Method, system and equipment for testing power conversion efficiency of energy storage system and medium

Abstract

The invention belongs to the field of energy storage systems, and provides an energy storage system power conversion efficiency test method, system, device and medium, and the method comprises the steps: obtaining historical power conversion efficiency data of a plurality of energy storage systems and corresponding environment parameters; environment temperature, humidity and load are divided into a plurality of intervals; performing time serialization processing on the historical power conversion efficiency data and the interval environment parameters; calculating the sensitivity of the power conversion efficiency of each energy storage system in each environment parameter interval to the environment parameter change; dividing the plurality of energy storage systems into a plurality of categories; fitting a mathematical expression between power conversion efficiency and environmental parameters for each interval of each type of energy storage system; gradually determining the system power conversion efficiency corresponding to the upper limit point or the lower limit point of the interval according to the system category and the expression of the interval to which the current environmental parameters belong; and calculating the system power conversion efficiency in the target environment through interval-by-interval calculation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of energy storage systems, and specifically relates to a method and system for testing the power conversion efficiency of an energy storage system. BACKGROUND

[0002] As a key regulating device in power systems, energy storage systems can serve as a bridge between power generation and consumption, improving the stability and flexibility of power systems. However, the power conversion efficiency of energy storage systems is significantly affected by various external environmental parameters, such as temperature, humidity, and load changes.

[0003] The rate of change of power conversion efficiency of different energy storage systems under different temperature conditions varies significantly. For example, lithium-ion battery energy storage systems may experience a significant decrease in efficiency at high temperatures, as high temperatures accelerate the instability of internal chemical reactions. For energy storage systems based on vanadium flow batteries, high temperatures may have a smaller impact on efficiency, but low temperatures may increase the viscosity of the electrolyte, thereby reducing efficiency.

[0004] Humidity mainly affects the power conversion efficiency by affecting the insulation performance, heat dissipation efficiency, and stability of key components within the system. For example, high air humidity may cause some internal electronic components of energy storage systems to leak electricity, reducing efficiency. Some advanced sealed energy storage systems are not sensitive to humidity, and their efficiency change rate may be lower.

[0005] Different energy storage systems also have different response characteristics to load changes. For example, for small energy storage devices, load fluctuations may cause power electronic devices to frequently switch, thereby significantly reducing efficiency. For large energy storage systems, load changes may optimize internal energy distribution, reduce efficiency change rate, and maintain high conversion performance.

[0006] The difference in the rate of change of power conversion efficiency of different energy storage systems under the same environmental conditions is due to differences in their internal structure, technical principles, and operating characteristics. For example, when the ambient temperature rises from 25°C to 35°C, the efficiency of lithium battery systems may decrease by 5%, while the efficiency of flow battery systems may decrease by less than 2%. When the humidity changes from 40% RH to 80% RH, the efficiency of some unsealed systems may decrease by more than 10%, while the efficiency of sealed systems may change by no more than 1%.

[0007] In order to compare the power conversion efficiency of energy storage systems, the environmental parameters need to be fixed at the same level, but the current environmental parameters may not be the target environmental parameters. Since the power conversion efficiency is different under different environmental parameters, it is difficult to unify all systems to one environmental parameter for comparison.

[0008] The above differences show that a single, static test method cannot comprehensively evaluate the power conversion efficiency performance of energy storage systems under complex and variable environmental conditions. Therefore, a test method that takes into account different environmental parameters and their synergistic effects is needed to accurately reflect the efficiency variation of different energy storage systems under different environmental conditions and facilitate the comparison of systems under the same target environmental parameters. SUMMARY

[0009] To solve the problems in the prior art, the present application provides a power conversion efficiency test method for energy storage systems, comprising the following steps: Obtain historical power conversion efficiency data of multiple energy storage systems and their corresponding environmental parameters, including environmental temperature, humidity and load; Divide the environmental temperature, humidity and load into multiple intervals, and convert each set of environmental parameter values into corresponding interval representations; When testing the power conversion efficiency of an energy storage system, the category of the energy storage system to be tested and the current environmental parameters are obtained in real time, and the interval to which the parameters belong is determined; According to the expression of the system category and the interval to which the current environmental parameters belong, the system power conversion efficiency corresponding to the upper or lower limit point of the interval is gradually determined; Through interval-by-interval calculation, the interval corresponding to the target environmental parameters is finally determined, and the system power conversion efficiency under the target environmental parameters is calculated according to the mathematical expression between the power conversion efficiency and the environmental parameters of the interval corresponding to the target environmental parameters.

[0010] Further, the time series data is normalized to generate a first feature vector set; based on the first feature vector set, the sensitivity of the power conversion efficiency of each energy storage system to the change of environmental parameters in each environmental parameter interval is calculated, and the sensitivity represents the response degree of the power conversion efficiency to the change of environmental parameters; According to the sensitivity of each energy storage system in different environmental parameter intervals, a clustering algorithm is used to divide the multiple energy storage systems into multiple categories; the energy storage systems in each category have similar sensitivity of power conversion efficiency to environmental parameters, and the environmental parameters are in the same interval; A mathematical expression between power conversion efficiency and environmental parameters is fitted for each interval of the energy storage systems in each category.

[0011] Further, the sensitivity is a quantitative description of the power conversion efficiency change trend of the energy storage system in each environmental parameter interval, and is used to reflect the response degree of the power conversion efficiency to the environmental parameter; the higher the sensitivity, the more significant the influence of the environmental parameter on the efficiency; by comparing the change amplitude of the environmental parameter in the adjacent interval and the corresponding power conversion efficiency change, the influence of the environmental temperature, humidity and load on the power conversion efficiency is determined, and the sensitivity is divided into three levels of high, medium and low.

[0012] Further, the use of a clustering algorithm to divide a plurality of energy storage systems into a plurality of categories comprises: In an environmental parameter interval, the sensitivity of each system is vectorized, and the sensitivity vector is used as an input feature to cluster the systems in the same environmental parameter interval using a clustering algorithm.

[0013] Further, the step-by-step determination of the system power conversion efficiency corresponding to the upper or lower limit point of the interval according to the expression of the system category and the interval to which the current environmental parameter belongs comprises: determining whether the environmental parameter takes the upper limit or the lower limit according to the change direction of the current environmental parameter and the target environmental parameter.

[0014] Further, the final determination of the interval corresponding to the target environmental parameter through interval-by-interval calculation, and the calculation of the system power conversion efficiency under the target environment according to the mathematical expression between the power conversion efficiency and the environmental parameter of the interval corresponding to the target environmental parameter comprises: According to the specific value of the target environmental parameter, determine the multiple intervals it spans or involves, arrange these intervals in order to form a complete interval path; For each interval in the path, call the corresponding mathematical expression to calculate the power conversion efficiency of the target environmental parameter in the interval; If the target environmental parameter is located at the junction of multiple intervals, the system will interpolate or smooth the calculation results of adjacent intervals to ensure the continuity of the power conversion efficiency; After the specific value of the target environmental parameter is covered by the complete path, the system synthesizes the calculation results of each interval to output the final power conversion efficiency. The present application also provides a power conversion efficiency test system for an energy storage system, comprising the following modules: An acquisition module is used to acquire historical power conversion efficiency data of a plurality of energy storage systems and corresponding environmental parameters, wherein the environmental parameters include environmental temperature, humidity and load; A division module is used to divide the environmental temperature, humidity and load into a plurality of intervals, and convert each group of environmental parameter values into a corresponding interval representation; A test module is used to acquire the category of the energy storage system to be tested and the current environmental parameter in real time when testing the power conversion efficiency of the energy storage system, and determine the interval to which the parameter belongs; The computing module is used for determining the system power conversion efficiency corresponding to the upper limit or lower limit point of the interval according to the system category and the expression of the interval to which the current environmental parameter belongs; the system power conversion efficiency corresponding to the target environmental parameter is finally determined through the interval-by-interval calculation, and the system power conversion efficiency under the target environment is calculated according to the mathematical expression between the power conversion efficiency and the environmental parameter of the interval corresponding to the target environmental parameter.

[0015] Further, the system further comprises: The computing module is used for normalizing the time-sequenced data to generate a first feature vector set; the sensitivity of the power conversion efficiency of each energy storage system to the change of the environmental parameter in each environmental parameter interval is calculated based on the first feature vector set, and the sensitivity represents the response degree of the power conversion efficiency to the change of the environmental parameter; the clustering module is used for dividing the plurality of energy storage systems into a plurality of categories by using a clustering algorithm according to the sensitivity of each energy storage system in different environmental parameter intervals; the energy storage systems in each category have similar sensitivity of the power conversion efficiency to the environmental parameter, and the environmental parameter is in the same interval. The fitting module is used for fitting a mathematical expression between the power conversion efficiency and the environmental parameter for each interval of the energy storage system of each category.

[0016] The application further provides a computer device comprising a memory and a processor, when instructions in the memory are executed by the processor, the computer device realizes the foregoing energy storage system power conversion efficiency test method.

[0017] The application further provides a non-temporary computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the foregoing energy storage system power conversion efficiency test method.

[0018] The energy storage system power conversion efficiency test method and system provided by the application can comprehensively evaluate and dynamically predict the power conversion efficiency of the energy storage system under different environmental conditions by combining historical operation data and real-time environmental parameters, and has the following beneficial effects: The method fully considers the comprehensive influence of environmental temperature, humidity and load and other environmental parameters, and can accurately reflect the synergistic effect of different parameters and the comprehensive influence on the efficiency.

[0019] The efficiency value of the different energy storage systems under the current environmental parameter is adjusted to the target environmental parameter level, so as to ensure that the influence of environmental differences is excluded when compared. Through interval-by-interval calculation, mathematical expression fitting and real-time data, the power conversion efficiency under the target environmental parameter can be accurately predicted, the accuracy of the test result is ensured, and the demand for high-precision evaluation in actual application is met. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative effort.

[0021] Figure 1 is a flowchart of the method of the present application; Figure 2 is a further improvement of the method of the present application; Figure 3 is a system block diagram of the present application; Figure 4 is a further improvement of the system of the present application; Figure 5 is a system architecture diagram of the present application. DETAILED DESCRIPTION

[0022] The preferred description of the present application is made below in combination with the drawings and the specific embodiments.

[0023] The present embodiment solves the above problems by the following steps: In one embodiment, with reference to Figure 1 The present application provides a method for testing the power conversion efficiency of an energy storage system, which aims to comprehensively evaluate the power conversion efficiency of the energy storage system under different environmental conditions. By combining historical operation data and real-time environmental parameters, using time series processing, sensitivity analysis and clustering algorithms, a dynamic correlation model between power conversion efficiency and environmental parameters is constructed, which can accurately predict and test the power conversion efficiency of the energy storage system in complex and variable operating environments, and provide a scientific basis for system performance optimization and operation strategy.

[0024] Specifically, the method comprises the following steps: Obtain historical power conversion efficiency data of a plurality of energy storage systems and their corresponding environmental parameters, including environmental temperature, humidity and load.

[0025] The historical power conversion efficiency data refers to the efficiency value between input power and output power measured by the plurality of energy storage systems during actual operation, which is usually recorded in time series form, reflecting the energy conversion performance of the energy storage system under different working conditions. This data can come from the monitoring records, test reports or other data acquisition equipment of the energy storage system, and contains the power conversion efficiency trend and performance of the system under various environmental conditions.

[0026] The environmental parameters refer to external environmental factors that may affect the power conversion efficiency of the energy storage system, including the following three types: Ambient Temperature, representing the temperature condition of the operating environment of the energy storage system, in Celsius (°C). Ambient temperature has a significant impact on the heat dissipation performance of the energy storage system, the stability and efficiency of the power conversion equipment. For example, high temperature can lead to efficiency decline.

[0027] Humidity, representing the air humidity of the operating environment of the energy storage system, usually expressed in relative humidity (%RH). Changes in humidity can affect the electrical performance, heat dissipation efficiency of the energy storage equipment, and the stability of internal components.

[0028] Load, referring to the output power demand that the energy storage system bears during operation, in kilowatts (kW) or megawatts (MW). Changes in load may cause fluctuations in the working state of the power conversion equipment, thereby affecting the power conversion efficiency.

[0029] Extract the above historical power conversion efficiency data and corresponding environmental parameters from the operation log, monitoring equipment or data acquisition platform of the energy storage system; through high-precision sensors, data acquisition modules and storage units, ensure that the data obtained is complete, accurate and covers the state of the system under various operating conditions.

[0030] By obtaining the historical power conversion efficiency data of multiple energy storage systems and their corresponding environmental parameters, it provides basic data support for subsequent analysis of the impact of environmental factors on power conversion efficiency and the establishment of efficiency prediction models.

[0031] Divide the ambient temperature, humidity and load into multiple intervals, and convert each set of environmental parameter values into corresponding interval representations.

[0032] Divide the ambient temperature, humidity and load into several discrete intervals according to the numerical range, each interval corresponding to a specific range.

[0033] Ambient Temperature Intervals: Temperature Interval 1: -20°C to 0°C; Temperature Interval 2: 0°C to 20°C; Temperature Interval 3: 20°C to 40°C; Temperature Interval 4: Above 40°C.

[0034] Humidity Intervals: Humidity Interval 1: 10%RH to 30%RH; Humidity Interval 2: 30%RH to 60%RH; Humidity Interval 3: 60%RH to 90%RH.

[0035] Load Intervals: Load Interval 1: 10kW to 100kW; Load interval 2: 100 kW to 300 kW; Load interval 3: 300 kW to 500 kW.

[0036] Each interval is represented by a number or a simplified symbol to reduce data complexity. For example: Temperature interval 1, Humidity interval 2, Load interval 3, etc.

[0037] Convert the original environmental parameter values to their corresponding interval identifiers. For example: An environmental temperature of 25°C corresponds to "Temperature interval 3"; A humidity of 45% RH corresponds to "Humidity interval 2"; A load of 200 kW corresponds to "Load interval 2".

[0038] Time sequence the historical power conversion efficiency data and the intervalized environmental parameters.

[0039] Historical power conversion efficiency data refers to the historical record of power conversion efficiency changes over time recorded by the energy storage system during actual operation, usually expressed as the ratio of input power to output power. This data can reflect the performance of the energy storage system under different environmental conditions, such as the fluctuation pattern or stability of efficiency.

[0040] Time sequencing refers to organizing historical power conversion efficiency data and corresponding intervalized environmental parameters in chronological order to form an ordered time series data structure, which is used to capture the dynamic change pattern of the energy storage system's operating state. Time sequencing can preserve the time correlation of data to support subsequent dynamic analysis and model training.

[0041] Sort the historical power conversion efficiency data and environmental parameters according to the collected time stamps to ensure that the time sequence of the data is consistent with the actual operation.

[0042] Divide the time series into fixed-length time windows, and arrange the data points in each window in chronological order. For example, take 5 minutes as a time interval to form continuous time segments.

[0043] Align the power conversion efficiency data and intervalized environmental parameters at each time point to ensure that each time point has complete data characteristics.

[0044] Time sequencing can completely preserve the time correlation of data and capture the pattern of power conversion efficiency changes over time, providing a basis for dynamic analysis and prediction.

[0045] The time series data structure facilitates intuitive analysis of the changes in power conversion efficiency and corresponding environmental parameters at each time point, helping to identify the main reasons for efficiency fluctuations.

[0046] When testing the power conversion efficiency of the energy storage system, the category of the energy storage system to be tested and the current environmental parameters are obtained in real time, and the interval to which the parameters belong is determined.

[0047] The acquisition device continuously monitors the operating state of the energy storage system through fixed time intervals (e.g., every second or every minute) and records environmental temperature, humidity, and load data in real time. The acquired parameter values are bound with timestamps to ensure the data has temporal continuity and real-time nature.

[0048] The environmental parameter values are distributed into specific ranges according to the preset interval division standard.

[0049] Exemplarily: According to historical data, a certain energy storage system is determined to be a third-class system, and the current environmental parameters are: The environmental temperature of 25°C corresponds to "temperature interval 2"; The humidity of 55% corresponds to "humidity interval 1"; The load of 220kW corresponds to "load interval 2".

[0050] The final generated interval identifier is: [temperature interval 2, humidity interval 1, load interval 2].

[0051] According to the expression of the system category and the interval to which the current environmental parameters belong, the system power conversion efficiency corresponding to the upper or lower limit point of the interval is gradually determined.

[0052] The expression of the interval to which the current environmental parameters belong is generated based on clustering results and mathematical fitting, and is specifically used to describe the law of the change of the power conversion efficiency of the energy storage system with the environmental parameters.

[0053] The expression of each interval can accurately quantify the relationship between the power conversion efficiency and the parameter change, for example, how the efficiency value changes with the change of the environmental temperature, humidity, or load.

[0054] The system calls the corresponding mathematical expression from the preset expression library according to the interval identifier of the current environmental parameters (e.g., "temperature interval 2", "humidity interval 1", "load interval 3"), determines the mathematical expression as f3(temperature, humidity, load), and gradually determines the system power conversion efficiency corresponding to the upper or lower limit point of the interval according to the change direction of the current environmental parameters and the target environmental parameters, thereby facilitating the calculation of the power conversion efficiency across the interval. For example, the current environmental temperature is 25°C and the target environmental temperature is 8°C, so the change direction of the temperature parameter is negative and the temperature environmental parameter takes the lower limit; the current environmental humidity is 55% and the target environmental humidity is 60%, so the change direction of the humidity parameter is positive and the humidity environmental parameter takes the upper limit.

[0055] Exemplarily, in the third-class system: Current environmental parameters: temperature is 25°C; humidity is 55%; load is 250kW.

[0056] The corresponding interval is: temperature interval 20°C to 30°C; humidity interval 50% to 60%; load interval 200KW to 300KW, the expression of the interval is f3(temperature, humidity, load); Calculate the lower limit point of the system power conversion efficiency, that is, calculate the conversion efficiency when the environmental temperature is 20°C, the humidity is 50%, and the load interval is 200KW, that is, f3(20°C, 50%, 200KW).

[0057] Through interval-by-interval calculation, the interval corresponding to the target environmental parameters is finally determined, and the system power conversion efficiency under the target environment is calculated according to the mathematical expression between the power conversion efficiency of the interval corresponding to the target environmental parameters and the environmental parameters.

[0058] Interval-by-interval calculation means that according to the interval to which the target environmental parameters belong, the mathematical expression of each interval is combined to calculate the change process of the energy storage system power conversion efficiency in turn, until the efficiency value corresponding to the target environmental parameters is accurately determined.

[0059] This process needs to combine the power conversion efficiency calculation results of each related interval with the specific value of the target environmental parameters to generate the final efficiency output, ensuring the comprehensiveness and accuracy of the evaluation.

[0060] According to the specific value of the target environmental parameters (such as temperature, humidity, load), determine the multiple intervals it crosses or involves. The system arranges these intervals in turn to form a complete interval path for step-by-step efficiency calculation.

[0061] For each interval in the path, call the corresponding mathematical expression to calculate the power conversion efficiency of the target environmental parameters in that interval.

[0062] If the target environmental parameters are located at the intersection of multiple intervals, the system will interpolate or smooth the calculation results of adjacent intervals to ensure the continuity of the power conversion efficiency.

[0063] After the specific value of the target environmental parameters is covered by the complete path, the system integrates the calculation results of each interval to output the final power conversion efficiency.

[0064] Exemplarily: Current environmental parameters: temperature is 25°C; humidity is 55%; load is 250kW.

[0065] The corresponding interval is: Temperature interval 20°C to 30°C; Humidity interval 50% to 60%; Load range: 200kW to 300kW.

[0066] The expression of this range is f3(temperature, humidity, load), which is used to describe the variation of the power conversion efficiency of the energy storage system in the above range.

[0067] The target environmental parameters are environmental temperature 8°C, humidity 45%, and load 180kW.

[0068] Since the target environmental parameters are not within the current range, the system power conversion efficiency corresponding to the target environmental parameters needs to be determined step by step through the method of range-by-range calculation.

[0069] First range calculation (20°C to 30°C, 50% to 60%, 200kW to 300kW): Lower limit point parameters: The lower limit point of the current range is environmental temperature 20°C, humidity 50%, and load 200kW.

[0070] Calculate the power conversion efficiency of the lower limit point: use the expression f3(20°C, 50%, 200kW), assuming the calculation result is a power conversion efficiency of 88%.

[0071] Judge the coverage of the target parameters: the target parameters (8°C, 45%, 180kW) are not covered by the first range, so further calculation is needed.

[0072] Second range calculation (10°C to 20°C, 40% to 50%, 150kW to 200kW): Range and expression: Environmental temperature range: 10°C to 20°C; Humidity range: 40% to 50%; Load range: 150kW to 200kW; The expression is f2(temperature, humidity, load).

[0073] Lower limit point parameters: environmental temperature 10°C, humidity 40%, and load 150kW.

[0074] Calculate the power conversion efficiency of the lower limit point: use the expression f2(10°C, 40%, 150kW), assuming the calculation result is a power conversion efficiency of 80%.

[0075] Judge the coverage of the target parameters: the target parameters (8°C, 45%, 180kW) are still not covered by the second range, so further calculation is needed.

[0076] Third range calculation (0°C to 10°C, 40% to 50%, 150kW to 200kW): Range and expression: Ambient temperature range: 0°C to 10°C; Humidity range: 40% to 50%; Load range: 150kW to 200kW; The expression is f1(temperature, humidity, load).

[0077] Target parameters are calculated directly: The target environmental parameters are directly substituted into the expression f1(8°C,45%,180kW), assuming the calculation result is a power conversion efficiency of 75%.

[0078] Through interval-by-interval calculations, the power conversion efficiency corresponding to the target environmental parameters (8°C, 45%, 180kW) is 75%.

[0079] Interval-by-interval calculations ensure that every parameter is taken into account, and the target environmental conditions are approximated step by step through interval-by-interval expressions, thereby improving the accuracy and comprehensiveness of the calculations.

[0080] When the target environmental parameters span multiple intervals, this method can flexibly integrate the information from each interval, ensuring that the calculation results have high reliability and applicability.

[0081] By defining the efficiency value for each range, the key ranges affecting efficiency can be identified, providing a clear basis for decision-making in optimizing the operation of energy storage systems.

[0082] like Figure 2 As shown, in another embodiment, the method for determining conversion efficiency is further improved.

[0083] Specifically, it includes the following steps: Acquire historical power conversion efficiency data and corresponding environmental parameters for multiple energy storage systems, including ambient temperature, humidity, and load. The specific implementation method for this step is the same as in the previous embodiment, and all content of the previous embodiment can be incorporated into this step.

[0084] The ambient temperature, humidity, and load are divided into multiple intervals, and each set of environmental parameter values ​​is converted into a corresponding interval representation. The specific implementation method of this step is the same as that of the previous embodiment, and all the contents of the previous embodiment can be completely incorporated into this step.

[0085] The historical power conversion efficiency data and the intervalized environmental parameters are processed into a time series.

[0086] Historical power conversion efficiency data refers to the historical record of how the power conversion efficiency of an energy storage system changes over time during actual operation. It is usually expressed as the ratio of input power to output power. This data can reflect the performance of the energy storage system under different environmental conditions, such as the fluctuation pattern or stability of efficiency.

[0087] Time series processing refers to organizing historical power conversion efficiency data and corresponding intervalized environmental parameters in chronological order to form an ordered time series data structure, which is used to capture the dynamic change rules of the energy storage system's operating state. Time series processing can preserve the time correlation of data to support subsequent dynamic analysis and model training.

[0088] The historical power conversion efficiency data and environmental parameters are sorted according to the collected timestamps to ensure that the time sequence of the data is consistent with the actual operating conditions.

[0089] The time series is divided into fixed-length time windows, and the data points in each window are arranged in chronological order. For example, a 5-minute interval is used to form continuous time segments.

[0090] The power conversion efficiency data and intervalized environmental parameters at each time point are aligned to ensure that each time point has complete data features.

[0091] Time series processing can completely preserve the time correlation of data and capture the rules of power conversion efficiency changes over time, providing a basis for dynamic analysis and prediction.

[0092] The time series data structure facilitates intuitive analysis of the changes in power conversion efficiency and corresponding environmental parameters at each time point, helping to identify the main reasons for efficiency fluctuations.

[0093] The time series data is normalized to generate a first feature vector set; based on the first feature vector set, the sensitivity of the power conversion efficiency of each energy storage system to environmental parameter changes in each environmental parameter interval is calculated, which represents the response degree of power conversion efficiency to environmental parameter changes.

[0094] Normalization refers to standardizing the power conversion efficiency data and environmental parameter values after time series processing, converting the numerical range of each feature to a unified scale, thereby eliminating dimensional differences and making different feature values comparable in subsequent calculations.

[0095] The time series data is traversed to extract the maximum and minimum values of each feature, scaled according to the preset numerical range, and the numerical values of power conversion efficiency, environmental temperature, humidity, and load are mapped to the same interval, such as 0 to 1. This process generates a unified feature representation to facilitate subsequent sensitivity calculations.

[0096] After normalization, power conversion efficiency and environmental parameters are represented by unified standardized values to form a first feature vector set, each vector containing power conversion efficiency and corresponding environmental temperature, humidity, and load features.

[0097] Sensitivity is a quantitative description of the trend of power conversion efficiency of energy storage systems in each environmental parameter interval, which reflects the degree of response of power conversion efficiency to environmental parameters. The higher the sensitivity, the more significant the impact of the environmental parameter on the efficiency.

[0098] Based on the first feature vector set generated by normalization processing, statistical analysis is performed on the data of each energy storage system in different intervals. The sensitivity calculation determines the influence of environmental temperature, humidity and load on power conversion efficiency by comparing the change amplitude of environmental parameters in adjacent intervals and the corresponding power conversion efficiency changes.

[0099] The sensitivity of each energy storage system in different environmental parameter intervals is stored in vector form. For example, assuming that the power conversion efficiency of a certain energy storage system changes greatly in environmental temperature interval 1, while the humidity and load interval 1 change little, its sensitivity feature vector is represented as: [high, low, low], where "high" means that the environmental parameter has a significant impact on the power conversion efficiency, and "low" means that the impact is small.

[0100] For each energy storage system and each environmental parameter, a sensitivity result is generated. For example: The environmental temperature sensitivity is "high"; the humidity sensitivity is "medium"; and the load sensitivity is "low".

[0101] Exemplarily: From interval 1 to interval 2, the efficiency increases from 0.0 to 1.0, with a change of 1.0; From interval 1 to interval 2, the efficiency increases from 0.0 to 0.5, with a change of 0.5; From interval 1 to interval 2, the efficiency increases from 0.0 to 0.2, with a change of 0.2.

[0102] The temperature sensitivity is "high" because the efficiency change is large and directly affected by temperature; The humidity sensitivity is "medium" because the efficiency change is small; The load sensitivity is "low" because the efficiency change is the smallest.

[0103] The output result is the sensitivity feature vector: [high, medium, low], corresponding to temperature, humidity and load respectively.

[0104] Sensitivity calculation can clearly determine the degree of influence of different environmental parameters on power conversion efficiency, quantify complex environmental influences, and provide scientific basis for the operation optimization of energy storage systems.

[0105] The sensitivity calculation result provides high-quality feature data for subsequent clustering analysis and efficiency prediction model, which helps to improve the accuracy and adaptability of the model.

[0106] The sensitivity calculation result can intuitively explain the dominant factor of the efficiency change of the energy storage system, and helps users quickly understand the system performance and develop optimization measures.

[0107] According to the sensitivity of each energy storage system in different environmental parameter intervals, a plurality of energy storage systems are divided into a plurality of categories by using a clustering algorithm; the energy storage systems in each category have similar sensitivity of power conversion efficiency to environmental parameters, and the environmental parameters are in the same interval.

[0108] In the foregoing step, the sensitivity represents the response degree of the power conversion efficiency of the energy storage system to the change of the environmental parameter (such as the environmental temperature, humidity, load). By calculating the sensitivity in different environmental parameter intervals, the performance characteristics of the energy storage system under various environmental conditions can be determined.

[0109] In this step, by using the sensitivity information, a plurality of energy storage systems are classified by using a clustering algorithm, and the energy storage systems with similar performance characteristics are classified into one category, so that the energy storage systems in different categories can be uniformly analyzed and the efficiency prediction model can be constructed.

[0110] Common clustering algorithms include K-means, DBSCAN, and hierarchical clustering, and the present application does not specifically limit which algorithm is used, and a person skilled in the art can determine it according to the data volume and other factors.

[0111] The sensitivity of each energy storage system in different environmental parameter intervals is used as an input feature, for example, a sensitivity vector: [temperature sensitivity, humidity sensitivity, load sensitivity].

[0112] The clustering algorithm compares the sensitivity vectors of a plurality of energy storage systems, and classifies the energy storage systems with similar sensitivity characteristics into the same category. The energy storage systems in each category have consistent response laws to environmental parameters, and are distributed in similar environmental parameter intervals.

[0113] The energy storage systems in each category have the following common characteristics: The sensitivity characteristics to environmental parameters are similar, for example, sensitive to temperature but not sensitive to load; The power conversion efficiency change trend is consistent in the same environmental parameter interval.

[0114] The clustering result ensures that the energy storage systems in each category can maintain consistent performance under specific environmental conditions, thereby facilitating targeted optimization of operating parameters or establishment of a prediction model.

[0115] For example, assume that the sensitivity vectors of three energy storage systems are: System A: [high temperature sensitivity, low humidity sensitivity, and medium load sensitivity]; System B: [high temperature sensitivity, low humidity sensitivity, and medium load sensitivity]; System C: [low temperature sensitivity, high humidity sensitivity, and high load sensitivity]. In this example, the clustering algorithm classifies the three energy storage systems into two categories: Category 1: System A and System B Category 2: System C The energy storage systems in Category 1 have similar sensitivity characteristics to environmental parameters, and the power conversion efficiency change trend is consistent in the same environmental parameter interval. The energy storage systems in Category 2 have different sensitivity characteristics to environmental parameters, and the power conversion efficiency change trend is inconsistent in the same environmental parameter interval. System C: [Low temperature sensitivity, medium humidity sensitivity, high load sensitivity].

[0116] The clustering result is: System A and System B have similar sensitivity characteristics and are classified into Category 1; System C has significantly different sensitivity characteristics and is classified into Category 2 separately.

[0117] The output classification is: Category 1 (high temperature sensitivity category): System A, System B; Category 2 (high load sensitivity category): System C.

[0118] In this step, the clustering algorithm classifies energy storage systems with similar characteristics into the same category. Systems with similar sensitivity have similar power conversion efficiency variation rates under similar environmental parameters. This characteristic can improve the accuracy of the conversion efficiency function.

[0119] A mathematical expression between power conversion efficiency and environmental parameters is fitted for each interval of energy storage systems in each category.

[0120] Based on the sensitivity and power conversion efficiency characteristics of each category of energy storage systems in the clustering result, a mathematical relationship between power conversion efficiency and environmental parameters (such as environmental temperature, humidity, and load) is established to form a general prediction model, facilitating efficient prediction of power conversion efficiency under different environmental conditions.

[0121] The mathematical expression fitting is based on the following basic data: Normalized power conversion efficiency data; Environment parameters divided by intervals (such as temperature intervals, humidity intervals, and load intervals); Sensitivity characteristics of each category of energy storage systems in the clustering result.

[0122] Extract the power conversion efficiency and corresponding environmental parameter data of each category of energy storage systems from the clustering result; ensure that the data is normalized to avoid fitting errors caused by different dimensions.

[0123] Select an appropriate fitting model based on the data characteristics of each category of energy storage systems. For linear characteristics, select a linear model; for nonlinear characteristics, select a polynomial model or a nonlinear regression model based on machine learning.

[0124] Use a fitting algorithm (such as the least squares method or gradient descent method) to train the data of each category of energy storage systems, optimize the model parameters, and make the fitting result as close as possible to the true relationship between power conversion efficiency and environmental parameters. For each interval of each category, generate a mathematical expression that can predict power conversion efficiency based on input environmental parameters.

[0125] Exemplarily: Suppose a certain type of energy storage system is divided into the following intervals according to environmental parameter sensitivity analysis: Environmental temperature interval: Interval 1: 0°C to 10°C; Interval 2: 10°C to 20°C.

[0126] Humidity interval: Interval 1: 40% to 50%; Interval 2: 50% to 60%.

[0127] Load interval: Interval 1: 100kW to 200kW; Interval 2: 200kW to 300kW.

[0128] For the interval data of this type of energy storage system, the following power conversion efficiency and environmental parameter relationships are obtained: Environmental temperature interval 1, humidity interval 1, load interval 1: Data points: Temperature 5°C, humidity 45%, load 150kW, power conversion efficiency 90%; Temperature 8°C, humidity 45%, load 180kW, power conversion efficiency 88%.

[0129] Environmental temperature interval 2, humidity interval 2, load interval 2: Data points: Temperature 15°C, humidity 55%, load 220kW, power conversion efficiency 85%; Temperature 18°C, humidity 55%, load 280kW, power conversion efficiency 82%.

[0130] Environmental temperature interval 1, humidity interval 1, load interval 1: Suppose the power conversion efficiency (η) has a linear relationship with temperature (T), humidity (H), and load (L), and the fitted expression is: η = 92 - 0.5 × (T - 5) - 0.1 × (H - 45) - 0.4 × (L - 150) Environmental temperature interval 2, humidity interval 2, load interval 2: Suppose the power conversion efficiency (η) has a nonlinear relationship with environmental parameters, and the fitted expression is: η = 90 - 0.3 × (T - 15)^2 - 0.2 × (H - 55)^2 - 0.5 × (L - 220)^2 When testing the power conversion efficiency of the energy storage system, the category of the energy storage system to be tested and the current environmental parameters are obtained in real time, and the interval to which the parameters belong is determined.

[0131] The acquisition device continuously monitors the operating state of the energy storage system through fixed time intervals (e.g., every second or every minute) and records environmental temperature, humidity, and load data in real time. The acquired parameter values are bound with timestamps to ensure the data has temporal continuity and real-time nature.

[0132] The environmental parameter values are distributed into specific ranges according to the preset interval division standard.

[0133] Exemplarily: According to historical data, a certain energy storage system is determined to be a third-class system, and the current environmental parameters are: The environmental temperature of 25°C corresponds to "temperature interval 2"; The humidity of 55% corresponds to "humidity interval 1"; The load of 220kW corresponds to "load interval 2".

[0134] The finally generated interval identifier is: [temperature interval 2, humidity interval 1, load interval 2].

[0135] According to the expression of the system category and the interval to which the current environmental parameters belong, the system power conversion efficiency corresponding to the upper or lower limit point of the interval is gradually determined.

[0136] The expression of the interval to which the current environmental parameters belong is generated based on clustering results and mathematical fitting, and is specially used to describe the law of the power conversion efficiency of the energy storage system changing with the environmental parameters.

[0137] The expression of each interval can accurately quantify the relationship between the power conversion efficiency and the parameter change, for example, how the efficiency value changes with the change of the environmental temperature, humidity, or load.

[0138] The system calls the corresponding mathematical expression from the preset expression library according to the interval identifier of the current environmental parameters (e.g., "temperature interval 2", "humidity interval 1", "load interval 3"), determines the mathematical expression as f3(temperature, humidity, load), and gradually determines the system power conversion efficiency corresponding to the upper or lower limit point of the interval according to the change direction of the current environmental parameters and the target environmental parameters, thereby facilitating the calculation of the power conversion efficiency across the interval. For example, the current environmental temperature is 25°C and the target environmental temperature is 8°C, so the change direction of the temperature parameter is negative and the temperature environmental parameter takes the lower limit; the current environmental humidity is 55% and the target environmental humidity is 60%, so the change direction of the humidity parameter is positive and the humidity environmental parameter takes the upper limit.

[0139] Exemplarily, in the third-class system: Current environmental parameters: ambient temperature is 25°C; humidity is 55%; load is 250kW.

[0140] The corresponding interval is: ambient temperature interval 20°C to 30°C; humidity interval 50% to 60%; load interval 200KW to 300KW, the expression of the interval is f3(temperature, humidity, load); Calculate the lower limit point of the system power conversion efficiency, that is, calculate the conversion efficiency when the ambient temperature is 20°C, the humidity is 50%, and the load interval is 200KW, that is, f3(20°C, 50%, 200KW).

[0141] Through interval-by-interval calculation, the interval corresponding to the target environmental parameters is finally determined, and the system power conversion efficiency under the target environment is calculated according to the mathematical expression between the power conversion efficiency of the interval corresponding to the target environmental parameters and the environmental parameters.

[0142] Interval-by-interval calculation means that according to the interval to which the target environmental parameters belong, the mathematical expression of each interval is combined to calculate the change process of the energy storage system power conversion efficiency in turn, until the efficiency value corresponding to the target environmental parameters is accurately determined.

[0143] This process needs to combine the power conversion efficiency calculation results of each related interval with the specific value of the target environmental parameters to generate the final efficiency output, ensuring the comprehensiveness and accuracy of the evaluation.

[0144] According to the specific value of the target environmental parameters (such as temperature, humidity, load), determine the multiple intervals it spans or involves. The system arranges these intervals in turn to form a complete interval path for step-by-step efficiency calculation.

[0145] For each interval in the path, call the corresponding mathematical expression to calculate the power conversion efficiency of the target environmental parameters in that interval.

[0146] If the target environmental parameters are located at the intersection of multiple intervals, the system will interpolate or smooth the calculation results of adjacent intervals to ensure the continuity of the power conversion efficiency.

[0147] After the specific value of the target environmental parameters is covered by the complete path, the system integrates the calculation results of each interval to output the final power conversion efficiency.

[0148] Exemplarily: Current environmental parameters: ambient temperature is 25°C; humidity is 55%; load is 250kW.

[0149] The corresponding interval is: Ambient temperature interval 20°C to 30°C; Humidity interval 50% to 60%; Load range: 200kW to 300kW.

[0150] The expression of this range is f3(temperature, humidity, load), which is used to describe the variation of the power conversion efficiency of the energy storage system in the above range.

[0151] The target environmental parameters are environmental temperature 8°C, humidity 45%, and load 180kW.

[0152] Since the target environmental parameters are not within the current range, the system power conversion efficiency corresponding to the target environmental parameters needs to be determined step by step through the method of range-by-range calculation.

[0153] First range calculation (20°C to 30°C, 50% to 60%, 200kW to 300kW): Lower limit point parameters: The lower limit point of the current range is environmental temperature 20°C, humidity 50%, and load 200kW.

[0154] Calculate the power conversion efficiency of the lower limit point: use the expression f3(20°C, 50%, 200kW), assuming the calculation result is a power conversion efficiency of 88%.

[0155] Determine the coverage of the target parameters: the target parameters (8°C, 45%, 180kW) are not covered by the first range, so further calculation is needed.

[0156] Second range calculation (10°C to 20°C, 40% to 50%, 150kW to 200kW): Range and expression: Temperature range: 10°C to 20°C; Humidity range: 40% to 50%; Load range: 150kW to 200kW; The expression is f2(temperature, humidity, load).

[0157] Lower limit point parameters: environmental temperature 10°C, humidity 40%, and load 150kW.

[0158] Calculate the power conversion efficiency of the lower limit point: use the expression f2(10°C, 40%, 150kW), assuming the calculation result is a power conversion efficiency of 80%.

[0159] Determine the coverage of the target parameters: the target parameters (8°C, 45%, 180kW) are still not covered by the second range, so further calculation is needed.

[0160] Third range calculation (0°C to 10°C, 40% to 50%, 150kW to 200kW): Range and expression: Temperature range: 0°C to 10°C; Humidity range: 40% to 50%; Load range: 150kW to 200kW; The expression is f1(temperature, humidity, load).

[0161] Direct calculation of target parameters: directly substitute the target environmental parameters into the expression f1(8°C, 45%, 180kW), assuming the calculation result is a power conversion efficiency of 75%.

[0162] Through interval-by-interval calculation, the power conversion efficiency corresponding to the target environmental parameters (8°C, 45%, 180kW) is 75%.

[0163] Interval-by-interval calculation ensures that each part of the parameter is considered, and the target environmental conditions are gradually approached through interval-by-interval expressions, improving the accuracy and comprehensiveness of the calculation.

[0164] When the target environmental parameters span multiple intervals, this method can flexibly integrate the information of each interval, ensuring that the calculation result has high reliability and applicability.

[0165] By specifying the efficiency value of each interval, the key interval that affects the efficiency can be identified, providing clear decision-making basis for energy storage system operation optimization. See Figure 3 In another embodiment, the present application also provides a power conversion efficiency test system for an energy storage system, comprising: An acquisition module for acquiring historical power conversion efficiency data of a plurality of energy storage systems and their corresponding environmental parameters, including environmental temperature, humidity, and load; A division module for dividing the environmental temperature, humidity, and load into multiple intervals, and converting each set of environmental parameter values into corresponding interval representations; A test module for acquiring the category of the energy storage system to be tested and the current environmental parameters in real time when performing power conversion efficiency tests on the energy storage system, and determining the interval to which the parameters belong; A calculation module for gradually determining the system power conversion efficiency corresponding to the upper or lower limit point of the interval according to the expression of the system category and the interval to which the current environmental parameters belong; through interval-by-interval calculation, the interval corresponding to the target environmental parameters is finally determined, and the system power conversion efficiency under the target environmental parameters is calculated according to the mathematical expression between the power conversion efficiency of the interval corresponding to the target environmental parameters and the environmental parameters.

[0166] See Figure 4 In another embodiment, the present application also provides a further improved power conversion efficiency test system for an energy storage system, comprising: obtain historical power conversion efficiency data of a plurality of energy storage systems and corresponding environmental parameters, the environmental parameters including temperature, humidity and load; divide the temperature, humidity and load into a plurality of intervals, and convert each set of environmental parameter values into a corresponding interval representation; serialize the historical power conversion efficiency data and the intervalized environmental parameters; normalize the time-serialized data to generate a first feature vector set; based on the first feature vector set, calculate the sensitivity of the power conversion efficiency of each energy storage system to changes in environmental parameters in each environmental parameter interval, the sensitivity representing the degree of response of the power conversion efficiency to changes in environmental parameters; cluster the plurality of energy storage systems into a plurality of categories according to the sensitivity of each energy storage system in different environmental parameter intervals; the energy storage systems in each category have similar sensitivity of power conversion efficiency to environmental parameters, and the environmental parameters are in the same interval; fit a mathematical expression between power conversion efficiency and environmental parameters for each interval of the energy storage systems in each category; when testing the power conversion efficiency of an energy storage system, obtain the category of the energy storage system to be tested and the current environmental parameters in real time, and determine the interval to which the parameters belong; calculate the system power conversion efficiency corresponding to the upper or lower limit point of the interval according to the expression of the system category and the interval to which the current environmental parameters belong; by calculating the interval by interval, finally determine the interval corresponding to the target environmental parameters, and calculate the system power conversion efficiency under the target environmental parameters according to the mathematical expression between power conversion efficiency and environmental parameters in the interval corresponding to the target environmental parameters.

[0167] It should be noted that the above-mentioned explanation and description of the energy storage system power conversion efficiency test method embodiment also applies to the device of the present application embodiment, which will not be described here.

[0168] In order to realize the above-mentioned embodiment, the present application embodiment further proposes a computer device, Figure 5 is a structural schematic diagram of the computer device. When the instruction processor in the computer device executes, the energy storage system power conversion efficiency test method in the above-mentioned embodiment is realized.

[0169] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to execute the energy storage system power conversion efficiency testing method in the aforementioned embodiments.

[0170] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0172] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, 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 described in the various embodiments of this application. 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.

[0173] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims. For some module structures not specifically defined in this invention, the content described in the prior art shall prevail. The prior art mentioned in the foregoing background art section and specific embodiments section can be considered as part of this invention and used to understand the meaning of some technical features or parameters.

Claims

1. A method for testing the power conversion efficiency of an energy storage system, characterized in that, The method includes the following steps: Acquire historical power conversion efficiency data of multiple energy storage systems and their corresponding environmental parameters, including ambient temperature, humidity and load; The ambient temperature, humidity, and load are divided into multiple intervals, and each set of environmental parameter values ​​is converted into a corresponding interval representation; When conducting power conversion efficiency tests on energy storage systems, the system acquires the type of the energy storage system under test and the current environmental parameters in real time, and determines the range to which the parameters belong. Based on the system category and the expression of the current environmental parameter range, the system power conversion efficiency corresponding to the upper or lower limit of the range is determined step by step; By calculating interval by interval, the interval corresponding to the target environmental parameters is finally determined. The system power conversion efficiency under the target environment is then calculated based on the mathematical expression between the power conversion efficiency of the interval corresponding to the target environmental parameters and the environmental parameters.

2. The method for testing the power conversion efficiency of an energy storage system according to claim 1, characterized in that, The method further includes: The historical power conversion efficiency data and the intervalized environmental parameters are processed into a time series; the time series data are normalized to generate a first feature vector set; based on the first feature vector set, the sensitivity of the power conversion efficiency of each energy storage system to changes in environmental parameters within each environmental parameter interval is calculated, whereby the sensitivity represents the degree of response of the power conversion efficiency to changes in environmental parameters. Based on the sensitivity of each energy storage system to different environmental parameter ranges, a clustering algorithm is used to divide multiple energy storage systems into multiple categories; the energy storage systems in each category have similar power conversion efficiency sensitivity to environmental parameters, and the environmental parameters are in the same range; For each interval of each type of energy storage system, fit a mathematical expression between power conversion efficiency and environmental parameters.

3. The method for testing the power conversion efficiency of an energy storage system according to claim 2, characterized in that, The sensitivity is a quantitative description of the trend of power conversion efficiency of the energy storage system within various environmental parameter ranges, used to reflect the degree of response of power conversion efficiency to environmental parameters; the higher the sensitivity, the more significant the impact of the environmental parameter on efficiency; by comparing the change range of environmental parameters in adjacent ranges and the corresponding changes in power conversion efficiency, the magnitude of the impact of ambient temperature, humidity and load on power conversion efficiency is determined, and the sensitivity is divided into three levels: high, medium and low.

4. The method for testing the power conversion efficiency of an energy storage system according to claim 2, characterized in that, The method of using clustering algorithms to classify multiple energy storage systems into multiple categories includes: Within a range of environmental parameters, the sensitivity of each system is vectorized. Using the sensitivity vector as input features, a clustering algorithm is used to cluster the systems within the same range of environmental parameters.

5. The method for testing the power conversion efficiency of an energy storage system according to claim 2, characterized in that, The step of gradually determining the system power conversion efficiency corresponding to the upper or lower limit of the interval based on the system category and the expression of the interval to which the current environmental parameters belong includes: determining whether the environmental parameters should be taken as the upper or lower limit based on the direction of change of the current environmental parameters and the target environmental parameters.

6. The method for testing the power conversion efficiency of an energy storage system according to claim 2, characterized in that, The process of calculating interval by interval to ultimately determine the interval corresponding to the target environmental parameters, and calculating the system power conversion efficiency under the target environment based on the mathematical expression between the power conversion efficiency of the interval corresponding to the target environmental parameters and the environmental parameters, includes: Based on the specific values ​​of the target environmental parameters, determine the multiple intervals it crosses or involves, and arrange these intervals in sequence to form a complete interval path; For each interval in the path, the corresponding mathematical expression is invoked to calculate the power conversion efficiency of the target environment parameters in that interval; If the target environmental parameters are located at the intersection of multiple intervals, the system will interpolate or smooth the calculation results of adjacent intervals to ensure the continuity of power conversion efficiency. After the specific values ​​of the target environmental parameters are covered by the complete path, the system integrates the calculation results of each interval and outputs the final power conversion efficiency.

7. A power conversion efficiency testing system for an energy storage system, characterized in that, The system includes the following modules: The acquisition module is used to acquire historical power conversion efficiency data of multiple energy storage systems and their corresponding environmental parameters, including ambient temperature, humidity and load. The segmentation module is used to divide the ambient temperature, humidity and load into multiple intervals, and convert each set of environmental parameter values ​​into the corresponding interval representation; The testing module is used to acquire the type of the energy storage system under test and the current environmental parameters in real time when conducting power conversion efficiency tests on energy storage systems, and to determine the range to which the parameters belong. The calculation module is used to progressively determine the system power conversion efficiency corresponding to the upper or lower limit of the interval based on the system category and the expression of the interval to which the current environmental parameters belong; through interval-by-interval calculation, the interval corresponding to the target environmental parameters is finally determined, and the system power conversion efficiency under the target environment is calculated based on the mathematical expression between the power conversion efficiency of the interval corresponding to the target environmental parameters and the environmental parameters.

8. The energy storage system power conversion efficiency testing system according to claim 7, characterized in that, The system also includes: The serialization module is used to perform time serialization processing on the historical power conversion efficiency data and the intervalized environmental parameters. The normalization module is used to normalize the time-series data and generate a first feature vector set. Based on the first feature vector set, the sensitivity of the power conversion efficiency of each energy storage system to changes in environmental parameters within each environmental parameter range is calculated. The sensitivity represents the degree of response of the power conversion efficiency to changes in environmental parameters. The clustering module is used to divide multiple energy storage systems into multiple categories based on the sensitivity of each energy storage system to different environmental parameter ranges using a clustering algorithm; the energy storage systems in each category have similar power conversion efficiency sensitivity to environmental parameters, and the environmental parameters are in the same range; The fitting module is used to fit a mathematical expression between power conversion efficiency and environmental parameters for each interval of each type of energy storage system.

9. A computer device, comprising a memory and a processor, characterized in that, When the instructions in the memory are executed by the processor, the computer device implements the energy storage system power conversion efficiency test method as described in any one of claims 1-6.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the energy storage system power conversion efficiency test method as described in any one of claims 1-6.

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