Multi-level health degree assessment method, system and equipment for energy storage power station and storage medium

Abstract

The embodiment of the invention provides a multi-level health degree assessment method, system and device for an energy storage power station and a storage medium. The method comprises the following steps: collecting single cell data of the battery management system in real time; calculating a Z score of each parameter of the single cell, linearly mapping each Z score into a centesimal system sub score, and carrying out weighted average on each parameter sub score to obtain the health degree of the single cell; calculating the range and the standard deviation of the health degrees of all the single cells in the battery pack, and performing weighted summation to obtain the health degree of the battery pack; calculating the range and the standard deviation of the health degrees of all the battery packs in the battery cluster, and performing weighted summation to obtain the health degree of the battery cluster; and when the health degree of any level is lower than a preset health degree threshold value, generating an alarm signal of the corresponding level. According to the embodiment of the invention, multi-level health degree evaluation and fault early warning are realized based on multiple dimension indexes, so that problems of the energy storage power station can be handled in time, the cycle life of an energy storage system is prolonged, and stable operation of a power grid is guaranteed.

Description

Technical Field

[0001] The embodiments of the present disclosure belong to the technical field, and specifically relate to a multi-level health assessment method, system, device, and storage medium for an energy storage power station. Background Art

[0002] As energy storage power stations play an increasingly prominent role in supporting the new energy sector, their fault warnings and health scores are crucial to their safe operation. The multi-level, consistent health assessment of cells, packs, and clusters in energy storage power station battery systems is crucial to system efficiency and safety.

[0003] As the basic unit, the consistency of battery cell parameters such as voltage and internal resistance directly affects the performance of the pack. The series-parallel structure of the battery cells within the pack must ensure balanced charging and discharging to avoid accelerated local aging. The coordinated operation of multiple packs in the cluster depends on overall parameter matching. If there are differences between layers, it will lead to reduced energy utilization and increased risk of thermal runaway.

[0004] However, the existing technology lacks a unified health scoring system based on multi-dimensional indicators that can achieve fault early warning. Summary of the Invention

[0005] The embodiments of the present disclosure aim to solve at least one of the technical problems existing in the prior art and provide a multi-level health assessment method, system, device and storage medium for an energy storage power station.

[0006] One aspect of the present disclosure provides a multi-level health assessment method for an energy storage power station, the method comprising:

[0007] Real-time collection of single cell data of the battery management system; wherein the single cell data includes the voltage, temperature, internal resistance and energy parameters of the single cell;

[0008] Calculate the Z score of each parameter of the single cell, linearly map each Z score to a percentage sub-score, and perform a weighted average of the sub-scores to obtain the health of the single cell;

[0009] Calculate the range and standard deviation of the health of all cells in the battery pack, and perform weighted summation of the range and standard deviation of the health of the cells to obtain the health of the battery pack;

[0010] Calculate the range and standard deviation of the health of all battery packs in the battery cluster, and perform weighted summation of the range and standard deviation of the battery pack health to obtain the battery cluster health;

[0011] When the health of any level of a single cell, battery pack, or battery cluster is lower than the preset health threshold, an alarm signal of the corresponding level is generated.

[0012] Furthermore, the Z score of each parameter of the single cell is calculated by the following formula:

[0013]

[0014] Where Z i represents the Z score of the i-th parameter, X is the sample value of the single cell, μ is the mean value of the sample set, and σ is the standard deviation of the sample set.

[0015] Furthermore, the percentage sub-score is calculated by the following formula:

[0016]

[0017] Where S i represents the percentage score of the i-th parameter, Z i represents the Z score of the i-th parameter, and c is an adjustable parameter set according to the current technical standards of energy storage power stations.

[0018] Further, the weight of the voltage parameter sub-score is greater than the weight of the temperature parameter sub-score; and / or,

[0019] The standard deviation is weighted more than the range.

[0020] Another aspect of the present disclosure provides a multi-level health assessment system for an energy storage power station, the system comprising:

[0021] An acquisition module is used to collect the single cell data of the battery management system in real time; wherein the single cell data includes the voltage, temperature, internal resistance and energy parameters of the single cell;

[0022] The cell health module is used to calculate the Z score of each parameter of a single cell, linearly map each Z score to a percentage sub-score, and perform a weighted average of the sub-scores of each parameter to obtain the cell health;

[0023] The battery pack health module is used to calculate the range and standard deviation of the health of all single cells in the battery pack, and to obtain the battery pack health by weighted summing the range and standard deviation of the health of the single cells;

[0024] The battery cluster health module is used to calculate the range and standard deviation of the health of all battery packs in the battery cluster, and perform a weighted sum of the range and standard deviation of the battery pack health to obtain the battery cluster health;

[0025] The alarm module is used to generate an alarm signal at the corresponding level when the health of any level of a single cell, battery pack, or battery cluster falls below a preset health threshold.

[0026] Furthermore, the Z score of each parameter of the single cell is calculated by the following formula:

[0027]

[0028] Where Z i represents the Z score of the i-th parameter, X is the sample value of the single cell, μ is the mean value of the sample set, and σ is the standard deviation of the sample set.

[0029] Furthermore, the percentage sub-score is calculated by the following formula:

[0030]

[0031] Where S i represents the percentage score of the i-th parameter, Z i represents the Z score of the i-th parameter, and c is an adjustable parameter set according to the current technical standards of energy storage power stations.

[0032] Further, the weight of the voltage parameter sub-score is greater than the weight of the temperature parameter sub-score; and / or,

[0033] The standard deviation is weighted more than the range.

[0034] Another aspect of the present disclosure provides an electronic device, comprising:

[0035] at least one processor; and,

[0036] A memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enables the at least one processor to implement the multi-level health assessment method for energy storage power stations described above.

[0037] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned multi-level health assessment method for an energy storage power station.

[0038] The disclosed embodiments provide a multi-level health assessment method, system, device, and storage medium for energy storage power stations. Leveraging statistics, the system implements multi-level health assessment and fault warning functions based on multiple dimensional indicators, such as temperature, voltage, energy, and internal resistance, of single battery cells. The system also provides timely feedback on the operating status of each level to operation and maintenance personnel, enabling timely resolution of energy storage power station issues, improving the cycle life of the energy storage system, and ensuring stable grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a multi-level health assessment method for an energy storage power station according to an embodiment of the present disclosure;

[0040] Figure 2This is a structural diagram of a multi-level health assessment system for an energy storage power station according to another embodiment of the present disclosure;

[0041] Figure 3 This is a schematic structural diagram of an electronic device according to another embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0043] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0044] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0045] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of this disclosure. As used in this disclosure, the term "and / or" includes any one of the associated listed items and all combinations of one or more of them.

[0046] Those skilled in the art will understand that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present disclosure, and therefore cannot be used to limit the scope of protection of the present disclosure.

[0047] like Figure 1 As shown, one embodiment of the present disclosure provides a multi-level health assessment method for an energy storage power station, the method comprising:

[0048] Step S1, real-time collection of single cell data of the battery management system; wherein the single cell data includes voltage, temperature, internal resistance and energy parameters of the single cell.

[0049] Specifically, the battery management system (BMS) of the energy storage power station collects real-time data on individual cells, including cell voltage, cell current, maximum cell temperature, cell number with maximum temperature, maximum cell voltage, cell number with maximum voltage, and battery charge and discharge status. The collected data is uploaded to a cloud server via a CAN bus or Ethernet interface, transmitted in real time using protocols such as Modbus / TCP, and stored in a MySQL database on the cloud server. This embodiment obtains real-time cell data from the MySQL database, including voltage, temperature, internal resistance, and energy parameters.

[0050] Step S2: Calculate the Z score of each parameter of the single cell, linearly map each Z score to a percentage sub-score, and perform a weighted average of the sub-scores of each parameter to obtain the health of the single cell.

[0051] Specifically, the Z-score algorithm, which combines the cell's temperature, voltage, internal resistance, and energy parameters, is used to score the cell's health. The Z-score, also known as the standard score, describes how far a data point deviates from the mean of its dataset. The unit is standard deviation, indicating how many standard deviations the data point is from the dataset's mean.

[0052] First, calculate the Z scores of the temperature, voltage, internal resistance, and energy parameters of the single cell using the following formula:

[0053]

[0054] Where Z i represents the Z score of the i-th parameter, X is the sample value of the single cell, μ is the mean value of the sample set, and σ is the standard deviation of the sample set.

[0055] The standard deviation σ of the sample set of single cells can be calculated by the following formula:

[0056]

[0057] Where σ is the standard deviation, X j is the jth sample value, is the sample mean, n is the number of samples. The standard deviation represents the degree to which all samples in the sample set deviate from the mean.

[0058] Each Z score was then linearly mapped to a percentile subscore using the following formula:

[0059]

[0060] Where S i represents the percentage score of the i-th parameter, Z i represents the Z score of the i-th parameter, and c is an adjustable parameter set according to the current technical standards of energy storage power stations.

[0061] The weighted average of the percentage-based sub-scores for each parameter obtained in the above process is used to determine the health level S1 of the corresponding single cell. Based on engineering practice, when performing the weighted average calculation, the voltage parameter sub-score should generally be weighted more heavily than the temperature parameter sub-score.

[0062] Step S3: Calculate the range and standard deviation of the health of all single cells in the battery pack, and perform weighted summation on the range and standard deviation of the health of the single cells to obtain the health of the battery pack.

[0063] Specifically, the health scores for the battery pack (PACK) and battery cluster (Cluster) levels are obtained by weighted summing the range and standard deviation of the health S1 of each cell in each level. Both the range and standard deviation represent the degree of dispersion of the sample set, where the range focuses on the difference between the maximum and minimum values in the set, while the standard deviation focuses on the degree to which all samples deviate from the mean. When performing the weighted summation calculation, the weight of the standard deviation should generally be greater than the weight of the range.

[0064] The range of health S1 of each cell in the battery pack is calculated using the following formula:

[0065] R1=S 1max -S 1min

[0066] In the formula, R1 is the extreme difference of the health of each monomer, S 1max is the maximum health value of the single cell in the battery pack, S 1min The minimum health value of the cells in the battery pack.

[0067] The standard deviation of the health S1 of each cell in the battery pack is calculated using the following formula:

[0068]

[0069] Where σ1 is the standard deviation of the health of each cell in the battery pack, S 1j is the health of the jth monomer, is the mean value of the individual health, and n is the number of samples.

[0070] The range R1 and standard deviation σ1 obtained in the above process are weightedly summed to obtain the health status S2 of the battery pack.

[0071] Step S4: Calculate the range and standard deviation of the health of all battery packs in the battery cluster, and perform weighted summation on the range and standard deviation of the health of the battery packs to obtain the health of the battery cluster.

[0072] Specifically, the range of the health status S2 of each battery pack in the battery cluster is calculated using the following formula:

[0073] R2=S 2max -S 2min

[0074] Where R2 is the extreme difference in health of each battery pack, S 2max is the maximum health of the battery pack in the battery cluster, S 2min The minimum health value of the battery packs in the battery cluster.

[0075] The standard deviation of the health S2 of each battery pack in the battery cluster is calculated using the following formula:

[0076]

[0077] Where σ2 is the standard deviation of the health of each cell in the battery pack, S 2j is the health of the j-th battery pack, is the mean health value of the battery pack, and n is the number of samples.

[0078] The range R2 and standard deviation σ2 obtained in the above process are weightedly summed to obtain the health status S3 of the battery cluster.

[0079] Step S5: When the health of any level of a single cell, a battery pack, or a battery cluster is lower than a preset health threshold, an alarm signal of the corresponding level is generated.

[0080] Specifically, the health status S1, S2, and S3 of each level of single cells, battery packs, and battery clusters are calculated and monitored in real time. When the health of any level is lower than the preset health threshold, an alarm signal corresponding to the abnormal level is issued through text messages, software pop-ups, sound and light alarms, etc., thereby feeding back the health status of each level to the operation and maintenance personnel, making it easier for them to locate the fault and handle it in a timely manner.

[0081] A multi-level health assessment method for energy storage power stations in the disclosed embodiments utilizes statistics and is based on multiple dimensional indicators such as temperature, voltage, energy, and internal resistance of single battery cells. This method implements multi-level health assessment and fault warning functions, and promptly provides operational status feedback at each level to operation and maintenance personnel. This allows for timely resolution of energy storage power station issues, improves the cycle life of the energy storage system, and ensures stable grid operation.

[0082] like Figure 2 As shown, another embodiment of the present disclosure provides a multi-level health assessment system for an energy storage power station, the system comprising:

[0083] The acquisition module 210 is used to collect the single cell data of the battery management system in real time; wherein the single cell data includes the voltage, temperature, internal resistance and energy parameters of the single cell;

[0084] The cell health module 220 is used to calculate the Z score of each parameter of the single cell, linearly map each Z score to a percentage sub-score, and perform a weighted average of the sub-scores of each parameter to obtain the single cell health;

[0085] The battery pack health module 230 is used to calculate the range and standard deviation of the health of all single cells in the battery pack, and perform a weighted sum of the range and standard deviation of the health of the single cells to obtain the battery pack health;

[0086] The battery cluster health module 240 is used to calculate the range and standard deviation of the health of all battery packs in the battery cluster, and perform a weighted sum of the range and standard deviation of the battery pack health to obtain the battery cluster health;

[0087] The alarm module 250 is used to generate an alarm signal of the corresponding level when the health of any level of a single cell, a battery pack, or a battery cluster is lower than a preset health threshold.

[0088] For example, the Z score of each parameter of the single cell is calculated by the following formula:

[0089]

[0090] Where Z i represents the Z score of the i-th parameter, X is the sample value of the single cell, μ is the mean value of the sample set, and σ is the standard deviation of the sample set.

[0091] Exemplarily, the percentage sub-score is calculated by the following formula:

[0092]

[0093] Where S i represents the percentage score of the i-th parameter, Z i represents the Z score of the i-th parameter, and c is an adjustable parameter set according to the current technical standards of energy storage power stations.

[0094] Exemplarily, the weight of the voltage parameter sub-score is greater than the weight of the temperature parameter sub-score; and / or,

[0095] The standard deviation is weighted more than the range.

[0096] Specifically, a multi-level health assessment system for an energy storage power station in an embodiment of the present disclosure is used to implement the multi-level health assessment method for an energy storage power station described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.

[0097] A multi-level health assessment system for energy storage power stations in the disclosed embodiments utilizes statistics and implements multi-level health assessment and fault warning functions based on multiple dimensional indicators such as temperature, voltage, energy, and internal resistance of single battery cells. It also provides timely feedback on the operating status of each level to operation and maintenance personnel, allowing for timely resolution of energy storage power station issues, improving the cycle life of the energy storage system and ensuring stable grid operation.

[0098] like Figure 3 As shown, another embodiment of the present disclosure provides an electronic device, including:

[0099] At least one processor 301; and a memory 302 in communication with the at least one processor 301, for storing one or more programs, which, when executed by the at least one processor 301, enable the at least one processor 301 to implement the multi-level health assessment method for energy storage power stations described above.

[0100] The memory 302 and processor 301 are connected using a bus. The bus can include any number of interconnected buses and bridges, connecting various circuits of one or more processors 301 and memory 302. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor 301 is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor 301.

[0101] The processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory 302 can be used to store data used by the processor 301 when performing operations.

[0102] Yet another embodiment of the present disclosure provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned multi-level health assessment method for an energy storage power station.

[0103] The computer-readable storage medium may be included in the system or electronic device of the present disclosure, or may exist independently.

[0104] Computer-readable storage media may be any tangible medium that contains or stores a program, which may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, optical fiber, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0105] The computer-readable storage medium may also include a data signal propagated in baseband or as part of a carrier wave, which carries the computer-readable program code. Specific examples include but are not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0106] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A multi-level health assessment method for energy storage power stations, characterized in that: The method comprises: Real-time collection of single cell data of the battery management system; wherein the single cell data includes the voltage, temperature, internal resistance and energy parameters of the single cell; Calculate the Z score of each parameter of the single cell, linearly map each Z score to a percentage sub-score, and perform a weighted average of the sub-scores to obtain the health of the single cell; Calculate the range and standard deviation of the health of all cells in the battery pack, and perform weighted summation of the range and standard deviation of the health of the cells to obtain the health of the battery pack; Calculate the range and standard deviation of the health of all battery packs in the battery cluster, and perform weighted summation of the range and standard deviation of the battery pack health to obtain the battery cluster health; When the health of any level of a single cell, battery pack, or battery cluster is lower than the preset health threshold, an alarm signal of the corresponding level is generated.

2. The method according to claim 1, characterized in that The Z score of each parameter of the single cell is calculated by the following formula: Where Z i represents the Z score of the i-th parameter, X is the sample value of the single cell, μ is the mean value of the sample set, and σ is the standard deviation of the sample set.

3. The method according to claim 2, characterized in that The percentile sub-score is calculated by the following formula: Where S i represents the percentage score of the i-th parameter, Z i represents the Z score of the i-th parameter, and c is an adjustable parameter set according to the current technical standards of energy storage power stations.

4. The method according to claim 1, wherein The voltage parameter sub-score has a greater weight than the temperature parameter sub-score; and / or, The standard deviation is weighted more than the range.

5. A multi-level health assessment system for energy storage power stations, characterized in that: The system comprises: An acquisition module is used to collect the single cell data of the battery management system in real time; wherein the single cell data includes the voltage, temperature, internal resistance and energy parameters of the single cell; The cell health module is used to calculate the Z score of each parameter of a single cell, linearly map each Z score to a percentage sub-score, and perform a weighted average of the sub-scores of each parameter to obtain the cell health; The battery pack health module is used to calculate the range and standard deviation of the health of all single cells in the battery pack, and to obtain the battery pack health by weighted summing the range and standard deviation of the health of the single cells; The battery cluster health module is used to calculate the range and standard deviation of the health of all battery packs in the battery cluster, and perform a weighted sum of the range and standard deviation of the battery pack health to obtain the battery cluster health; The alarm module is used to generate an alarm signal at the corresponding level when the health of any level of a single cell, battery pack, or battery cluster falls below a preset health threshold.

6. The system according to claim 5, characterized in that The Z score of each parameter of the single cell is calculated by the following formula: Where Z i represents the Z score of the i-th parameter, X is the sample value of the single cell, μ is the mean value of the sample set, and σ is the standard deviation of the sample set.

7. The system according to claim 6, characterized in that The percentile sub-score is calculated by the following formula: Where S i represents the percentage score of the i-th parameter, Z i represents the Z score of the i-th parameter, and c is an adjustable parameter set according to the current technical standards of energy storage power stations.

8. The system according to claim 5, wherein: The voltage parameter sub-score has a greater weight than the temperature parameter sub-score; and / or, The standard deviation is weighted more than the range.

9. An electronic device, characterized in that: include: at least one processor; as well as, A memory communicatively connected to the at least one processor, for storing one or more programs, which, when executed by the at least one processor, enables the at least one processor to implement the multi-level health assessment method for an energy storage power station according to any one of claims 1 to 4.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-level health assessment method for an energy storage power station according to any one of claims 1 to 4 is implemented.

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