A method for cascade utilization of a battery, a terminal device and a storage medium

Abstract

The application discloses a kind of battery's gradient utilization method, terminal equipment and storage medium, the method determines corresponding key battery characteristics and reference threshold according to current application scene, with each key battery characteristics as coordinate axis, constructs the battery characteristic coordinate system corresponding to current scene;According to the reference threshold of each key battery characteristics, the available battery area is constructed in battery characteristic coordinate system;The key battery characteristic parameter value of the secondary battery to be utilized is extracted, and the first projection point corresponding to the secondary battery to be utilized is generated in the battery characteristic coordinate system;When determining that the first projection point is in the available battery area, the secondary battery to be utilized is used in current application scene. Through the implementation of the embodiment of the application, the dynamic adaptation of application scene and secondary battery is realized, and the problem that the secondary battery does not match the application scene is avoided.

Description

Technical Field

[0001] This invention relates to the field of battery reuse, and more particularly to a method, terminal device and storage medium for battery reuse. Background Technology

[0002] With the development of new energy vehicles, the number of retired batteries is gradually increasing. However, retired batteries are not completely ineffective. The power batteries retired from new energy vehicles usually retain 70% to 80% of their remaining capacity. This remaining capacity is sufficient to meet the energy storage needs in other scenarios. Therefore, the secondary use of retired power batteries is gradually increasing.

[0003] Current technologies for reusing batteries typically test retired batteries according to uniform parameter testing standards, reusing only those that meet the standards. However, they fail to consider the varying battery characteristic requirements for different application scenarios. Taking residential and commercial energy storage as examples, residential energy storage systems often consist of small- to medium-capacity modules connected in series, and the consistency of individual cell parameters directly determines system safety and lifespan. Long cycle life covers the core operating cycle of a residential photovoltaic system, avoiding frequent maintenance disruptions to family life. Furthermore, the unique characteristic of residential scenarios is the close proximity of battery equipment to the living environment, making safety the highest priority. Thermal runaway temperature is a key indicator for measuring a battery's resistance to overheating risk. The higher this temperature, the lower the probability of the battery catching fire or exploding under abnormal conditions (such as overcharging, short circuits, and high-temperature environments), fundamentally protecting the personal safety of family members and the safety of family property. Therefore, for residential energy storage scenarios, the key battery characteristics to focus on are individual cell consistency, cycle life, and thermal runaway temperature. For commercial energy storage scenarios, the core requirements are peak shaving and valley filling, peak-valley arbitrage, and demand-side response, needing to cope with significant fluctuations in commercial load. The charge / discharge rate directly determines the battery's power output capability. Furthermore, commercial energy storage requires 1-2 charge / discharge cycles daily and often operates at high rates; therefore, cycle life directly determines the system's operational lifespan and economic viability. Charge / discharge efficiency is directly related to energy loss and arbitrage profits in commercial energy storage. In commercial scenarios with high daily charge / discharge frequencies, even a 1% decrease in efficiency will significantly compress the profit margin for peak-valley arbitrage over the long term. Therefore, for commercial energy storage scenarios, the key battery characteristics to focus on are charge / discharge rate, cycle life, and charge / discharge efficiency.

[0004] Existing battery reuse solutions are tested and screened based on uniform parameter testing standards, without taking into account the differences in application scenarios. This often results in the selected batteries being unsuitable for the current application scenario, leading to mismatch between the reused batteries and the application scenario, and low utilization efficiency. Summary of the Invention

[0005] This invention provides a method for the cascade utilization of batteries, a terminal device, and a storage medium, which can reduce the problem that existing secondary battery processing schemes often result in batteries that are not compatible with current application scenarios.

[0006] One embodiment of the present invention provides a method for the cascade utilization of batteries, comprising:

[0007] Based on the current application scenario, key battery characteristics and baseline thresholds for each key battery characteristic are determined. These baseline thresholds characterize the minimum threshold values ​​that the corresponding key battery characteristics of the secondary battery must meet when used in the current application scenario. The application scenarios include: residential energy storage, commercial energy storage, and grid energy storage. When the application scenario is residential energy storage, the key battery characteristics are: cell consistency, cycle life, and thermal runaway temperature. When the application scenario is commercial energy storage, the key battery characteristics are: charge / discharge rate, cycle life, and charge / discharge efficiency. When the application scenario is grid energy storage, the key battery characteristics are: available capacity, power response speed, and cell consistency.

[0008] Using each key battery characteristic as a coordinate axis, a battery feature coordinate system corresponding to the current scenario is constructed, and a usable battery region is constructed within the battery characteristic coordinate system based on the benchmark thresholds of each key battery characteristic; the usable battery region is used to characterize the parameter range of the key battery characteristics of the secondary battery when the current application scenario is met.

[0009] Extract the key battery characteristic parameter values ​​of the secondary battery to be utilized, and generate the first projection point corresponding to the secondary battery to be utilized in the battery feature coordinate system based on the key battery characteristic parameter values ​​of the secondary battery to be utilized.

[0010] When the first projection point is determined to be within the available battery area, the secondary battery to be utilized is applied in the current application scenario.

[0011] Furthermore, it also includes: when there are multiple sets of first projection points corresponding to the secondary batteries to be utilized within the available battery area, constructing a first threshold sectional surface corresponding to each reference threshold in the battery feature coordinate system according to the reference threshold.

[0012] Obtain the ideal thresholds for key battery characteristics in the current application scenario, and construct the second threshold sectional plane corresponding to each ideal threshold in the battery feature coordinate system based on the ideal thresholds;

[0013] Calculate the first vertical distance from each first threshold section to the corresponding second threshold section;

[0014] For each secondary battery to be utilized, calculate the second vertical distance from the first projection point to each first threshold section; calculate the ratio of each second vertical distance to the corresponding first vertical distance to obtain the parameter redundancy ratio of each key battery characteristic; calculate the weighted parameter redundancy of each key battery characteristic based on the parameter redundancy ratio of each key battery characteristic and the weight coefficient of each key battery characteristic parameter.

[0015] Based on the redundancy of the weighted parameters of each key battery characteristic, the secondary batteries to be utilized are grouped to form different battery clusters.

[0016] Furthermore, the step of grouping the secondary batteries to be utilized into different battery clusters based on the weighted parameter redundancy of each key battery characteristic includes:

[0017] Using the weighted parameter redundancy of key battery characteristics as the coordinate axis, a redundancy coordinate system is constructed, and according to the weighted parameter redundancy of each key battery characteristic of each secondary battery to be utilized, the corresponding second projection point is generated in the redundancy coordinate system.

[0018] The second projection point with the smallest standard deviation of the weighted parameter redundancy of each key battery characteristic among all the second projection points is taken as the center projection point.

[0019] Calculate the Euclidean distance from each second projection point to the central projection point;

[0020] Based on the Euclidean distance, the secondary batteries to be utilized are grouped using the K-means algorithm, and then a battery cluster is constructed based on the secondary batteries to be utilized within the same group.

[0021] Furthermore, the step of grouping the secondary batteries to be utilized into different battery clusters based on the weighted parameter redundancy of each key battery characteristic includes:

[0022] Based on the weighted parameter redundancy of each key battery characteristic, the comprehensive redundancy score of each secondary battery to be utilized is calculated.

[0023] Based on the comprehensive redundancy score of each secondary battery to be utilized, each secondary battery to be utilized is divided into several levels of groups; each group corresponds to a comprehensive redundancy score range, and the comprehensive redundancy score of each secondary battery to be utilized in the same group is within the corresponding comprehensive redundancy score range.

[0024] Based on each group, different levels of battery clusters are constructed.

[0025] Furthermore, it also includes: when there are multiple application scenarios, and the first projection point corresponding to a secondary battery to be utilized is within the available battery area of ​​each application scenario, the secondary battery to be utilized is applied to the application scenario with the highest comprehensive redundancy score based on the comprehensive redundancy score of the secondary battery to be utilized in each application scenario.

[0026] Furthermore, based on the current application scenario, the key battery characteristics of the current application scenario and the benchmark thresholds for each key battery characteristic are determined, including:

[0027] When the current application scenario is a home energy storage scenario, the peak daily average electricity load of the current household, the peak maximum load of the home energy storage, and a preset voltage deviation baseline value are obtained; the load demand intensity of the current household is determined based on the peak daily average electricity load and the peak maximum load of the home energy storage; the maximum voltage difference threshold of the individual unit corresponding to the current household is determined based on the load demand intensity, the voltage deviation baseline value, and the preset load influence coefficient; the maximum voltage difference threshold of the individual unit is used as the benchmark threshold corresponding to the consistency of the individual units in the home energy storage scenario.

[0028] Obtain the current household's planned usage years, the shortest planned usage years of the household energy storage, the longest planned usage years of the household energy storage, and the base value of the remaining cycle count; determine the relative demand intensity of the usage years based on the current household's planned usage years, the shortest planned usage years of the household energy storage, and the longest planned usage years of the household energy storage; determine the battery's remaining cycle count threshold corresponding to the current household based on the relative demand intensity of the usage years, the base value of the remaining cycle count, and the preset age influence coefficient; and use the battery's remaining cycle count threshold as the benchmark threshold corresponding to the cycle life in the household energy storage scenario.

[0029] The system obtains the average maximum daily temperature at the current home energy storage installation location, the ideal installation temperature for home energy storage, and the baseline safe temperature value; it determines the relative temperature deviation based on the average maximum daily temperature at the home energy storage installation location and the ideal installation temperature for home energy storage; it determines the thermal runaway temperature threshold corresponding to the current home based on the relative temperature deviation, the baseline safe temperature value, and a preset ambient temperature influence coefficient; and it uses the thermal runaway temperature threshold as the benchmark threshold corresponding to the thermal runaway temperature in the home energy storage scenario.

[0030] Furthermore, based on the current application scenario, the key battery characteristics of the current application scenario and the benchmark thresholds for each key battery characteristic are determined, including:

[0031] When the current application scenario is commercial energy storage, the following steps are taken: First, obtain the daily charge / discharge counts for the current commercial user, the minimum daily charge / discharge counts for commercial energy storage, the maximum daily charge / discharge counts for commercial energy storage, and the base value of the commercial energy storage charge / discharge rate. Then, determine the charge / discharge demand intensity based on these parameters. Finally, determine the charge / discharge rate threshold for the current commercial user based on the charge / discharge demand intensity, the base value of the commercial energy storage charge / discharge rate, and a preset frequency impact coefficient. This charge / discharge rate threshold is then used as the benchmark threshold for the charge / discharge rate in the commercial energy storage scenario.

[0032] Obtain the planned operating years of the current commercial user, the basic operating years of the commercial energy storage, and the preset basic value of the number of commercial cycles; determine the cycle life demand intensity based on the planned operating years of the current commercial user, the daily charge and discharge frequency of the current commercial user, and the basic operating years of the commercial energy storage; calculate the commercial cycle life threshold based on the cycle life demand intensity and the basic value of the number of commercial cycles; and use the commercial cycle life threshold as the benchmark threshold corresponding to the cycle life in the commercial energy storage scenario.

[0033] Obtain the peak-valley electricity price difference, the minimum economic price difference for commercial energy storage, the maximum economic price difference for commercial energy storage, and the preset basic value of charging and discharging efficiency in the current commercial user's region; calculate the relative return sensitivity based on the peak-valley electricity price difference, the minimum economic price difference for commercial energy storage, and the maximum economic price difference for commercial energy storage in the current commercial user's region; calculate the charging and discharging efficiency threshold based on the relative return sensitivity, the basic value of charging and discharging efficiency, and the preset price difference influence coefficient; and use the charging and discharging efficiency threshold as the benchmark threshold corresponding to the charging and discharging efficiency in the commercial energy storage scenario.

[0034] Furthermore, based on the current application scenario, the key battery characteristics of the current application scenario and the benchmark thresholds for each key battery characteristic are determined, including:

[0035] When the current application scenario is grid energy storage, the real-time load fluctuation amplitude of the power grid, the historical maximum load fluctuation amplitude of the power grid, and the basic value of the available capacity of the power grid are obtained; the relative load fluctuation intensity is determined based on the real-time load fluctuation amplitude and the historical maximum load fluctuation amplitude of the power grid; the available capacity threshold is determined based on the relative load fluctuation intensity, the basic value of the available capacity of the power grid, and the preset load fluctuation influence coefficient; the available capacity threshold is used as the benchmark threshold of the available capacity in the grid energy storage scenario.

[0036] The system obtains the real-time frequency, rated frequency, maximum allowable frequency deviation of the power grid, and a base value for frequency regulation response delay. It calculates the real-time frequency deviation based on the real-time and rated frequencies of the power grid, and determines the relative risk intensity of the frequency deviation based on the real-time frequency deviation and the maximum allowable frequency deviation of the power grid. It then determines a delay time threshold based on the relative risk intensity of the frequency deviation, the base value for frequency regulation response delay, and a preset frequency deviation influence coefficient. Finally, it uses the delay time threshold as a benchmark value for the power response speed in a power grid energy storage scenario.

[0037] Obtain the actual required battery cell cluster size, the baseline value of the battery cell cluster size, and the preset baseline value of grid voltage deviation for the power grid; determine the relative risk level of the battery cluster size based on the actual required battery cell cluster size and the baseline value of the battery cell cluster size; determine the maximum voltage deviation threshold of the individual cells in the power grid based on the relative risk level of the battery cluster size, the preset baseline value of grid voltage deviation, and the cluster size influence coefficient; and use the maximum voltage deviation threshold of the individual cells in the power grid energy storage scenario as the baseline value for cell consistency.

[0038] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments;

[0039] One embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the battery cascade utilization method according to any one of the present invention.

[0040] Based on the above method embodiments, the present invention provides a corresponding storage medium embodiment;

[0041] One embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform the battery cascade utilization method according to any one of the present invention.

[0042] The implementation of this invention provides the following beneficial effects: This invention provides a method for the tiered utilization of batteries, a terminal device, and a storage medium. The method first determines the key battery characteristics and corresponding benchmark thresholds based on the current application scenario. Then, using each key battery characteristic as a coordinate axis, a battery feature coordinate system corresponding to the current scenario is constructed. Based on the benchmark thresholds of each key battery characteristic, a usable battery region is constructed within the battery characteristic coordinate system. The usable battery region characterizes the parameter range of the key battery characteristics of the secondary battery when satisfying the current application scenario. Next, the key battery characteristic parameter values ​​of the secondary battery to be utilized are extracted, and based on these values, a first projection point corresponding to the secondary battery to be utilized is generated within the battery feature coordinate system. When the first projection point is determined to be within the usable battery region, the secondary battery to be utilized is applied in the current application scenario. By implementing this invention, key battery characteristic parameters corresponding to the secondary battery to be utilized can be extracted specifically according to actual application scenario requirements. The extracted parameters can be visually determined based on coordinate axes to determine whether they meet the requirements of the current application scenario. Only when they meet the requirements of the current application scenario are the secondary batteries to be utilized applied in the current application scenario. The key battery characteristics vary depending on the application scenario, thus achieving dynamic adaptation between the application scenario and the secondary battery, avoiding the problem of mismatch between the secondary battery and the application scenario. Attached Figure Description

[0043] Figure 1 This is a schematic flowchart of a method for the cascade utilization of batteries according to an embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, one embodiment of the present invention provides a method for the cascade utilization of batteries, which includes at least the following steps:

[0046] Step S1: Based on the current application scenario, determine the key battery characteristics and benchmark thresholds for each key battery characteristic in the current application scenario. The benchmark thresholds are used to characterize the minimum threshold values ​​that the parameter values ​​of the key battery characteristics corresponding to the secondary battery need to meet when the secondary battery is used in the current application scenario. The application scenarios include: home energy storage scenario, commercial energy storage scenario, and grid energy storage scenario. When the application scenario is a home energy storage scenario, the key battery characteristics are: cell consistency, cycle life, and thermal runaway temperature. When the application scenario is a commercial energy storage scenario, the key battery characteristics are: charge / discharge rate, cycle life, and charge / discharge efficiency. When the application scenario is a grid energy storage scenario, the key battery characteristics are: available capacity, power response speed, and cell consistency.

[0047] Specifically, this invention mainly includes three types of application scenarios: home energy storage, commercial energy storage, and grid energy storage.

[0048] Home energy storage systems are mostly composed of small- to medium-capacity modules connected in series, and the consistency of individual modules directly determines the system's safety and lifespan. Poor consistency can easily lead to localized overcharging and over-discharging during charging and discharging, which not only accelerates battery capacity decay and shortens system lifespan but may also cause safety accidents such as thermal runaway. Since homes are close to residential environments, the consequences of these safety risks are even more severe. Therefore, consistency is fundamental to ensuring the safe and stable operation of home energy storage. Secondly, home energy storage is often paired with photovoltaic (PV) systems, resulting in a long investment payback period (typically 8-10 years). Cycle life directly relates to the system's economic value and replacement costs. Insufficient cycle life (e.g., below 3000 cycles) will lead to premature battery retirement, requiring users to bear additional replacement costs and significantly reducing the benefits of "self-consumption and grid connection of surplus electricity." At the same time, a long cycle life covers the core operating cycle of the home PV system, avoiding frequent maintenance disruptions to family life. Furthermore, the unique characteristic of home scenarios is that battery equipment is closely adjacent to the living environment, making safety the highest priority. Thermal runaway temperature is a key indicator for measuring a battery's resistance to overheating risk. The higher this temperature, the lower the probability of the battery catching fire or exploding under abnormal operating conditions (such as overcharging, short circuits, and high-temperature environments). This fundamentally protects the personal safety of family members and the safety of family property, making it an uncompromising core safety parameter for home energy storage scenarios. Therefore, for home energy storage scenarios, this invention adopts three characteristic parameters—cell consistency, cycle life, and thermal runaway temperature—as the corresponding key battery characteristics.

[0049] For commercial energy storage scenarios, commercial energy storage needs to cope with significant fluctuations in commercial loads (such as shopping malls and industrial parks). The charge / discharge rate directly determines the battery's power output capability: a high discharge rate (≥1C) can meet the power supply demand during peak load periods, while a high charge rate (≥0.5C) can efficiently absorb cheap electricity or surplus solar / wind power during off-peak periods; demand-side response scenarios require short-term pulse rates of 2C-3C to quickly smooth load fluctuations and obtain response revenue. Insufficient rate will lead to a compression of arbitrage opportunities or an inability to meet dispatch requirements, directly impacting commercial profitability. Secondly, commercial energy storage needs to undergo 1-2 charge / discharge cycles daily and is often in a high-rate operating state; cycle life directly determines the system's operating cycle and economics. If the cycle life is too low (e.g., below 4000 cycles), it will lead to frequent battery replacements, increasing equipment costs and potentially affecting the normal operation of commercial users due to downtime maintenance (e.g., power outages in shopping malls, interruptions in factory production lines). A long cycle life can ensure stable operation for 5-8 years, covering the investment return cycle of commercial energy storage projects and guaranteeing continuous returns. Furthermore, charge / discharge efficiency is directly related to energy loss and arbitrage profits in commercial energy storage. Commercial scenarios involve high daily charge / discharge frequencies; a 1% decrease in efficiency over the long term will significantly compress the profit margin for peak-valley arbitrage due to accumulated energy loss. For example, if the system charge / discharge efficiency (AC-AC) drops from 85% to 80%, the annual revenue reduction due to energy loss may reach more than 10% of the project's total revenue. Simultaneously, high charge / discharge efficiency improves energy utilization efficiency, reduces the load pressure on the grid, makes it easier to meet grid connection standards, and ensures stable project operation. Therefore, for commercial energy storage scenarios, this invention adopts charge / discharge rate, cycle life, and charge / discharge efficiency as the corresponding key battery characteristics.

[0050] For grid-connected energy storage scenarios, grid-connected energy storage is a large-scale application. Its core functions are renewable energy consumption, grid frequency regulation and peak shaving, and reserve capacity supply, requiring precise matching with the grid's large-scale energy throughput demands. Rated capacity determines the upper limit of the battery system's energy storage, while the actual usable capacity (with clearly defined attenuation coefficients under different operating conditions) directly affects the accuracy of dispatch commands. Insufficient usable capacity can lead to power shortages during peak load periods or power curtailment during renewable energy generation periods, disrupting the grid's supply-demand balance. In reserve capacity scenarios, insufficient usable capacity may also cause power outages during grid faults, threatening the safe and stable operation of the grid. Secondly, the key value of grid-connected energy storage lies in its rapid response to grid dispatch commands, especially in frequency regulation scenarios. Power response speed (typically required to be ≤20ms) determines whether the battery can promptly smooth grid frequency fluctuations and maintain grid frequency stability. A slow response speed will result in substandard frequency regulation, failure to obtain dispatch benefits, and may even lead to grid safety accidents due to excessive frequency deviation. Meanwhile, rapid power regulation capability enhances the grid's ability to accept new energy sources (photovoltaics and wind power), mitigating the impact of the intermittency and volatility of new energy power generation on the grid. This is a core parameter for grid energy storage to adapt to grid dispatch requirements. Furthermore, grid energy storage systems consist of a massive number of battery cells (typically ≥10MWh), and the consistency of these cells directly determines the overall system performance, lifespan, and safety redundancy. Excessive deviations in cell voltage, capacity, and internal resistance can lead to unbalanced charging and discharging, causing some cells to prematurely enter their decay phase, thus dragging down the available capacity and cycle life of the entire system. More seriously, inconsistency can trigger localized overheating, and thermal runaway in large-scale battery clusters has a chain reaction, leading to widespread battery failures and even safety accidents at energy storage power stations, causing fatal impacts on regional grid stability. Therefore, high consistency is fundamental to the stable operation of large-scale grid energy storage systems. Therefore, for grid energy storage scenarios, this invention adopts available capacity, power response speed, and cell consistency as the corresponding key battery characteristics.

[0051] The benchmark thresholds for each key battery characteristic in each application scenario can be fixed values ​​set with reference to industry standards or general safety standards.

[0052] To further improve the compatibility of the second battery with the current application scenario, in another optional embodiment of the present invention, a benchmark threshold is dynamically set in real time based on the actual scenario requirements. The specific benchmark threshold settings for each application scenario are as follows:

[0053] In a preferred embodiment, when the current application scenario is a residential energy storage scenario, the peak daily average electricity load of the current household, the peak maximum load of the residential energy storage, and a preset voltage deviation baseline value are obtained; the load demand intensity of the current household is determined based on the peak daily average electricity load and the peak maximum load of the residential energy storage; the maximum voltage difference threshold of the individual unit corresponding to the current household is determined based on the load demand intensity, the voltage deviation baseline value, and the preset load influence coefficient; and the maximum voltage difference threshold of the individual unit is used as the benchmark threshold corresponding to the consistency of the individual units in the residential energy storage scenario.

[0054] Obtain the current household's planned usage years, the shortest planned usage years of the household energy storage, the longest planned usage years of the household energy storage, and the base value of the remaining cycle count; determine the relative demand intensity of the usage years based on the current household's planned usage years, the shortest planned usage years of the household energy storage, and the longest planned usage years of the household energy storage; determine the battery's remaining cycle count threshold corresponding to the current household based on the relative demand intensity of the usage years, the base value of the remaining cycle count, and the preset age influence coefficient; and use the battery's remaining cycle count threshold as the benchmark threshold corresponding to the cycle life in the household energy storage scenario.

[0055] The system obtains the average maximum daily temperature at the current home energy storage installation location, the ideal installation temperature for home energy storage, and the baseline safe temperature value; it determines the relative temperature deviation based on the average maximum daily temperature at the home energy storage installation location and the ideal installation temperature for home energy storage; it determines the thermal runaway temperature threshold corresponding to the current home based on the relative temperature deviation, the baseline safe temperature value, and a preset ambient temperature influence coefficient; and it uses the thermal runaway temperature threshold as the benchmark threshold corresponding to the thermal runaway temperature in the home energy storage scenario.

[0056] Specifically, for residential energy storage scenarios, the baseline threshold for individual unit consistency can be calculated using the following formula:

[0057] ;

[0058] in, This is the threshold for the maximum voltage difference between individual cells in the current household, which is the benchmark threshold for individual cell consistency in the above-mentioned household energy storage scenario; The preset voltage deviation baseline value is a fixed value set with reference to industry standards or general safety standards. In this invention, it can be set to 50mV with reference to standards such as GB / T 36276. The preset load impact factor can be calibrated using measured data; for illustrative purposes, its value can be set to 0.4. P represents the peak daily average electricity load of the current household, which is the actual maximum power of all electrical devices in a single household when they are started simultaneously within a day. In real-world scenarios, this can be collected by a smart meter. The maximum peak load for energy storage is a fixed value designed for home energy storage systems that does not change with individual households. For example, it can be set to 5kW to cover high-load scenarios for most households, such as when multiple appliances are started at the same time. This represents the current load demand intensity of households.

[0059] The specific formula for calculating the baseline threshold corresponding to its cycle life is as follows:

[0060] ;

[0061] in, The remaining battery cycle count threshold for the current household is the benchmark threshold for cycle life in the home energy storage scenario. The remaining number of cycles mentioned above is a base value set with reference to industry standards; for example, it can be set to 1000 times. The factor representing the impact of the number of years of use can be calibrated using measured data; a possible value is 0.8. T represents the planned usage years of the current household. For the shortest planned service life of home energy storage, For the longest planned service life of home energy storage, and These are all pre-set fixed values, for illustrative purposes only. It can be set to 1 year. It can be set to 8 years. This refers to the relative demand intensity to the aforementioned service life.

[0062] The specific formula for calculating the baseline threshold corresponding to its thermal runaway temperature is as follows:

[0063] ;

[0064] in, This is the thermal runaway temperature threshold corresponding to the current household, that is, the benchmark threshold corresponding to the thermal runaway temperature in the home energy storage scenario. The above-mentioned basic safe temperature value can be referenced from the battery safety standard, which sets it to 130℃ (general safe temperature). The environmental temperature influence coefficient can be calibrated through actual measurements in various temperature environments to ensure that the threshold increases synchronously when the temperature rises. In this invention, it can be set to 1.2. The average daily maximum temperature at the current home energy storage installation location is affected by the specific installation location and season, and the corresponding value can be collected by a temperature sensor. The ideal installation temperature for home energy storage is a fixed value that can be set to 20°C for illustration purposes. This refers to the aforementioned relative temperature deviation.

[0065] For commercial energy storage scenarios, in a preferred embodiment, the following steps are taken: First, obtain the current daily charge / discharge frequency of the commercial user, the minimum daily charge / discharge frequency of commercial energy storage, the maximum daily charge / discharge frequency of commercial energy storage, and the base value of the commercial energy storage charge / discharge rate. Then, determine the charge / discharge demand intensity based on these parameters. Finally, determine the charge / discharge rate threshold corresponding to the current commercial user based on the charge / discharge demand intensity, the base value of the commercial energy storage charge / discharge rate, and a preset frequency influence coefficient. This charge / discharge rate threshold is then used as the benchmark threshold for the charge / discharge rate in the commercial energy storage scenario.

[0066] Obtain the planned operating years of the current commercial user, the basic operating years of the commercial energy storage, and the preset basic value of the number of commercial cycles; determine the cycle life demand intensity based on the planned operating years of the current commercial user, the daily charge and discharge frequency of the current commercial user, and the basic operating years of the commercial energy storage; calculate the commercial cycle life threshold based on the cycle life demand intensity and the basic value of the number of commercial cycles; and use the commercial cycle life threshold as the benchmark threshold corresponding to the cycle life in the commercial energy storage scenario.

[0067] Obtain the peak-valley electricity price difference, the minimum economic price difference for commercial energy storage, the maximum economic price difference for commercial energy storage, and the preset basic value of charging and discharging efficiency in the current commercial user's region; calculate the relative return sensitivity based on the peak-valley electricity price difference, the minimum economic price difference for commercial energy storage, and the maximum economic price difference for commercial energy storage in the current commercial user's region; calculate the charging and discharging efficiency threshold based on the relative return sensitivity, the basic value of charging and discharging efficiency, and the preset price difference influence coefficient; and use the charging and discharging efficiency threshold as the benchmark threshold corresponding to the charging and discharging efficiency in the commercial energy storage scenario.

[0068] Specifically, the reference threshold corresponding to the charge / discharge rate can be calculated using the following formula:

[0069] ;

[0070] in, The above-mentioned charge / discharge rate thresholds are the benchmark thresholds corresponding to the charge / discharge rate in commercial energy storage scenarios. The above-mentioned basic values ​​for the charge and discharge rates of commercial energy storage can be set with reference to commercial energy storage standards. This is the frequency impact coefficient; n represents the number of times a commercial user charges and discharges per day (e.g., 3 times per day, which can be determined through the operation plan). This represents the minimum number of charge / discharge cycles per day for commercial energy storage. This represents the highest number of charge-discharge cycles per day for commercial energy storage. and These are all fixed values ​​set based on commercial energy storage demand, and are for illustrative purposes only. It can be set to 2 times. It can be set to 6 times; This refers to the charging and discharging demand intensity mentioned above.

[0071] Specifically, the baseline threshold for cycle life in commercial energy storage scenarios can be calculated using the following formula:

[0072] ;

[0073] in, The aforementioned commercial cycle life threshold is the benchmark threshold corresponding to the cycle life in commercial energy storage scenarios. The above-mentioned basic value for the number of commercial cycles can be set with reference to commercial energy storage standards, and can be set to 3000 cycles for illustration purposes; The planned operating life for current commercial users, i.e., the planned operating life required when current commercial users set up commercial energy storage systems. The basic operating period for commercial energy storage is a pre-set fixed value. For example, the shortest common operating cycle for commercial energy storage, 3 years, can be used as the basic operating period. This refers to the cycle life requirement intensity.

[0074] Specifically, the benchmark threshold for charging and discharging efficiency in commercial energy storage scenarios can be calculated using the following formula:

[0075] ;

[0076] in, The above-mentioned charging and discharging efficiency thresholds are the benchmark thresholds for charging and discharging efficiency in commercial energy storage scenarios. This is a baseline value for charge / discharge efficiency, which can be illustratively set to 85%. This is the price difference impact coefficient, which can be set to 5% as an example. This represents the peak-valley electricity price difference in the region where the current commercial user is located. For the lowest economic price spread for commercial energy storage, This represents the highest economic price difference for commercial energy storage. This is the relative return sensitivity.

[0077] In a preferred embodiment, for grid energy storage scenarios, when the current application scenario is grid energy storage, the real-time load fluctuation amplitude of the grid, the historical maximum load fluctuation amplitude of the grid, and the basic value of the grid's available capacity are obtained; the relative load fluctuation intensity is determined based on the real-time load fluctuation amplitude and the historical maximum load fluctuation amplitude of the grid; the available capacity threshold is determined based on the relative load fluctuation intensity, the basic value of the grid's available capacity, and a preset load fluctuation influence coefficient; and the available capacity threshold is used as the benchmark threshold for available capacity in the grid energy storage scenario.

[0078] The system obtains the real-time frequency, rated frequency, maximum allowable frequency deviation of the power grid, and a base value for frequency regulation response delay. It calculates the real-time frequency deviation based on the real-time and rated frequencies of the power grid, and determines the relative risk intensity of the frequency deviation based on the real-time frequency deviation and the maximum allowable frequency deviation of the power grid. It then determines a delay time threshold based on the relative risk intensity of the frequency deviation, the base value for frequency regulation response delay, and a preset frequency deviation influence coefficient. Finally, it uses the delay time threshold as a benchmark value for the power response speed in a power grid energy storage scenario.

[0079] Obtain the actual required battery cell cluster size, the baseline value of the battery cell cluster size, and the preset baseline value of grid voltage deviation for the power grid; determine the relative risk level of the battery cluster size based on the actual required battery cell cluster size and the baseline value of the battery cell cluster size; determine the maximum voltage deviation threshold of the individual cells in the power grid based on the relative risk level of the battery cluster size, the preset baseline value of grid voltage deviation, and the cluster size influence coefficient; and use the maximum voltage deviation threshold of the individual cells in the power grid energy storage scenario as the baseline value for cell consistency.

[0080] Specifically, the baseline threshold for available capacity in grid energy storage scenarios can be calculated using the following formula:

[0081] ;

[0082] in, This is the available capacity threshold, which is the benchmark threshold for available capacity in the context of grid energy storage. This is the baseline value for available grid capacity, and can be set at 70% with reference to grid energy storage standards. The load fluctuation impact coefficient can be set to 0.3 for illustrative purposes. This represents the real-time load fluctuation range of the power grid. This represents the maximum historical load fluctuation range of the power grid. This represents the relative intensity of load fluctuation.

[0083] Specifically, the benchmark value for power response speed in grid energy storage scenarios can be calculated using the following formula:

[0084] ;

[0085] in, This is the delay time threshold, which serves as the benchmark value for power response speed in grid energy storage scenarios; The frequency modulation response delay is a basic value, which can be set to 500ms (to meet the general frequency modulation requirements within ±0.1Hz frequency deviation). is the frequency deviation influence coefficient, which can be denoted as 0.8 for illustrative purposes; f is the real-time frequency of the power grid; The rated frequency of the power grid; This is the absolute deviation between the real-time frequency and the rated frequency of the power grid, i.e., the aforementioned real-time frequency deviation. This represents the maximum allowable frequency deviation of the power grid. This refers to the relative risk intensity of frequency deviation.

[0086] Specifically, the benchmark value for individual cell consistency in grid-connected energy storage scenarios can be calculated using the following formula:

[0087] ;

[0088] in, This is the maximum voltage deviation threshold for a single grid cell, which is the benchmark value for cell consistency in the grid energy storage scenario. This is the baseline value for grid voltage deviation, which can be set to 30mV for illustrative purposes. The cluster size influence coefficient is denoted by 0.2, which can be taken as an illustration; M is the actual cluster size of the battery cells required by the power grid, i.e., the actual number of battery cells required. This is a baseline value for the size of a battery cell cluster, which is a fixed value and can be illustratively set to 1000 cells. The relative risk level for the size of the battery cluster.

[0089] The above embodiments allow for the dynamic setting of benchmark thresholds for different key battery characteristics in different application scenarios based on actual needs, further improving the matching degree between secondary batteries and application scenarios.

[0090] Step S2: Using each key battery characteristic as the coordinate axis, construct a battery feature coordinate system corresponding to the current scenario, and construct a usable battery region within the battery characteristic coordinate system based on the benchmark threshold of each key battery characteristic; the usable battery region is used to characterize the parameter range of the key battery characteristics of the secondary battery when the current application scenario is met.

[0091] Specifically, a three-dimensional spatial coordinate system is constructed based on the three key battery characteristics in the current application scenario to obtain the aforementioned battery characteristic coordinate system. Then, based on the determined benchmark thresholds, a usable battery region can be constructed within the battery characteristic coordinate system. This usable battery region can characterize the range of key battery characteristic parameters that a secondary battery matching the current application scenario needs to satisfy.

[0092] Step S3: Extract the key battery characteristic parameter values ​​of the secondary battery to be utilized, and generate the first projection point corresponding to the secondary battery to be utilized in the battery feature coordinate system based on the key battery characteristic parameter values ​​of the secondary battery to be utilized.

[0093] Specifically, the parameter values ​​corresponding to the secondary battery to be utilized are extracted based on the current application scenario. Then, according to the specific parameter values, the corresponding mapping point, i.e., the first projection point, is generated in the aforementioned battery feature coordinate system. It should be noted that in this invention, the secondary battery to be utilized is a battery module, which contains several individual battery cells.

[0094] Step S4: When it is determined that the first projection point is within the available battery area, the secondary battery to be utilized is applied in the current application scenario.

[0095] Specifically, after generating the first projection point, it is determined whether the first projection point is within the available battery area of ​​the battery characteristic coordinate system. If it is, it means that the key battery characteristic parameter values ​​of the secondary battery to be utilized meet the requirements of the current application scenario. At this time, the secondary battery to be utilized can be applied to the current application scenario. Otherwise, it means that the secondary battery to be utilized does not meet the requirements of the current application scenario and is not compatible with the current application scenario. Therefore, the secondary battery to be utilized is removed.

[0096] In a preferred embodiment, the method further includes: when there are multiple groups of secondary batteries with first projection points corresponding to them within the available battery area, constructing a first threshold sectional surface corresponding to each benchmark threshold in the battery feature coordinate system based on a benchmark threshold; obtaining ideal thresholds for key battery characteristics in the current application scenario, and constructing a second threshold sectional surface corresponding to each ideal threshold in the battery feature coordinate system based on the ideal thresholds; calculating a first vertical distance from each first threshold sectional surface to the corresponding second threshold sectional surface; calculating a second vertical distance from each projection point corresponding to a secondary battery to be utilized to each first threshold sectional surface for each first projection point; calculating the ratio of each second vertical distance to the corresponding first vertical distance to obtain the parameter redundancy ratio of each key battery characteristic; calculating the weighted parameter redundancy of each key battery characteristic based on the parameter redundancy ratio of each key battery characteristic and the weighting coefficient of each key battery characteristic parameter; and grouping each secondary battery to be utilized according to the weighted parameter redundancy of each key battery characteristic to form different battery clusters.

[0097] In this embodiment, if multiple first projection points corresponding to the available secondary batteries exist within the available battery area, it indicates that multiple secondary batteries meet the requirements of the current application scenario. At this point, each battery is further evaluated. A first threshold section defines the acceptable baseline, and a second section determines the ideal target upper limit. The first vertical distance between the first and second sections represents the total scale of performance improvement potential. For each first projection point, the second vertical distance from the first projection point to each first section measures the degree to which the secondary battery exceeds the baseline threshold in each battery characteristic dimension. Furthermore, the ratio of each second vertical distance to the corresponding first vertical distance determines the parameter redundancy percentage of each key battery characteristic. This redundancy percentage further measures the performance excellence of the secondary battery. For example, if the charge / discharge efficiency redundancy percentage of a secondary battery is 80%, it means that the distance of its efficiency parameter from the acceptable line (second vertical distance) accounts for 80% of the total distance from the acceptable line to the ideal line (first vertical distance), indicating near-ideal performance. If the percentage is only 20%, the performance is only slightly above the acceptable line, with insufficient margin. This quantification process allows for further performance evaluation of previously undefined qualified batteries. Subsequently, based on the percentage of parameter redundancy, multiple secondary batteries can be finely grouped to achieve refined scenario matching.

[0098] In a preferred embodiment, grouping the secondary batteries to be utilized into different battery clusters based on the weighted parameter redundancy of each key battery characteristic includes:

[0099] Using the weighted parameter redundancy of key battery characteristics as the coordinate axis, a redundancy coordinate system is constructed, and based on the weighted parameter redundancy of each key battery characteristic parameter of each secondary battery to be utilized, a corresponding second projection point is generated in the redundancy coordinate system.

[0100] The second projection point with the smallest standard deviation of the weighted parameter redundancy of each key battery characteristic among all the second projection points is taken as the center projection point.

[0101] Calculate the Euclidean distance from each second projection point to the central projection point;

[0102] Based on the Euclidean distance, the secondary batteries to be utilized are grouped using the K-means algorithm, and then a battery cluster is constructed based on the secondary batteries to be utilized within the same group.

[0103] In this embodiment, the weighted parameter redundancy of each key battery characteristic is used as the coordinate axis, and a coordinate system is established to obtain the aforementioned redundancy coordinate system. Then, based on the weighted parameter redundancy of each key battery characteristic parameter of each secondary battery to be utilized, each secondary battery to be utilized is projected onto the redundancy coordinate system, forming the aforementioned second projection points. Next, the second projection point with the smallest standard deviation of the weighted parameter redundancy of each key battery characteristic is selected as the center projection point. The smallest standard deviation means that the difference in weighted redundancy at this center projection point is the smallest, rather than a point with extremely high redundancy in one characteristic but extremely low redundancy in others. Using this as the center point ensures that the core characteristics of the batteries within the cluster are at similar levels. The Euclidean distance from each second projection point to the center projection point is calculated. Then, based on the Euclidean distance, the secondary batteries to be utilized are grouped using the K-means algorithm. Finally, based on the secondary batteries to be utilized within the same group, a battery cluster is constructed, and the secondary batteries to be utilized within the same battery cluster can be utilized together subsequently. This approach ensures that each battery cluster has balanced performance, with rechargeable batteries within the same cluster used together and those in different clusters used separately. This prevents the entire cluster from failing prematurely due to a single weakness, significantly improving the stability of the subsequent system operation.

[0104] In another optional embodiment, the step of grouping the secondary batteries to be utilized according to the weighted parameter redundancy of each key battery characteristic to form different battery clusters includes:

[0105] Based on the weighted parameter redundancy of each key battery characteristic, the comprehensive redundancy score of each secondary battery to be utilized is calculated.

[0106] Based on the comprehensive redundancy score of each secondary battery to be utilized, each secondary battery to be utilized is divided into several levels of groups; each group corresponds to a comprehensive redundancy score range, and the comprehensive redundancy score of each secondary battery to be utilized in the same group is within the corresponding comprehensive redundancy score range.

[0107] Based on each group, different levels of battery clusters are constructed.

[0108] In this embodiment, the comprehensive redundancy score of each secondary battery to be utilized can be obtained by directly summing the weighted redundancy parameters of each key battery characteristic. Illustratively, based on the redundancy score, each secondary battery to be utilized is divided into three groups: high redundancy, medium redundancy, and low redundancy, with each group corresponding to a redundancy range. Secondary batteries to be utilized within the same group form a battery cluster, thus creating three levels of battery clusters. Based on the different demand levels of the current application scenario, different levels of battery clusters are configured to meet the corresponding demand levels. Through this embodiment, different levels of battery clusters can serve as a further refined demand configuration for the current application scenario. For example, in a residential energy storage scenario: a high-redundancy battery cluster can serve as the primary long-term energy storage for peak-valley arbitrage and daily power supply; a medium-redundancy battery cluster can serve as auxiliary energy storage to supplement primary or seasonal use; and a low-redundancy battery cluster can serve as temporary emergency energy storage, only for backup during power outages, thereby achieving further refined adaptation to the current application scenario.

[0109] In a preferred embodiment, the method further includes: when there are multiple application scenarios and the first projection point corresponding to a secondary battery to be utilized is within the available battery area of ​​each application scenario, the secondary battery to be utilized is applied to the application scenario with the highest comprehensive redundancy score based on the comprehensive redundancy score of the secondary battery to be utilized in each application scenario.

[0110] In this embodiment, when multiple application scenarios exist, the secondary batteries to be utilized are assigned to the application scenario with the highest overall redundancy score. This achieves optimal allocation of battery resources across scenarios, solving the selection dilemma when the same battery is adapted to multiple scenarios. By using the quantitative standard of the highest overall redundancy score, each group of secondary batteries to be utilized can be matched with the scenario that best utilizes its performance value, achieving global optimized configuration.

[0111] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0112] One embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the battery cascade utilization method according to any one of the present invention.

[0113] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0114] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0115] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0116] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0117] An embodiment of the present invention provides a storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the battery cascade utilization method according to any one of the present invention.

[0118] The storage medium is a computer-readable medium, which may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0119] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for the cascade utilization of batteries, characterized in that, include: Based on the current application scenario, determine the key battery characteristics of the current application scenario and the benchmark thresholds for each key battery characteristic; The benchmark threshold is used to characterize the minimum threshold value that the key battery characteristics of the secondary battery must meet when it is used in the current application scenario. The application scenarios include: residential energy storage, commercial energy storage, and grid energy storage. When the application scenario is residential energy storage, the key battery characteristics are: cell consistency, cycle life, and thermal runaway temperature. When the application scenario is commercial energy storage, the key battery characteristics are: charge / discharge rate, cycle life, and charge / discharge efficiency. When the application scenario is grid energy storage, the key battery characteristics are: available capacity, power response speed, and cell consistency. Using each key battery characteristic as a coordinate axis, a battery feature coordinate system corresponding to the current scenario is constructed, and a usable battery region is constructed within the battery characteristic coordinate system based on the benchmark thresholds of each key battery characteristic; the usable battery region is used to characterize the parameter range of the key battery characteristics of the secondary battery when the current application scenario is met. Extract the key battery characteristic parameter values ​​of the secondary battery to be utilized, and generate the first projection point corresponding to the secondary battery to be utilized in the battery feature coordinate system based on the key battery characteristic parameter values ​​of the secondary battery to be utilized. When the first projection point is determined to be within the available battery area, the secondary battery to be utilized is applied in the current application scenario; Specifically, based on the current application scenario, the key battery characteristics and benchmark thresholds for each key battery characteristic are determined, including: When the current application scenario is a home energy storage scenario, the peak daily average electricity load of the current household, the peak maximum load of the home energy storage, and a preset voltage deviation baseline value are obtained; based on the peak daily average electricity load and the peak maximum load of the home energy storage, the load demand intensity of the current household is determined; based on the load demand intensity, the voltage deviation baseline value, and the preset load influence coefficient, the maximum voltage difference threshold of the individual unit corresponding to the current household is determined; the maximum voltage difference threshold of the individual unit is used as the benchmark threshold corresponding to the consistency of the individual units in the home energy storage scenario. Obtain the current household's planned usage years, the shortest planned usage years of the household energy storage, the longest planned usage years of the household energy storage, and the base value of the remaining cycle count; determine the relative demand intensity of the usage years based on the current household's planned usage years, the shortest planned usage years of the household energy storage, and the longest planned usage years of the household energy storage; determine the battery's remaining cycle count threshold corresponding to the current household based on the relative demand intensity of the usage years, the base value of the remaining cycle count, and the preset age influence coefficient; and use the battery's remaining cycle count threshold as the benchmark threshold corresponding to the cycle life in the household energy storage scenario. The system obtains the average maximum daily temperature at the current home energy storage installation location, the ideal installation temperature for home energy storage, and the baseline safe temperature value; it determines the relative temperature deviation based on the average maximum daily temperature at the home energy storage installation location and the ideal installation temperature for home energy storage; it determines the thermal runaway temperature threshold corresponding to the current home based on the relative temperature deviation, the baseline safe temperature value, and a preset ambient temperature influence coefficient; and it uses the thermal runaway temperature threshold as the benchmark threshold corresponding to the thermal runaway temperature in the home energy storage scenario. When the current application scenario is commercial energy storage, the following steps are taken: First, obtain the daily charge / discharge counts for the current commercial user, the minimum daily charge / discharge counts for commercial energy storage, the maximum daily charge / discharge counts for commercial energy storage, and the base value of the commercial energy storage charge / discharge rate. Then, determine the charge / discharge demand intensity based on these parameters. Finally, determine the charge / discharge rate threshold for the current commercial user based on the charge / discharge demand intensity, the base value of the commercial energy storage charge / discharge rate, and a preset frequency impact coefficient. This charge / discharge rate threshold is then used as the benchmark threshold for the charge / discharge rate in the commercial energy storage scenario. Obtain the planned operating years of the current commercial user, the basic operating years of the commercial energy storage, and the preset basic value of the number of commercial cycles; determine the cycle life demand intensity based on the planned operating years of the current commercial user, the daily charge and discharge frequency of the current commercial user, and the basic operating years of the commercial energy storage; calculate the commercial cycle life threshold based on the cycle life demand intensity and the basic value of the number of commercial cycles; and use the commercial cycle life threshold as the benchmark threshold corresponding to the cycle life in the commercial energy storage scenario. The system obtains the peak-valley electricity price difference, the minimum economic price difference for commercial energy storage, the maximum economic price difference for commercial energy storage, and a preset baseline value for charging and discharging efficiency in the current commercial user's region. It then calculates the relative return sensitivity based on these factors. Finally, it calculates the charging and discharging efficiency threshold based on the relative return sensitivity, the baseline value for charging and discharging efficiency, and a preset price difference influence coefficient. This threshold is then used as the benchmark threshold for charging and discharging efficiency in the commercial energy storage scenario. When the current application scenario is grid energy storage, the real-time load fluctuation amplitude of the power grid, the historical maximum load fluctuation amplitude of the power grid, and the basic value of the available capacity of the power grid are obtained; the relative load fluctuation intensity is determined based on the real-time load fluctuation amplitude and the historical maximum load fluctuation amplitude of the power grid; the available capacity threshold is determined based on the relative load fluctuation intensity, the basic value of the available capacity of the power grid, and the preset load fluctuation influence coefficient; the available capacity threshold is used as the benchmark threshold of the available capacity in the grid energy storage scenario. The system obtains the real-time frequency, rated frequency, maximum allowable frequency deviation of the power grid, and a base value for frequency regulation response delay. It calculates the real-time frequency deviation based on the real-time and rated frequencies of the power grid, and determines the relative risk intensity of the frequency deviation based on the real-time frequency deviation and the maximum allowable frequency deviation of the power grid. It then determines a delay time threshold based on the relative risk intensity of the frequency deviation, the base value for frequency regulation response delay, and a preset frequency deviation influence coefficient. Finally, it uses the delay time threshold as a benchmark value for the power response speed in a power grid energy storage scenario. Obtain the actual required battery cell cluster size, the baseline value of the battery cell cluster size, and the preset baseline value of grid voltage deviation for the power grid; determine the relative risk level of the battery cluster size based on the actual required battery cell cluster size and the baseline value of the battery cell cluster size; determine the maximum voltage deviation threshold of the individual cells in the power grid based on the relative risk level of the battery cluster size, the preset baseline value of grid voltage deviation, and the cluster size influence coefficient; and use the maximum voltage deviation threshold of the individual cells in the power grid energy storage scenario as the baseline value for cell consistency.

2. The method for the cascade utilization of batteries as described in claim 1, characterized in that, Also includes: When there are multiple sets of secondary batteries to be utilized, corresponding to the first projection points in the available battery area, a first threshold sectional surface corresponding to each benchmark threshold is constructed in the battery feature coordinate system according to the benchmark threshold. Obtain the ideal thresholds for key battery characteristics in the current application scenario, and construct the second threshold sectional plane corresponding to each ideal threshold in the battery feature coordinate system based on the ideal thresholds; Calculate the first vertical distance from each first threshold section to the corresponding second threshold section; For each secondary battery to be utilized, calculate the second vertical distance from the first projection point to each first threshold tangent. Calculate the ratio of each second vertical distance to the corresponding first vertical distance to obtain the parameter redundancy ratio of each key battery characteristic; calculate the weighted parameter redundancy of each key battery characteristic based on the parameter redundancy ratio of each key battery characteristic and the weight coefficient of each key battery characteristic parameter. Based on the redundancy of the weighted parameters of each key battery characteristic, the secondary batteries to be utilized are grouped to form different battery clusters.

3. The method for the cascade utilization of batteries as described in claim 2, characterized in that, The process of grouping the secondary batteries to be utilized into different battery clusters based on the weighted parameter redundancy of each key battery characteristic includes: Using the weighted parameter redundancy of key battery characteristics as the coordinate axis, a redundancy coordinate system is constructed, and based on the weighted parameter redundancy of each key battery characteristic parameter of each secondary battery to be utilized, a corresponding second projection point is generated in the redundancy coordinate system. The second projection point with the smallest standard deviation of the weighted parameter redundancy of each key battery characteristic among all the second projection points is taken as the center projection point. Calculate the Euclidean distance from each second projection point to the central projection point; Based on the Euclidean distance, the secondary batteries to be utilized are grouped using the K-means algorithm, and then a battery cluster is constructed based on the secondary batteries to be utilized within the same group.

4. The method for the cascade utilization of batteries as described in claim 2, characterized in that, The process of grouping the secondary batteries to be utilized into different battery clusters based on the weighted parameter redundancy of each key battery characteristic includes: Based on the weighted parameter redundancy of each key battery characteristic, the comprehensive redundancy score of each secondary battery to be utilized is calculated. Based on the comprehensive redundancy score of each secondary battery to be utilized, each secondary battery to be utilized is divided into several levels of groups; each group corresponds to a comprehensive redundancy score range, and the comprehensive redundancy score of each secondary battery to be utilized in the same group is within the corresponding comprehensive redundancy score range. Based on each group, different levels of battery clusters are constructed.

5. The method for the cascade utilization of batteries as described in claim 4, characterized in that, Also includes: When there are multiple application scenarios, and the first projection point corresponding to a secondary battery to be utilized is within the available battery area of ​​each application scenario, the secondary battery to be utilized is applied to the application scenario with the highest comprehensive redundancy score based on the comprehensive redundancy score of the secondary battery to be utilized in each application scenario.

6. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the battery reuse method according to any one of claims 1-5.

7. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the battery reuse method as described in any one of claims 1 to 5.

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