on the application scenario.

[0101] S202: Based on the above evaluation conditions, the battery to be treated is subjected to charge-discharge cycle tests to obtain the degradation rate of the battery to be treated under the above evaluation conditions.

[0102] Here, the aforementioned processing device can perform a preset number of charge-discharge cycle tests on the battery to be processed under the aforementioned evaluation conditions to determine the initial discharge capacity and the final discharge capacity of the battery to be processed. Then, based on the initial discharge capacity and the final discharge capacity, the initial SOH and the final SOH of the battery to be processed are calculated. Thus, based on the initial SOH and the final SOH, and the aforementioned preset number of cycles, the degradation rate of the battery to be processed under the aforementioned evaluation conditions is obtained.

[0103] Wherein, SOH represents the percentage of the battery's current discharge capacity relative to its rated capacity. The preset number of cycles can be determined based on actual conditions, for example, 50 cycles.

[0104] The aforementioned processing device performs a preset number of charge-discharge cycle tests on the battery to be processed, determines the initial discharge capacity and the final discharge capacity of the battery, calculates the percentage of the initial discharge capacity relative to the battery's rated capacity, and the percentage of the final discharge capacity relative to the battery's rated capacity, and then determines the initial state of discharge (SOH) and the final state of discharge (SOH) of the battery to be processed. Further, the processing device can determine the capacitance percentage difference of the battery to be processed based on the initial SOH and the final SOH, then calculate the ratio of this capacitance percentage difference to the preset number of cycles, and use this ratio as the degradation rate of the battery to be processed under the aforementioned evaluation conditions.

[0105] For example, if a battery has a rated capacity of 160Ah, the above-mentioned processing device performs 50 charge-discharge cycle tests at 0.5C and 80% DOD (10%~90% SOC) under an ambient temperature of 25℃. The initial discharge capacity (130Ah) and the final discharge capacity (129Ah) after the battery is fully charged are determined. The percentage of the initial discharge capacity relative to the battery's rated capacity and the percentage of the final discharge capacity relative to the battery's rated capacity are then calculated. The initial SOH and the final SOH are determined to be 81.25% and 80.625%, respectively. The capacitance percentage difference under these conditions is calculated, and the ratio of this capacitance percentage difference to the number of cycles is used as the degradation rate, i.e., the degradation rate is 0.0125% / cycle.

[0106] S203: Based on the type and model of the battery to be processed, obtain the curve showing the relationship between the target number of cycles and the decay rate.

[0107] In this embodiment of the application, the processing device can pre-store the relationship curves between the number of cycles and the decay rate of multiple batteries of different types and models, and thus, based on the relationship curves, obtain the relationship curve between the target number of cycles and the decay rate corresponding to the type and model of the battery to be processed.

[0108] S204: Based on the above-mentioned curve showing the relationship between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the above-mentioned evaluation conditions, determine the lifespan of the battery to be treated.

[0109] Here, the processing device can obtain the number of cycles of the battery under the evaluation conditions based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery under the evaluation conditions. Then, based on the number of cycles, it can determine the lifespan of the battery under the evaluation conditions.

[0110] This application embodiment obtains a battery to be processed and determines the corresponding evaluation conditions. Then, based on these evaluation conditions, it performs charge-discharge cycle tests on the battery to obtain its degradation rate. Based on the relationship curve between the number of cycles and the degradation rate, the lifespan of the battery to be processed is determined. This solves the problems of long testing cycles and low efficiency in existing battery life prediction methods, making it suitable for engineering applications. Furthermore, this application embodiment, based on the relationship curve between the number of cycles and the degradation rate, can predict battery lifespan under different degradation rates, making battery life prediction more comprehensive. In addition, this application embodiment can also predict the lifespan of batteries under different evaluation conditions, meeting various application needs.

[0111] Furthermore, before obtaining the relationship curve between the target number of cycles and the degradation rate based on the type and model of the battery to be processed, the aforementioned processing device also considers conducting charge-discharge cycle tests on multiple different types and models of batteries under preset experimental evaluation conditions to determine the discharge capacity of multiple different types and models of batteries at different number of cycles. Then, based on the discharge capacity of multiple different types and models of batteries at different number of cycles, the state of charge (SOH) of multiple different types and models of batteries at different number of cycles is calculated. Based on the SOH of multiple different types and models of batteries at different number of cycles, the degradation rate of multiple different types and models of batteries at different number of cycles is obtained. Based on the degradation rate, the relationship curve between the number of cycles and the degradation rate of multiple different types and models of batteries is determined, thus quickly establishing the relationship curve between the number of cycles and the degradation rate, shortening the subsequent battery life prediction cycle. Moreover, by comprehensively considering the characteristics of battery degradation (degradation situation in different stages), the remaining battery life curve can be predicted, improving the accuracy of battery life prediction. Figure 3 This is a flowchart illustrating another battery life prediction method proposed in an embodiment of this application. Figure 3 As shown, the method includes:

[0112] S301: Obtain the battery to be processed and determine the evaluation conditions corresponding to the battery to be processed.

[0113] S302: Based on the above evaluation conditions, the battery to be treated is subjected to charge-discharge cycle tests to obtain the degradation rate of the battery to be treated under the evaluation conditions.

[0114] The implementation of steps S301-S302 is the same as that of steps S201-S202 above, and will not be repeated here.

[0115] S303: Under preset experimental evaluation conditions, charge-discharge cycle tests are conducted on multiple different types and models of batteries to determine the discharge capacity of these multiple different types and models of batteries at different cycle numbers.

[0116] The aforementioned preset experimental evaluation conditions can be determined based on actual conditions. For example, taking a retired battery from a logistics vehicle as an example, the battery module has a rated capacity of 160Ah, and its normal operating conditions are -30℃ to 60℃, a maximum charge / discharge current of 1C at a high temperature of 55℃, and a maximum depth of charge / discharge of 100% DOD. The aforementioned preset experimental evaluation conditions can be a charge / discharge cycle test at 1C and 100% DOD under an ambient temperature of 55℃. The aforementioned processing device performs a charge / discharge cycle test at 1C and 100% DOD under an ambient temperature of 55℃ to determine the discharge capacity after a full charge (136Ah), the discharge capacity after 500 cycles (104Ah), and the discharge capacity after 800 cycles (88Ah).

[0117] Here, the aforementioned processing device performs cyclic charge-discharge tests at high temperature and high rate (the battery decays faster under high temperature and high rate testing) to shorten the battery evaluation cycle.

[0118] S304: Based on the discharge capacity of the above-mentioned batteries of different categories and models at different cycle counts, calculate the SOH of the above-mentioned batteries of different categories and models at the above-mentioned different cycle counts.

[0119] For example, the processing device calculates the percentage of discharge capacity relative to the rated capacity of the batteries of the various types and models at different cycle numbers, thereby determining the SOH of the batteries of the various types and models at the different cycle numbers.

[0120] For example, the aforementioned processing device calculates the percentage of the battery's rated capacity relative to its full charge discharge capacity (136 Ah), discharge capacity after 500 cycles (104 Ah), and discharge capacity after 800 cycles (88 Ah), thereby determining the initial SOH (State of Harm) of the battery at 85%, 65%, and 55% at 800, 800, and 1000 cycles, respectively. For example, as... Figure 4 As shown, Figure 4 The graph presents a relationship between the number of battery cycles and state of equilibrium (SOH). The graph shows the relationship between the number of battery cycles and SOH under two conditions: one under specific conditions (corresponding to the longer curve in the graph), and the other under accelerated aging conditions (corresponding to the shorter curve in the graph). Here, the specific conditions can be determined based on actual circumstances, while accelerated aging conditions can be high temperature and high rate conditions, such as the 1C, 100% DOD charge-discharge cycle test conducted at an ambient temperature of 55℃ as mentioned above.

[0121] S305: Based on the SOH of the above-mentioned batteries of different types and models at different cycle numbers, obtain the degradation rate of the above-mentioned batteries of different types and models at different cycle numbers, and determine the relationship curve between the cycle number and degradation rate of the above-mentioned batteries of different types and models based on the degradation rate of the above-mentioned batteries of different types and models at different cycle numbers.

[0122] Here, the aforementioned processing device can first determine the initial SOH of the battery from the SOH of the multiple different types and models of batteries at the different number of cycles, then calculate the capacitance percentage difference between the initial SOH and the other remaining SOH, and then calculate the ratio of the capacitance percentage difference to the corresponding number of cycles. This ratio is used as the decay rate at the corresponding number of cycles, thereby determining the relationship curve between the number of cycles and the decay rate of the battery.

[0123] S306: Based on the relationship curves between the number of cycles and the degradation rate of the batteries of different categories and models mentioned above, obtain the relationship curve between the target number of cycles and the degradation rate corresponding to the category and model of the battery to be processed mentioned above.

[0124] S307: Based on the above-mentioned curve showing the relationship between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the above-mentioned evaluation conditions, determine the lifespan of the battery to be treated.

[0125] The implementation of steps S306-S307 is similar to that of steps S203-S204 above, and will not be described again here.

[0126] This application's embodiments can quickly establish a curve showing the relationship between the number of cycles and the degradation rate, thereby shortening the subsequent battery life prediction cycle. Furthermore, this application's embodiments comprehensively consider the characteristics of battery degradation (degradation at different stages), enabling the prediction of the remaining battery life curve and improving the accuracy of battery life prediction. Additionally, this application's embodiments can also predict battery life under different evaluation conditions, meeting various application needs.

[0127] Corresponding to the battery life prediction method in the above embodiments, Figure 5 This is a schematic diagram of the battery life prediction device provided in an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. Figure 5This is a schematic diagram of a battery life prediction device provided in an embodiment of this application. The battery life prediction device 50 includes: a battery acquisition module 501, a degradation rate acquisition module 502, a relationship curve acquisition module 503, and a lifespan determination module 504. The battery life prediction device here can be the aforementioned processing device itself, or a chip or integrated circuit that implements the functions of the processing device. It should be noted that the division of the battery acquisition module, degradation rate acquisition module, relationship curve acquisition module, and lifespan determination module is only a logical functional division; physically, they can be integrated or independent.

[0128] The battery acquisition module 501 is used to acquire the battery to be processed and determine the evaluation conditions corresponding to the battery to be processed.

[0129] The degradation rate acquisition module 502 is used to perform charge-discharge cycle tests on the battery to be treated according to the evaluation conditions, and obtain the degradation rate of the battery to be treated under the evaluation conditions.

[0130] The relationship curve acquisition module 503 is used to obtain the relationship curve between the target number of cycles and the decay rate according to the type and model of the battery to be processed.

[0131] The lifetime determination module 504 is used to determine the lifetime of the battery to be treated based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions.

[0132] In one possible implementation, the attenuation rate obtaining module 502 is specifically used for:

[0133] Under the evaluation conditions, the battery to be treated is subjected to a preset number of charge-discharge cycle tests to determine the initial discharge capacity and the final discharge capacity of the battery to be treated.

[0134] Calculate the initial SOH and final SOH of the battery to be processed based on the initial discharge capacity and the final discharge capacity of the battery to be processed.

[0135] Based on the initial SOH and final SOH of the battery to be treated, and the preset number of times, the degradation rate of the battery to be treated under the evaluation conditions is obtained.

[0136] In one possible implementation, the attenuation rate obtaining module 502 is specifically used for:

[0137] The capacitance percentage difference of the battery to be processed is determined based on the initial SOH and the final SOH of the battery to be processed.

[0138] Calculate the ratio of the capacitance percentage difference of the battery to be treated to the preset number of times, and use the ratio as the degradation rate of the battery to be treated under the evaluation conditions.

[0139] In one possible implementation, the lifetime determination module 504 is specifically used for:

[0140] Based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions, the number of cycles of the battery to be treated under the evaluation conditions is obtained.

[0141] The lifespan of the battery to be treated is determined based on the number of cycles performed under the evaluation conditions.

[0142] In one possible implementation, the battery acquisition module 501 is specifically used for:

[0143] Obtain pre-stored battery selection conditions, which are determined based on battery output.

[0144] According to the battery selection criteria, the battery to be processed is obtained from the retired batteries.

[0145] In one possible implementation, the battery acquisition module 501 is further configured to:

[0146] Determine whether the appearance of the battery to be processed meets the preset appearance requirements;

[0147] If the appearance of the battery to be processed meets the preset appearance requirements, then the evaluation conditions corresponding to the battery to be processed are determined.

[0148] The apparatus provided in this application embodiment can be used to perform the above-described... Figure 2 The technical solutions of the method embodiments described above are similar in implementation principle and technical effect, and will not be repeated here.

[0149] Figure 6 This is a schematic diagram of another battery life prediction device provided in an embodiment of this application. Figure 5 Based on the embodiment shown, the battery life prediction device 50 further includes a relationship curve determination module 505.

[0150] In one possible implementation, the relationship curve determination module 505 is used to perform charge-discharge cycle tests on multiple different types and models of batteries under preset experimental evaluation conditions before the relationship curve acquisition module 503 obtains the relationship curve between the target cycle number and the decay rate according to the type and model of the battery to be processed, and to determine the discharge capacity of the multiple different types and models of batteries at different cycle numbers.

[0151] Based on the discharge capacity of the various types and models of batteries at different cycle counts, calculate the SOH of the various types and models of batteries at different cycle counts;

[0152] Based on the State of Health (SOH) of the various types and models of batteries at different cycle counts, the degradation rate of the various types and models of batteries at different cycle counts is obtained, and based on the degradation rate of the various types and models of batteries at different cycle counts, the relationship curve between the cycle count and the degradation rate of the various types and models of batteries is determined.

[0153] In one possible implementation, the relationship curve obtaining module 503 is specifically used for:

[0154] Obtain the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries;

[0155] Based on the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries, obtain the relationship curve between the target number of cycles and the degradation rate corresponding to the type and model of the battery to be processed.

[0156] The apparatus provided in this application embodiment can be used to perform the above-described... Figure 3 The technical solutions of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0157] Optionally, Figure 7A and 7B A schematic diagram of a possible basic hardware architecture for the battery life prediction device described in this application is provided.

[0158] See Figure 7A and 7B The battery life prediction device 700 includes at least one processor 701 and a communication interface 703. Optionally, it may also include a memory 702 and a bus 704.

[0159] In the battery life prediction device 700, the number of processors 701 can be one or more. Figure 7A and 7BOnly one processor 701 is illustrated. Optionally, processor 701 can be a central processing unit (CPU), a graphics processing unit (GPU), or a digital signal processor (DSP). If the battery life prediction device 700 has multiple processors 701, the types of the multiple processors 701 can be different or the same. Optionally, the multiple processors 701 of the battery life prediction device 700 can also be integrated into a multi-core processor.

[0160] The memory 702 stores computer instructions and data; the memory 702 may store computer instructions and data required to implement the battery life prediction method provided in this application, for example, the memory 702 stores instructions for implementing the steps of the battery life prediction method. The memory 702 may be any one or any combination of the following storage media: non-volatile memory (e.g., read-only memory (ROM), solid-state drive (SSD), hard disk drive (HDD), optical disk), volatile memory.

[0161] The communication interface 703 can provide information input / output for the at least one processor. It may also include any one or any combination of the following devices: a network interface (e.g., an Ethernet interface), a wireless network card, or other devices with network access capabilities.

[0162] Optionally, the communication interface 703 can also be used for data communication between the battery life prediction device 700 and other computing devices or terminals.

[0163] Further optional, Figure 7A and 7B Bus 704 is represented by a thick line. Bus 704 connects processor 701 to memory 702 and communication interface 703. In this way, through bus 704, processor 701 can access memory 702 and can also use communication interface 703 to exchange data with other computing devices or terminals.

[0164] In this application, the battery life prediction device 700 executes computer instructions in the memory 702, causing the battery life prediction device 700 to implement the battery life prediction method provided in this application, or causing the battery life prediction device 700 to deploy the battery life prediction apparatus described above.

[0165] From the perspective of logical functional division, for example, such as Figure 7AAs shown, the memory 702 may include a battery acquisition module 501, a degradation rate acquisition module 502, a relationship curve acquisition module 503, and a lifespan determination module 504. This inclusion refers only to the fact that the instructions stored in the memory, when executed, can respectively implement the functions of the battery acquisition module, degradation rate acquisition module, relationship curve acquisition module, and lifespan determination module, and is not limited to the physical structure.

[0166] For example, such as Figure 7B As shown, the memory 702 may include a relation curve determination module 505. This inclusion refers only to the ability of the memory to implement the function of the relation curve determination module when the instructions stored in the memory are executed, and is not limited to the physical structure.

[0167] In addition, the aforementioned battery life prediction device can, in addition to the above... Figure 7A and 7B Besides being implemented through software, it can also be implemented as a hardware module or as a circuit unit through hardware.

[0168] This application provides a computer-readable storage medium, the computer program product including computer instructions that instruct a computing device to execute the battery life prediction method provided in this application.

[0169] This application provides a computer program product, including computer instructions, which are executed by a processor using the above-described battery life prediction method.

[0170] This application provides a chip including at least one processor and a communication interface, wherein the communication interface provides information input and / or output to the at least one processor. Furthermore, the chip may also include at least one memory for storing computer instructions. The at least one processor is used to invoke and execute the computer instructions to perform the battery life prediction method provided in this application.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

Claims

1. A method for predicting battery life, characterized in that, include: Obtain the battery to be processed and determine the evaluation conditions corresponding to the battery to be processed. The evaluation conditions are determined according to the application requirements of the battery to be processed. The evaluation conditions include the charge / discharge rate, depth of charge / discharge and ambient temperature of the battery. According to the evaluation conditions, the battery to be treated is subjected to charge-discharge cycle test to obtain the degradation rate of the battery to be treated under the evaluation conditions. Based on the type and model of the battery to be processed, obtain the relationship curve between the target number of cycles and the degradation rate; Based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions, the lifespan of the battery to be treated is determined. Before obtaining the curve showing the relationship between the target number of cycles and the degradation rate based on the type and model of the battery to be processed, the method further includes: Under preset experimental evaluation conditions, charge-discharge cycle tests were conducted on multiple different types and models of batteries to determine the discharge capacity of the multiple different types and models of batteries at different number of cycles. Based on the discharge capacity of the various types and models of batteries at different cycle counts, calculate the percentage of the discharge capacity of the various types and models of batteries at different cycle counts relative to the rated capacity of the batteries; Based on the percentage of discharge capacity relative to the rated capacity of the batteries of different categories and models at different cycle counts, the degradation rate of the batteries of different categories and models at different cycle counts is obtained, and the relationship curve between the cycle count and degradation rate of the batteries of different categories and models is determined according to the degradation rate of the batteries of different categories and models at different cycle counts. The step of obtaining the relationship curve between the target number of cycles and the degradation rate based on the type and model of the battery to be processed includes: Obtain the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries; Based on the relationship curves between the number of cycles and the decay rate of the multiple different types and models of batteries, a target relationship curve between the number of cycles and the decay rate corresponding to the type and model of the battery to be processed is obtained. After acquiring the battery to be processed, the process further includes: Determine whether the appearance of the battery to be processed meets the preset appearance requirements; The determination of the evaluation conditions corresponding to the battery to be processed includes: If the appearance of the battery to be processed meets the preset appearance requirements, then the evaluation conditions corresponding to the battery to be processed are determined.

2. The method according to claim 1, characterized in that, The step of performing charge-discharge cycle tests on the battery to be treated according to the evaluation conditions to obtain the degradation rate of the battery to be treated under the evaluation conditions includes: Under the evaluation conditions, the battery to be treated is subjected to a preset number of charge-discharge cycle tests to determine the initial discharge capacity and the final discharge capacity of the battery to be treated. Based on the initial discharge capacity and the final discharge capacity of the battery to be processed, calculate the percentage of the initial discharge capacity relative to the rated capacity of the battery and the percentage of the final discharge capacity relative to the rated capacity of the battery. Based on the percentage of the initial discharge capacity of the battery to be treated relative to the rated capacity of the battery and the percentage of the final discharge capacity relative to the rated capacity of the battery, as well as the preset number of discharges, the degradation rate of the battery to be treated under the evaluation conditions is obtained.

3. The method according to claim 2, characterized in that, The method of obtaining the degradation rate of the battery under the evaluation conditions based on the percentage of the initial discharge capacity relative to the rated capacity and the percentage of the final discharge capacity relative to the rated capacity, and the preset number of discharges, includes: The capacitance percentage difference of the battery to be processed is determined based on the percentage of the initial discharge capacity relative to the rated capacity of the battery and the percentage of the final discharge capacity relative to the rated capacity of the battery. Calculate the ratio of the capacitance percentage difference of the battery to be treated to the preset number of times, and use the ratio as the degradation rate of the battery to be treated under the evaluation conditions.

4. The method according to any one of claims 1 to 3, characterized in that, The determination of the lifespan of the battery to be treated based on the relationship curve between the target number of cycles and the degradation rate, and the degradation rate of the battery under the evaluation conditions, includes: Based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions, the number of cycles of the battery to be treated under the evaluation conditions is obtained. The lifespan of the battery to be treated is determined based on the number of cycles performed under the evaluation conditions.

5. The method according to any one of claims 1 to 3, characterized in that, The process of acquiring the battery to be processed includes: Obtain pre-stored battery selection conditions, which are determined based on battery output. According to the battery selection criteria, the battery to be processed is obtained from the retired batteries.

6. A battery life prediction device, characterized in that, include: A battery acquisition module is used to acquire a battery to be processed and determine the evaluation conditions corresponding to the battery to be processed. The evaluation conditions are determined according to the application requirements of the battery to be processed. The evaluation conditions include the battery's charge / discharge rate, depth of charge / discharge, and ambient temperature. The degradation rate acquisition module is used to perform charge-discharge cycle tests on the battery to be treated according to the evaluation conditions, and obtain the degradation rate of the battery to be treated under the evaluation conditions. The relationship curve acquisition module is used to obtain the relationship curve between the target number of cycles and the decay rate based on the type and model of the battery to be processed; A lifetime determination module is used to determine the lifetime of the battery to be treated based on the relationship curve between the target number of cycles and the decay rate, and the decay rate of the battery to be treated under the evaluation conditions. Before obtaining the relationship curve between the target number of cycles and the degradation rate based on the type and model of the battery to be processed, the relationship curve determination module is further configured to perform charge-discharge cycle tests on multiple different types and models of batteries under preset experimental evaluation conditions to determine the discharge capacity of the multiple different types and models of batteries at different number of cycles; calculate the percentage of the discharge capacity of the multiple different types and models of batteries at different number of cycles relative to the rated capacity of the battery based on the discharge capacity of the multiple different types and models of batteries at different number of cycles relative to the rated capacity of the battery; obtain the degradation rate of the multiple different types and models of batteries at different number of cycles based on the percentage of the discharge capacity of the multiple different types and models of batteries at different number of cycles, and determine the relationship curve between the number of cycles and the degradation rate of the multiple different types and models of batteries based on the degradation rate of the multiple different types and models of batteries at different number of cycles; The relationship curve acquisition module is specifically used to obtain the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries; and to obtain the relationship curve between the target number of cycles and the degradation rate corresponding to the type and model of the battery to be processed based on the relationship curves between the number of cycles and the degradation rate of the multiple different types and models of batteries. The battery acquisition module is further configured to determine whether the appearance of the battery to be processed meets the preset appearance requirements; if the appearance of the battery to be processed meets the preset appearance requirements, then the evaluation conditions corresponding to the battery to be processed are determined.

7. A battery life prediction device, characterized in that, include: processor; Memory; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor, the computer program including instructions for performing the method as described in any one of claims 1-5.

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