Aging test method and system based on old battery

Abstract

The invention relates to the technical field of battery recovery test, and discloses an old battery aging test method and system, and the method comprises the steps: collecting subject objective attribute data of old batteries in stock of the same specification, and the subject objective attribute data comprises the brand, model, delivery time, initial capacity and internal resistance of the batteries; meanwhile, subjective use environment data of the battery are collected, the subjective use environment data comprise the use temperature range, the number of times of charging and discharging, the charging and discharging rate and use scenes, and the use scenes comprise an electric vehicle and an energy storage system; constructing a multi-dimensional accelerated aging test model by using the subject objective attribute data and the subjective use environment data; in the model, corresponding temperature acceleration factors are set according to different use temperature ranges, corresponding circulation acceleration factors are set according to the charging and discharging times and the charging and discharging multiplying power, and an accelerated aging test is carried out on the old battery. According to the invention, the test accuracy can be improved by simulating the aging process in various actual use scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery recycling testing, in particular to an aging test method and system based on old batteries. BACKGROUND

[0002] In today's energy field, the application of batteries is extremely wide, from daily electronic equipment to large-scale energy storage systems, from electric vehicles to distributed energy projects, the development of battery technology is driving the transformation of various industries. With the rapid development of the new energy industry, the use of battery packs has increased dramatically, and in many projects, battery packs are facing replacement or shelving due to technological development, performance not meeting standards, or local defects. Among these replaced or shelved battery packs, there are a large number of old batteries with intact single cell functions, with a capacity retention rate of up to 80% or more, still having high use value. However, the traditional treatment method for such old batteries has many obvious shortcomings. Common practices such as direct disposal or simple storage shelving not only fail to fully consider the recycling value of old batteries, but also cause a series of problems. A large number of old batteries idle occupy a large amount of valuable storage space, greatly increasing the cost of inventory management; each battery production consumes raw materials and energy, and reusable batteries being idle is a great waste of resources, which does not conform to the concept of circular economy development and exacerbates the resource shortage situation; if the discarded batteries are not properly treated, they will also cause potential pollution to the environment. The existing battery aging test method either has a long test cycle and low efficiency, making it difficult to meet the demand for rapid evaluation of the performance of old batteries, or lacks test accuracy, and cannot accurately simulate the complex aging process of the battery in actual application, resulting in inaccurate evaluation of the performance of the battery and prediction of the service life. In terms of understanding the aging mechanism of the battery, the existing technology is also difficult to achieve deep understanding by monitoring the internal microstructure changes and performance degradation characteristics of the battery in real time. Therefore, it is urgent to develop a method and system that can quickly and accurately evaluate the performance of old batteries, deeply understand the aging mechanism of the battery, and effectively realize the secondary use of old batteries, which is also the technical problem that the present application aims to solve, and the technical goal is to achieve efficient and accurate testing of old batteries and promote green use of old batteries. SUMMARY

[0003] The present application aims to provide an aging test method based on old batteries to solve the problem that the existing method cannot achieve deep testing by monitoring the internal microstructure changes and performance degradation characteristics of the battery in real time.

[0004] To solve the above technical problems, the present application adopts the following technical solutions: Scheme one: a kind of based on old battery aging test method, comprising the following steps: the main objective attribute data of the same specification inventory old battery is collected, the main objective attribute data includes the brand, model, factory time, initial capacity, internal resistance of battery;The subjective use environment data of battery is collected simultaneously, the subjective use environment data includes the use temperature range, charge-discharge times, charge-discharge rate, use scene, the use scene includes electric vehicle and energy storage system;The main objective attribute data and subjective use environment data are used to construct a multi-dimensional accelerated aging test model;In the model, according to different use temperature range setting corresponding temperature acceleration factor, according to charge-discharge times and charge-discharge rate set corresponding cycle acceleration factor, the old battery is accelerated aging test.

[0005] Beneficial effect: by collecting the main objective attribute data and subjective use environment data of battery, it can provide rich and comprehensive data basis for subsequent accurate simulation of complex aging process of battery in actual application, so that the test result is more close to the real aging condition of battery. The construction of multi-dimensional accelerated aging test model combines a variety of key factors affecting battery aging and sets corresponding acceleration factor, which can greatly shorten the test cycle, obtain battery aging data in a short time and improve test efficiency. At the same time, through simulating the aging process under a variety of actual use scenes, the test accuracy is improved.

[0006] Preferably, in the process of accelerated aging test, the internal microstructure changes of battery are monitored in real time, including electrode material crystal structure change, electrolyte composition change and electrode and electrolyte interface film change;The performance degradation characteristics of battery are monitored simultaneously, including capacity attenuation, internal resistance increase and charge-discharge efficiency reduction, and the monitoring data are fed back to the multi-dimensional accelerated aging test model in real time, and the model is optimized and adjusted.

[0007] Beneficial effect: real-time monitoring of internal microstructure changes and performance degradation characteristics of battery and feedback optimization test model can help to realize deep insight into the aging mechanism of battery, further improve the test accuracy, and make the test result more accurately reflect the internal reason and actual process of battery aging.

[0008] Preferably, based on the real-time monitoring data of internal microstructure changes and performance degradation characteristics of battery, a battery aging state evaluation system is established, which is divided into several grades according to the aging degree of battery, including mild aging, moderate aging and severe aging, and the corresponding performance evaluation conclusion and service life prediction range are given for each grade.

[0009] Beneficial effects: Establish a battery aging state evaluation system, which can intuitively and clearly show the battery aging state, provide clear battery performance evaluation conclusions and service life prediction range for users, and facilitate users to take appropriate measures such as continued use, step-by-step utilization or scrap disposal according to the battery aging level.

[0010] Preferably, after completing the aging test, the test data is analyzed and processed, and the data of the old battery with obvious abnormal performance is eliminated. For the remaining battery data, the old batteries with similar internal resistance are found out according to the aging data, and are matched in groups according to the preset series-parallel connection mode, which is 2 series 9 parallel.

[0011] Beneficial effects: The analysis and processing of test data and the battery grouping matching based on aging data can effectively screen out reusable old batteries and reasonably group them, providing a basis for the secondary use of old batteries, avoiding resource waste, and improving the value of old batteries.

[0012] Scheme two: An old battery aging test system based on the old battery aging test method as described above, comprising a central processing unit and a data acquisition module, a test model construction module, a monitoring module, an evaluation module and a data processing module connected thereto; The data acquisition module is used to acquire the main objective attribute data and subjective use environment data of the same specification of the old battery in stock; The test model construction module constructs a multi-dimensional accelerated aging test model according to the data collected by the data acquisition module; The monitoring module is used to monitor the internal microstructure changes and performance degradation characteristics of the battery in real time during the accelerated aging test; The evaluation module establishes a battery aging state evaluation system based on the data monitored by the monitoring module; The data processing module is used to analyze and process the test data after completing the aging test.

[0013] Beneficial effects: Through the cooperative work of each module, the system realizes the full-process automatic operation from data acquisition, test model construction, aging monitoring, state evaluation to data processing, improves the accuracy and efficiency of the test, reduces human error, and provides a complete solution for the aging test of old batteries.

[0014] Preferably, the data acquisition module comprises a sensor unit and a data transmission unit, the sensor unit is used to acquire the main objective attribute data and subjective use environment data of the battery, and the data transmission unit transmits the collected data to the test model construction module and the data processing module in real time.

[0015] Beneficial effects: The design of the sensor unit and data transmission unit of the data acquisition module ensures the comprehensiveness and real-time nature of data acquisition, providing accurate and timely data support for subsequent modules and ensuring the efficient operation of the entire test system.

[0016] Preferably, the test model construction module includes a parameter setting sub-module and a model operation sub-module, the parameter setting sub-module is used to set the temperature acceleration factor and the cycle acceleration factor in the multi-dimensional accelerated aging test model, and the model operation sub-module performs aging test simulation operation according to the set parameters and the collected data.

[0017] Beneficial effects: The sub-module design of the test model construction module makes the parameter setting flexible and convenient, and can be personalized according to different battery types and use scenarios, while the model operation sub-module ensures the accuracy and efficiency of the aging test simulation operation, improving the reliability of the test model.

[0018] Preferably, the monitoring module includes a microstructure monitoring unit and a performance monitoring unit, the microstructure monitoring unit uses devices including an X-ray diffractometer and a scanning electron microscope to monitor the internal microstructure changes of the battery, and the performance monitoring unit monitors the performance degradation characteristics of the battery by monitoring the voltage, current and capacity parameters of the battery.

[0019] Beneficial effects: The monitoring module uses professional equipment and multiple parameter monitoring methods to comprehensively and accurately obtain the internal microstructure changes and performance degradation characteristic data of the battery, providing strong data support for in-depth understanding of the battery aging mechanism and accurate assessment of the battery aging state.

[0020] Preferably, the evaluation module includes a grade division sub-module and a report generation sub-module, the grade division sub-module divides the aging grade according to the battery aging degree, and the report generation sub-module generates the corresponding performance evaluation report and service life prediction report according to the aging grade.

[0021] Beneficial effects: The sub-module design of the evaluation module makes the aging grade division clear and explicit, and the report generation is standardized and comprehensive, providing users with detailed and intuitive battery aging evaluation results, making it convenient for users to understand the battery state and make decisions.

[0022] Preferably, the multi-dimensional accelerated aging test model includes an input layer, a processing layer, an output layer and a feedback optimization module; the input layer receives the objective attribute parameters and the subjective use environment parameters; the processing layer includes a temperature and cycle acceleration factor calculation unit and an aging process coupling unit, which calculates the acceleration factor and maps the accelerated test and the actual aging time; the output layer outputs the battery aging test results, including real-time capacity, internal resistance, aging grade and life prediction; the feedback optimization module dynamically adjusts the parameters according to the monitoring data.

[0023] Beneficial effects: By fusing the main objective attribute and the multi-parameter of the subjective use environment, the precise quantification and accelerated simulation of the complex aging process of the battery are realized, and the test cycle is greatly shortened; the hierarchical structure design ensures the comprehensiveness of the input data, the rigor of the processing logic and the practicality of the output results, and can accurately output the key indicators such as capacity, internal resistance, aging grade and life prediction; the feedback optimization mechanism can dynamically adjust the parameters based on the real-time monitoring data, significantly improve the adaptability and test accuracy of different battery types and use scenarios, and provide efficient and reliable technical support for the aging characteristics analysis and secondary utilization of old batteries.

[0024] Working principle of the application: The old battery aging test method and system based on the application first collects the main objective attribute data (such as battery brand, model, factory time, initial capacity, internal resistance) and subjective use environment data (such as use temperature range, charge and discharge times, charge and discharge rate, use scenario) of the same specification inventory old battery through the data acquisition module. Using these data, the test model construction module constructs a multi-dimensional accelerated aging test model, sets corresponding acceleration factors (such as temperature acceleration factor, cycle acceleration factor) in the model according to different influencing factors, and performs accelerated aging test on the old battery. In the test process, the monitoring module uses professional equipment (such as X-ray diffractometer, scanning electron microscope to monitor microstructure changes, and monitors performance degradation characteristics by monitoring voltage, current and capacity parameters) to monitor the internal microstructure changes and performance degradation characteristics of the battery in real time, and feeds back the monitoring data to the test model construction module to optimize and adjust the model. The evaluation module establishes a battery aging state evaluation system according to the monitoring data, divides the aging grade and gives the performance evaluation conclusion and the use life prediction range. Finally, the data processing module analyzes and processes the test data, selects the reusable batteries and performs grouping matching. Advantages of the application: Test cycle is greatly shortened: Through the multi-dimensional accelerated aging test model, combined with the setting of acceleration factors for various factors affecting battery aging, the long-term aging process of the battery can be simulated in a short time, the aging data can be obtained, the test efficiency can be improved, and the demand for rapid evaluation of the performance of old batteries can be met. High test accuracy: Collecting battery main objective attribute data and subjective use environment data, constructing a multi-dimensional accelerated aging test model close to actual application scenarios, and optimizing the model by real-time monitoring of battery internal microstructure changes and performance degradation characteristics make the test results more accurately reflect the aging condition of the battery in actual use, and improve the test accuracy. Deep insight into the aging mechanism: Real-time monitoring of the internal microstructure changes and performance degradation characteristics of the battery helps to deeply understand the internal reasons and actual process of battery aging, provides more accurate data for battery aging research, and promotes the further development of battery technology. Promote the green utilization of old batteries: Screen out reusable old batteries through aging tests, and reasonably match them into groups based on aging data to achieve secondary utilization of old batteries, avoid resource waste, comply with the concept of green energy development, and at the same time reduce inventory management costs and mitigate potential environmental pollution risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flowchart of an embodiment of the present invention.

[0026] Figure 2 2 is a system block diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following is further described in detail through specific implementation methods: The embodiment is basically as shown in the attached Figure 1 As shown: The old battery aging test method of the present invention includes the following steps: Collect objective attribute data of old batteries of the same specifications in stock, including the brand, model, production time, initial capacity, and internal resistance of the battery; At the same time, subjective usage environment data of the battery is collected, and the subjective usage environment data includes the usage temperature range, the number of charge and discharge times, the charge and discharge rate, and the usage scenarios, and the usage scenarios include electric vehicles and energy storage systems; Using the objective attribute data and subjective usage environment data of the subject, a multi-dimensional accelerated aging test model is constructed; In this model, the corresponding temperature acceleration factor is set according to different operating temperature ranges, and the corresponding cycle acceleration factor is set according to the number of charge and discharge cycles and the charge and discharge rate to perform accelerated aging tests on old batteries.

[0028] like Figure 2 As shown, the old battery aging test system includes a central processing unit and a data acquisition module, a test model construction module, a monitoring module, an evaluation module and a data processing module connected thereto; the central processing unit performs data calculation and processing functions according to existing technologies.

[0029] The data collection module is used to collect the main objective attribute data and subjective usage environment data of old batteries of the same specifications in stock; The test model construction module constructs a multi-dimensional accelerated aging test model based on the data collected by the data collection module; The monitoring module is used to monitor the internal microstructure changes and performance degradation characteristics of the battery in real time during the accelerated aging test; The evaluation module establishes a battery aging status evaluation system based on the data monitored by the monitoring module; The data processing module is used for analyzing and processing test data after completing the aging test.

[0030] The specific implementation process is as follows: The process of constructing a multi-dimensional accelerated aging test model using objective attribute data of the main body and subjective use environment data can be divided into four core steps of data acquisition and preprocessing, parameter setting, model building, and real-time optimization, which are as follows: (1) Data acquisition and preprocessing Two types of basic data are collected through the data acquisition module: one is the objective attribute data of the main body, including the brand, model, factory time, initial capacity, and internal resistance of the battery; the other is the subjective use environment data, including the use temperature range, charge and discharge times, charge and discharge rate, and use scenario. The collected data is cleaned to eliminate outliers and standardized to provide a high-quality data basis for model input.

[0031] (2) Acceleration factor parameter setting Based on the preprocessed data, key parameters are set for different dimensions: according to the use temperature range, intervals are divided and temperature acceleration factors are set (1.2 for 0-10℃, 1 for 10-25℃, and 1.3 for 25-40℃); according to the combination of charge and discharge times and charge and discharge rate, cycle acceleration factors are set (1.2 for 500 charge and discharge times + 0.5C rate, 1.5 for 800 charge and discharge times + 1C rate, and 1.8 for 1000 charge and discharge times + 1.5C rate). Parameter setting needs to be dynamically adjusted in combination with battery types (such as lithium ion batteries and lead-acid batteries) and use scenarios (such as electric vehicles and energy storage systems). The dynamic adjustment range of the temperature acceleration factor and the cycle acceleration factor of the present application is shown in Table 1.

[0032] Table 1

[0033] In Table 1, the temperature acceleration factor, the basic interval is divided according to 0-10℃, 10-25℃, and 25-40℃, and the reference values of lithium ion batteries are 1.2, 1.0, and 1.3, respectively; the lead-acid battery is more sensitive to temperature, and is adjusted by 5%-15% higher than the lithium ion battery in the same interval; the electric vehicle scenario is adjusted by 5%-10% higher than the energy storage system scenario due to greater temperature fluctuations.

[0034] The cycle acceleration factor, the basic combination is increased according to the charge and discharge intensity (500 times + 0.5C → 800 times + 1C → 1000 times + 1.5C), and the reference values of lithium ion batteries are 1.2, 1.5, and 1.8, respectively; the lead-acid battery is less resistant to cycles, and is adjusted by 5%-15% higher than the lithium ion battery; the electric vehicle scenario is adjusted by 5%-10% higher than the energy storage system scenario due to higher charge and discharge intensity.

[0035] Dynamic adjustment logic: the acceleration factor of lead-acid batteries is generally higher than that of lithium-ion batteries, and the acceleration factor of electric vehicle scenarios is generally higher than that of energy storage system scenarios, and the adjustment ranges of the two can be superimposed (for example, when lead-acid batteries are used in electric vehicles, the dual increase of battery type and scenario must be considered at the same time). The final value needs to be calibrated based on actual test data.

[0036] (3) Model construction A multi-dimensional coupling model is constructed by linking objective attribute data (such as initial capacity and internal resistance) with subjective usage environment data (such as temperature range and charge and discharge parameters) and set acceleration factors. The core of the model is to compress the aging process under actual usage conditions through acceleration factors, thereby achieving accelerated testing (for example, using temperature and cycle acceleration factors to simulate battery aging under more stringent conditions, shortening testing time).

[0037] (4) Real-time optimization and iteration During accelerated aging testing, the monitoring module collects real-time data on changes in the battery's internal microstructure (such as electrode material lattice distortion) and performance degradation characteristics (such as capacity, voltage, and current). This data is fed back into the model to fine-tune parameters such as the acceleration factor. If a deep learning neural network is introduced, the algorithm must also be trained using historical aging data, allowing the model to autonomously learn the aging patterns of different batteries, automatically optimize parameters, and improve model adaptability.

[0038] The multi-dimensional accelerated aging test model is a coupled model that integrates multiple parameters. Its core is to quantify and accelerate the complex aging process of batteries in actual use through acceleration factors. The model structure and its components are defined as follows: (1) Overall structure of the model The model consists of four parts: input layer, processing layer, output layer and feedback optimization module: Input layer: receives two types of data, one is the subject's objective attribute parameters (denoted as , including brand, model, and production time , initial capacity , internal resistance ); the second is the subjective use environment parameter (denoted as , including operating temperature range , charge and discharge times , charge and discharge rate , Usage scenarios ).

[0039] Processing layer: The core is to calculate the acceleration factor and couple the aging process. It includes: Temperature acceleration factor calculation unit: according to temperature range Output temperature acceleration factor (For example, when T∈[25,40℃], = 1.3); Cycle acceleration factor calculation unit: output cycle acceleration factor according to charge-discharge times N and rate K (when N = 800 times and K = 1C, = 1.5); Aging process coupling unit: integrate O and E by acceleration factor, calculate the mapping relationship between accelerated aging test time t and actual aging time (simplified as = t x ( x ), that is, accelerated test t hours is equivalent to actual use t x x hours of aging).

[0040] Output layer: output battery aging test results, including real-time capacity , internal resistance , aging degree level (mild / moderate / severe) and remaining life prediction L.

[0041] Feedback optimization module: receive real-time data of monitoring module (such as internal microstructure change M, performance decline feature P), dynamically adjust and (such as when detecting lattice distortion, fine-tuning factor value); if deep learning is introduced, the neural network is used to optimize parameters autonomously.

[0042] (2) Key parameter definition Temperature acceleration factor ( ): represents the intensification coefficient of temperature on aging speed, > 1, the higher the temperature, the faster the aging speed; Cycle acceleration factor ( ): represents the intensification coefficient of charge-discharge times and rate on aging speed, > 1, the stronger the cycle cumulative effect, the faster the aging speed; Aging degree: taking capacity attenuation rate as the core index, defined as , corresponding to mild (<20%), moderate (20%-40%), severe (>40%) aging.

[0043] This model realizes precise simulation and accelerated test of old battery aging process through coupling and dynamic optimization of multi-dimensional parameters.

[0044] Example one Based on an old battery aging test method: a batch of same specification lithium ion batteries retired from a certain brand of electric vehicle as an example, first, the data acquisition equipment is used to collect the objective attribute data of each battery, the battery brand is A, the model is XX, the factory time is 20XX, the initial capacity is marked as 100Ah, and the average internal resistance is 0.5mΩ measured by a professional internal resistance tester. At the same time, collect the subjective use environment data, the temperature range of this batch of batteries is mainly 0-40℃, the charge and discharge times reach 800 times, the average charge and discharge rate is 1C, and the use scene is electric vehicle power supply. Using the above collected data, in the multi-dimensional accelerated aging test model construction software, the temperature acceleration factor is set: when the temperature is 0-10℃, the acceleration factor is 1.2; 10-25℃, the acceleration factor is 1; 25-40℃, the acceleration factor is 1.3. The cycle acceleration factor is set according to the charge and discharge times and the charge and discharge rate, when the charge and discharge times reach 800 times and the charge and discharge rate is 1C, the cycle acceleration factor is set to 1.5. Put the battery into the aging test equipment, and carry out accelerated aging test according to the set acceleration factor. During the aging test, the X-ray diffraction instrument is used to detect the crystal structure of the battery electrode material every 1 hour, the voltage, current and capacity data of the battery are monitored every half hour, and the battery performance degradation characteristics are recorded. If the battery capacity attenuation is found to be 80Ah, the test time is recorded as 200 hours, and it is found that the crystal structure of the electrode material appears partial lattice distortion. These monitoring data are fed back to the multi-dimensional accelerated aging test model construction software, and the model parameters are fine-tuned. According to the monitoring data, the pre-established battery aging state evaluation system is used to judge the aging degree of the battery as moderate aging. In the evaluation system, when the battery capacity attenuation is 80%-60% of the initial capacity, it is considered as moderate aging, and the performance evaluation conclusion is that the battery performance has decreased, but still has certain use value, and the service life prediction range is 200-300 times of charge and discharge under the current use condition. After completing the aging test, all the test data are analyzed and processed, and the battery data with abnormal capacity attenuation to 50Ah is excluded. For the remaining battery data, according to the aging data, the batteries with similar internal resistance are found out, and the batteries are matched in groups according to the mode of 2 strings and 9 parallel, and a plurality of groups of batteries for secondary utilization are obtained. Example two Based on an old battery aging test system: a set of old battery aging test system is constructed, which includes a data acquisition module, a test model construction module, a monitoring module, an evaluation module and a data processing module; the sensor unit of the data acquisition module adopts high-precision temperature sensors, current sensors, voltage sensors and capacity monitoring sensors, which are used to collect data such as battery temperature, charging and discharging current, voltage and capacity, and at the same time, a code scanning device is equipped to read the brand, model and other information of the battery. The data transmission unit adopts a wireless transmission module to transmit the collected data to the test model construction module and the data processing module in real time. The parameter setting submodule in the test model construction module sets the temperature acceleration factor and the cycle acceleration factor in the multi-dimensional accelerated aging test model through the man-machine interface according to the battery type and the use scene by the operator. The model operation submodule adopts a high-performance computing chip to perform aging test simulation operation according to the set parameters and the collected data. For example, for a batch of lead-acid batteries for energy storage, the operator sets the temperature acceleration factor to 1.1 when the temperature is 15-30℃ in the parameter setting submodule; the cycle acceleration factor is 1.3 when the number of charging and discharging cycles reaches 500 times and the charging and discharging rate is 0.5C.

[0045] The microstructure monitoring unit of the monitoring module is equipped with a miniaturized X-ray diffractometer and a scanning electron microscope to detect the internal microstructure of the battery regularly, and the performance monitoring unit obtains the battery performance degradation characteristics by monitoring the voltage, current and capacity parameters of the battery in real time. The monitored data is transmitted to the evaluation module through the data line. The grade division submodule of the evaluation module divides the aging grade according to the pre-set battery aging degree standard, such as mild aging when the battery capacity attenuation is 90%-80% of the initial capacity, moderate aging when it is 80%-60%, and severe aging when it is less than 60%. The report generation submodule generates detailed performance evaluation report and service life prediction report according to the aging grade, and the report content includes the change of battery performance indicators, aging grade, performance evaluation conclusion, service life prediction range, etc., and the report is stored in the storage device of the data processing module in the form of electronic document. The data processing module analyzes and processes the test data after completing the aging test, uses data analysis software to filter out the battery data with abnormal performance and eliminate it, and for the remaining normal data, finds out the batteries with similar internal resistance according to the aging data, matches them in groups according to the preset series-parallel mode (such as 2 series 9 parallel), outputs the group matching result to the display device, and stores it in the database for subsequent query and use. Compared with the prior art, the advantages of the present application are: Innovative test method: The prior art mostly uses single factor or simple model for battery aging test, which cannot comprehensively consider the complex conditions in the actual use of the battery. The present application first proposes to construct a multi-dimensional accelerated aging test model by combining the analysis of the objective attributes of the subject and the analysis of the subjective use environment. This multi-dimensional comprehensive consideration method is pioneering in the field of battery aging test and cannot be obtained by simply combining conventional technical means. Real-time monitoring and deep insight: Most current technologies only focus on the changes of external performance parameters of the battery, and rarely monitor the internal microstructure changes of the battery. The present application monitors the internal microstructure changes and performance degradation characteristics of the battery in real time, and feeds them back to optimize the test model, thereby achieving deep insight into the battery aging mechanism. This technical path and implementation method have not been reported in the prior art, and require in-depth innovation understanding and practical breakthroughs in battery aging theory and test technology. Promote green use of old batteries: The existing technology mostly simply discards or stores old batteries. The present application screens reusable batteries through aging test and reasonably matches them in groups, realizes the secondary use of old batteries, forms a complete green use scheme from test to application, opens up a new technical direction in the treatment and resource utilization of old batteries, and is not a conventional improvement based on the prior art.

[0046] Example three Unlike example one, the aging test method for old batteries in this embodiment introduces a deep learning neural network algorithm into the multi-dimensional accelerated aging test model. A large amount of historical battery aging data and real-time monitoring data are input into the algorithm for training, so that the algorithm can automatically analyze the aging rules of different types of batteries in various complex use scenarios, and further automatically optimize the acceleration factors and other parameter settings in the model. The introduction of the deep learning neural network algorithm enables the multi-dimensional accelerated aging test model to automatically learn and adapt to the aging characteristics of different batteries, automatically optimize parameters, greatly improve the model's adaptation ability to complex scenarios, make the test more intelligent and accurate, further improve the test efficiency, and reduce the cost and error of manual parameter adjustment.

[0047] Taking a lithium-ion battery of the same specification retired by another brand as an example, on the basis of the method of embodiment one, a deep learning neural network algorithm is introduced. 1000 sets of historical aging data of this brand and the same type of battery in the past 5 years are collected, including the aging conditions under different temperatures, charge-discharge times, and charge-discharge rates, as well as the corresponding real-time monitoring data. These data are input into the deep learning neural network algorithm for training. Through analysis and learning, the algorithm masters the aging rules of this type of battery under different scenarios. When testing the retired battery of this brand collected newly, the real-time collected main objective attribute data, subjective use environment data, and monitored microstructure changes and performance degradation characteristic data are input into the trained algorithm, and the algorithm automatically optimizes the temperature acceleration factor and cycle acceleration factor in the multi-dimensional accelerated aging test model. For example, when the battery use temperature is 30-40℃ and the charge-discharge rate is 1.2C, the algorithm optimizes the temperature acceleration factor from the originally set 1.3 to 1.35, and the cycle acceleration factor from 1.5 to 1.55, so that the model can more accurately simulate the battery aging process under this scenario. After testing, the deviation rate of the test results from the actual aging condition of the battery is reduced by 15% and the test efficiency is improved by 20% after using this method.

[0048] Embodiment Four Different from embodiment two, the old battery aging test system based on the old battery aging test system of this embodiment, the test model construction module further comprises an AI algorithm unit, which adopts a deep learning neural network algorithm to receive and process a large amount of historical battery aging data and real-time monitoring data, and automatically optimizes the acceleration factors and other parameter settings in the multi-dimensional accelerated aging test model through learning analysis. The addition of the AI algorithm unit enables the test model construction module to have autonomous learning and optimization capabilities, dynamically adjusts the model parameters according to different battery types and use scenarios, improves the intelligent level of the system, ensures that the test model always maintains high accuracy and adaptability, and provides more efficient support for old battery aging test.

[0049] On the basis of the system of embodiment two, the test model building module adds an AI algorithm unit, which carries a deep learning neural network algorithm. The system pre-stores a large amount of historical aging data of various types of batteries (such as lithium-ion batteries, lead-acid batteries, etc.). When testing a batch of new lead-acid energy storage batteries, the data acquisition module collects relevant data of the batteries and transmits them to the test model building module, the AI algorithm unit receives these real-time data and real-time monitoring data from the monitoring module, and learns and analyzes in combination with historical data. For this batch of lead-acid batteries in the use scenario of 25-35℃ and 0.8C charge-discharge rate, the algorithm automatically adjusts the temperature acceleration factor from 1.1 to 1.12 and the cycle acceleration factor from 1.3 to 1.33. Through actual application verification, the consistency of the battery service life prediction results obtained by the system with the actual use situation is improved by 18%, greatly improving the test performance of the system.

[0050] In the prior art, the parameter setting of the multi-dimensional accelerated aging test model depends on manual adjustment based on experience or limited data, which is difficult to accurately adapt to various complex battery types and use scenarios, resulting in limited test accuracy and efficiency. The newly added content in this part introduces a deep learning neural network algorithm, allowing the model to autonomously learn a large amount of historical data and real-time data and automatically optimize parameters. From a technical perspective, this breaks the limitations of traditional manual parameter adjustment, enabling the model to have intelligent autonomous optimization capabilities, which is an innovation in the construction of test models. Through the deep mining and adaptation of different battery aging laws by the algorithm, the accuracy and efficiency of the test can be significantly improved, providing a more advanced technical means for aging test of old batteries. This combination of artificial intelligence algorithms and multi-dimensional accelerated aging test models is relatively rare in existing technology, reflecting outstanding substantive features and significant progress, and has high creativity.

[0051] The above is only an embodiment of the present application, and well-known specific technical solutions and / or characteristics in the scheme are not described in detail. It should be noted that for those skilled in the art, without departing from the technical solutions of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the patent. The protection scope claimed in this application should be subject to the content of its claims, and the specific implementation in the description can be used to explain the content of the claims.

Claims

1. A method for testing aging of old batteries, characterized in that: The following steps are involved: The objective attribute data of the main body of old batteries with the same specifications in stock are collected, and the objective attribute data include the brand, model, production time, initial capacity, and internal resistance of the battery; at the same time, the subjective usage environment data of the battery are collected, and the subjective usage environment data include the usage temperature range, charge and discharge times, charge and discharge rate, and usage scenarios, and the usage scenarios include electric vehicles and energy storage systems; using the objective attribute data and subjective usage environment data, a multi-dimensional accelerated aging test model is constructed; in this model, corresponding temperature acceleration factors are set according to different usage temperature ranges, and corresponding cycle acceleration factors are set according to the charge and discharge times and charge and discharge rate, and accelerated aging tests are performed on old batteries.

2. The old battery aging test method according to claim 1, characterized in that: During the accelerated aging test, the changes in the internal microstructure of the battery are monitored in real time. The microstructural changes include changes in the crystal structure of the electrode material, changes in the electrolyte composition, and changes in the interface film between the electrode and the electrolyte. At the same time, the battery performance degradation characteristics are monitored, including capacity decay, increased internal resistance, and reduced charge and discharge efficiency. The monitoring data is fed back to the multi-dimensional accelerated aging test model in real time to optimize and adjust the model.

3. The old battery aging test method according to claim 2, characterized in that: Based on real-time monitoring of battery internal microstructure changes and performance degradation characteristic data, a battery aging status assessment system is established. The system is divided into multiple levels according to the degree of battery aging, including mild aging, moderate aging and severe aging, and corresponding performance evaluation conclusions and service life prediction range are given for each level.

4. The old battery aging test method according to any one of claims 1 to 3, characterized in that: After completing the aging test, the test data is analyzed and processed to eliminate the old battery data with obvious abnormal performance. For the remaining battery data, old batteries with similar internal resistance are found based on the aging data, and they are matched into groups according to the preset series-parallel method, which is 2 series and 9 parallel.

5. A system for testing old battery aging, characterized in that: The method for testing old batteries according to claim 1 comprises a central processing unit and a data acquisition module, a test model building module, a monitoring module, an evaluation module and a data processing module connected thereto; The data collection module is used to collect the objective attribute data and subjective usage environment data of old batteries of the same specifications in stock; The test model construction module constructs a multi-dimensional accelerated aging test model based on the data collected by the data collection module; The monitoring module is used to monitor the internal microstructure changes and performance degradation characteristics of the battery in real time during the accelerated aging test; The evaluation module establishes a battery aging status evaluation system based on the data monitored by the monitoring module; The data processing module is used to analyze and process the test data after the aging test is completed.

6. The old battery aging test system according to claim 5, characterized in that: The data acquisition module includes a sensor unit and a data transmission unit. The sensor unit is used to collect the main objective attribute data and subjective usage environment data of the battery. The data transmission unit transmits the collected data to the test model construction module and the data processing module in real time.

7. The old battery aging test system according to claim 5, characterized in that: The test model construction module includes a parameter setting submodule and a model operation submodule. The parameter setting submodule is used to set the temperature acceleration factor and cycle acceleration factor in the multi-dimensional accelerated aging test model. The model operation submodule performs aging test simulation operations based on the set parameters and collected data.

8. The old battery aging test system according to claim 5, characterized in that: The monitoring module includes a microstructure monitoring unit and a performance monitoring unit. The microstructure monitoring unit uses equipment including an X-ray diffractometer and a scanning electron microscope to monitor changes in the internal microstructure of the battery. The performance monitoring unit monitors the battery performance degradation characteristics by monitoring the battery's voltage, current and capacity parameters.

9. The old battery aging test system according to claim 5, characterized in that: The evaluation module includes a level classification submodule and a report generation submodule. The level classification submodule classifies the battery into aging levels according to the degree of battery aging. The report generation submodule generates a corresponding performance evaluation report and a service life prediction report according to the aging level.

10. The old battery aging test system according to claim 1, characterized in that: The multi-dimensional accelerated aging test model includes an input layer, a processing layer, an output layer, and a feedback optimization module. The input layer receives the objective attribute parameters of the subject and the subjective usage environment parameters. The processing layer contains a temperature and cycle acceleration factor calculation unit and an aging process coupling unit to calculate the acceleration factor and map the accelerated test to the actual aging time. The output layer outputs battery aging test results, including real-time capacity, internal resistance, aging level and life prediction; The feedback optimization module dynamically adjusts parameters based on monitoring data.

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