Energy storage battery management method, device and equipment and readable storage medium

Abstract

The application discloses an energy storage battery management method and device, equipment and a readable storage medium. The method comprises the following steps: determining the configuration type of a target energy storage battery; acquiring a shelving aging function and a cyclic use aging function matched with the target energy storage battery according to the configuration type; acquiring target environmental parameters, target shelving data and target charging and discharging data of the target energy storage battery; calculating a first health loss degree based on the target environmental parameters and the target shelving data and in combination with the shelving aging function; calculating a second health loss degree based on the target charging and discharging data and the cyclic use aging function; and evaluating whether the target energy storage battery needs to be replaced based on the first health loss degree and the second health loss degree. It can be seen that the application can guarantee the evaluation accuracy and better meet the real-time and high-efficiency requirements of the power grid system for the safety monitoring of a large number of energy storage batteries.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and more specifically, to a method, apparatus, device, and readable storage medium for energy storage battery management. Background Technology

[0002] In the field of power grid technology, energy storage batteries can effectively achieve peak and frequency regulation, smooth power fluctuations, and improve power supply stability and energy utilization efficiency. Therefore, energy storage batteries have been deployed and applied on a large scale in power grid systems. However, the complex and variable operating conditions within the power grid mean that there is no unified fixed standard for the obsolescence time of energy storage batteries. This makes it impossible to effectively manage them through simple timed replacement, thus compromising the safety of power grid operation.

[0003] In existing technologies, neural network methods are often used to predict the health of energy storage batteries and promptly identify those that need to be replaced, thus mitigating safety risks, in order to address the aforementioned problems. However, the neural networks used in these methods involve multi-layered structures, including input / output layer architectures and hidden layer node designs, which consume significant system resources. Therefore, they cannot perform parallel health assessments of multiple energy storage batteries in resource-constrained scenarios, and are ill-suited to the real-time and efficient safety monitoring requirements of power grid systems for a large number of energy storage batteries. Summary of the Invention

[0004] In view of this, this application provides an energy storage battery management method, apparatus, device, and readable storage medium to address the shortcomings of low real-time performance and efficiency in existing energy storage battery management technologies.

[0005] To achieve the above objectives, the following solution is proposed:

[0006] A method for managing energy storage batteries, comprising:

[0007] Determine the configuration type of the target energy storage battery;

[0008] Based on the configuration type, obtain the shelving aging function and the cyclic aging function matched for the target energy storage battery;

[0009] Obtain the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery;

[0010] Based on the target environmental parameters and the target storage data, and combined with the storage aging function, a first health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging in the target storage state.

[0011] Based on the target charge-discharge data and the cyclic aging function, a second health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging under the target charge-discharge state.

[0012] Based on the first health loss level and the second health loss level, assess whether the target energy storage battery needs to be replaced.

[0013] Optionally, obtaining the set-off aging function and the cyclic aging function matched to the target energy storage battery according to the configuration type includes:

[0014] Acquire multiple first data subsets corresponding to the idle state, and multiple second data subsets corresponding to the charge / discharge state; wherein each first data subset contains the historical idle period, idle environment parameters, idle health loss degree, and different idle battery parameter values ​​corresponding to the historical energy storage battery of the configuration type; each second data subset contains the historical charge / discharge period, usage health loss degree, and different usage battery parameter values ​​corresponding to the historical energy storage battery of the configuration type.

[0015] Perform a polynomial fit on each first data subset to generate a shelving aging function that matches the configuration type;

[0016] Multinomial fitting is performed on each second subset of data to generate a recurring aging model that matches the configuration type.

[0017] Optionally, acquiring a plurality of first data subsets corresponding to the idle state and a plurality of second data subsets corresponding to the charge / discharge state includes:

[0018] Identify multiple historical energy storage batteries that match the configuration type, and determine the replacement information, historical health loss sequence, historical operation sequence, and historical environment sequence for each historical energy storage battery. The historical operation sequence includes the time points and battery parameter values ​​corresponding to different operating states.

[0019] From the historical operation sequence of each historical energy storage battery, determine multiple historical storage periods and multiple historical charge and discharge periods for the corresponding historical energy storage battery, and extract the parameter values ​​of each stored battery corresponding to each historical storage period and the parameter values ​​of each used battery corresponding to each historical charge and discharge period from each historical operation sequence.

[0020] By combining the historical storage periods, historical charge and discharge periods, historical environmental sequences, and historical health loss sequences of each historical energy storage battery, the storage health loss and storage environment parameters corresponding to each historical storage period, as well as the usage health loss corresponding to each historical charge and discharge period, are generated.

[0021] Each historical storage period, its corresponding storage environment parameters, the parameter values ​​of each stored battery, and the degree of storage health loss are combined to form a first data subset;

[0022] Each historical charge / discharge period, along with its corresponding battery parameter values ​​and usage health loss, is combined to form a second data subset.

[0023] Optionally, acquiring the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery includes:

[0024] Obtain the target temperature and target humidity of the target energy storage battery;

[0025] Obtain the target storage battery's target storage time, target state of charge, and target storage voltage;

[0026] The target usage time, target overcharge count, target over-discharge count, target depth of discharge, target current value, and target current rate of the target energy storage battery are obtained.

[0027] Optionally, the step of calculating a first health loss degree, characterizing the level of health damage to the target energy storage battery after aging in the target storage state, based on the target environmental parameters and the target storage data, combined with the storage aging function, includes:

[0028] The first degree of health loss is calculated by substituting the target temperature, target humidity, target storage time, target state of charge, and target storage voltage into the storage aging function.

[0029] Optionally, the step of calculating a second health loss degree, based on the target charge-discharge data and the cycle-use aging function, to characterize the level of health damage to the target energy storage battery after aging under the target charge-discharge state, includes:

[0030] The second health loss degree is calculated by substituting the target usage duration, target overcharge count, target over-discharge count, target discharge depth, target current value, and target current rate into the cyclic aging function.

[0031] Optionally, assessing whether the target energy storage battery needs to be replaced based on the first health loss level and the second health loss level includes:

[0032] Determine a first weight to characterize the impact of the target idle state on the target energy storage battery;

[0033] Determine a second weight to characterize the degree of influence of the target charge / discharge state on the target energy storage battery;

[0034] The sum of the first product and the second product is calculated as the health loss value of the target energy storage battery; wherein, the first product is the product between the first health loss degree and the first weight, and the second product is the product between the second health loss degree and the second weight;

[0035] Determine the health variables corresponding to the target energy storage battery;

[0036] The value of the health status variable is updated based on the health loss value;

[0037] When the updated variable value is lower than a preset health threshold, it is determined that the target energy storage battery needs to be replaced.

[0038] If the updated variable value is not lower than the health threshold, it is determined that the target energy storage battery does not need to be replaced.

[0039] An energy storage battery management device, comprising:

[0040] The configuration type determination module is used to determine the configuration type of the target energy storage battery;

[0041] The function acquisition module is used to acquire the shelving aging function and the cyclic aging function matched to the target energy storage battery according to the configuration type.

[0042] The data acquisition module is used to acquire the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery.

[0043] The function combination module is used to calculate the first health loss degree, which characterizes the level of health damage of the target energy storage battery after aging in the target storage state, based on the target environmental parameters and the target storage data, combined with the storage aging function.

[0044] The loss calculation module is used to calculate a second health loss degree based on the target charge-discharge data and the cyclic aging function, which characterizes the level of health damage of the target energy storage battery after aging under the target charge-discharge state.

[0045] The battery evaluation module is used to evaluate whether the target energy storage battery needs to be replaced based on the first health loss level and the second health loss level.

[0046] An energy storage battery management device includes a memory and a processor;

[0047] The memory is used to store programs;

[0048] The processor is used to execute the program to implement the various steps of the above-described energy storage battery management method.

[0049] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the various steps of the above-described energy storage battery management method.

[0050] As can be seen from the above technical solution, the energy storage battery management method provided in this application can determine the configuration type of the target energy storage battery; based on the configuration type, obtain the matching shelving aging function and cyclic use aging function for the target energy storage battery; based on this, this application can quantify the shelving aging law and usage aging law of energy storage batteries of the same configuration type through the shelving aging function and cyclic use aging function, realizing personalized matching and scenario-based quantification, improving the reliability of health loss assessment caused by different scenarios, significantly reducing assessment error compared with traditional general model assessment, and more accurately reflecting the actual health status of the energy storage battery; obtain the target environmental parameters, target shelving data, and target charge / discharge data of the target energy storage battery; based on the target environmental parameters and the target shelving data, combined with the shelving aging function, calculate the calculation to characterize the health of the target energy storage battery after aging in the target shelving state. The application calculates a first health loss level based on the target charge / discharge data and the cyclic aging function, and a second health loss level to characterize the health damage level of the target energy storage battery after aging under the target charge / discharge state. These two targeted aging patterns can be used to replace the generalized model for calculating the health loss level, solving the calculation deviation problem caused by different aging patterns of batteries with different configurations, improving the calculation reliability of this application, and requiring fewer resources for function calculation. In resource-constrained scenarios, the health of multiple energy storage batteries can be evaluated in parallel. Based on the first and second health loss levels, this application can assess whether the target energy storage battery needs to be replaced. Therefore, this application can comprehensively evaluate battery health in two main aging scenarios: idle state and charge / discharge state, solving the problem of incomplete evaluation caused by unclear loss type segmentation and the neglect of some losses in traditional evaluations. As can be seen, this application can complete the health assessment of the target energy storage battery with less computing resources, comprehensively considering the losses caused by storage and recycling. While ensuring assessment accuracy, it better meets the real-time and high-efficiency requirements of the power grid system for the safety monitoring of a large number of energy storage batteries. This effectively reduces the overall cost of battery health monitoring in large-scale energy storage power stations, balancing assessment accuracy and operational efficiency, and ensuring the stable and reliable operation of the grid-side energy storage system. Through the above-described scenario-based quantification and comprehensive assessment process, the current actual health status of the energy storage battery can be accurately obtained, providing a precise basis for decision-making regarding battery replacement and avoiding safety hazards or resource waste caused by errors in battery health assessment. Attached Figure Description

[0051] 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 embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0052] Figure 1 This is a flowchart of an energy storage battery management method disclosed in an embodiment of this application;

[0053] Figure 2 This is a structural block diagram of an energy storage battery management device disclosed in an embodiment of this application;

[0054] Figure 3 This is a hardware structure block diagram of an energy storage battery management device disclosed in an embodiment of this application. Detailed Implementation

[0055] 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 skilled in the art without creative effort are within the scope of protection of this application.

[0056] This application provides an energy storage battery management method, which can be applied to various energy storage battery management systems or power grid equipment management systems, as well as to various computer terminals or smart terminals. The executing entity can be the processor or server of the computer terminal or smart terminal.

[0057] Next, combine Figure 1 The energy storage battery management method described in this application is detailed, including the following steps:

[0058] Step S1: Determine the configuration type of the target energy storage battery.

[0059] Specifically, considering the potentially significant differences in aging patterns of energy storage batteries under different usage environments, different aging functions for both rest and cycle use can be applied to batteries operating in different environments. For example, energy storage batteries with different chemical systems (such as lithium iron phosphate and ternary lithium), or those with different capacity specifications or from different manufacturers, exhibit significantly different aging characteristics. Therefore, energy storage batteries can be pre-classified based on their configuration type, and corresponding aging function can be matched accordingly. The target energy storage battery is the one currently requiring a health assessment to determine whether replacement is necessary; its configuration type can be directly determined based on its product information.

[0060] The operating environment is generally affected by the power grid system where the energy storage system is located. Different power grid systems vary in terms of voltage stability, frequency fluctuations, and load changes, all of which affect the performance and aging rate of energy storage batteries. For example, in power grid systems in some densely industrial areas, the frequent start-ups and shutdowns of large industrial equipment can cause significant fluctuations in grid voltage and frequency. In such environments, energy storage batteries need to undergo frequent charge-discharge adjustments, which significantly accelerates their aging process.

[0061] Different battery configurations have varying tolerance to these environmental fluctuations and different aging patterns. Therefore, it is necessary to determine the configuration type specifically to provide a basis for accurate calculation of health loss.

[0062] Meanwhile, the aging patterns of different energy storage batteries may vary greatly. Therefore, different aging functions for resting and aging functions for recycling can be adopted for different models of energy storage batteries.

[0063] Therefore, the configuration type can be used to characterize data that can characterize the usage environment, such as the model identifier, configuration location identifier, and / or the grid identifier of the target energy storage battery.

[0064] Step S2: Based on the configuration type, obtain the shelving aging function and the cyclic aging function matched to the target energy storage battery.

[0065] Specifically, the corresponding aging functions and recurring aging functions that match the configuration type can be retrieved from a database that stores the correspondence between different usage environment types and different aging functions and recurring aging functions.

[0066] The storage aging model can be used to characterize the quantitative relationship between battery health loss and temperature, humidity, state of charge (SOC), storage voltage, and storage duration under storage conditions.

[0067] Cyclic aging models can be used to characterize the quantitative relationship between battery health degradation and usage time, overcharge cycles, over-discharge cycles, depth of discharge (DOD), current value, and current rate under charge-discharge cycle conditions. By matching a dedicated aging function for the corresponding configuration type, it is possible to better adapt to the actual aging degradation pattern of that type of battery and avoid evaluation biases caused by general models.

[0068] Cyclic aging functions can be used to characterize the quantitative relationship between battery health loss and usage time, overcharge cycles, over-discharge cycles, depth of discharge, current value, and current rate under charge and discharge conditions.

[0069] Step S3: Obtain the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery.

[0070] Specifically, through the equipment ledger management system, select ledger query in the main menu bar, select equipment query in the sub-menu bar, enter the identifier of the target energy storage battery, and retrieve the target environmental parameters, target idle data, and target charge and discharge data of the target energy storage battery from the latest health status replacement time to the current time.

[0071] The target environmental parameters mainly record the temperature and humidity information of the environment in which the battery operates, which can be directly collected by the temperature and humidity sensors built into the energy storage battery cabinet.

[0072] Target shelving data and target charge / discharge data can be directly extracted from the operation logs of the energy storage battery management system.

[0073] The energy storage battery management system can record the battery's operating status in real time for each period, distinguish between idle periods and charging / discharging periods, and then extract relevant data for the corresponding periods to ensure the completeness and accuracy of data acquisition and avoid deviations in subsequent evaluation results due to missing data.

[0074] The equipment ledger management system can record the basic configuration information, replacement time, and historical operation data of each energy storage battery. It can quickly locate the target energy storage battery and extract various types of data required by time range, ensuring the efficiency of the data acquisition process.

[0075] The target environmental parameters specifically include the target temperature and the target humidity;

[0076] The target shelving data specifically includes the target shelving duration, target state of charge, and target shelving voltage;

[0077] The target charge and discharge data specifically includes target usage time, target overcharge count, target over-discharge count, target depth of discharge, target current value, and target current rate. These data cover the core factors affecting battery aging in the two main scenarios of storage and charge and discharge, providing complete data support for subsequent accurate calculation of health loss.

[0078] Step S4: Based on the target environmental parameters and the target storage data, and in conjunction with the storage aging function, calculate the first health loss degree, which characterizes the level of health damage of the target energy storage battery after aging in the target storage state.

[0079] Specifically, a computation thread can be used to input the target environmental parameters and the target shelved data into the shelving aging function to calculate the first degree of health loss.

[0080] The first health loss level can directly reflect the degree of health degradation of the target energy storage battery in the current evaluation cycle due to the combined effects of environmental conditions and storage parameters under the storage state, providing quantitative results of loss in the storage scenario for subsequent comprehensive evaluation.

[0081] Step S5: Based on the target charge-discharge data and the cyclic aging function, calculate the second health loss degree, which characterizes the level of health damage of the target energy storage battery after aging under the target charge-discharge state.

[0082] Specifically, a computation thread can be used to substitute the target charge and discharge data into the cyclic aging function to calculate the second health loss degree.

[0083] The second health loss level can directly reflect the degree of health degradation of the target energy storage battery during the current evaluation cycle caused by the combined effect of various usage parameters during charge and discharge cycles. It can accurately quantify the loss of battery health during charge and discharge cycles and is the core quantitative basis for comprehensively evaluating the battery health status.

[0084] The first and second health loss levels correspond to the quantitative results of the loss in two main aging scenarios, respectively. This method of splitting the calculation can clearly distinguish the contribution of different scenarios to battery aging, avoid the confusion between different aging factors leading to inaccurate assessments, and provide an accurate scenario-based loss basis for subsequent comprehensive assessments.

[0085] Step S6: Based on the first health loss level and the second health loss level, assess whether the target energy storage battery needs to be replaced.

[0086] Specifically, the health loss value of the target energy storage battery from the latest health update time to the current time can be determined by combining the first health loss value and the second health loss value.

[0087] Specifically, the first health loss is the health loss caused by the idle scenario, and the second health loss is the health loss caused by the charging and discharging scenario. The total health loss value of the target energy storage battery can be obtained by directly adding the two. If there are historical health assessment records, the historical health loss values ​​can also be accumulated on the basis of the current total health loss value to obtain the final health loss value.

[0088] The current health of the target energy storage battery can be determined by the difference between the health status at the latest health status update time and the health loss value.

[0089] When the current health level is lower than the health threshold, it can be determined that the target energy storage battery needs to be replaced.

[0090] Health thresholds can be preset based on historical operating data and grid safety requirements. Different health thresholds can be set for different types of energy storage batteries to ensure that replacement decisions are adapted to the actual usage requirements of the batteries.

[0091] If the current health level is greater than or equal to the health threshold, it is determined that the target energy storage battery does not need to be replaced and can continue to be used normally. At the same time, the calculated health loss value and the current health level are recorded to provide a data basis for the next assessment.

[0092] Through the above-mentioned process of quantification and comprehensive evaluation in different scenarios, the actual health status of the energy storage battery can be accurately obtained, providing a precise basis for decision-making on whether to replace the battery and avoiding safety hazards or waste of resources caused by errors in battery health assessment.

[0093] As can be seen from the above technical solution, the energy storage battery management method provided in this application can determine the configuration type of the target energy storage battery; based on the configuration type, obtain the matching shelving aging function and cyclic use aging function for the target energy storage battery; based on this, this application can quantify the shelving aging law and usage aging law of energy storage batteries of the same configuration type through the shelving aging function and cyclic use aging function, realizing personalized matching and scenario-based quantification, improving the reliability of health loss assessment caused by different scenarios, significantly reducing assessment error compared with traditional general model assessment, and more accurately reflecting the actual health status of the energy storage battery; obtain the target environmental parameters, target shelving data, and target charge / discharge data of the target energy storage battery; based on the target environmental parameters and the target shelving data, combined with the shelving aging function, calculate the calculation to characterize the health of the target energy storage battery after aging in the target shelving state. The application calculates a first health loss level based on the target charge / discharge data and the cyclic aging function, and a second health loss level to characterize the health damage level of the target energy storage battery after aging under the target charge / discharge state. These two targeted aging patterns can be used to replace the generalized model for calculating the health loss level, solving the calculation deviation problem caused by different aging patterns of batteries with different configurations, improving the calculation reliability of this application, and requiring fewer resources for function calculation. In resource-constrained scenarios, the health of multiple energy storage batteries can be evaluated in parallel. Based on the first and second health loss levels, this application can assess whether the target energy storage battery needs to be replaced. Therefore, this application can comprehensively evaluate battery health in two main aging scenarios: idle state and charge / discharge state, solving the problem of incomplete evaluation caused by unclear loss type segmentation and the neglect of some losses in traditional evaluations. As can be seen, this application can use less computing resources to complete the health assessment of the target energy storage battery by comprehensively considering the losses caused by storage and the losses caused by cyclic use. While ensuring the accuracy of the assessment, it better meets the real-time and high-efficiency requirements of the power grid system for the safety monitoring of a large number of energy storage batteries. It can effectively reduce the overall cost of battery health monitoring in large-scale energy storage power stations, balance assessment accuracy and operating efficiency, and ensure the stable and reliable operation of the grid-side energy storage system.

[0094] In some embodiments of this application, the process of obtaining the shelving aging function and the cyclic aging function matching the target energy storage battery according to the configuration type is described in detail, and the steps are as follows:

[0095] S20. Obtain a plurality of first data subsets corresponding to the idle state, and a plurality of second data subsets corresponding to the charging and discharging state.

[0096] Specifically, the equipment ledger management system can be used to obtain replacement information, historical health loss sequence, historical operation sequence, and historical environment sequence of multiple historical energy storage batteries that match the configuration type.

[0097] Different historical energy storage batteries can come from different manufacturers and have different models. Different models of historical energy storage batteries with the same configuration can be configured with different storage aging functions and cycle aging functions.

[0098] Information can be extracted from the replacement information, historical health loss sequence, historical operation sequence and historical environment sequence of each historical energy storage battery, and relevant data of each historical energy storage battery in the standby state can be separated and integrated to obtain multiple first data subsets corresponding to the standby state.

[0099] Information such as replacement information, historical health loss sequence, historical operation sequence and historical environment sequence of each historical energy storage battery can be extracted, and relevant data of each historical energy storage battery in charge and discharge state at each time period can be separated and integrated to obtain multiple second data subsets corresponding to the charge and discharge state.

[0100] Each first data subset contains the historical storage period, storage environment parameters, storage health loss degree, and different storage battery parameter values ​​corresponding to the configuration type of the historical energy storage battery; each second data subset contains the historical charge and discharge period, usage health loss degree, and different usage battery parameter values ​​corresponding to the configuration type of the historical energy storage battery.

[0101] S21. Perform polynomial fitting on each first data subset to generate a shelving aging function that matches the configuration type.

[0102] Specifically, a polynomial fitting can be performed on each first subset of data in various ways to generate a shelving aging function that matches the configuration type.

[0103] For example, the least squares method can be used to perform polynomial fitting on each first subset of data to generate a shelving aging function that matches the configuration type.

[0104] Alternatively, a machine learning regression algorithm can be used to train a storage aging prediction model based on multiple subsets of initial data. The trained prediction model can then be used as the storage aging function for this configuration type, ensuring that the function accurately matches the actual aging patterns of batteries in storage scenarios. During the fitting or training process, the generated results can be verified using a preset error threshold. If the error of the fitted function exceeds the threshold, the fitting parameters are adjusted or more historical data is added for retraining until the error requirement is met, thus ensuring the accuracy of the generated storage aging function.

[0105] S22. Perform polynomial fitting on each second data subset to generate a recurring aging model that matches the configuration type.

[0106] Specifically, a multinomial fitting can be performed on each second subset of data in various ways to generate a cyclic aging model that matches the configuration type.

[0107] For example, the least squares method can be used to perform multinomial fitting on each second subset of data to generate a recurring aging model that matches the configuration type.

[0108] Alternatively, machine learning regression algorithms can be used to train a cyclic aging prediction model based on multiple second data subsets. The trained prediction model can then be used as the cyclic aging function matching this configuration type, ensuring that the function accurately adapts to the actual aging patterns of this battery type under charge-discharge cycle scenarios. During the fitting or training process, the generated results can also be verified using a preset error threshold. If the error of the fitted function exceeds the threshold, the fitting parameters are adjusted or more historical data is added for retraining until the error requirement is met. This ensures the accuracy of the generated cyclic aging function and provides a precise quantitative model foundation for subsequent calculations of health loss in different scenarios.

[0109] As can be seen from the above technical solution, this embodiment provides an optional method for obtaining the matching storage aging function and cycle-use aging function for the target energy storage battery based on the configuration type. Through this method, the matching storage aging function and cycle-use aging function can be accurately generated based on historical data of different configuration types. This method fully considers the differences in aging characteristics between different usage environments and different models of energy storage batteries, making the generated aging function more targeted and accurate. It can more accurately quantify the aging patterns of energy storage batteries in storage and charge / discharge states, providing a reliable basis for subsequent health assessments.

[0110] In some embodiments of this application, the process of step S20, obtaining a plurality of first data subsets corresponding to the idle state, and a plurality of second data subsets corresponding to the charge / discharge state, is described in detail as follows:

[0111] S200: Determine multiple historical energy storage batteries that match the configuration type, and determine the replacement information, historical health loss sequence, historical operation sequence, and historical environment sequence for each historical energy storage battery.

[0112] Specifically, the historical operation sequence may include the time points and battery parameter values ​​corresponding to different operating states.

[0113] The battery parameters can include any combination of the following: resting time, state of charge, resting voltage, usage time, overcharge cycles, over-discharge cycles, depth of discharge, current value, and current rate.

[0114] The types of battery parameters included in different operating states can be different.

[0115] For example, the battery parameter type corresponding to the standby state can be standby battery parameters, such as standby duration, state of charge, and standby voltage;

[0116] The battery parameters for charge / discharge states can be used as battery parameters, such as usage time, overcharge cycles, over-discharge cycles, depth of discharge, current value, and current rate.

[0117] Historical environmental sequences can include temperature and humidity values ​​at different points in time, reflecting the impact of the battery's environment on aging at different times.

[0118] The historical health loss sequence can be an empty set or it can contain the health of the target energy storage battery at different health update times.

[0119] The replacement information for each historical energy storage battery includes the battery's commissioning time, decommissioning time, and actual health status at the time of decommissioning, which can provide a final verification benchmark for the labeling and fitting of data subsets.

[0120] S201. Determine multiple historical storage periods and multiple historical charge / discharge periods from the historical operation sequence of each historical energy storage battery, and extract the parameter values ​​of each stored battery corresponding to each historical storage period and the parameter values ​​of each used battery corresponding to each historical charge / discharge period from each historical operation sequence.

[0121] Specifically, each historical operation sequence can be classified into time periods to identify multiple historical idle periods and multiple historical charging and discharging periods;

[0122] The system can extract the values ​​of the stored battery parameters corresponding to each historical storage period and the values ​​of the used battery parameters corresponding to each historical charge / discharge period from each historical operation sequence.

[0123] S202. Combining the historical storage battery's various historical storage periods, various historical charge and discharge periods, historical environmental sequences, and historical health loss sequences, generate the corresponding storage health loss degree and storage environment parameters for each historical storage battery's historical storage period, as well as the usage health loss degree for each historical charge and discharge period.

[0124] Specifically, the historical health loss sequence of each historical energy storage battery can be matched with the corresponding historical storage periods, and the health difference between the start and end times of each historical storage period can be calculated as the storage health loss.

[0125] The historical environmental sequence of each historical energy storage battery can be matched with the corresponding historical storage periods, and the average environmental parameter value of each historical storage period can be calculated as the storage environmental parameter.

[0126] The historical health loss sequence of each historical energy storage battery can be matched with the corresponding historical charge and discharge periods, and the health difference between the start and end times of each historical charge and discharge period can be calculated as the health loss.

[0127] S203. Combine each historical storage period and its corresponding storage environment parameters, each storage battery parameter value, and storage health loss degree to form a first data subset.

[0128] Specifically, the first data subset can be formed by combining the environmental parameters of the same energy storage battery during the same historical storage period, the parameter values ​​of each stored battery, and the degree of health loss during storage, so as to provide accurate and effective data samples for the storage aging model.

[0129] S204. Combine each historical charge / discharge period and its corresponding battery parameter values ​​and health loss to form a second data subset.

[0130] Specifically, the parameter values ​​and health loss of each battery in use during the same historical charge and discharge period of the same energy storage battery can be combined to form a second data subset, providing accurate and effective data samples for the aging function of cyclic use.

[0131] As can be seen from the above technical solution, this embodiment provides an optional method for obtaining multiple first data subsets corresponding to the idle state and multiple second data subsets corresponding to the charge / discharge state. Through this method, the accuracy and completeness of each data subset can be ensured by time period classification and parameter extraction, thereby more realistically reflecting the aging characteristics of the energy storage battery under different usage scenarios and improving the reliability of the idle aging function and the cyclic aging function.

[0132] In some embodiments of this application, the process of step S3, obtaining the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery, is described in detail below:

[0133] S30. Obtain the target temperature and target humidity of the target energy storage battery.

[0134] Specifically, the average temperature and average humidity values ​​in the target energy storage battery compartment can be obtained from the latest health update time to the current time as the target temperature and target humidity, respectively.

[0135] The target temperature can be obtained from the environmental sensors installed in the corresponding energy storage power station, and the target humidity is also obtained by real-time collection and statistics from the environmental sensors. This can accurately reflect the actual environmental conditions of the target energy storage battery during the current evaluation period, and provide environmental parameter support for subsequent calculation of aging loss.

[0136] S31. Obtain the target storage battery's target storage time, target state of charge, and target storage voltage.

[0137] Specifically, the total time the target energy storage battery has been idle since the latest health update to the current time can be used as the target idle time.

[0138] The average standby voltage from the latest health update time to the current time can be obtained as the target standby voltage.

[0139] The target storage duration, target state of charge, and target storage voltage together constitute the target storage data, which fully covers the core parameters affecting the aging of energy storage batteries under storage scenarios, ensuring the accuracy of the calculation of the first degree of health loss.

[0140] S32. Obtain the target usage time, target overcharge count, target over-discharge count, target discharge depth, target current value, and target current rate of the target energy storage battery.

[0141] Specifically, the total usage time of the target energy storage battery from the latest health update time to the current time can be used as the target usage time;

[0142] The total number of overcharges of the target energy storage battery from the latest health update time to the current time can be counted as the target overcharge count;

[0143] The total number of over-discharges of the target energy storage battery from the latest health update time to the current time can be counted as the target over-discharge count;

[0144] The average depth of discharge of the target energy storage battery from the latest health update time to the current time can be obtained as the target depth of discharge;

[0145] The average current of the target energy storage battery from the latest health update time to the current time can be obtained as the target current value;

[0146] The average current rate of the target energy storage battery from the latest health update time to the current time can be obtained as the target current rate.

[0147] It can normalize the target temperature, target humidity, target storage time, target state of charge, target storage voltage, target usage time, target overcharge count, target over-discharge count, target depth of discharge, target current value, and target current rate.

[0148] Normalized data eliminates the impact of differences in parameter magnitudes on calculation results, improving the stability and accuracy of health loss calculation. The parameters mentioned above comprehensively cover all core parameters affecting aging of the target energy storage battery under both idle and charge-discharge cycle scenarios since the last health assessment, providing accurate input data support for subsequent scenario-specific health loss calculations.

[0149] As can be seen from the above technical solution, this embodiment provides an optional method for obtaining the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery. Through this method, key parameters of the target energy storage battery under different states can be obtained comprehensively and accurately, providing detailed data support for subsequent health assessments and ensuring the accuracy and reliability of the assessment results.

[0150] In some embodiments of this application, the process of calculating step S4, which involves calculating a first health loss degree to characterize the level of health damage to the target energy storage battery after aging in the target storage state, based on the target environmental parameters and the target storage data, combined with the storage aging function, is described in detail below:

[0151] S40. Substitute the target temperature, target humidity, target storage time, target state of charge, and target storage voltage into the storage aging function to calculate the first degree of health loss.

[0152] Specifically, the aging settling function can be defined as follows:

[0153] ;

[0154] In the formula, The first health loss level is represented by A; the target state of charge is represented by T; the target temperature is represented by U; the target settling voltage is represented by Q; and the target humidity is represented by Q. The target settling time; e is the first error coefficient; 1 is the charge factor; b is the environmental factor; c is the storage factor; d is the humidity factor.

[0155] The target temperature, target humidity, target storage time, target state of charge, and target storage voltage can be substituted into the above storage aging function to calculate the first degree of health loss.

[0156] As can be seen from the above technical solution, this embodiment provides an optional method for calculating the first health loss degree based on the target environmental parameters and the target storage data, combined with the storage aging function. Through this method, the key parameters of the target energy storage battery in the target storage state can be accurately substituted into the storage aging function to obtain the first health loss degree, which can intuitively reflect the degree of health damage caused by aging in the storage state. The quantitative result is more accurate and closely matches the actual storage aging situation of the target battery, effectively avoiding the problem of distortion in the estimation of the storage aging degree under different environments and operating parameters.

[0157] In some embodiments of this application, the process of calculating step S5, based on the target charge-discharge data and the cyclic aging function, a second health loss degree used to characterize the level of health damage of the target energy storage battery after aging under the target charge-discharge state, is described in detail below:

[0158] S50. Substitute the target usage time, target overcharge count, target over-discharge count, target discharge depth, target current value, and target current rate into the cyclic aging function to calculate the second health loss degree.

[0159] Specifically, the aging function can be used cyclically as follows:

[0160] ;

[0161] In the formula, The second degree of health loss; f is the rate coefficient; denoted as target current rate; g is the current coefficient; The target current value; Target temperature; k is the usage coefficient; t is the target usage duration; P is the sum of the target overcharge count and the target over-discharge count; m is the count coefficient; This is the second error coefficient.

[0162] The target temperature, target usage time, target overcharge count, target over-discharge count, target depth of discharge, target current value, and target current rate can be substituted into the cyclic aging function to calculate the second health loss degree.

[0163] As can be seen from the above technical solution, this embodiment provides an optional method for calculating the second health loss degree based on the target charge-discharge data and the cyclic aging function. Through this method, key parameters of the target energy storage battery under the target charge-discharge state can be substituted into the cyclic aging function to obtain the second health loss degree, which directly reflects the degree of health damage caused by aging under charge-discharge states. This achieves scenario-specific quantification of health loss, effectively distinguishes the impact of different parameters on battery aging during charge-discharge processes, further improves the accuracy of the health loss quantification results, and solves the problem of large deviations in the current unified estimation of aging degree across all scenarios.

[0164] In some embodiments of this application, step S6, which involves assessing whether the target energy storage battery needs to be replaced based on the first health loss level and the second health loss level, is described in detail below:

[0165] S60. Determine a first weight to characterize the impact of the target shelving state on the target energy storage battery.

[0166] Specifically, based on the historical operating data of the target energy storage battery, the proportion of time in the overall operating cycle that is in the standby state can be analyzed, as well as the degree of impact of battery aging on battery performance and lifespan in the standby state, and the first weight can be determined comprehensively.

[0167] S61. Determine a second weight to characterize the degree of influence of the target charge / discharge state on the target energy storage battery.

[0168] Specifically, based on the historical operating data of the target energy storage battery, the proportion of time in the charge and discharge state in the overall operating cycle can be analyzed, as well as the degree of impact of battery aging on battery performance and lifespan during the charge and discharge state, and the second weight can be determined comprehensively.

[0169] S62. Calculate the sum of the first product and the second product as the health loss value of the target energy storage battery; wherein, the first product is the product between the first health loss degree and the first weight, and the second product is the product between the second health loss degree and the second weight.

[0170] Specifically, the product of the first health loss degree and the first weight can be calculated as the first product;

[0171] The second product can be calculated by multiplying the second health loss degree by the second weight.

[0172] S63. Determine the health variable corresponding to the target energy storage battery.

[0173] Specifically, the health variables of the pre-generated target energy storage battery can be obtained.

[0174] This health variable is used to record the health status of the target energy storage battery at different times.

[0175] S64. Update the value of the health variable based on the health loss value.

[0176] Specifically, the difference between the value of the health status variable and the value of health loss can be used as the new value of the health status variable.

[0177] S65. When the updated variable value is lower than the preset health threshold, it is determined that the target energy storage battery needs to be replaced.

[0178] Specifically, the latest variable value can be compared with a preset health threshold. If the latest variable value is lower than the preset health threshold, it is determined that the target energy storage battery needs to be replaced.

[0179] S66. When the updated variable value is not lower than the health threshold, it is determined that the target energy storage battery does not need to be replaced.

[0180] Specifically, the health threshold can indicate the end of the lifespan of the corresponding energy storage battery.

[0181] The average health of each historical energy storage battery, matched with the configuration type, at the end of its life can be used as the health threshold.

[0182] For example, the health threshold can be 0.8.

[0183] As can be seen from the above technical solution, this embodiment provides an optional method for assessing whether the target energy storage battery needs to be replaced based on the first health loss degree and the second health loss degree. By introducing weight coefficients corresponding to different states and combining the first and second health loss degrees calculated for different scenarios, the overall health loss value of the target energy storage battery for the current cycle is comprehensively calculated. Then, by updating the health variable and comparing it with a preset threshold, the health status of the target energy storage battery can be accurately determined. This comprehensively considers the aging of the energy storage battery under both idle and charge / discharge states, providing a scientific and reasonable basis for the maintenance and replacement of the energy storage battery. It promptly identifies aging batteries that need replacement and avoids premature or delayed replacement, effectively reducing usage costs and ensuring the stable operation of the energy storage system. This ensures the overall stability and safety of the energy storage system and avoids operational failures caused by battery aging.

[0184] Next, we will combine Figure 2 The energy storage battery management device provided in this application is described in detail. The energy storage battery management device described below can be compared with the energy storage battery management method described above.

[0185] See Figure 2It can be observed that the energy storage battery management device may include:

[0186] Configuration type determination module 10 is used to determine the configuration type of the target energy storage battery;

[0187] The function acquisition module 20 is used to acquire the shelving aging function and the cyclic aging function matched to the target energy storage battery according to the configuration type.

[0188] Data acquisition module 30 is used to acquire target environmental parameters, target storage data and target charge and discharge data of the target energy storage battery;

[0189] The function combination module 40 is used to calculate, based on the target environmental parameters and the target shelving data, and in conjunction with the shelving aging function, a first health loss degree that characterizes the level of health damage to the target energy storage battery after aging in the target shelving state.

[0190] The loss calculation module 50 is used to calculate a second health loss degree based on the target charge and discharge data and the cyclic aging function, which characterizes the level of health damage of the target energy storage battery after aging in the target charge and discharge state.

[0191] The battery evaluation module 60 is used to evaluate whether the target energy storage battery needs to be replaced based on the first health loss degree and the second health loss degree.

[0192] As can be seen from the above technical solutions, the energy storage battery management device provided in this application can combine the configuration type determination module 10, function acquisition module 20, data acquisition module 30, and function combination module 40 to use the above two types of targeted aging laws to replace the generalized model for calculating the health loss degree. This solves the calculation deviation problem caused by different aging laws of batteries with different configurations, improves the calculation reliability of this application, and requires fewer resources for function calculation. In resource-limited scenarios, the health of multiple energy storage batteries can be evaluated in parallel. This application can evaluate whether the target energy storage battery needs to be replaced based on the first health loss degree and the second health loss degree. Based on this, this application can comprehensively evaluate the battery health in two main battery aging scenarios: idle state and charge / discharge state. This solves the problem of incomplete evaluation caused by unclear loss type separation and the neglect of some losses in traditional evaluation. As can be seen, the energy storage battery management device of this application can complete the health assessment by using less computing resources, taking into account the losses caused by the target energy storage battery due to being idle and the losses caused by cyclic use. While ensuring the accuracy of the assessment, it better meets the real-time and high-efficiency requirements of the power grid system for the safety monitoring of a large number of energy storage batteries. It can effectively reduce the overall cost of battery health monitoring in large-scale energy storage power stations, balance assessment accuracy and operating efficiency, and ensure the stable and reliable operation of the grid-side energy storage system.

[0193] Furthermore, the function acquisition module 20 may include:

[0194] A data subset acquisition unit is used to acquire multiple first data subsets corresponding to the idle state, and multiple second data subsets corresponding to the charge / discharge state; wherein each first data subset includes the historical idle period, idle environment parameters, idle health loss degree, and different idle battery parameter values ​​corresponding to the historical energy storage battery of the configuration type; each second data subset includes the historical charge / discharge period, usage health loss degree, and different usage battery parameter values ​​corresponding to the historical energy storage battery of the configuration type.

[0195] The aging function fitting unit is used to perform polynomial fitting on each first data subset to generate an aging function that matches the configuration type.

[0196] A cyclic aging model fitting unit is used to perform multinomial fitting on each second subset of data to generate a cyclic aging model that matches the configuration type.

[0197] Furthermore, the data subset acquisition unit may include:

[0198] The first data subset acquisition subunit is used to determine multiple historical energy storage batteries that match the configuration type, and to determine the replacement information, historical health loss sequence, historical operation sequence and historical environment sequence of each historical energy storage battery, wherein the historical operation sequence includes the time points and battery parameter values ​​corresponding to different operating states.

[0199] The second data subset acquisition subunit is used to determine multiple historical storage periods and multiple historical charge and discharge periods of the corresponding historical energy storage battery from the historical operation sequence of each historical energy storage battery, and to extract the parameter values ​​of each stored battery corresponding to each historical storage period and the parameter values ​​of each used battery corresponding to each historical charge and discharge period from each historical operation sequence.

[0200] The third data subset acquisition subunit is used to combine each historical storage battery’s historical storage period, each historical charge and discharge period, historical environment sequence and historical health loss sequence to generate the storage health loss degree and storage environment parameters corresponding to each historical storage battery’s historical storage period, and the usage health loss degree corresponding to each historical charge and discharge period.

[0201] The fourth data subset acquisition subunit is used to combine each historical storage period and its corresponding storage environment parameters, the parameter values ​​of each stored battery and the storage health loss degree to form a first data subset;

[0202] The fifth data subset acquisition subunit is used to combine each historical charge and discharge period and its corresponding battery parameter values ​​and usage health loss degree to form a second data subset.

[0203] Furthermore, the data acquisition module 30 may include:

[0204] The first data acquisition unit is used to acquire the target temperature and target humidity of the target energy storage battery.

[0205] The second data acquisition unit is used to acquire the target storage battery's target storage time, target state of charge, and target storage voltage.

[0206] The third data acquisition unit is used to acquire the target usage time, target overcharge count, target over-discharge count, target discharge depth, target current value, and target current rate of the target energy storage battery.

[0207] Furthermore, the function combination module 40 may include:

[0208] The first health loss calculation unit is used to substitute the target temperature, target humidity, target storage time, target state of charge, and target storage voltage into the storage aging function to calculate the first health loss.

[0209] Furthermore, the loss calculation module 50 may include:

[0210] The second health loss calculation unit is used to substitute the target usage time, target overcharge times, target over-discharge times, target discharge depth, target current value, and target current rate into the cyclic aging function to calculate the second health loss.

[0211] Furthermore, the battery evaluation module 60 may include:

[0212] The first battery evaluation unit is used to determine a first weight to characterize the degree of influence of the target idle state on the target energy storage battery.

[0213] The second battery evaluation unit is used to determine a second weight for characterizing the degree of influence of the target charge and discharge state on the target energy storage battery.

[0214] The third battery evaluation unit is used to calculate the sum of the first product and the second product as the health loss value of the target energy storage battery; wherein, the first product is the product between the first health loss degree and the first weight, and the second product is the product between the second health loss degree and the second weight.

[0215] The fourth battery evaluation unit is used to determine the health variables corresponding to the target energy storage battery;

[0216] The fifth battery evaluation unit is used to update the value of the health status variable based on the health loss value;

[0217] The sixth battery evaluation unit is used to determine that the target energy storage battery needs to be replaced when the updated variable value is lower than a preset health threshold.

[0218] The seventh battery evaluation unit is used to determine that the target energy storage battery does not need to be replaced when the updated variable value is not lower than the health threshold.

[0219] The energy storage battery management device provided in this application embodiment can be applied to energy storage battery management equipment, such as PC terminals, cloud platforms, servers, and server clusters. Optionally, Figure 3 The hardware structure block diagram of the energy storage battery management device is shown below. Figure 3 The hardware structure of an energy storage battery management device may include: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;

[0220] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0221] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0222] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0223] The memory stores a program, which the processor can call. The program is used for:

[0224] Determine the configuration type of the target energy storage battery;

[0225] Based on the configuration type, obtain the shelving aging function and the cyclic aging function matched for the target energy storage battery;

[0226] Obtain the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery;

[0227] Based on the target environmental parameters and the target storage data, and combined with the storage aging function, a first health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging in the target storage state.

[0228] Based on the target charge-discharge data and the cyclic aging function, a second health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging under the target charge-discharge state.

[0229] Based on the first health loss level and the second health loss level, assess whether the target energy storage battery needs to be replaced.

[0230] Optionally, the refined and extended functions of the program can be referred to the above description.

[0231] This application embodiment also provides a readable storage medium that can store a program suitable for execution by a processor, the program being used for:

[0232] Determine the configuration type of the target energy storage battery;

[0233] Based on the configuration type, obtain the shelving aging function and the cyclic aging function matched for the target energy storage battery;

[0234] Obtain the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery;

[0235] Based on the target environmental parameters and the target storage data, and combined with the storage aging function, a first health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging in the target storage state.

[0236] Based on the target charge-discharge data and the cyclic aging function, a second health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging under the target charge-discharge state.

[0237] Based on the first health loss level and the second health loss level, assess whether the target energy storage battery needs to be replaced.

[0238] Optionally, the refined and extended functions of the program can be referred to the above description.

[0239] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0240] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0241] In the several embodiments provided in this application, it should be understood that the disclosed devices, electronic devices, computer storage media, computer program products, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units and modules 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, or the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0242] 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.

[0243] Furthermore, the functional units in the various embodiments of the present invention 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 as a software functional unit.

[0244] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0245] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments of this application can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing energy storage batteries, characterized in that, include: Determine the configuration type of the target energy storage battery; Based on the configuration type, obtain the shelving aging function and the cyclic aging function matched for the target energy storage battery; Obtain the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery; Based on the target environmental parameters and the target storage data, and combined with the storage aging function, a first health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging in the target storage state. Based on the target charge-discharge data and the cyclic aging function, a second health loss degree is calculated to characterize the level of health damage of the target energy storage battery after aging under the target charge-discharge state. Based on the first health loss level and the second health loss level, assess whether the target energy storage battery needs to be replaced.

2. The energy storage battery management method according to claim 1, characterized in that, The step of obtaining the resting aging function and the cyclic use aging function matched to the target energy storage battery according to the configuration type includes: Acquire multiple first data subsets corresponding to the idle state, and multiple second data subsets corresponding to the charge / discharge state; wherein each first data subset contains the historical idle period, idle environment parameters, idle health loss degree, and different idle battery parameter values ​​corresponding to the historical energy storage battery of the configuration type; each second data subset contains the historical charge / discharge period, usage health loss degree, and different usage battery parameter values ​​corresponding to the historical energy storage battery of the configuration type. Perform a polynomial fit on each first data subset to generate a shelving aging function that matches the configuration type; Multinomial fitting is performed on each second subset of data to generate a recurring aging model that matches the configuration type.

3. The energy storage battery management method according to claim 2, characterized in that, The acquisition of multiple first data subsets corresponding to the idle state and multiple second data subsets corresponding to the charging / discharging state includes: Identify multiple historical energy storage batteries that match the configuration type, and determine the replacement information, historical health loss sequence, historical operation sequence, and historical environment sequence for each historical energy storage battery. The historical operation sequence includes the time points and battery parameter values ​​corresponding to different operating states. From the historical operation sequence of each historical energy storage battery, determine multiple historical storage periods and multiple historical charge and discharge periods for the corresponding historical energy storage battery, and extract the parameter values ​​of each stored battery corresponding to each historical storage period and the parameter values ​​of each used battery corresponding to each historical charge and discharge period from each historical operation sequence. By combining the historical storage periods, historical charge and discharge periods, historical environmental sequences, and historical health loss sequences of each historical energy storage battery, the storage health loss and storage environment parameters corresponding to each historical storage period, as well as the usage health loss corresponding to each historical charge and discharge period, are generated. Each historical storage period, its corresponding storage environment parameters, the parameter values ​​of each stored battery, and the degree of storage health loss are combined to form a first data subset; Each historical charge / discharge period, along with its corresponding battery parameter values ​​and usage health loss, is combined to form a second data subset.

4. The energy storage battery management method according to claim 1, characterized in that, The acquisition of the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery includes: Obtain the target temperature and target humidity of the target energy storage battery; Obtain the target storage battery's target storage time, target state of charge, and target storage voltage; The target usage time, target overcharge count, target over-discharge count, target depth of discharge, target current value, and target current rate of the target energy storage battery are obtained.

5. The energy storage battery management method according to claim 4, characterized in that, The calculation of the first health loss degree, characterizing the level of health damage to the target energy storage battery after aging in the target storage state, based on the target environmental parameters and the target storage data, combined with the storage aging function, includes: The first degree of health loss is calculated by substituting the target temperature, target humidity, target storage time, target state of charge, and target storage voltage into the storage aging function.

6. The energy storage battery management method according to claim 4, characterized in that, The calculation of a second health loss degree, based on the target charge-discharge data and the cyclic aging function, to characterize the level of health damage to the target energy storage battery after aging under the target charge-discharge state, includes: The second health loss degree is calculated by substituting the target usage duration, target overcharge count, target over-discharge count, target discharge depth, target current value, and target current rate into the cyclic aging function.

7. The energy storage battery management method according to claim 1, characterized in that, The step of assessing whether the target energy storage battery needs to be replaced based on the first health loss level and the second health loss level includes: Determine a first weight to characterize the impact of the target idle state on the target energy storage battery; Determine a second weight to characterize the degree of influence of the target charge / discharge state on the target energy storage battery; The sum of the first product and the second product is calculated as the health loss value of the target energy storage battery; wherein, the first product is the product between the first health loss degree and the first weight, and the second product is the product between the second health loss degree and the second weight; Determine the health variables corresponding to the target energy storage battery; The value of the health status variable is updated based on the health loss value; When the updated variable value is lower than a preset health threshold, it is determined that the target energy storage battery needs to be replaced. If the updated variable value is not lower than the health threshold, it is determined that the target energy storage battery does not need to be replaced.

8. An energy storage battery management device, characterized in that, include: The configuration type determination module is used to determine the configuration type of the target energy storage battery; The function acquisition module is used to acquire the shelving aging function and the cyclic aging function matched to the target energy storage battery according to the configuration type. The data acquisition module is used to acquire the target environmental parameters, target storage data, and target charge / discharge data of the target energy storage battery. The function combination module is used to calculate the first health loss degree, which characterizes the level of health damage of the target energy storage battery after aging in the target storage state, based on the target environmental parameters and the target storage data, combined with the storage aging function. The loss calculation module is used to calculate a second health loss degree based on the target charge-discharge data and the cyclic aging function, which characterizes the level of health damage of the target energy storage battery after aging under the target charge-discharge state. The battery evaluation module is used to evaluate whether the target energy storage battery needs to be replaced based on the first health loss level and the second health loss level.

9. An energy storage battery management device, characterized in that, Including memory and processor; The memory is used to store programs; The processor is configured to execute the program to implement each step of the energy storage battery management method as described in any one of claims 1-7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the energy storage battery management method as described in any one of claims 1-7.

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