Energy storage system performance analysis method, device, equipment, storage medium and product

Abstract

Embodiments of the present application provide a kind of energy storage system performance analysis method, device, equipment, storage medium and product.The method comprises: obtaining the life cycle data of energy storage system, the life cycle data includes: operating data and production process data;Based on the operating data, the performance index data set of the energy storage system is determined;Based on each performance index value in the performance index data set and the production process data, performance analysis data set is constructed;Based on the performance analysis data set, the performance of the energy storage system is analyzed.The method improves the comprehensiveness of energy storage system performance analysis, provides scientific basis for production process optimization and iterative design of energy storage system.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to a method, apparatus, equipment, storage medium and product for energy storage system performance analysis. Background Technology

[0002] Energy storage systems are widely used in scenarios such as grid peak shaving, new energy grid connection, backup power supply, and electric vehicle charging stations due to their ability to store and release electrical energy.

[0003] In existing technologies, performance analysis of energy storage systems almost always focuses on the control strategies of the energy storage system. During the analysis, the energy storage system is assumed to be a hardware black box that cannot be adjusted or optimized, and the performance of the energy storage system is analyzed, such as basic attributes like efficiency, lifespan, and reliability.

[0004] However, the hardware effects of an energy storage system can cause fundamental differences in its performance, and these differences cannot be eliminated by simple software strategy adjustments, but are crucial to improving the performance of the energy storage system. Summary of the Invention

[0005] This application provides methods, apparatus, equipment, storage media, and products for energy storage system performance analysis, which improves the comprehensiveness of energy storage system performance analysis and provides a scientific basis for production process optimization and iterative design of energy storage systems.

[0006] In a first aspect, embodiments of this application provide a method for analyzing the performance of an energy storage system, including:

[0007] Acquire lifecycle data of the energy storage system, including operational data and production process data;

[0008] Based on the operational data, a performance index dataset for the energy storage system is determined;

[0009] Based on the performance index values ​​in the performance index dataset and the production process data, a performance analysis dataset is constructed.

[0010] Based on the performance analysis dataset, the energy storage system is subjected to performance analysis.

[0011] In one possible implementation, the operational data includes: system operational data of the energy storage system and component operational data of the target components in the energy storage system; the performance index dataset includes: system performance index values, component performance index values, and performance index deviation values.

[0012] The process of determining the performance index dataset of the energy storage system based on the operational data includes:

[0013] Based on the system operation data of the energy storage system and the component operation data of the target components in the energy storage system, the system performance index value and component performance index value of the energy storage system are determined respectively.

[0014] Based on the system performance index value and the component performance index value of the same dimension, the performance index deviation value is obtained.

[0015] In one possible implementation, the performance indicators include energy efficiency, full capacity, and lifetime, and the target component includes a battery cell; the performance indicator deviation value obtained based on the system performance indicator value and the component performance indicator value of the same dimension includes at least one of the following:

[0016] Calculate the average cell energy efficiency of multiple cells in the energy storage system, and calculate the difference between the system energy efficiency value of the energy storage system and the average cell energy efficiency to obtain the energy efficiency deviation;

[0017] Calculate the average cell lifespan of multiple cells in the energy storage system, and calculate the difference between the system lifespan of the energy storage system and the average cell lifespan to obtain the lifespan deviation;

[0018] Calculate the total full capacity of the multiple cells in the energy storage system, and calculate the difference between the total full capacity of the energy storage system and the total full capacity of the cells to obtain the full capacity deviation.

[0019] In one possible implementation, the production process data includes: system production process data of the energy storage system and component production process data of the target components in the energy storage system;

[0020] The construction of a performance analysis dataset based on the performance index values ​​in the performance index dataset and the production process data includes:

[0021] For a target performance indicator in the performance indicator dataset, at least one influencing factor is determined from other performance indicators in the performance indicator dataset and the production process parameters corresponding to the production process data; the target performance indicator is any one of the performance indicators in the performance indicator dataset.

[0022] The performance analysis dataset is obtained based on the performance index values ​​corresponding to the target performance index, the performance index values ​​corresponding to the at least one influencing factor, the system production process data, and the component production process data.

[0023] In one possible implementation, before determining the performance index dataset of the energy storage system based on the operational data, the method further includes:

[0024] Obtain the system unique identifier of the energy storage system, determine the system operation data and system production process data of the energy storage system, and determine the target unique identifier that has a binding relationship with the system unique identifier;

[0025] The component operation data and component production process data of the target component corresponding to the target unique identifier are determined from the operation data and production process data, respectively.

[0026] In one possible implementation, performance analysis of the energy storage system is performed based on the performance analysis dataset, including:

[0027] Based on the index values ​​corresponding to the at least one influencing factor and / or the production process data, quantify the quantitative relationship between the at least one influencing factor and the target performance index of the energy storage system;

[0028] Based on the quantitative relationship, the performance analysis results of the energy storage system are generated.

[0029] In one possible implementation, based on the index values ​​corresponding to the at least one influencing factor and / or the production process data, the quantitative relationship between the at least one influencing factor and the target performance index of the energy storage system is quantified, including:

[0030] Based on the index values ​​of the target performance index, the index values ​​corresponding to the at least one influencing factor, and / or the production process data, a regression model for the target performance index and each influencing factor is trained.

[0031] Based on the index values ​​and / or the production process data, determine the sample distribution space corresponding to each influencing factor;

[0032] Within the sample distribution space, a perturbation algorithm is used to determine the sensitivity of the target performance index to each influencing factor, thereby obtaining a quantitative relationship between the energy storage system performance and each influencing factor.

[0033] Secondly, embodiments of this application provide an energy storage system performance analysis device, including: an acquisition module, used to acquire life cycle data of the energy storage system, the life cycle data including: operation data and production process data;

[0034] The processing module is used to determine the performance index dataset of the energy storage system based on the operational data.

[0035] The processing module is also used to construct a performance analysis dataset based on the performance index values ​​in the performance index dataset and the production process data;

[0036] The analysis module is used to perform performance analysis on the energy storage system based on the performance analysis dataset.

[0037] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0038] The memory stores computer-executed instructions;

[0039] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0040] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0041] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0042] The energy storage system performance analysis method, apparatus, equipment, storage medium, and product provided in this application acquire lifecycle data of the energy storage system, including operational data and production process data. Based on the operational data, a performance index dataset of the energy storage system is determined. Based on the performance index values ​​in the performance index dataset and the production process data, a performance analysis dataset is constructed. Based on the performance analysis dataset, the performance of the energy storage system is analyzed. By determining performance indicators through operational data and analyzing the correlation between performance indicators and production process data, problems related to production process defects are deduced. The impact of each influencing factor's corresponding production process on the energy storage system performance is quantified, establishing a correlation between energy storage system performance and production process. This solves the technical problem of separating production process and system performance in traditional analysis, avoids the limitations of existing technologies that rely solely on control strategies when analyzing energy storage system performance, improves the comprehensiveness of energy storage system performance analysis, and provides a scientific basis for production process optimization and iterative design of energy storage systems. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] Figure 1 A flowchart illustrating a performance analysis method for an energy storage system provided in this application embodiment. Figure 1 ;

[0045] Figure 2 A flowchart illustrating a performance analysis method for an energy storage system provided in this application embodiment. Figure 2 ;

[0046] Figure 3 A schematic diagram of the structure of an energy storage system performance analysis device provided in an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0048] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0050] "Multiple" refers to two or more, and other quantifiers are similar. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0051] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0052] It should be noted that, in the embodiments of this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0053] Energy storage systems are widely used in scenarios such as grid peak shaving, new energy grid connection, backup power supply, and electric vehicle charging stations due to their ability to store and release electrical energy.

[0054] In existing technologies, performance analysis of energy storage systems almost always focuses on the control strategies of the energy storage system. During the analysis, the energy storage system is assumed to be a hardware black box that cannot be adjusted or optimized. For example, by monitoring multiple cells in the energy storage system, the timing data of the cells is obtained, and the performance of the energy storage system is analyzed based on the timing data of the cells and a pre-trained model, such as basic attributes like efficiency, lifespan, and reliability.

[0055] However, differences in the manufacturing processes of energy storage systems can lead to fundamental differences in their performance, and these differences cannot be eliminated by simple software strategy adjustments, but are crucial to improving the performance of energy storage systems.

[0056] The energy storage system performance analysis method provided in this application analyzes the performance of the energy storage system by combining the life cycle data of the energy storage system, which solves the problem in the prior art that it is difficult to deduce production process defects from operational data.

[0057] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0058] Figure 1 A flowchart illustrating a performance analysis method for an energy storage system provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method includes:

[0059] S101. Obtain lifecycle data of the energy storage system. Lifecycle data includes: operation data and production process data.

[0060] Operational data refers to data collected during the operation of the energy storage system. This data can be obtained through sensors, operational monitoring systems, and other means. Operational data may include one or more of the following: system operation data and operation data of various components within the energy storage system. For example, operational data may include discharge data, charging data, environmental data, auxiliary energy consumption data, current data, and voltage data.

[0061] Production process data refers to the process parameters recorded during the production process. These parameters can be obtained from the energy storage system's production records. Production process data may include one or more of the system's overall production process data and the component production process data of each part of the energy storage system. For example, production process data may include welding data, coating data, etc.

[0062] In this step, the operating data and production process data of the energy storage system can be obtained through sensor devices, operation monitoring systems, and production recording systems.

[0063] Specifically, the system collects data such as discharge data, charging data, environmental data, auxiliary energy consumption data, current data, and voltage data during the operation of the energy storage system through sensor devices and operation monitoring systems to obtain operational data; and obtains process parameters of the energy storage system recorded during the production process through the production record system to obtain production process data.

[0064] For example, the unique identifier of the energy storage system can be used to filter out the operation data and production process data of the energy storage system associated with the unique identifier from the data collected by sensor devices, operation monitoring systems and process parameters stored in the production record system.

[0065] S102. Based on the operational data, determine the performance index dataset of the energy storage system.

[0066] The performance index dataset is a collection of performance index values ​​corresponding to various performance indicators of the energy storage system. These performance indicators can include system performance indicators of the energy storage system, as well as component performance indicators of individual components within the system. For example, performance indicators can include energy efficiency, total capacity, and lifespan.

[0067] In this step, based on the acquired operational data, the performance index values ​​corresponding to each performance index of the energy storage system are calculated, and the performance index dataset of the energy storage system is obtained.

[0068] Specifically, based on the acquired operating data such as discharge data, charging data, environmental data, auxiliary energy consumption data, current data, and voltage data, the performance index values ​​corresponding to each performance index of the energy storage system are calculated to obtain the performance index dataset of the energy storage system.

[0069] For example, charging data may include charging energy, and discharging data may include discharging energy and discharging current. The energy efficiency of a single charge-discharge cycle is obtained based on the ratio of discharging energy to charging energy. The total system capacity is determined by the product of discharging current, average voltage, and discharging time.

[0070] S103. Based on the performance index values ​​and production process data in the performance index dataset, construct a performance analysis dataset.

[0071] The performance analysis dataset is used to perform performance analysis on energy storage systems, including various performance index values ​​and production process data.

[0072] In this step, at least one performance indicator is determined from each performance indicator, the corresponding performance indicator value is determined from the performance indicator dataset, and a performance analysis dataset is constructed based on the corresponding performance indicator value and production process data.

[0073] For example, the performance index dataset includes system performance index value P1, component performance index value P2, and performance index deviation value ΔP between system performance index value P1 and component performance index value P2 of the same dimension. The production process data includes system production process data M1 and component production process data M2. The sample structure of the constructed performance analysis dataset can be {P1, P2, ΔP, M1, M2}.

[0074] S104. Based on the performance analysis dataset, perform performance analysis on the energy storage system.

[0075] The performance analysis dataset includes the target performance metrics to be analyzed, i.e., sample labels, as well as the influencing factors used for analysis, i.e., features.

[0076] Existing technologies do not establish a direct correlation between process parameters and performance indicators in the production process of energy storage systems, resulting in a lack of scientific basis for optimizing production processes.

[0077] In this step, the target performance indicators to be analyzed are analyzed by using the performance index values ​​and / or production process data corresponding to the influencing factors in the performance analysis dataset, that is, the performance analysis of the energy storage system is performed.

[0078] Specifically, the analysis model can be trained by using the data corresponding to the target performance index in the performance analysis dataset as output and the data corresponding to each influencing factor as input. For example, the analysis model can be trained using regression algorithms, correlation analysis, machine learning algorithms, or deep learning algorithms. The sample distribution space is determined based on the data corresponding to each influencing factor. Then, the data corresponding to each influencing factor is fine-tuned and input into the analysis model. Based on the output of the analysis model before and after fine-tuning, the sensitivity of each influencing factor to the target performance index is calculated, quantifying the impact of each influencing factor on the target performance index to be analyzed, thus achieving performance analysis of the energy storage system.

[0079] This application provides a method for performance analysis of energy storage systems. It acquires lifecycle data of the energy storage system, including operational and production process data. Based on the operational data, it determines a performance index dataset for the energy storage system. Based on the performance index values ​​and production process data, it constructs a performance analysis dataset. Based on this dataset, it performs performance analysis on the energy storage system. By determining performance indicators through operational data and analyzing the correlation between performance indicators and production process data, it infers problems related to production process defects, quantifies the impact of each influencing factor's corresponding production process on the energy storage system's performance, and establishes a correlation between energy storage system performance and production process. This solves the technical problem of separating production process from system performance in traditional analysis, avoids the limitations of existing technologies that rely solely on control strategies when analyzing energy storage system performance, improves the comprehensiveness of energy storage system performance analysis, and provides a scientific basis for production process optimization and iterative design of energy storage systems.

[0080] Figure 2 A flowchart illustrating a performance analysis method for an energy storage system provided in this application embodiment. Figure 2 In this embodiment, the operational data includes: system operational data of the energy storage system and component operational data of the target component in the energy storage system; the performance index dataset includes: system performance index values, component performance index values, and performance index deviation values; and the production process data includes: system production process data of the energy storage system and component production process data of the target component in the energy storage system. This embodiment... Figure 2 Based on the examples, a method for performance analysis of energy storage systems is described in detail, such as... Figure 2 As shown, the method includes:

[0081] S201. Obtain lifecycle data of the energy storage system. Lifecycle data includes: operation data and production process data.

[0082] Steps S201 and S101 are similar and will not be described again here.

[0083] S202. Obtain the system unique identifier of the energy storage system, determine the system operation data and system production process data of the energy storage system, and determine the target unique identifier that is bound to the system unique identifier.

[0084] The binding relationship is established through the unique identifier of the energy storage system and the unique identifier of the components. The unique identifier can be, for example, a serial number or a production batch number.

[0085] System operation data refers to system-level operation data during the operation of an energy storage system, such as charging-side data, discharging measurement data, and the start and end times of the charging and discharging process.

[0086] System production process data refers to the process parameters recorded by the energy storage system during the production process, such as welding strength, welding time, welding angle, aluminum busbar thickness, and aluminum busbar surface flatness.

[0087] A target unique identifier refers to the unique identifier of each target component located within an energy storage system. A target component can be any component within the energy storage system, or a specific component relevant to analyzing the system's performance. Examples of components include battery cells, fans, air conditioners, liquid cooling units, electrical components, and structural components.

[0088] Binding relationship refers to the association between an energy storage system and its components established through system unique identifiers and component unique identifiers.

[0089] Different energy storage systems contain different components. Therefore, when conducting performance analysis, the analysis should be based on the same energy storage system and the components within that system.

[0090] In this step, the system's unique identifier (UII) is obtained, and the UII is used to determine the system's operational and production data. Using the UII, target unique identifiers that are linked to the system's unique identifier are retrieved from the production process data recorded during production.

[0091] S203. Determine the component operation data and component production process data of the target component corresponding to the target unique identifier from the operation data and production process data, respectively.

[0092] Among them, component operation data refers to the process parameters of the target component of the energy storage system during operation, such as the terminal voltage of each cell, the current of each cell, etc.

[0093] Component manufacturing process data refers to the process parameters in the component manufacturing process, such as coating thickness, coating uniformity, coating speed, winding force, etc.

[0094] In this step, the target unique identifier is used to filter the running data and production process data to determine the component running data and component production process data of the target component corresponding to the target unique identifier.

[0095] S204. Based on the system operation data of the energy storage system and the component operation data of the target components in the energy storage system, determine the system performance index value and component performance index value of the energy storage system respectively.

[0096] Among them, system performance indicators refer to system-level performance indicators, while component performance indicators refer to component-level performance indicators.

[0097] In this step, based on the system operation data of the energy storage system, the system performance index values ​​corresponding to the system performance indicators of the energy storage system are determined, and based on the component operation data of the target components in the energy storage system, the component performance index values ​​corresponding to the component performance indicators of each component in the energy storage system are determined.

[0098] S205. Based on the system performance index values ​​and component performance index values ​​of the same dimension, obtain the performance index deviation value.

[0099] Among them, the performance index deviation value refers to the difference between the system performance index value and the component performance index value under the same dimension.

[0100] In existing technologies, the deviation between the system performance indicators of the energy storage system and the component performance indicators of the target components is not quantified, making it difficult to identify performance bottlenecks. Iteration in the production process of energy storage systems mainly relies on experience-based optimization.

[0101] In this step, the performance index deviation value is obtained by calculating the difference between the system performance index value and the component performance index value in the same dimension.

[0102] By determining the deviation values ​​of performance indicators, the impact of hardware performance differences on the performance of energy storage systems is quantified, providing data guidance for the iteration of energy storage system production processes and enabling production process optimization to shift from experience-driven to data-driven.

[0103] In one possible implementation, performance indicators include energy efficiency, total capacity, and lifespan; the target component includes a battery cell; and the performance indicator deviation value, based on the system performance indicator value and component performance indicator value of the same dimension, includes at least one of the following:

[0104] Calculate the average cell energy efficiency of multiple cells in the energy storage system, and calculate the difference between the system energy efficiency value and the average cell energy efficiency to obtain the energy efficiency deviation;

[0105] The average cell lifespan of multiple cells in the energy storage system is calculated, and the difference between the system lifespan and the average cell lifespan is calculated to obtain the lifespan deviation.

[0106] Calculate the total capacity of the multiple cells in the energy storage system, and calculate the difference between the total system capacity and the total cell capacity to obtain the total capacity deviation.

[0107] By calculating the difference between the system performance index value and the component performance index value in the same dimension, the performance index deviation value is obtained, which quantifies the impact of hardware performance differences on the performance of the energy storage system.

[0108] S206. For the target performance index in the performance index dataset, determine at least one influencing factor from other performance indicators in the performance index dataset and the production process parameters corresponding to the production process data.

[0109] The target performance metric is any one of the performance metrics in the performance metric dataset.

[0110] In this step, the target performance indicators and influencing factors are determined according to the analysis objectives. When the analysis objective is system-level performance, the target performance indicators can be either performance indicator deviations or system performance indicators; when the analysis objective is component-level performance, the target performance indicators can be component performance indicators.

[0111] S207. Based on the performance index values ​​corresponding to the target performance index and the performance index values ​​corresponding to at least one influencing factor, the system production process data and the component production process data, a performance analysis dataset is obtained.

[0112] After determining the target performance indicators and influencing factors, the corresponding indicator values ​​for the target performance indicators are determined from the performance indicator dataset. The data corresponding to each influencing factor is determined from the performance indicator dataset, system production process data, and component production process data, thus obtaining the performance analysis dataset.

[0113] For example, the target performance index can be the system performance index, and the influencing factors can be the component performance index, the performance index deviation, and the production process parameters corresponding to the system production process data and the component production process data can be used as influencing factors. Based on the performance index value corresponding to the target performance index and the data corresponding to each influencing factor, the first performance analysis dataset {P1, P2, ΔP, M1, M2} can be obtained.

[0114] For example, the target performance index can be the performance index deviation, and the influencing factors can be the production process parameters corresponding to the system production process data. Based on the performance index value corresponding to the target performance index and the data corresponding to each influencing factor, the second performance analysis dataset {ΔP, M1} can be obtained.

[0115] For example, the target performance index can be the component performance index, and the influencing factors can be the production process parameters corresponding to the component production process data. Based on the performance index value corresponding to the target performance index and the data corresponding to each influencing factor, the third performance analysis dataset {P2, M2} can be obtained.

[0116] S208. Based on the target performance index value, the index value corresponding to at least one influencing factor, and / or production process data, train a regression model for the target performance index and each influencing factor.

[0117] In this step, the index value corresponding to at least one influencing factor and / or production process data are used as input to the regression model, and the index value of the target performance indicator is used as output, forming a regression model of the target performance indicator and each influencing factor.

[0118] S209. Based on the indicator values ​​and / or production process data, determine the sample distribution space corresponding to each influencing factor.

[0119] The sample distribution space is used to indicate the range of values ​​for the corresponding index values ​​and / or production process data for each influencing factor.

[0120] Based on the index values ​​and / or production process data corresponding to each influencing factor, determine the range of values ​​for the index values ​​and / or production process data corresponding to each influencing factor, and obtain the sample distribution space.

[0121] S210. Using a perturbation algorithm within the sample distribution space, determine the target performance index and the sensitivity of each influencing factor, obtain the quantitative relationship between the energy storage system performance and each influencing factor, and generate the performance analysis results of the energy storage system based on the quantitative relationship.

[0122] Based on the value range of the corresponding index values ​​and / or production process data of each influencing factor, the data corresponding to each influencing factor is fine-tuned and then input into the regression model. Based on the output of the regression model before and after fine-tuning, the sensitivity of each influencing factor to the target performance index is calculated, the impact of each influencing factor on the target performance index to be analyzed is quantified, and the performance analysis of the energy storage system is realized.

[0123] For example, for the first performance analysis dataset {P1, P2, ΔP, M1, M2}, a perturbation algorithm is used in the sample distribution space to obtain the sensitivity between the system performance index P1 and the component performance index P2, the performance index deviation ΔP, the system production process data M1, and the component production process data M2, thereby obtaining the quantitative relationship between the energy storage system performance and each influencing factor.

[0124] For example, for the second performance analysis dataset {ΔP, M1}, a perturbation algorithm is used in the sample distribution space to obtain the sensitivity between the performance index deviation ΔP and the system production process data M1, thereby obtaining the quantitative relationship between the energy storage system performance and the production process parameters.

[0125] For example, for the third performance analysis dataset {P2, M2}, a perturbation algorithm is used in the sample distribution space to obtain the sensitivity between the component performance index P2 and the component production process data M2, thereby obtaining the quantitative relationship between the component performance and its production process data.

[0126] This application provides a method for performance analysis of an energy storage system. It determines the corresponding target unique identifier, system operation data, and system production process data through the system's unique identifier. Based on the target unique identifier, it determines component operation data and component production process data. Based on the system operation data and component operation data, it determines system performance index values, component performance index values, and performance index deviation values, respectively. A performance analysis dataset is constructed based on these data. Energy storage system performance analysis is then performed based on this data. This method achieves a direct correlation between performance indicators and hardware performance differences, improves the accuracy of performance bottleneck identification, avoids the limitations of existing technologies that rely solely on control strategies when analyzing energy storage system performance, and enhances the comprehensiveness of energy storage system performance analysis. It provides a data benchmark for production process optimization and iterative design of energy storage systems.

[0127] Figure 3 A schematic diagram of the structure of an energy storage system performance analysis device provided in this application embodiment is shown below. Figure 3 As shown, the energy storage system performance analysis device 30 provided in this embodiment includes:

[0128] The acquisition module 301 is used to acquire lifecycle data of the energy storage system, which includes: operation data and production process data.

[0129] Processing module 302 is used to determine the performance index dataset of the energy storage system based on the operating data;

[0130] Processing module 302 is also used to construct a performance analysis dataset based on the performance index values ​​and production process data in the performance index dataset;

[0131] Analysis module 303 is used to perform performance analysis on energy storage systems based on performance analysis datasets.

[0132] In one possible implementation, the operational data includes: system operational data of the energy storage system and component operational data of the target component in the energy storage system; the performance index dataset includes: system performance index values, component performance index values, and performance index deviation values; the processing module 302 is further used to determine the system performance index values ​​and component performance index values ​​of the energy storage system based on the system operational data of the energy storage system and the component operational data of the target component in the energy storage system; and to obtain the performance index deviation values ​​based on the system performance index values ​​and component performance index values ​​of the same dimension.

[0133] In one possible implementation, the performance indicators include energy efficiency, total capacity, and lifetime. The target components include: battery cells; processing module 302 is further configured to calculate the average energy efficiency of multiple battery cells in the energy storage system, and calculate the difference between the system energy efficiency value of the energy storage system and the average battery cell energy efficiency to obtain the energy efficiency deviation; processing module 302 is further configured to calculate the average lifetime of multiple battery cells in the energy storage system, and calculate the difference between the system lifetime value of the energy storage system and the average battery cell lifetime to obtain the lifetime deviation; processing module 302 is further configured to calculate the sum of the total capacity of multiple battery cells in the energy storage system, and calculate the difference between the system total capacity value of the energy storage system and the sum of the total battery cell capacity to obtain the total capacity deviation.

[0134] In one possible implementation, the production process data includes: system production process data of the energy storage system and component production process data of the target component in the energy storage system; the processing module 302 is further configured to determine at least one influencing factor from other performance indicators in the performance indicator dataset and the production process parameters corresponding to the production process data, for the target performance indicator in the performance indicator dataset; the target performance indicator is any one of the performance indicators in the performance indicator dataset; and a performance analysis dataset is obtained based on the performance indicator value corresponding to the target performance indicator and the performance indicator value corresponding to at least one influencing factor, the system production process data, and the component production process data.

[0135] In one possible implementation, the acquisition module 301 is further configured to acquire the system unique identifier of the energy storage system, determine the system operation data and system production process data of the energy storage system, and determine the target unique identifier that is bound to the system unique identifier; and determine the component operation data and component production process data of the target component corresponding to the target unique identifier from the operation data and production process data, respectively.

[0136] In one possible implementation, the analysis module 303 is further configured to quantify the quantitative relationship between at least one influencing factor and the target performance index of the energy storage system based on the index value of the target performance index, the index value corresponding to at least one influencing factor, and / or production process data; and generate the performance analysis results of the energy storage system based on the quantitative relationship.

[0137] In one possible implementation, the analysis module 303 is further configured to train a regression model for the target performance index and each influencing factor based on the index value and / or production process data corresponding to at least one influencing factor; determine the sample distribution space corresponding to each influencing factor based on the index value and / or production process data; and use a perturbation algorithm within the sample distribution space to determine the sensitivity of the target performance index to each influencing factor, thereby obtaining a quantitative relationship between the energy storage system performance and each influencing factor.

[0138] The energy storage system performance analysis device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0139] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0140] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0141] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0142] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0143] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0144] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0145] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0146] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0147] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0148] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0149] The division of units is merely a logical functional division; 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 indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0150] 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.

[0151] In addition, 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.

[0152] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0153] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0154] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for performance analysis of an energy storage system, characterized in that, include: Acquire lifecycle data of the energy storage system, including operational data and production process data; Based on the operational data, a performance index dataset for the energy storage system is determined; Based on the performance index values ​​in the performance index dataset and the production process data, a performance analysis dataset is constructed. Based on the performance analysis dataset, the energy storage system is subjected to performance analysis.

2. The method according to claim 1, characterized in that, The operational data includes: system operational data of the energy storage system and component operational data of the target components in the energy storage system; the performance index dataset includes: system performance index values, component performance index values, and performance index deviation values. The process of determining the performance index dataset of the energy storage system based on the operational data includes: Based on the system operation data of the energy storage system and the component operation data of the target components in the energy storage system, the system performance index value and component performance index value of the energy storage system are determined respectively. Based on the system performance index value and the component performance index value of the same dimension, the performance index deviation value is obtained.

3. The method according to claim 2, characterized in that, The performance indicators include energy efficiency, full capacity, and lifespan; the target component includes a battery cell; the performance indicator deviation value obtained based on the system performance indicator value and the component performance indicator value of the same dimension includes at least one of the following: Calculate the average cell energy efficiency of multiple cells in the energy storage system, and calculate the difference between the system energy efficiency value of the energy storage system and the average cell energy efficiency to obtain the energy efficiency deviation; Calculate the average cell lifespan of multiple cells in the energy storage system, and calculate the difference between the system lifespan of the energy storage system and the average cell lifespan to obtain the lifespan deviation; Calculate the total full capacity of the multiple cells in the energy storage system, and calculate the difference between the total full capacity of the energy storage system and the total full capacity of the cells to obtain the full capacity deviation.

4. The method according to claim 2 or 3, characterized in that, The production process data includes: system production process data of the energy storage system and component production process data of the target components in the energy storage system; The construction of a performance analysis dataset based on the performance index values ​​in the performance index dataset and the production process data includes: For a target performance indicator in the performance indicator dataset, at least one influencing factor is determined from other performance indicators in the performance indicator dataset and the production process parameters corresponding to the production process data; the target performance indicator is any one of the performance indicators in the performance indicator dataset. The performance analysis dataset is obtained based on the performance index values ​​corresponding to the target performance index, the performance index values ​​corresponding to the at least one influencing factor, the system production process data, and the component production process data.

5. The method according to claim 4, characterized in that, Before determining the performance index dataset of the energy storage system based on the operational data, the method further includes: Obtain the system unique identifier of the energy storage system, determine the system operation data and system production process data of the energy storage system, and determine the target unique identifier that has a binding relationship with the system unique identifier; The component operation data and component production process data of the target component corresponding to the target unique identifier are determined from the operation data and production process data, respectively.

6. The method according to claim 4, characterized in that, The performance analysis of the energy storage system based on the performance analysis dataset includes: Based on the index values ​​corresponding to the at least one influencing factor and / or the production process data, quantify the quantitative relationship between the at least one influencing factor and the target performance index of the energy storage system; Based on the quantitative relationship, the performance analysis results of the energy storage system are generated.

7. The method according to claim 6, characterized in that, The quantitative relationship between the at least one influencing factor and the target performance index of the energy storage system, based on the index values ​​corresponding to the at least one influencing factor and / or the production process data, includes: Based on the index values ​​of the target performance index, the index values ​​corresponding to the at least one influencing factor, and / or the production process data, a regression model for the target performance index and each of the influencing factors is trained. Based on the index values ​​and / or the production process data, determine the sample distribution space corresponding to each influencing factor; Within the sample distribution space, a perturbation algorithm is used to determine the sensitivity of the target performance index to each influencing factor, thereby obtaining a quantitative relationship between the energy storage system performance and each influencing factor.

8. A performance analysis device for an energy storage system, characterized in that, include: The acquisition module is used to acquire lifecycle data of the energy storage system, including: operational data and production process data. The processing module is used to determine the performance index dataset of the energy storage system based on the operational data. The processing module is also used to construct a performance analysis dataset based on the performance index values ​​in the performance index dataset and the production process data; The analysis module is used to perform performance analysis on the energy storage system based on the performance analysis dataset.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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