Energy storage system evaluation method and device and electronic equipment

Abstract

The invention discloses an energy storage system evaluation method and device and electronic equipment. According to the method, comprehensive state data of the energy storage system are obtained, wherein the comprehensive state data comprise battery performance data, electric power data, communication data and temperature control data; determining attributes of the comprehensive state data, and preprocessing the comprehensive state data according to the attributes of the comprehensive state data to obtain a preprocessed comprehensive state data matrix; obtaining weights corresponding to the battery performance data, the power data, the communication data and the temperature control data, and obtaining an index weight matrix according to the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data; and based on the comprehensive state data matrix and the index weight matrix, evaluating the difference of the energy storage system relative to an ideal state so as to obtain a comprehensive evaluation index of the energy storage system. According to the invention, energy storage system performance evaluation can be carried out at any time according to actual data, and real-time comprehensive guidance is provided for operation of the energy storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage system evaluation, and more specifically, relates to an energy storage system evaluation method, device and electronic equipment. Background Art

[0002] At present, the evaluation of energy storage systems mainly focuses on the economic indicators and safety indicators of energy storage projects. For the performance evaluation of energy storage systems, professionals mainly operate energy storage systems and related test equipment according to the test steps in the standards, and then record and analyze the test results. In addition, the existing standards are usually used before the energy storage system is put into operation to obtain the performance evaluation results before the energy storage system is put into operation.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that the related technology has at least the following problems: there is a lack of evaluation of the comprehensive performance of the energy storage system during normal operation, and the evaluation of the energy storage system is not comprehensive, often biased towards economic and safety indicators; the participation of professionals is required, such as test operations and result analysis, so that the evaluation cannot be performed during the operation of the energy storage system, that is, real-time evaluation cannot be achieved. Summary of the invention

[0004] In view of the above defects or improvement needs of the prior art, the embodiments of the present invention provide an energy storage system evaluation method, device and electronic device to solve the technical problem that the related technology cannot guide the performance evaluation of the energy storage system in real time and comprehensively.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides an energy storage system evaluation method, the method comprising: obtaining comprehensive status data of the energy storage system, the comprehensive status data comprising battery performance data, power data, communication data and temperature control data; determining attributes of the comprehensive status data, and preprocessing the comprehensive status data according to the attributes of the comprehensive status data to obtain a preprocessed comprehensive status data matrix; obtaining weights corresponding to the battery performance data, the power data, the communication data and the temperature control data, and obtaining an indicator weight matrix according to the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data; based on the comprehensive status data matrix and the indicator weight matrix, evaluating the gap between the energy storage system and the ideal state, thereby obtaining a comprehensive evaluation index of the energy storage system.

[0006] Optionally, the acquisition of comprehensive status data of the energy storage system includes: collecting message data during the operation of the energy storage system; and obtaining battery performance data, power data, communication data, and temperature control data according to the message data. The message data can cover various information during the operation of the energy storage system, thereby obtaining comprehensive battery performance, power, communication, and temperature control data, which is helpful for a comprehensive understanding of the system status; in addition, the collection of real-time message data can realize real-time monitoring of the operating status of the energy storage system.

[0007] Optionally, the determining of the attributes of the comprehensive state data and preprocessing the comprehensive state data according to the attributes of the comprehensive state data to obtain a matrix of preprocessed comprehensive state data include: determining the attributes of the comprehensive state data, the attributes including benefit type, cost type and interval type; determining the normalization processing method corresponding to the comprehensive state data according to the type corresponding to the attribute of the comprehensive state data; normalizing the comprehensive state data according to the determined normalization processing method, thereby obtaining the normalized comprehensive state data; generating a matrix of comprehensive state data based on the normalized comprehensive state data. Among them, by dividing the comprehensive state data into benefit type, cost type and interval type attributes, it is helpful to analyze and process the data more finely; determining the appropriate normalization processing method for different attribute types can effectively eliminate the dimension and value range differences between different attributes, making the data more comparable; and, through normalization processing, the data can be unified to the same scale, which is conducive to subsequent data analysis and comparison.

[0008] Optionally, determining a normalization processing method corresponding to the comprehensive status data according to a type corresponding to an attribute of the comprehensive status data includes:

[0009] When the attribute of the comprehensive status data is benefit type, the normalization processing method corresponding to the comprehensive status data is determined as: b = a / a max ;

[0010] When the attribute of the comprehensive status data is cost type, the normalization processing method corresponding to the comprehensive status data is determined as: b = 1-a / a max ;

[0011] When the attribute of the comprehensive state data is interval type, assuming that the given optimal attribute is [j, k], p is the intolerable lower limit, and q is the intolerable upper limit, then the normalization processing method corresponding to the comprehensive state data is determined as follows:

[0012]

[0013] When the attribute of the comprehensive status data is of interval type, and the attribute of the comprehensive status data has a corresponding upper limit and a lower limit, the normalization processing method corresponding to the comprehensive status data is determined as follows:

[0014]

[0015] Among them, a is the attribute value of the current comprehensive state data, b is the attribute value of the comprehensive state data after normalization, and a max is the maximum value of the attribute, a min Is the minimum value of the attribute.

[0016] Among them, determining different normalization methods according to different attributes helps to retain attribute characteristics, improve data accuracy, and promote more refined data analysis, thereby providing more advantages for the processing and application of comprehensive status data.

[0017] Optionally, the obtaining of the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data, and obtaining the indicator weight matrix according to the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data, includes: assigning weights to the data modules corresponding to the battery performance data, the power data, the communication data and the temperature control data to obtain a first weight matrix; calculating four groups of second weight matrices according to the first weight matrix and the battery performance data, the power data, the communication data and the temperature control data; combining the four groups of second weight matrices to obtain the indicator weight matrix. By assigning different weights to different data types, the importance of each data type in the overall evaluation or decision can be determined in a personalized manner according to specific needs or goals.

[0018] Optionally, based on the comprehensive state data matrix and the indicator weight matrix, the gap between the energy storage system and the ideal state is evaluated to obtain the comprehensive evaluation index of the energy storage system, including: calculating the weight forward matrix of the energy storage system according to the comprehensive state data matrix and the indicator weight matrix; obtaining the positive ideal solution matrix and the negative ideal solution matrix; calculating the distance from the state of the energy storage system to the positive ideal solution according to the weight forward matrix and the positive ideal solution matrix, which is the first distance; calculating the distance from the state of the energy storage system to the negative ideal solution according to the weight forward matrix and the negative ideal solution matrix, which is the second distance; calculating the comprehensive evaluation index of the energy storage system according to the first distance and the second distance. Among them, by quantifying the comprehensive performance evaluation of the energy storage system, the comprehensive evaluation index is finally obtained, and this quantification can improve the scientificity, accuracy and operability of evaluation and decision-making.

[0019] Optionally, the acquisition of the positive ideal solution matrix and the negative ideal solution matrix includes: acquiring the record matrix of the positive ideal solution and the record matrix of the negative ideal solution according to the attributes of the comprehensive state data; performing data preprocessing on the record matrix of the positive ideal solution and the record matrix of the negative ideal solution respectively to obtain the preprocessed data processing matrix of the positive ideal solution and the data processing matrix of the negative ideal solution; calculating the positive ideal solution matrix according to the data processing matrix of the positive ideal solution and the indicator weight matrix; calculating the negative ideal solution matrix according to the data processing matrix of the negative ideal solution and the indicator weight matrix. Introducing positive and negative ideal solutions helps to transform the multi-attribute decision-making problem into a single-attribute problem, thereby simplifying the complex multi-attribute decision-making process.

[0020] An embodiment of the present invention also provides an energy storage system evaluation device, the device comprising: a comprehensive status data acquisition module, used to acquire comprehensive status data of the energy storage system, the comprehensive status data comprising battery performance data, power data, communication data and temperature control data; a data preprocessing module, used to determine the attributes of the comprehensive status data, and preprocess the comprehensive status data according to the attributes of the comprehensive status data to obtain a preprocessed comprehensive status data matrix; an indicator weight matrix acquisition module, used to acquire the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data, and obtain the indicator weight matrix according to the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data; a comprehensive evaluation index calculation module, used to evaluate the gap between the energy storage system and the ideal state based on the comprehensive status data matrix and the indicator weight matrix, so as to obtain the comprehensive evaluation index of the energy storage system.

[0021] An embodiment of the present invention also provides an electronic device, comprising: a memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the electronic device implements the energy storage system evaluation method as described above.

[0022] An embodiment of the present invention further provides a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by an electronic device, the electronic device implements the energy storage system evaluation method as described above.

[0023] In general, the above technical solutions conceived by the embodiments of the present invention have the following beneficial effects compared with the prior art:

[0024] By automatically collecting various data, including battery performance data, power data, communication data, and temperature control data, the energy storage system can process these collected data in real time to evaluate the performance of the energy storage system. This process can ensure that the performance evaluation of the energy storage system is real-time, so that operation and maintenance personnel can understand the status of the energy storage system in a timely manner, and take timely measures to optimize the performance of the energy storage system and ensure the stable operation of the energy storage system. In addition, by comprehensively considering data such as battery performance, power, communication, and temperature control, the status of the energy storage system can be comprehensively evaluated; by determining attributes and preprocessing, and assigning weights to each data type, it ensures reasonable consideration of different data types; by comparing the gap between the actual state of the energy storage system and the ideal state, the performance of the system can be quantitatively evaluated to provide guidance for system optimization, and the comprehensive evaluation index obtained by the quantification can comprehensively consider the weights of various data and provide decision makers with an intuitive evaluation basis.

[0025] Through the solution provided by the embodiment of the present invention, technicians can evaluate the performance of the energy storage system according to actual data at any time, and provide real-time comprehensive guidance for the operation of the energy storage system, thereby solving the technical problem that it was previously impossible to provide real-time and comprehensive guidance for the performance evaluation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of an energy storage system evaluation method provided by an embodiment of the present invention;

[0027] Figure 2 is a flow chart of a method for obtaining a preprocessed comprehensive state data matrix provided by an embodiment of the present invention;

[0028] Figure 3 is a flow chart of a method for obtaining an indicator weight matrix provided by an embodiment of the present invention;

[0029] Figure 4 is a flow chart of a method for obtaining a comprehensive evaluation index of an energy storage system provided by an embodiment of the present invention;

[0030] Figure 5 is a structural schematic diagram of an energy storage system evaluation device provided by an embodiment of the present invention;

[0031] Figure 6 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0033] Energy storage system is a system that can store energy and release it when needed. It can help balance the difference between energy supply and demand, improve energy efficiency, and deal with energy fluctuations. The evaluation of energy storage system currently focuses on the economic indicators and safety indicators of energy storage projects. Economic indicators include cost-benefit analysis, energy cost savings, reduced operating costs, investment payback period, etc. Safety indicators include system stability, safety design, emergency response, environmental impact, etc. These two aspects play a vital role in determining the deployment and operation of energy storage systems.

[0034] However, there is indeed an important defect in the evaluation of energy storage systems, that is, the lack of a comprehensive evaluation of the comprehensive performance of energy storage systems during normal operation. Usually, the evaluation focuses on economic and safety indicators, while ignoring the comprehensive performance of energy storage systems in actual operation. It is of great significance to conduct a comprehensive performance evaluation of energy storage systems in actual operation, which can fully understand the performance of energy storage systems in actual operation, find existing problems and bottlenecks, and provide a basis for system optimization and improvement; by evaluating the dynamic response capability and stability of the system under different working conditions, it can ensure that the system can stably and reliably provide the required functions in actual operation; comprehensive performance evaluation helps to reduce the operating cost of the system, improve system efficiency and reduce energy waste by optimizing system parameters and operating strategies; through comprehensive performance evaluation, system problems can be discovered in time and improved; and so on. Therefore, comprehensive performance evaluation not only helps to optimize the performance and operating efficiency of energy storage systems, but also improves the stability and reliability of energy storage systems, reduces operating costs, and thus promotes the widespread application and development of energy storage technology in the energy field.

[0035] Based on this, the embodiments of the present invention provide a method and device for evaluating an energy storage system, which mainly solve the technical problem that it was impossible to guide the performance evaluation of the energy storage system in real time and comprehensively in the past.

[0036] The comprehensive evaluation method provided in the embodiment of the present invention can be applied to various types of energy storage systems, including but not limited to: battery energy storage systems, liquid flow battery energy storage systems, fuel cell energy storage systems, etc.

[0037] The energy storage system evaluation method and device are described below through specific embodiments.

[0038] See also Figure 1 , Figure 1 is a flow chart of an energy storage system evaluation method provided by an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0039] S11. Obtain comprehensive status data of the energy storage system, where the comprehensive status data includes battery performance data, power data, communication data, and temperature control data.

[0040] Among them, comprehensive status data refers to the multi-dimensional data set required to evaluate the overall performance of the energy storage system. Comprehensive status data includes battery performance data, power data, communication data and temperature control data. Battery performance data is data used to evaluate the health and performance of the battery, including SOH (State of Health), battery maximum voltage difference, etc.; SOH reflects the degree to which the current performance of the battery compares with the performance of a new battery; the maximum voltage difference of the battery is the maximum voltage difference between each single cell in the battery pack. Excessive voltage difference may indicate that a battery is faulty or unbalanced. Power data is used to reflect the power output and conversion capacity of the energy storage system, including the rated power discharge time of the day, the total positive active energy of the day, the conversion efficiency and the charge-discharge conversion time, etc.; the rated power discharge time of the day is used to indicate the total time that the battery is discharged at the rated power in a certain day; the total positive active energy of the day is used to indicate the total active energy released by the battery in a day, which can be expressed in kilowatt-hours (kWh); the conversion efficiency is the energy conversion efficiency during the battery charging and discharging process, indicating the ratio of input energy to output energy; the charge-discharge conversion time is the time required for the battery to switch between the charging state and the discharging state. Communication data includes reply message time, communication bit error rate, clock error and packet loss rate, etc.; the reply message time is the time required for the system to send a reply after receiving a request; the communication bit error rate is the ratio of data errors that occur during data transmission, reflecting the reliability of communication; the clock error is the deviation between the system's internal clock and the standard time; the packet loss rate is the proportion of data packets lost during data transmission. Temperature control data is used to monitor the temperature status of the energy storage system, for example, including air conditioning return air temperature / coolant return temperature, PCS ambient temperature, single cell temperature difference and maximum single cell temperature. Air conditioning return air temperature / coolant return temperature is the temperature of the air or liquid returned by the air conditioning or cooling system; PCS (Power Conversion System) ambient temperature is the temperature of the environment surrounding the power conversion system; single cell temperature difference is the difference between the highest single cell temperature and the lowest single cell temperature in the battery cluster. Excessive single cell temperature difference may affect the consistency of the battery cluster, and then affect the overall performance of the battery cluster; the highest single cell temperature refers to the highest single cell temperature in the energy storage system battery cluster. If the highest single cell temperature is too high, the battery decay rate will be accelerated.

[0041] Ways to obtain the above-mentioned comprehensive status data may include: installing sensors on key components of the energy storage system to monitor battery performance, power output, communication status and temperature in real time. These sensors may include voltage sensors, current sensors, temperature sensors, etc. Then use data acquisition equipment (such as data loggers, etc.) to collect data from the sensors. Then transmit the data from the data acquisition equipment to the centralized control system or monitoring platform through a suitable communication protocol. Next, analyze and process the data based on the centralized control system or monitoring platform to extract battery performance data, power data, communication data and temperature control data. It is also possible to use data visualization tools based on a visualization platform to display comprehensive status data and generate regular reports.

[0042] In some embodiments, obtaining comprehensive status data of the energy storage system includes: collecting message data during the operation of the energy storage system; obtaining battery performance data, power data, communication data and temperature control data according to the message data. Among them, a data collector or a monitoring system can be used to capture the message data during the operation of the energy storage system in real time. The data collector or the monitoring system can communicate with each component of the energy storage system. During the communication process, ensure that the appropriate communication protocol (such as CAN, TCP / IP, etc.) is used to obtain message data from each module (battery management system, inverter, electric meter, temperature control system, etc.). Parse the collected message data to extract key information. The message contains a variety of information, such as voltage, current, temperature, status indication, etc. Convert the parsed data into a usable format to facilitate subsequent processing and analysis. According to the parsed data, it can be classified into battery performance data, power data, communication data and temperature control data to obtain the comprehensive status data. In this embodiment, the message data can cover various information during the operation of the energy storage system, so as to obtain comprehensive battery performance, power, communication and temperature control data, which is helpful to fully understand the system status; in addition, the collection of real-time message data can realize real-time monitoring of the operating status of the energy storage system.

[0043] The obtained comprehensive status data can also be stored in a database for subsequent query and analysis. Visualization tools can also be used to visualize the obtained comprehensive status data to facilitate operators to intuitively understand the status of the energy storage system.

[0044] S12. Determine the attributes of the comprehensive state data, and preprocess the comprehensive state data according to the attributes of the comprehensive state data to obtain a matrix of preprocessed comprehensive state data.

[0045] This attribute is used to describe the characteristics or classification of comprehensive status data. This attribute can be used to quantify the performance of the energy storage system, making it easier to analyze and compare.

[0046] For example, attributes can be used to classify data according to their properties (such as benefit type, cost type, interval type), which helps to choose the appropriate method for analyzing and processing data. Benefit type attributes are those where the larger the data value, the better the performance. Cost type attributes are those where the smaller the data value, the better the performance, which is related to reducing losses or costs. Interval type attributes are those that perform best within a specific range, and their upper and lower limits need to be paid attention to.

[0047] In some embodiments, see Figure 2 , dividing the comprehensive status data into benefit-type, cost-type and interval-type attributes is helpful for more detailed analysis and processing of the data. Determining appropriate normalization processing methods for different attribute types can effectively eliminate the differences in dimensions and value ranges between different attributes, making the data more comparable. Therefore, the above step S12 specifically includes:

[0048] S121. Determine the attributes of the comprehensive status data, which include benefit type, cost type and interval type.

[0049] The benefit attribute is that the larger the data value, the better, which indicates some positive performance or effect. For example, the above-mentioned SOH, rated power discharge time of the day, total positive active energy of the day, and conversion efficiency can be determined as benefit attributes.

[0050] Cost attributes are those where the smaller the data value, the better the performance. For example, the maximum voltage difference of the battery, charge and discharge conversion time, message reply time, communication bit error rate, clock error, packet loss rate, and temperature difference of a single cell can be determined as cost attributes.

[0051] Interval attributes perform best within a specific range and have an ideal range. For example, the above air conditioner return air temperature, PCS ambient temperature, and maximum cell temperature can be determined as interval attributes.

[0052] S122. Determine a normalization processing method corresponding to the comprehensive status data according to the type corresponding to the attribute of the comprehensive status data.

[0053] When the attribute of the comprehensive status data is benefit type, the normalization processing method corresponding to the comprehensive status data is determined as: b = a / a max ;

[0054] When the attribute of the comprehensive status data is cost type, the normalization processing method corresponding to the comprehensive status data is determined as: b = 1-a / a max ;

[0055] When the attribute of the comprehensive state data is interval type, assuming that the given optimal attribute is [j, k], p is the intolerable lower limit, and q is the intolerable upper limit, then the normalization processing method corresponding to the comprehensive state data is determined as:

[0056]

[0057] When the attribute of the comprehensive state data is of interval type, and the attribute of the comprehensive state data has a corresponding upper limit and a lower limit, and being close to the upper limit is preferred, the normalization processing method corresponding to the comprehensive state data is determined as follows:

[0058]

[0059] Among them, a is the attribute value of the current comprehensive state data, b is the attribute value of the comprehensive state data after normalization, and a max is the maximum value of the attribute, a min Is the minimum value of the attribute.

[0060] In this embodiment, determining different normalization methods according to different attributes helps to retain attribute characteristics, improve data accuracy, and promote more refined data analysis, thereby providing more advantages for the processing and application of comprehensive status data.

[0061] S123, performing normalization processing on the comprehensive status data according to the determined normalization processing method, so as to obtain normalized comprehensive status data.

[0062] S124. Generate a comprehensive state data matrix based on the normalized comprehensive state data.

[0063] The comprehensive state data matrix is ​​a structure for storing the normalized comprehensive state data. Through the above steps S121 to S123, the normalized comprehensive state data can be obtained, and these data are combined into a matrix to obtain the comprehensive state data matrix. For example, the comprehensive state data is a matrix A = [a1 a2 ... an], and after normalizing the data according to the properties of the data (benefit-type attributes, cost-type attributes, interval-type attributes), the comprehensive state data matrix B = [b1b2 ... bn] is obtained.

[0064] In this embodiment, the data is scaled to a specific range through normalization processing. Because the initial units of various indicators in the scoring system corresponding to the energy storage system are not uniform, it is necessary to convert data of different magnitudes to the same scale through normalization processing to eliminate the dimension for subsequent distance calculation.

[0065] S13, obtaining the weights corresponding to the battery performance data, power data, communication data and temperature control data, and obtaining an indicator weight matrix according to the weights corresponding to the battery performance data, power data, communication data and temperature control data.

[0066] In this embodiment, by assigning different weights to different data types, the importance of each data type in the overall evaluation or decision can be determined in a personalized manner according to specific needs or goals. Figure 3 , step S13 specifically includes:

[0067] S131, assigning weights to the data modules corresponding to the battery performance data, the power data, the communication data, and the temperature control data to obtain a first weight matrix.

[0068] Among them, battery performance data, power data, communication data and temperature control data are indicators for evaluating the performance of the energy storage system. The four types of data each contain their corresponding indicators. The data modules corresponding to the battery performance data, power data, communication data and temperature control data can be considered as modules for evaluating the indicators corresponding to the four types of data. For example, the battery performance evaluation module, power evaluation module, communication evaluation module and temperature control evaluation module constitute the evaluation module corresponding to the energy storage system.

[0069] The battery performance evaluation module is used to evaluate the performance of batteries used in energy storage systems, and includes indicators related to battery health, efficiency and performance, including but not limited to SOH, battery maximum voltage difference, etc.

[0070] The power evaluation module is used to evaluate the power of the energy storage system during operation, and includes indicators related to energy output and utilization efficiency, including but not limited to the rated power discharge time of the day, the total positive active power of the day, conversion efficiency, charge-discharge conversion time, etc.

[0071] The communication evaluation module is used to evaluate the communication quality of the energy storage system, and includes indicators related to data transmission and system communication quality, including but not limited to reply message time, communication bit error rate, clock error, packet loss rate, etc.

[0072] The temperature control evaluation module is used to evaluate the temperature control effect of the energy storage system. It includes indicators related to environmental control and temperature management, including but not limited to air conditioning return air temperature / coolant return temperature, PCS ambient temperature, single cell temperature difference, maximum single cell temperature, etc.

[0073] By assigning weights to each data module, the importance of different modules in the overall evaluation can be reflected. Therefore, the weights of the above four modules can be determined subjectively by combining actual factors such as the actual application scenarios of the energy storage system and customer attention, thereby obtaining the first weight matrix.

[0074] S132. Four sets of second weight matrices are calculated respectively according to the first weight matrix and the battery performance data, the power data, the communication data, and the temperature control data.

[0075] According to the above step S131, it can be known that the first weight matrix includes the weight values ​​corresponding to the above four modules. In this step, subjective weighting is performed for battery performance data, power data, communication data, and temperature control data, that is, subjective weighting is performed for the indicators corresponding to each of the four types of data. When weighting the indicators corresponding to each of the four types of data, the sum of the weight values ​​of all indicators corresponding to the same type of data is equal to the weight value of the evaluation module corresponding to the type.

[0076] For example, determine that the weight value of the xth module in the above four evaluation modules is σ x , and then subjectively assign weights to determine the weights of y indicators under this module [δ1δ2…δ y ], and σ x =δ1+…+δ y , there are y indicators under the xth module.

[0077] The second weight matrix is ​​used to reflect the weight distribution of each indicator under each evaluation module in the above four evaluation modules, ensuring that the sum of all indicator weights under one evaluation module is equal to the weight value of its corresponding evaluation module. Four sets of second weight matrices can be obtained in the above manner, and the sum of all weight values ​​corresponding to the four sets of second weight matrices is 1.

[0078] S133. Combine four groups of second weight matrices to obtain an indicator weight matrix.

[0079] The weight matrices of the evaluation modules can be connected in order to generate an indicator weight matrix, which reflects the importance of each indicator in the comprehensive evaluation of the energy storage system, and corresponds to the above-mentioned normalized comprehensive state data.

[0080] S14. Based on the comprehensive state data matrix and the indicator weight matrix, the gap between the energy storage system and the ideal state is evaluated to obtain a comprehensive evaluation index of the energy storage system.

[0081] By combining the comprehensive state data matrix with the indicator weight matrix, the gap between the current state and the ideal state of the energy storage system can be effectively evaluated, thereby obtaining a comprehensive evaluation index. This process provides a quantitative basis for the performance evaluation and improvement of the energy storage system.

[0082] For details, please refer to Figure 4 , step S14 specifically includes:

[0083] S141. Calculate the weight forward matrix of the energy storage system based on the comprehensive state data matrix and the indicator weight matrix.

[0084] In order to eliminate the directional differences between indicators, all indicators can be compared in the same direction. Especially when there are benefit indicators (the larger the better) and cost indicators (the smaller the better), the forward processing is particularly important, so the weight forward matrix is ​​calculated.

[0085] The comprehensive state data matrix and the indicator weight matrix are multiplied using the Hadamard product, that is, the corresponding elements of the two matrices are multiplied to obtain the weight forward matrix.

[0086] The weight forward matrix unifies the comprehensive status data and indicator weights in the same direction. The matrix provides a basis for subsequent evaluation calculations and ensures that all indicators reflect their importance in a consistent manner in the evaluation.

[0087] S142. Obtain a positive ideal solution matrix and a negative ideal solution matrix.

[0088] Wherein, obtaining the positive ideal solution matrix and the negative ideal solution matrix includes:

[0089] According to the attributes of the comprehensive status data, the record matrix of the positive ideal solution and the record matrix of the negative ideal solution are obtained; the attributes include the above-mentioned benefit type, cost type and interval type. According to the attributes of each data item in the comprehensive status data, its positive ideal solution is determined. For example, if SOH is a benefit type attribute, its positive ideal solution is the maximum value. The maximum value is used as the first value of the record matrix of the positive ideal solution. Then, the attributes of the remaining data are judged in turn, and the positive ideal solution corresponding to each data is recorded in the record matrix of the positive ideal solution. According to the attributes of each data item in the comprehensive status data, its negative ideal solution is determined. For example, if SOH is a benefit type attribute, its negative ideal solution is the minimum value. The minimum value is used as the first value of the record matrix of the negative ideal solution. Then, the attributes of the remaining data are judged in turn, and the negative ideal solution corresponding to each data is recorded in the record matrix of the negative ideal solution.

[0090] Data preprocessing is performed on the record matrix of the positive ideal solution and the record matrix of the negative ideal solution respectively to obtain the preprocessed data processing matrix of the positive ideal solution and the data processing matrix of the negative ideal solution. The data preprocessing may be a normalization process, and the corresponding normalization process mode may be selected according to the attribute of the data, and the above embodiment may be referred to for details.

[0091] The positive ideal solution matrix is ​​obtained according to the data processing matrix and the indicator weight matrix of the positive ideal solution; wherein, the data processing matrix of the positive ideal solution and the indicator weight matrix are subjected to a matrix Hadamard product operation to obtain the positive ideal solution matrix.

[0092] The negative ideal solution matrix is ​​obtained by calculating the negative ideal solution data processing matrix and the indicator weight matrix. Among them, the negative ideal solution matrix is ​​obtained by performing a matrix Hadamard product operation on the negative ideal solution data processing matrix and the indicator weight matrix.

[0093] Among them, the positive ideal solution can be regarded as the best performance on each indicator, which represents the situation where the optimal value is taken on each attribute, that is, the maximized value or the minimized value is obtained among all indicators, which depends on the attribute of the indicator (benefit type or cost type). The positive ideal solution matrix is ​​the matrix obtained by performing the Hadamard product operation on the matrix composed of the positive ideal solution and the indicator weight matrix. The negative ideal solution can be regarded as the worst performance on each indicator, which represents the situation where the most unfavorable value is taken on each attribute, that is, the minimized value or the maximized value is obtained among all indicators, which also depends on the attribute of the indicator. The negative ideal solution matrix is ​​the matrix obtained by performing the Hadamard product operation on the matrix composed of the negative ideal solution and the indicator weight matrix. In this embodiment, the introduction of positive and negative ideal solutions helps to transform the multi-attribute decision-making problem into a single-attribute problem, thereby simplifying the complex multi-attribute decision-making process.

[0094] Among them, for benefit-type attributes, the positive ideal solution is the maximum value of the attribute in the evaluation object; for cost-type attributes, the positive ideal solution is the minimum value of the attribute in the evaluation object, and the evaluation object is the comprehensive state data itself, such as SOH. For interval-type attributes, the positive ideal solution is determined according to the specific situation.

[0095] For benefit-type attributes, the value of the negative ideal solution is the minimum value of the attribute in the evaluation object; for cost-type attributes, the value of the negative ideal solution is the maximum value of the attribute in the evaluation object; for interval-type attributes, the negative ideal solution is determined according to the specific situation.

[0096] For example, the comprehensive state data matrix is ​​recorded as B = [b1 b2…b n ], the indicator weight matrix is ​​recorded as C = [c1c2…c n ], the weight forward matrix is ​​recorded as D = B⊙C = [d1 d2…d n ], where the ⊙ symbol indicates the matrix Hadamard product, which means d1 = b1 × c1. From this, we can get the positive ideal solution and the negative ideal solution as:

[0097]

[0098] Correspondingly, the positive ideal solution matrix is ​​E * =[e1 * e2 * …e n * ], the negative ideal solution matrix is ​​E 0 =[e1 0 e20 …e n 0 ], where n is a positive integer and n is the number of comprehensive status data.

[0099] S143. Calculate the distance from the state of the energy storage system to the positive ideal solution according to the weight forward matrix and the positive ideal solution matrix, where the distance is the first distance.

[0100] The first distance refers to the distance between the state of the energy storage system and the positive ideal solution, which is used to evaluate the difference between the current state of the energy storage system and the optimal state. A smaller first distance value indicates that the state of the energy storage system is closer to the positive ideal solution, while a larger first distance value indicates that there is a larger gap between the state of the energy storage system and the optimal state.

[0101] The first distance can be calculated by the following formula:

[0102]

[0103] Among them, d j is the data of the jth item in the weight forward matrix, is the jth data item in the positive ideal solution matrix, s * is the first distance.

[0104] S144. Calculate the distance from the state of the energy storage system to the negative ideal solution according to the weight forward matrix and the negative ideal solution matrix, where the distance is the second distance.

[0105] The second distance refers to the distance between the state of the energy storage system and the negative ideal solution, which is used to evaluate the performance of the current state of the energy storage system relative to the worst state. A smaller second distance value indicates that the state of the energy storage system is closer to the worst state, while a larger second distance value indicates that the state of the energy storage system is far away from the worst state.

[0106] The second distance can be calculated by the following formula:

[0107]

[0108] Among them, d j is the data of the jth item in the weight forward matrix, is the jth data item in the negative ideal solution matrix, s 0 is the second distance.

[0109] S145. Calculate a comprehensive evaluation index of the energy storage system according to the first distance and the second distance.

[0110] The comprehensive evaluation index f of the energy storage system is calculated according to the following formula:

[0111] f=s0 / (s 0 +s * )×100;

[0112] Among them, the first distance s * It measures the distance between the current state of the system and the ideal solution, that is, the performance of the system relative to the optimal state. 0 It measures the distance from the current state of the system to the negative ideal solution, that is, the performance of the system relative to the worst state. 0 Represents the distance from the system state to the negative ideal solution, the denominator s 0 +s * Represents the total distance from the system to the optimal and worst states. By dividing the distance from the system to the worst state by the sum of the total distances, a ratio is obtained, which reflects the degree of the current state of the system relative to the worst state. Multiplying by 100 is to map the ratio to a percentage range, so that the value range of the evaluation index f is between 0 and 100. The larger the f, the closer the state of the energy storage system is to the ideal state. Conversely, the smaller the f, the less ideal the state of the energy storage system is.

[0113] The comprehensive evaluation index f of the energy storage system takes into account the first distance and the second distance, and is used to evaluate the comprehensive performance of the current state of the energy storage system relative to the positive ideal solution and the negative ideal solution. Through such a comprehensive evaluation index, the state of the energy storage system can be more comprehensively evaluated, and an indicator can be provided to compare the advantages and disadvantages of different system states, thereby providing an important reference for decision-making and optimization.

[0114] The energy storage system evaluation method provided in the embodiment of the present invention can automatically collect various data, including battery performance data, power data, communication data, and temperature control data, and the energy storage system can process these collected data in real time to evaluate the performance of the energy storage system. This process can ensure that the performance evaluation of the energy storage system is real-time, so that the operation and maintenance personnel can timely understand the status of the energy storage system, and take timely measures to optimize the performance of the energy storage system and ensure the stable operation of the energy storage system. In addition, by comprehensively considering data such as battery performance, power, communication, and temperature control, the state of the energy storage system can be comprehensively evaluated; by determining attributes and performing preprocessing, and assigning weights to each data type, the reasonable consideration of different data types is ensured; by comparing the gap between the actual state and the ideal state of the energy storage system, the performance of the system can be quantitatively evaluated to provide guidance for system optimization, and the comprehensive evaluation index obtained by the quantification can comprehensively consider the weights of various data to provide decision makers with an intuitive evaluation basis.

[0115] The following is a specific example to illustrate how to obtain the comprehensive evaluation index of the energy storage system.

[0116] For example, the comprehensive status data includes 14 items of data as shown in the following table, and determines the attributes of each item of data, as well as the unit, maximum value, minimum value, interval and related remarks of the data. The details are shown in the following Table 1.

[0117] Table 1

[0118]

[0119] Among them, SOH is a benefit attribute, the unit is %, the minimum acceptable value of SOH is usually 70%, and the maximum value is a 1,max is 100%, the SOH value provided by the BMS can be read as a1, and after linear transformation, b1=a1 / a 1,max .

[0120] The maximum voltage difference of the battery is a cost-type attribute, and its unit is V. The ideal minimum value of the maximum voltage difference of the battery is 0V, and the maximum value is a 2,max is 0.3V, and after linear transformation, we can get b2=1-a2 / a 2,max .

[0121] The rated power discharge time of the day is a benefit-type attribute, the unit is min, and the daily maximum value is determined according to the actual charge and discharge strategy. 3,max It can be set to 240min, then b3=a3 / a 3,max .

[0122] The total positive active energy of the day is a benefit-based attribute, with the unit of kWh. The daily maximum value is determined according to the actual charging and discharging strategy. 4,max It can be set to twice the rated capacity of the energy storage system, then b4=a4 / a 4,max .

[0123] Conversion efficiency is a benefit-based attribute, with the unit of %. Conversion efficiency is equal to the total positive active energy divided by the total negative active energy of the day. Its ideal maximum value is a 5,max Can be set to 100%, then b5 = a5 / a 5,max .

[0124] The charge and discharge conversion time is a cost attribute, the unit is s, the minimum value is 0.02s, the maximum value is a 6,max = 0.2s, then b6 = 1-a6 / a 6,max .

[0125] The reply message time is a cost attribute, with the unit of s. The ideal minimum value is 0.2s (determined according to the actual system communication cycle), and the maximum value is a 7,max It can be 3s. After linear transformation, we can get b7=1-a7 / a 7,max .

[0126] The communication bit error rate is a cost attribute, the unit is %, and the maximum value is a 8,max It can be 3%. After linear transformation, we can get b8=1-a8 / a 8,max .

[0127] Clock error is a cost attribute, the unit is s, and the maximum value is a 9,max can be 1s, and after linear transformation, we can get b9=1-a9 / a 9,max .

[0128] The packet loss rate is a cost attribute, the unit is %, and the maximum value is a 10,max can be 0.5%, and after linear transformation, we can get b 10 =1-a 10 / a 10,max .

[0129] The return air temperature of the air conditioner is an interval attribute, with the unit of °C. Assume that the given optimal range is [15, 35], -40 is the intolerable lower limit, and 55 is the intolerable upper limit. The specific value depends on the air conditioner manufacturer.

[0130]

[0131] PCS-ambient temperature is an interval attribute, with the unit of °C. Assume that the given optimal range is [15, 45], -30 is the lower limit that cannot be tolerated, and 55 is the upper limit that cannot be tolerated. The specific value depends on the PCS manufacturer.

[0132]

[0133] The temperature difference of a single cell is a cost-type attribute, and the unit is ℃. Generally, for an air-cooled system, the maximum value is a 13,max It can reach 15℃. The minimum value of general air-cooled industrial and commercial storage is 6℃, and that of liquid-cooled is 3℃. The specific value depends on the battery manufacturer. After linear transformation, b 13 =1-a 13 / a 13,max .

[0134] The maximum cell temperature is an interval attribute, with the unit of °C. Assume that the given optimal range is [10, 45], 0 is the intolerable lower limit, and 60 is the intolerable upper limit. The specific value depends on the battery manufacturer.

[0135]

[0136] Furthermore, the weight matrix corresponding to the four modules can be subjectively assigned as [0.2 0.4 0.1 0.3], and the weights of the indicators in each module can be assigned respectively, that is, the indicator weight matrix of the battery performance evaluation module is [0.05 0.15], where 0.05+0.15=0.2; the indicator weight matrix of the power evaluation module is [0.07 0.15 0.15 0.03], where 0.07+0.15+0.15+0.03=0.4; the indicator weight matrix of the communication evaluation module is [0.025 0.025 0.0250.025], where 0.025+0.025+0.025+0.025=0.1; the indicator weight matrix of the temperature control evaluation module is [0.050.050.1 0.1], where 0.05+0.05+0.1+0.1=0.3. Finally, the indicator weight matrix C=[0.050.150.07 0.15 0.15 0.03 0.025 0.025 0.025 0.025 0.05 0.05 0.1 0.1] is given, where the sum of all data in C is 1.

[0137] Next, the record matrix of the positive ideal solution is [100% 0 240 430 100 0.02 0.2 0 0 0 2525 625], and the record matrix of the negative ideal solution is [70% 0.3 0 0 0 0.2 3 3 1 0.5 55 55 15 55]; according to the attributes of the data, they are normalized respectively, and the normalized matrix of the positive ideal solution is [1 1 1 10.9 0.933 1 1 1 11 0.61], and the normalized matrix of the negative ideal solution is [0.7 0 0 0 0 0 0 0 0 0 0 00];

[0138] Next, the normalized matrix of the positive ideal solution and the indicator weight matrix are calculated to obtain the positive ideal solution matrix. This calculation is a matrix Hadamard product operation, that is:

[0139] Positive ideal solution matrix = [1 1 1 1 1 0.9 0.933 1 1 1 1 1 0.6 1]⊙[0.05 0.15 0.070.150.15 0.03 0.025 0.025 0.025 0.025 0.05 0.05 0.1 0.1] = [0.05 0.15 0.070.15 0.150.027 0.023325 0.025 0.025 0.025 0.05 0.05 0.06 0.1];

[0140] The negative ideal solution matrix is ​​obtained by calculating the normalized matrix of the negative ideal solution and the indicator weight matrix. The calculation is a matrix Hadamard product operation, that is:

[0141] Negative ideal solution matrix = [0.7 0 0 0 0 0 0 0 0 0 0 0 0 0]⊙[0.05 0.15 0.07 0.150.150.03 0.025 0.025 0.025 0.025 0.05 0.05 0.1 0.1] = [0.035 0 0 0 0 0 0 0 0 0 00 00].

[0142] Next, assuming that the data matrix A collected on that day is [100% 0.15 225 387.8 86.9% 0.10.30.5% 0 0 32 42 7 44], according to the above steps, the normalized matrix B can be obtained as [10.5 0.93750.9018 0.869 0.5 0.9 0.8333 1 1 1 1 0.5333 1].

[0143] Further, according to the above steps, the weight forward matrix D = B⊙C is obtained;

[0144] D=B⊙C=[1 0.5 0.9375 0.9018 0.869 0.5 0.9 0.8333 1 1 1 1 0.5333 1]⊙[0.05 0.15 0.07 0.15 0.15 0.03 0.025 0.025 0.025 0.025 0.05 0.05 0.1 0.1]

[0145] =[0.05 0.075 0.065625 0.13527 0.13035 0.015 0.0225 0.02083250.0250.025 0.05 0.05 0.05333 0.1].

[0146] Next, according to the weight forward matrix and the positive ideal solution matrix, that is, according to D = [0.05 0.075 0.065625 0.13527 0.13035 0.015 0.0225 0.0208325 0.025 0.025 0.025 0.05 0.050.05333 0.1], the positive ideal solution matrix = [0.05 0.15 0.07 0.15 0.15 0.027 0.023325 0.025 0.025 0.025 0.05 0.050.06 0.1], and the following formula:

[0147]

[0148] Calculate the distance from the state of the energy storage system to the positive ideal solution.

[0149] According to the weight forward matrix and the negative ideal solution matrix, that is, according to D = [0.05 0.0750.0656250.13527 0.13035 0.015 0.0225 0.0208325 0.025 0.025 0.05 0.050.05333 0.1], the negative ideal solution matrix = [0.035 0 0 0 0 0 0 0 0 0 0 0 0 0], and the following formula:

[0150]

[0151] Calculate the distance from the state of the energy storage system to the negative ideal solution.

[0152] Finally, according to the formula f = s 0 / (s 0 +s * )×100 to calculate the comprehensive evaluation index.

[0153] See also Figure 5 , Figure 5 Schematic diagram of the structure of an energy storage system evaluation device provided by an embodiment of the present invention. Figure 5 As shown, the energy storage system evaluation device 20 includes:

[0154] A comprehensive status data acquisition module 201 is used to acquire comprehensive status data of the energy storage system, the comprehensive status data including battery performance data, power data, communication data and temperature control data;

[0155] The data preprocessing module 202 is used to determine the attributes of the comprehensive state data and preprocess the comprehensive state data according to the attributes of the comprehensive state data to obtain a preprocessed comprehensive state data matrix;

[0156] The indicator weight matrix acquisition module 203 is used to obtain the weights corresponding to the battery performance data, power data, communication data and temperature control data, and obtain the indicator weight matrix according to the weights corresponding to the battery performance data, power data, communication data and temperature control data;

[0157] The comprehensive evaluation index calculation module 204 is used to evaluate the gap between the energy storage system and the ideal state based on the comprehensive state data matrix and the indicator weight matrix, so as to obtain the comprehensive evaluation index of the energy storage system.

[0158] The energy storage system evaluation device 20 may be a software module, which includes several instructions stored in a memory. The processor may access the memory and call the instructions for execution to complete the energy storage system evaluation method described in the above-mentioned embodiments.

[0159] In some embodiments, the energy storage system evaluation device 20 can also be constructed by hardware devices. For example, the energy storage system evaluation device 20 can be constructed by one or more chips, and each chip can work in coordination with each other to complete the energy storage system evaluation method described in each of the above embodiments. For another example, the energy storage system evaluation device 20 can also be constructed by various logic devices, such as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or any combination of these components.

[0160] It should be noted that the energy storage system evaluation device 20 can execute the energy storage system evaluation method for electronic devices provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in the embodiment of the energy storage system evaluation device 20, please refer to the energy storage system evaluation method for electronic devices provided in the embodiment of the present invention.

[0161] See also Figure 6 , Figure 6 30 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 30 can be used to execute the energy storage system evaluation method in the above embodiment. The electronic device 30 includes one or more processors 301 and a memory 302. The memory 302 is connected to the one or more processors 301, for example, connected to the processor 301 via a bus.

[0162] The processor 301 is configured to support the electronic device to perform the corresponding functions in the method in the above method embodiment. The processor 301 can be a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof. The above hardware chip can be an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0163] The memory 302 is used to store program codes, etc. The memory 302 may include a volatile memory (VM), such as a random access memory (RAM); the memory 302 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory 302 may also include a combination of the above-mentioned types of memories.

[0164] The memory 302 can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the energy storage system evaluation method in the embodiment of the present invention. The processor 301 executes various functional applications and data processing of the energy storage system evaluation method and the energy storage system evaluation device by running the non-volatile software programs, instructions and modules stored in the memory 302, that is, realizes the functions of each module or unit of the energy storage system evaluation method and the energy storage system evaluation device provided in the above method embodiment.

[0165] The memory 302 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function. The data storage area may store data created according to the use of the energy storage system evaluation device, etc. In some embodiments, the memory 302 may include a memory remotely arranged relative to the processor 301, and these remote memories may be connected to the energy storage system evaluation device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0166] The one or more modules are stored in the memory 302, and when executed by the one or more processors 301, the energy storage system evaluation method in any of the above method embodiments is executed, for example, the method steps described in the above method embodiments are executed to realize the functions of the modules described in the above device embodiments.

[0167] The electronic device of the embodiment of the present invention may specifically be an ultra-mobile personal computer device, a smart phone, a server or a server cluster, etc.

[0168] An embodiment of the present invention provides a non-volatile computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, such as Figure 6 A processor 301 in the embodiment may enable the one or more processors to execute the energy storage system evaluation method in any of the above method embodiments.

[0169] An embodiment of the present invention provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by the above-mentioned electronic device, the electronic device is enabled to execute the energy storage system evaluation method in any of the above-mentioned method embodiments.

[0170] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0171] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating an energy storage system, characterized in that: The method comprises: Acquire comprehensive status data of the energy storage system, wherein the comprehensive status data includes battery performance data, power data, communication data and temperature control data; Determining the attributes of the comprehensive state data, and preprocessing the comprehensive state data according to the attributes of the comprehensive state data to obtain a preprocessed comprehensive state data matrix; Obtaining weights corresponding to the battery performance data, the power data, the communication data, and the temperature control data, and obtaining an indicator weight matrix according to the weights corresponding to the battery performance data, the power data, the communication data, and the temperature control data; Based on the comprehensive state data matrix and the indicator weight matrix, the gap between the energy storage system and the ideal state is evaluated, so as to obtain a comprehensive evaluation index of the energy storage system.

2. The method according to claim 1, characterized in that The obtaining of comprehensive status data of the energy storage system includes: Collecting message data during the operation of the energy storage system; Battery performance data, power data, communication data and temperature control data are obtained according to the message data.

3. The method according to claim 1, characterized in that The determining the attributes of the comprehensive status data and preprocessing the comprehensive status data according to the attributes of the comprehensive status data to obtain a preprocessed comprehensive status data matrix includes: Determining attributes of the comprehensive status data, the attributes including benefit type, cost type and interval type; Determining a normalization processing method corresponding to the comprehensive status data according to the type corresponding to the attribute of the comprehensive status data; Normalizing the comprehensive status data according to the determined normalization processing method, so as to obtain the normalized comprehensive status data; A comprehensive state data matrix is ​​generated based on the normalized comprehensive state data.

4. The method according to claim 3, characterized in that The determining, according to the type corresponding to the attribute of the comprehensive status data, a normalization processing method corresponding to the comprehensive status data includes: When the attribute of the comprehensive status data is benefit type, the normalization processing method corresponding to the comprehensive status data is determined as: b = a / a max ; When the attribute of the comprehensive status data is cost type, the normalization processing method corresponding to the comprehensive status data is determined as: b = 1-a / a max ; When the attribute of the comprehensive state data is interval type, assuming that the given optimal attribute is [j, k], p is the intolerable lower limit, and q is the intolerable upper limit, then the normalization processing method corresponding to the comprehensive state data is determined as follows: When the attribute of the comprehensive status data is of interval type, and the attribute of the comprehensive status data has a corresponding upper limit and a lower limit, the normalization processing method corresponding to the comprehensive status data is determined as follows: Among them, a is the attribute value of the current comprehensive state data, b is the attribute value of the comprehensive state data after normalization, and a max is the maximum value of the attribute, a min Is the minimum value of the attribute.

5. The method according to claim 1, characterized in that The obtaining of the weights corresponding to the battery performance data, the power data, the communication data, and the temperature control data, and obtaining an indicator weight matrix according to the weights corresponding to the battery performance data, the power data, the communication data, and the temperature control data, includes: Assigning weights to data modules corresponding to the battery performance data, the power data, the communication data, and the temperature control data to obtain a first weight matrix; Four sets of second weight matrices are calculated respectively according to the first weight matrix and the battery performance data, the power data, the communication data, and the temperature control data; The four groups of second weight matrices are combined to obtain the indicator weight matrix.

6. The method according to any one of claims 1 to 5, characterized in that: The step of evaluating the gap between the energy storage system and the ideal state based on the comprehensive state data matrix and the indicator weight matrix, thereby obtaining a comprehensive evaluation index of the energy storage system, includes: Calculating a weight forward matrix of an energy storage system according to the comprehensive state data matrix and the indicator weight matrix; Obtain a positive ideal solution matrix and a negative ideal solution matrix; Calculate the distance from the state of the energy storage system to the positive ideal solution according to the weight forward matrix and the positive ideal solution matrix, where the distance is a first distance; Calculate the distance from the state of the energy storage system to the negative ideal solution according to the weight forward matrix and the negative ideal solution matrix, where the distance is the second distance; A comprehensive evaluation index of the energy storage system is calculated according to the first distance and the second distance.

7. The method according to claim 6, characterized in that The obtaining of the positive ideal solution matrix and the negative ideal solution matrix comprises: Acquire a record matrix of a positive ideal solution and a record matrix of a negative ideal solution according to the attributes of the comprehensive state data; Performing data preprocessing on the record matrix of the positive ideal solution and the record matrix of the negative ideal solution respectively to obtain a preprocessed data processing matrix of the positive ideal solution and a preprocessed data processing matrix of the negative ideal solution; Obtaining a positive ideal solution matrix by calculation according to the data processing matrix of the positive ideal solution and the indicator weight matrix; The negative ideal solution matrix is ​​obtained by calculation based on the data processing matrix of the negative ideal solution and the indicator weight matrix.

8. An energy storage system evaluation device, characterized in that: The device comprises: A comprehensive status data acquisition module, used to acquire comprehensive status data of the energy storage system, wherein the comprehensive status data includes battery performance data, power data, communication data and temperature control data; A data preprocessing module, used for determining the attributes of the comprehensive state data, and preprocessing the comprehensive state data according to the attributes of the comprehensive state data to obtain a preprocessed comprehensive state data matrix; An indicator weight matrix acquisition module is used to obtain the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data, and obtain an indicator weight matrix according to the weights corresponding to the battery performance data, the power data, the communication data and the temperature control data; A comprehensive evaluation index calculation module is used to evaluate the gap between the energy storage system and the ideal state based on the comprehensive state data matrix and the indicator weight matrix, so as to obtain the comprehensive evaluation index of the energy storage system.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the electronic device implements the energy storage system evaluation method as described in any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by an electronic device, the electronic device implements the energy storage system evaluation method according to any one of claims 1 to 7.

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