Intelligent load identification and distribution system and method of lithium battery energy storage system

Abstract

The invention belongs to the technical field of energy storage systems, and discloses an intelligent load identification and distribution system and method of a lithium battery energy storage system. The method comprises the following steps: selecting quantum dots based on a natural heuristic optimization algorithm; marking the quantum dots to a preset monitoring position of the load equipment; collecting and correcting to obtain a fluorescence signal; extracting fluorescence intensity characteristics, fluorescence lifetime characteristics and fluorescence spectrum characteristics based on the fluorescence signals; forming an equipment fingerprint by taking each feature as a dimension; storing the normal equipment fingerprint of the known equipment information into a fingerprint database; comparing the device fingerprint with all device fingerprints stored in a fingerprint database, and identifying to obtain device information of the load device; comparing the device fingerprint with a normal device fingerprint to calculate a device fingerprint deviation, and starting an adaptive adjustment mechanism to perform power distribution according to a calculation result; according to the method, multiple factors can be comprehensively considered, and optimized power distribution is carried out, so that the service life of the lithium battery is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage systems, and more specifically, to an intelligent load identification and distribution system and method for a lithium battery energy storage system. Background Art

[0002] The Chinese patent application with publication number CN119253823A discloses a power allocation method with threshold applied to an independent photovoltaic power generation hybrid energy storage system, the method comprising: S1: calculating the total power P that the hybrid energy storage system should bear H ; S2: Through the sliding average algorithm, the total power P that the hybrid energy storage system needs to bear is calculated. H Decompose to get the average power component P av ; S3: Lithium battery state of charge SOC LIB And supercapacitor state of charge SOC sc As the input of the fuzzy controller, the output of the fuzzy controller is the adjustment coefficient K LIB , P in step S2 av Multiply by the adjustment factor K LIB Get the power value P L'IB ; S4: The power value P in step S3 L'IB The final power value P required for the lithium battery to be stabilized is obtained through the threshold setting module LIB , the total power P borne by the hybrid energy storage system in step S1 H Subtract P LIB The remaining power value P sc_1 The supercapacitor takes over; S5: The lithium battery power value P obtained in step S4 is smoothed LIB Divide by the lithium battery voltage U LIB Get the reference current i of the lithium battery LIBref , lithium battery reference current i LIBref The actual current i LIB Subtract and get the lithium battery current error Δi LIB ; S6: lithium battery current error Δi in step S5 LIB Multiply by the lithium battery voltage to get the uncompensated power demand P of the lithium battery sc_2 , the uncompensated amount is borne by the supercapacitor; S7: P in step S4 sc_1 and P in step S6 sc_2 The power value P that the supercapacitor needs to smooth is obtained through the summation module sc ; S8: After the power distribution is completed, a dynamic evolution control strategy is used to control the power flow of the energy storage system. This invention can respond quickly to the power imbalance in the system when the external environment changes and the load changes, and maintain the bus voltage and system stability.

[0003] Although the above method can meet most scenarios, research and practical application of the above method and existing technology have found that the above method and existing technology have at least the following defects:

[0004] In practical applications, load changes are complex and rapid. When dealing with dynamic changes in load, the above method lacks real-time load identification capability, resulting in the inability to adjust the power allocation strategy in time according to the load changes; and the power allocation strategy of adjusting energy allocation by setting thresholds for lithium battery charging and discharging power is relatively simple and cannot provide more optimized power allocation for load devices.

[0005] In view of this, the present invention proposes an intelligent load identification and distribution system and method for a lithium battery energy storage system to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: an intelligent load identification and allocation method for a lithium battery energy storage system, comprising the following steps:

[0007] Selecting quantum dots with the best fluorescence emission wavelength and the best stability based on a nature-inspired optimization algorithm;

[0008] Marking quantum dots at a preset monitoring position of a load device; aligning a probe of a fluorescence spectrometer with the preset monitoring position marked with quantum dots, collecting a background light signal when the excitation light source is not started and an actual fluorescence signal when the excitation light source is started, and correcting the actual fluorescence signal to obtain a fluorescence signal;

[0009] Extract fluorescence intensity features, fluorescence lifetime features, and fluorescence spectrum features based on the corrected fluorescence signal; splice each feature as a dimension to form a device fingerprint;

[0010] The device fingerprint and corresponding device information of the known device information in the normal operating state are stored in the fingerprint database, and the device information includes the device type code, rated power, rated working voltage, rated working current and device priority code;

[0011] Compare the device fingerprint with all device fingerprints stored in the fingerprint database to identify and obtain the device information of the payload device;

[0012] The device fingerprint is compared with the device fingerprint under normal operating state corresponding to the device information, the device fingerprint deviation is calculated, and the device fingerprint deviation is compared with a preset fingerprint deviation threshold. When the device fingerprint deviation is greater than the preset fingerprint deviation threshold, the adaptive adjustment mechanism is started to allocate power.

[0013] Further, the method of obtaining quantum dots with optimal fluorescence emission wavelength and optimal stability includes:

[0014] Obtaining a preset quantum dot fluorescence emission wavelength range and a corresponding quantitative index, a preset stability range and a corresponding quantitative index;

[0015] Get Quantum dots affect parameters and a preset value range corresponding to each quantum dot impact parameter;

[0016] The number of ants in the default ant colony is , the pheromone volatility coefficient is , the pheromone importance factor is And the heuristic information importance factor is ;

[0017] Discretize the value range of each quantum dot influencing parameter to obtain the corresponding parameter value, and summarize all parameter values ​​to form a parameter space;

[0018] Initialize the pheromone matrix , The dimension of corresponds to the parameter space;

[0019] Each ant starts searching from an initial position in the parameter space. In each iteration, the ant selects the next parameter value based on the transition probability according to the pheromone concentration at the current position and the comprehensive heuristic information;

[0020] According to the fluorescence emission wavelength and stability of quantum dots, an evaluation function for comprehensive heuristic information is established; after each ant constructs a quantum dot parameter combination, the corresponding fitness is calculated, where the fitness value is equal to the evaluation function value;

[0021] In the entire parameter space, the pheromone concentration is updated based on the pheromone concentration update formula;

[0022] For the paths passed by ants whose fitness is higher than the preset fitness threshold, that is, the selected parameter combination, the pheromone concentration at the corresponding position is enhanced.

[0023] When the number of iterations hour, The algorithm is terminated when the preset maximum number of iterations is reached; or the maximum difference between the fitness values ​​of the population in two consecutive iterations is reached. When , the update is terminated, where is the threshold value for the change of the preset population fitness value;

[0024] After the update is terminated, the parameter combination with the highest fitness is selected from all the parameter combinations searched by the ants and the corresponding quantum dots are set as the quantum dots with the best fluorescence emission wavelength and the best stability.

[0025] Further, the method of obtaining heuristic information includes:

[0026] Calculate the fluorescence emission wavelength When the fluorescence emission wavelength is within the preset quantum dot fluorescence emission wavelength range, or when the fluorescence emission wavelength When the preset quantum dot fluorescence emission wavelength range is exceeded, the corresponding fluorescence emission wavelength heuristic information;

[0027] Calculate the stability When the stability is within the preset range, or when the stability When the preset stability range is exceeded, the corresponding stability heuristic information;

[0028] The comprehensive heuristic information is obtained by calculating the product of the fluorescence emission wavelength heuristic information and the stability heuristic information.

[0029] Furthermore, a corresponding fluorescence intensity is obtained based on the fluorescence signal, wherein a value of the fluorescence intensity is equal to a value of the fluorescence signal.

[0030] Furthermore, the method for obtaining the fluorescence intensity characteristic includes:

[0031] Calculate the average intensity of the fluorescence signal of the device under stable operation; take the average intensity of the fluorescence signal as the steady-state fluorescence intensity;

[0032] Calculate the difference between the maximum and minimum fluorescence intensity; As the range of fluorescence intensity variation;

[0033] The rate of change of fluorescence intensity over time is calculated as the fluorescence intensity change rate;

[0034] The steady-state fluorescence intensity, fluorescence intensity variation range and fluorescence intensity variation rate are spliced ​​together to obtain the fluorescence intensity characteristics.

[0035] Furthermore, the method for obtaining the fluorescence lifetime characteristics includes:

[0036] collection The fluorescence intensity at each moment is fitted to the fluorescence decay curve to obtain the fluorescence decay fitting curve about the fluorescence lifetime, and the The average fluorescence lifetime is the average fluorescence lifetime of the fluorescence signals. ;

[0037] The maximum value of the fluorescence intensity is obtained according to the fluorescence decay fitting curve, and the half-maximum value is calculated, which is half of the maximum value of the fluorescence intensity; the two time points corresponding to the value of the fluorescence decay fitting curve equal to the half-maximum value are solved. and ; Solve the two time points based on the fluorescence decay fitting curve and The difference between the two time points and The difference between is taken as the fluorescence lifetime distribution width;

[0038] The fluorescence lifetime characteristics are obtained by splicing the average fluorescence lifetime and the fluorescence lifetime distribution width.

[0039] Furthermore, the method for obtaining the fluorescence spectrum characteristics includes:

[0040] Based on the curve of fluorescence intensity versus fluorescence emission wavelength obtained by fluorescence spectrum measurement, the fluorescence emission wavelength and intensity corresponding to the peak with the largest intensity in the curve are obtained, the fluorescence emission wavelength corresponding to the peak with the largest intensity is taken as the main peak position, and the intensity corresponding to the peak with the largest intensity is taken as the main peak intensity;

[0041] Calculate two fluorescence emission wavelengths corresponding to half of the main peak intensity in the fluorescence spectrum, calculate the difference between the two fluorescence emission wavelengths, and use the difference between the two fluorescence emission wavelengths as the spectrum bandwidth;

[0042] A spectral curve is described using a Gaussian function, and the Gaussian function is fitted based on the fluorescence emission wavelength and the corresponding fluorescence intensity to obtain a spectral curve function; the spectral curve function is differentiated once to obtain a first-order derivative, and then differentiated twice to obtain a second-order derivative, and the position of the peak in the spectral curve function is found; the position offset of the same peak of the device under different states is calculated; the fluorescence intensity corresponding to each peak and the two fluorescence emission wavelengths corresponding to half of the fluorescence intensity corresponding to each peak are obtained, the difference between the two fluorescence emission wavelengths is calculated, and the difference between the two fluorescence emission wavelengths is used as the peak width;

[0043] Based on the fluorescence emission wavelength interval between the left and right sides of each peak and the peak position The shape parameters of each peak are calculated from the corresponding fluorescence intensity at 1 .

[0044] The position offset, fluorescence intensity, width and shape parameters of all peaks were concatenated to obtain the spectral shape;

[0045] The main peak position and main peak intensity, spectral bandwidth and spectral shape are spliced ​​to obtain the fluorescence spectral characteristics.

[0046] Further, an adaptive adjustment mechanism is initiated to perform power allocation;

[0047] Construct a linear priority evaluation function based on the fluorescence intensity variation range, average fluorescence lifetime, rated power and device priority coding; according to the number of load devices and the total power of the lithium battery energy storage system, the first The device performs power distribution; the Power allocated to each device Under the conditions of power limitation, energy storage system capacity constraint and equipment voltage constraint, the first Power allocated to each device Optimization.

[0048] Furthermore, the nature-inspired optimization algorithm is used to perform the Power allocated to each device During the optimization process, the corresponding parameter space is constructed based on power limitation, energy storage system power constraint and equipment voltage constraint for optimization.

[0049] Furthermore, the method of correcting the actual fluorescent signal to obtain the fluorescent signal includes: subtracting the background fluorescent signal from the actual fluorescent signal to obtain a corrected fluorescent signal.

[0050] Furthermore, the method for obtaining device information of a load device includes:

[0051] A distance measurement-based method is used to compare the device fingerprint with all device fingerprints in the fingerprint database, and the similarity between the device fingerprint and all device fingerprints stored in the fingerprint database is calculated; the similarity is compared with a preset similarity threshold, and when the similarity exceeds the preset similarity threshold, the corresponding device information is obtained.

[0052] An intelligent load identification and allocation system for a lithium battery energy storage system implements the intelligent load identification and allocation method for the lithium battery energy storage system, comprising:

[0053] Quantum dot optimization module: selects quantum dots with the best fluorescence emission wavelength and stability based on the nature-inspired optimization algorithm;

[0054] Signal acquisition module: mark the quantum dots to the preset monitoring position of the load device; align the probe of the fluorescence spectrometer with the preset monitoring position marked with the quantum dots, collect the background light signal when the excitation light source is not started and the actual fluorescence signal when the excitation light source is started, and correct the actual fluorescence signal to obtain the fluorescence signal;

[0055] Feature extraction module: extracts fluorescence intensity features, fluorescence lifetime features and fluorescence spectrum features based on the corrected fluorescence signal; each feature of the fluorescence intensity feature, fluorescence lifetime feature and fluorescence spectrum feature is spliced ​​as a dimension to form a device fingerprint;

[0056] Fingerprint pre-storage module: stores the device fingerprint and corresponding device information in the normal operating state of known device information into the fingerprint database. The device information includes device type code, rated power, rated working voltage, rated working current and device priority code;

[0057] Load identification module: compares the device fingerprint with all device fingerprints stored in the fingerprint database to identify and obtain the device information of the load device;

[0058] Power allocation module: compares the device fingerprint with the device fingerprint under normal operating state corresponding to the device information, calculates the device fingerprint deviation, compares the device fingerprint deviation with the preset fingerprint deviation threshold, and when the device fingerprint deviation is greater than the preset fingerprint deviation threshold, starts the adaptive adjustment mechanism for power allocation.

[0059] The technical effects and advantages of the intelligent load identification and distribution system and method of the lithium battery energy storage system of the present invention are as follows:

[0060] The present invention collects the fluorescence signal of the load device based on quantum dots with optimal fluorescence emission wavelength and optimal stability, and performs further feature extraction to obtain the device fingerprint. Based on the device fingerprint, it can accurately identify loads of different types and different power requirements, thereby achieving more refined allocation; it also distributes power through an adaptive adjustment mechanism, which can comprehensively consider multiple factors and perform more optimized power allocation, so as to extend the service life of the lithium battery and improve the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of an intelligent load identification and allocation method for a lithium battery energy storage system of the present invention;

[0062] Figure 2 This is a schematic diagram of the optimization results of quantum dots of the present invention;

[0063] Figure 3 A block diagram of an intelligent load identification and distribution system of a lithium battery energy storage system of the present invention;

[0064] Figure 4 This is a schematic diagram of the fingerprint pre-storage module interface of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Example 1

[0067] See also Figure 1 As shown, the intelligent load identification and allocation method of the lithium battery energy storage system described in this embodiment includes:

[0068] Quantum dots with optimal fluorescence emission wavelength and best stability are selected based on a nature-inspired optimization algorithm.

[0069] Methods for obtaining quantum dots with optimal fluorescence emission wavelength and optimal stability include:

[0070] Obtain a preset quantum dot fluorescence emission wavelength range and a corresponding quantitative index, a preset stability range and a corresponding quantitative index.

[0071] Get Quantum dots affect parameters And the preset value range corresponding to each quantum dot influencing parameter; such as working parameters, fluorescence emission performance and stability indicators.

[0072] The number of ants in the default ant colony is , the pheromone volatility coefficient is , the pheromone importance factor is And the heuristic information importance factor is .

[0073] The value range of each quantum dot influencing parameter is discretized to obtain the corresponding parameter value, and all parameter values ​​are summarized to form a parameter space.

[0074] Initialize the pheromone matrix , The dimension of corresponds to the parameter space.

[0075] Each ant starts searching from an initial position in the parameter space, and in each iteration, the ant selects the next parameter value based on the transition probability according to the pheromone concentration at the current position and the comprehensive heuristic information.

[0076] Methods for obtaining heuristic information include:

[0077] Calculate fluorescence emission wavelength heuristic information: When the fluorescence emission wavelength When the wavelength of the quantum dot fluorescence emission is within the preset range, the heuristic information ;in, is a mathematical constant; is the optimal fluorescence emission wavelength; is the fluorescence emission wavelength scale parameter; when the fluorescence emission wavelength Fluorescence emission wavelength heuristic information when the preset quantum dot fluorescence emission wavelength range is exceeded ;in, is the maximum value of the preset quantum dot fluorescence emission wavelength range; It is the minimum value of the preset quantum dot fluorescence emission wavelength range.

[0078] Compute stability heuristic information; when stability When within the preset stability range, the stability heuristic information ;in, For optimal stability; is the stability scale parameter; when stability When the preset stability range is exceeded, the stability heuristic information ;in, is the maximum value of the preset stability range; This is the preset minimum stability range.

[0079] Compute comprehensive heuristic information ;in, is the fluorescence emission wavelength heuristic information; To stabilize heuristic information; to synthesize heuristic information Only when the fluorescence emission wavelength and stability When both are close to the optimal value, the comprehensive heuristic information will approach 1, and any characteristic that deviates from the ideal value will cause Rapidly decrease.

[0080] The probability formula is used to obtain the Ants from the current position Select to location The transition probability for:

[0081]

[0082] in, For the The position at the iteration To location pheromone concentration; For location To location Comprehensive heuristic information of For location To location Comprehensive heuristic information of A set of parameter values ​​that can be selected by the current ant.

[0083] According to the fluorescence emission wavelength and stability of quantum dots, an evaluation function is established ; Each ant constructs a quantum dot parameter combination Then, calculate the corresponding fitness ;in, For the The quantum dot parameter combination corresponding to the ants; For the The first quantum dot parameter combination corresponding to the ant Quantum dot parameters; For the The first iteration The quantum dot parameter combination corresponding to the ants; For the The iteration The first quantum dot parameter combination corresponding to the ant quantum dot parameters; the higher the fitness, the closer the corresponding parameter combination is to the expected quantum dot characteristics.

[0084] In the entire parameter space, the formula is updated based on the pheromone concentration Update the pheromone concentration; For the The position at the iteration To location This means that over time, the pheromone concentration will gradually decrease, preventing ants from always relying on early search paths.

[0085] For the paths (i.e. the selected parameter combinations) that the ants with fitness higher than the preset fitness threshold pass through, the pheromone concentration at the corresponding position is enhanced. The ant is in the The fitness in the iteration is , the pheromone enhancement amount is ,but ,in, is a constant; after pheromone enhancement, the enhanced pheromone concentration is .

[0086] When the number of iterations hour, To set the maximum number of iterations, terminate the algorithm; or calculate the difference between the maximum values ​​of the population fitness in two consecutive iterations ,when When , it means the algorithm converges and terminates the algorithm, where is the threshold value for the change of the preset population fitness value; For the The maximum value of the population fitness at the iteration; For the The maximum value of the population fitness at the iteration.

[0087] Reference Figure 2After the algorithm terminates, the parameter combination with the highest fitness is selected from all the parameter combinations obtained by the ant search, that is, the corresponding optimization result in the figure, and the quantum dot fluorescence emission wavelength and stability corresponding to the parameter combination with the highest fitness are used as the optimal fluorescence emission wavelength and the optimal stability. The parameters of the quantum dots are set to the parameter combination with the highest fitness, and the quantum dots with the optimal fluorescence emission wavelength and the optimal stability are obtained.

[0088] The quantum dots are marked to the preset monitoring position of the load device by physical adsorption method; the probe of the fluorescence spectrometer is aligned with the preset monitoring position marked with the quantum dots, and the fluorescence spectrometer is used to collect the fluorescence signal of the quantum dots during the operation of the device, which can ensure the accurate collection of the fluorescence signal. The background light signal when the excitation light source is not started is collected to obtain the background fluorescence intensity sequence ;in, is the acquisition time; the actual fluorescence signal when the excitation light source is started is collected , subtract the background fluorescence signal from the actual fluorescence signal to obtain the corrected fluorescence signal ; Among them, the fluorescence intensity .

[0089] Based on the corrected fluorescence signal, the fluorescence intensity feature, fluorescence lifetime feature and fluorescence spectrum feature are extracted; each feature is spliced ​​as a dimension to form a device fingerprint; the resulting device fingerprint makes each load device correspond to a unique fingerprint vector, which can accurately distinguish the corresponding device from other devices; the corresponding power allocation can be made to the load device according to the device fingerprint, thereby improving the power utilization efficiency of the entire system.

[0090] When the device load increases, it will cause the temperature inside the device to rise, the electric or magnetic field to change, and the chemical reaction rate to change. These factors will affect the surrounding environment of the quantum dots, thereby changing their fluorescence intensity characteristics. Under high load conditions, more heat, higher electric field strength, or stronger chemical interactions may be generated inside the device. These factors will change the microscopic environment in which the quantum dots are located, affecting the fluorescence lifetime characteristics of their excited states. When the device load changes, factors such as the electric field, magnetic field, temperature, and chemical environment around the quantum dots will change, thereby affecting the energy level structure and transition probability of the quantum dots, and thus affecting the fluorescence spectral characteristics. Therefore, extracting fluorescence intensity characteristics, fluorescence lifetime characteristics, and fluorescence spectral characteristics can comprehensively reflect the dynamic changes in the environment in which the quantum dots are located during the operation of the device, providing richer information for device fingerprint recognition.

[0091] Methods for obtaining fluorescence intensity characteristics include:

[0092] Calculate the average intensity of the fluorescence signal of the device under stable operation ;in, and They are the start and end time of the stable operation of the fluorescence signal; is the number of time points corresponding to the fluorescence intensity; the average intensity of the fluorescence signal as the steady-state fluorescence intensity.

[0093] Calculate the difference between the maximum and minimum fluorescence intensity ;in, and are the maximum and minimum values ​​of fluorescence intensity respectively; the difference between the maximum and minimum values ​​of fluorescence intensity As the fluorescence intensity variation range.

[0094] Calculate the rate of change of fluorescence intensity over time ;in, for Fluorescence intensity at the time; is the time interval; the rate of change of fluorescence intensity over time as the fluorescence intensity change rate.

[0095] The steady-state fluorescence intensity, fluorescence intensity variation range and fluorescence intensity variation rate are spliced ​​together to obtain the fluorescence intensity characteristics.

[0096] The fluorescence lifetime characteristics include the average fluorescence lifetime and the fluorescence lifetime distribution width; the methods for obtaining the fluorescence lifetime characteristics include:

[0097] collection The fluorescence intensity at each moment is fitted to the fluorescence decay curve to obtain the fluorescence decay fitting curve about the fluorescence lifetime, such as ,in, is the fluorescence lifetime; is the fluorescence signal at the start of stable operation; The average fluorescence lifetime is the average fluorescence lifetime of the fluorescence signals. .

[0098] Get the maximum value of fluorescence intensity according to the fluorescence decay fitting curve , calculate the half maximum value , that is, the fitted maximum value of the fluorescence intensity Half of the fluorescence decay; solve the two time points corresponding to the value of the fitting curve equal to the half maximum value and ; Solve the two time points based on the fluorescence decay fitting curve and The difference between , get two time points and The difference ; Two time points and The difference between and is taken as the fluorescence lifetime distribution width.

[0099] The fluorescence lifetime characteristics are obtained by splicing the average fluorescence lifetime and the fluorescence lifetime distribution width.

[0100] The fluorescence spectrum characteristics include main peak position and main peak intensity, spectral bandwidth and spectral shape.

[0101] Methods for obtaining fluorescence spectral characteristics include:

[0102] Based on the curve of fluorescence intensity varying with fluorescence emission wavelength obtained by fluorescence spectrum measurement, the fluorescence emission wavelength and intensity corresponding to the peak with the largest intensity in the curve are obtained, the fluorescence emission wavelength corresponding to the peak with the largest intensity is taken as the main peak position, and the intensity corresponding to the peak with the largest intensity is taken as the main peak intensity.

[0103] The two fluorescence emission wavelengths corresponding to half of the main peak intensity in the fluorescence spectrum are calculated, the difference between the two fluorescence emission wavelengths is calculated, and the difference between the two fluorescence emission wavelengths is used as the spectrum bandwidth.

[0104] Use a Gaussian function to describe the spectral curve, and fit the Gaussian function based on the fluorescence emission wavelength and the corresponding fluorescence intensity to obtain the spectral curve function; perform a first-order derivative on the spectral curve function, and then perform a second-order derivative to obtain the second-order derivative, and find the position of the peak in the spectral curve function; calculate the position offset of the same peak of the device in different states ;in, and They are respectively the positions of the same peak of the device in different states; the fluorescence intensity corresponding to each peak and the two fluorescence emission wavelengths corresponding to half of the fluorescence intensity corresponding to each peak are obtained, the difference between the two fluorescence emission wavelengths is calculated, and the difference between the two fluorescence emission wavelengths is taken as the width of the peak.

[0105] Calculate shape parameters for each peak ;in, and The distance between the peak on the right and left sides of the peak and the fluorescence emission wavelength The corresponding fluorescence intensity.

[0106] The position offset, fluorescence intensity, width and shape parameters of all peaks were concatenated to obtain the spectral shape.

[0107] The main peak position and main peak intensity, spectral bandwidth and spectral shape are spliced ​​to obtain the fluorescence spectral characteristics.

[0108] The device fingerprint and corresponding device information under normal operating conditions of known device information are stored in the fingerprint database. The device information includes device type code, rated power, rated operating voltage, rated operating current and device priority code to facilitate subsequent identification and energy allocation.

[0109] When the load device is accurately identified, the corresponding device fingerprint and device information are added to the database. At the same time, the fingerprint data in the database is reviewed and updated according to the preset update interval to ensure the accuracy and timeliness of the data. If it is found that some device fingerprints have changed due to equipment aging, maintenance, etc., the corresponding records in the database are updated in time.

[0110] The device fingerprint is compared with all device fingerprints stored in the fingerprint database to identify and obtain the device information of the payload device. The method for obtaining the device information of the payload device includes:

[0111] A distance measurement-based method is used to compare the device fingerprint with all device fingerprints in the fingerprint database, and the similarity between the device fingerprint and all device fingerprints stored in the fingerprint database is calculated; the similarity is compared with a preset similarity threshold, and when the similarity exceeds the preset similarity threshold, the corresponding device information is obtained.

[0112] The device fingerprint is compared with the device fingerprint under normal operating state corresponding to the device information, the device fingerprint deviation is calculated, and the device fingerprint deviation is compared with a preset fingerprint deviation threshold. When the device fingerprint deviation is greater than the preset fingerprint deviation threshold, the adaptive adjustment mechanism is started to allocate power.

[0113] Construct a priority evaluation function: ; ; ; ;in, , and They are the results of fluorescence intensity variation range, average fluorescence lifetime and rated power after transformation; is the preset reference fluorescence lifetime value; is the adjustment index; is the rated power of the equipment; It is a value encoded according to the device type; is the activation parameter.

[0114] Assume the number of load devices is , the total power of the energy storage system is , No. The priority of a device is , then Power allocated to each device ;in, For the The priority of each device; when the Power allocated to each device Optimization is performed based on a nature-inspired optimization algorithm under the conditions of power constraints, energy storage system capacity constraints, and equipment voltage constraints.

[0115] Example 2

[0116] See also Figure 3 As shown, the intelligent load identification and distribution system of the lithium battery energy storage system described in this embodiment includes:

[0117] Quantum dot optimization module: selects quantum dots with optimal fluorescence emission wavelength and stability based on nature-inspired optimization algorithm.

[0118] Signal acquisition module: mark the quantum dots to the preset monitoring position of the load device; align the probe of the fluorescence spectrometer with the preset monitoring position marked with the quantum dots, collect the background light signal when the excitation light source is not started and the actual fluorescence signal when the excitation light source is started, and correct the actual fluorescence signal to obtain the fluorescence signal.

[0119] Feature extraction module: extracts fluorescence intensity features, fluorescence lifetime features and fluorescence spectrum features based on the corrected fluorescence signal; each feature of the fluorescence intensity feature, fluorescence lifetime feature and fluorescence spectrum feature is spliced ​​as a dimension to form a device fingerprint.

[0120] Fingerprint pre-storage module: stores the device fingerprint and corresponding device information of the known device information in the normal operating state into the fingerprint database. The device information includes the device type code, rated power, rated working voltage, rated working current and device priority code; the device name and device description can also be input accordingly. Figure 4 , in order to facilitate the later distinction of devices.

[0121] Load identification module: compares the device fingerprint with all device fingerprints stored in the fingerprint database to identify and obtain the device information of the load device.

[0122] Power allocation module: compares the device fingerprint with the device fingerprint under normal operating state corresponding to the device information, calculates the device fingerprint deviation, compares the device fingerprint deviation with the preset fingerprint deviation threshold, and when the device fingerprint deviation is greater than the preset fingerprint deviation threshold, starts the adaptive adjustment mechanism for power allocation.

[0123] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0124] Finally: 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, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent load identification and allocation method for a lithium battery energy storage system, characterized in that: The steps include: Selecting quantum dots with the best fluorescence emission wavelength and the best stability based on a nature-inspired optimization algorithm; Marking quantum dots at a preset monitoring position of a load device; aligning a probe of a fluorescence spectrometer with the preset monitoring position marked with quantum dots, collecting a background light signal when the excitation light source is not started and an actual fluorescence signal when the excitation light source is started, and correcting the actual fluorescence signal to obtain a fluorescence signal; Extract fluorescence intensity features, fluorescence lifetime features, and fluorescence spectrum features based on the corrected fluorescence signal; splice each feature as a dimension to form a device fingerprint; The device fingerprint and corresponding device information of the known device information in the normal operating state are stored in the fingerprint database, and the device information includes the device type code, rated power, rated working voltage, rated working current and device priority code; Compare the device fingerprint with all device fingerprints stored in the fingerprint database to identify and obtain the device information of the payload device; The device fingerprint is compared with the device fingerprint under normal operating state corresponding to the device information, the device fingerprint deviation is calculated, and the device fingerprint deviation is compared with a preset fingerprint deviation threshold. When the device fingerprint deviation is greater than the preset fingerprint deviation threshold, the adaptive adjustment mechanism is started to allocate power.

2. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 1 is characterized in that: Methods for obtaining quantum dots with optimal fluorescence emission wavelength and optimal stability include: Obtaining a preset quantum dot fluorescence emission wavelength range and corresponding quantitative index, a preset stability range and corresponding quantitative index; obtaining Quantum dots affect parameters and a preset value range corresponding to each quantum dot impact parameter; The number of ants in the default ant colony is , the pheromone volatility coefficient is , the pheromone importance factor is And the heuristic information importance factor is ; Discretize the value range of each quantum dot influencing parameter to obtain the corresponding parameter value, and summarize all parameter values ​​to form a parameter space; Initialize the pheromone matrix , The dimension of corresponds to the parameter space; Each ant starts searching from an initial position in the parameter space. In each iteration, the ant selects the next parameter value based on the transition probability according to the pheromone concentration at the current position and the comprehensive heuristic information; According to the fluorescence emission wavelength and stability of quantum dots, an evaluation function for comprehensive heuristic information is established; after each ant constructs a quantum dot parameter combination, the corresponding fitness is calculated; wherein the fitness value is equal to the evaluation function value; In the entire parameter space, the pheromone concentration is updated based on the pheromone concentration update formula; For the paths taken by ants whose fitness is higher than the preset fitness threshold, i.e. the selected parameter combination, the pheromone concentration at the corresponding position is enhanced; When the number of iterations hour, The preset maximum number of iterations terminates the update; or the maximum difference between the fitness values ​​of the population in two consecutive iterations When , the update is terminated, where is the threshold value for the change of the preset population fitness value; After the update is terminated, the parameter combination with the highest fitness is selected from all the parameter combinations searched by the ants and the corresponding quantum dots are set as the quantum dots with the best fluorescence emission wavelength and the best stability.

3. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 2 is characterized in that: Methods for obtaining heuristic information include: Calculate the fluorescence emission wavelength When the fluorescence emission wavelength is within the preset quantum dot fluorescence emission wavelength range, or when the fluorescence emission wavelength When the preset quantum dot fluorescence emission wavelength range is exceeded, the corresponding fluorescence emission wavelength heuristic information; Calculate the stability When the stability is within the preset range, or when the stability When the preset stability range is exceeded, the corresponding stability heuristic information; The comprehensive heuristic information is obtained by calculating the product of the fluorescence emission wavelength heuristic information and the stability heuristic information.

4. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 1, characterized in that: A corresponding fluorescence intensity is obtained based on the fluorescence signal, wherein a value of the fluorescence intensity is equal to a value of the fluorescence signal.

5. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 4 is characterized in that: Methods for obtaining fluorescence intensity characteristics include: Calculate the average intensity of the fluorescence signal of the device under stable operation; take the average intensity of the fluorescence signal as the steady-state fluorescence intensity; Calculate the difference between the maximum and minimum fluorescence intensity; As the range of fluorescence intensity variation; The rate of change of fluorescence intensity over time is calculated as the fluorescence intensity change rate; The steady-state fluorescence intensity, fluorescence intensity variation range and fluorescence intensity variation rate are spliced ​​together to obtain the fluorescence intensity characteristics.

6. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 4 is characterized in that: Methods for obtaining fluorescence lifetime characteristics include: collection The fluorescence intensity at each moment is fitted to the fluorescence decay curve to obtain the fluorescence decay fitting curve about the fluorescence lifetime, and the The average fluorescence lifetime is the average fluorescence lifetime of the fluorescence signals. ; The maximum value of the fluorescence intensity is obtained according to the fluorescence decay fitting curve, and the half-maximum value is calculated, which is half of the maximum value of the fluorescence intensity; the two time points corresponding to the value of the fluorescence decay fitting curve equal to the half-maximum value are solved. and ; Solve the two time points based on the fluorescence decay fitting curve and The difference between the two time points and The difference between is taken as the fluorescence lifetime distribution width; The fluorescence lifetime characteristics are obtained by splicing the average fluorescence lifetime and the fluorescence lifetime distribution width.

7. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 4, characterized in that: Methods for obtaining fluorescence spectral characteristics include: Based on the curve of fluorescence intensity versus fluorescence emission wavelength obtained by fluorescence spectrum measurement, the fluorescence emission wavelength and intensity corresponding to the peak with the largest intensity in the curve are obtained, the fluorescence emission wavelength corresponding to the peak with the largest intensity is taken as the main peak position, and the intensity corresponding to the peak with the largest intensity is taken as the main peak intensity; Calculate two fluorescence emission wavelengths corresponding to half of the main peak intensity in the fluorescence spectrum, calculate the difference between the two fluorescence emission wavelengths, and use the difference between the two fluorescence emission wavelengths as the spectrum bandwidth; A spectral curve is described using a Gaussian function, and the Gaussian function is fitted based on the fluorescence emission wavelength and the corresponding fluorescence intensity to obtain a spectral curve function; the spectral curve function is differentiated once to obtain a first-order derivative, and then differentiated twice to obtain a second-order derivative, and the position of the peak in the spectral curve function is found; the position offset of the same peak of the device under different states is calculated; the fluorescence intensity corresponding to each peak and the two fluorescence emission wavelengths corresponding to half of the fluorescence intensity corresponding to each peak are obtained, the difference between the two fluorescence emission wavelengths is calculated, and the difference between the two fluorescence emission wavelengths is used as the peak width; Based on the fluorescence emission wavelength interval between the left and right sides of each peak and the peak position The shape parameters of each peak are calculated from the corresponding fluorescence intensity at 1 . The position offset, fluorescence intensity, width and shape parameters of all peaks were concatenated to obtain the spectral shape; The main peak position and main peak intensity, spectral bandwidth and spectral shape are spliced ​​to obtain the fluorescence spectral characteristics.

8. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 1, characterized in that: The method of starting the adaptive adjustment mechanism to distribute power includes: Construct a linear priority evaluation function based on the fluorescence intensity variation range, average fluorescence lifetime, rated power and device priority coding; according to the number of load devices and the total power of the lithium battery energy storage system, the first The device performs power distribution; the Power allocated to each device Under the conditions of power limitation, energy storage system capacity constraint and equipment voltage constraint, the first Power allocated to each device Optimization.

9. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 8, characterized in that: The method is based on the nature-inspired optimization algorithm. Power allocated to each device During the optimization process, the corresponding parameter space is constructed based on power limitation, energy storage system power constraint and equipment voltage constraint for optimization.

10. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 9, characterized in that: The method for correcting the actual fluorescent signal to obtain the fluorescent signal includes: subtracting the background fluorescent signal from the actual fluorescent signal to obtain the corrected fluorescent signal.

11. The intelligent load identification and allocation method of the lithium battery energy storage system according to claim 10, characterized in that: Methods for obtaining device information of a load device include: A distance measurement-based method is used to compare the device fingerprint with all device fingerprints in the fingerprint database, and the similarity between the device fingerprint and all device fingerprints stored in the fingerprint database is calculated; the similarity is compared with a preset similarity threshold, and when the similarity exceeds the preset similarity threshold, the corresponding device information is obtained.

12. An intelligent load identification and distribution system for a lithium battery energy storage system, implementing the intelligent load identification and distribution method for a lithium battery energy storage system according to any one of claims 1 to 11, characterized in that: include: Quantum dot optimization module: selects quantum dots with the best fluorescence emission wavelength and stability based on the nature-inspired optimization algorithm; Signal acquisition module: mark the quantum dots to the preset monitoring position of the load device; align the probe of the fluorescence spectrometer with the preset monitoring position marked with the quantum dots, collect the background light signal when the excitation light source is not started and the actual fluorescence signal when the excitation light source is started, and correct the actual fluorescence signal to obtain the fluorescence signal; Feature extraction module: extracts fluorescence intensity features, fluorescence lifetime features and fluorescence spectrum features based on the corrected fluorescence signal; each feature of the fluorescence intensity feature, fluorescence lifetime feature and fluorescence spectrum feature is spliced ​​as a dimension to form a device fingerprint; Fingerprint pre-storage module: stores the device fingerprint and corresponding device information in the normal operating state of known device information into the fingerprint database. The device information includes device type code, rated power, rated working voltage, rated working current and device priority code; Load identification module: compares the device fingerprint with all device fingerprints stored in the fingerprint database to identify and obtain the device information of the load device; Power allocation module: compares the device fingerprint with the device fingerprint under normal operating state corresponding to the device information, calculates the device fingerprint deviation, compares the device fingerprint deviation with the preset fingerprint deviation threshold, and when the device fingerprint deviation is greater than the preset fingerprint deviation threshold, starts the adaptive adjustment mechanism for power allocation.

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