Energy management method and system for lifting machinery luffing process based on supercapacitor

Abstract

The present invention provides a supercapacitor-based energy management method and system for the luffing process of a lifting machinery, relating to the field of energy management technology. The method includes calculating the theoretical energy demand of the luffing process through a neural network prediction model, correcting the dynamic grouping coefficient to obtain a power allocation coefficient, and using this as the upper limit of the charge and discharge power of each group of supercapacitors. The charge and discharge current is adjusted by an adaptive PI controller. When the luffing angle decreases, the regenerative braking energy that exceeds the chargeable power of the supercapacitor is stored in a high-temperature superconducting energy storage inductor, and when conditions permit, this energy is charged into the supercapacitor according to the power allocation coefficient. When the luffing angle increases, a dynamic programming algorithm is used to optimize the output power ratio between the main power supply and each group of supercapacitors, and power coordinated control is achieved through a sliding mode and active interference rejection controller to ensure system voltage stability. The present invention can effectively improve energy utilization efficiency, extend the life of supercapacitors, and ensure system stability.

Description

Technical Field

[0001] The present invention relates to energy management technology, and in particular to a supercapacitor-based energy management method and system for a lifting machinery luffing process. Background Art

[0002] During luffing operations, the energy demand of cranes is often subject to dynamic fluctuations, including dramatic instantaneous power fluctuations and unstable energy recovery efficiency. Traditional energy management methods struggle to effectively address these power fluctuations, resulting in low system energy utilization and increased wear on mechanical components, impacting equipment life and operational reliability. Existing technologies lack specificity in energy distribution and power control, making it impossible to precisely manage complex energy flows.

[0003] Supercapacitors, due to their high power density, rapid charge and discharge capabilities, and long cycle life, are an ideal choice for crane energy management systems. However, in practical applications, supercapacitors suffer from capacity decay and uneven charge and discharge, resulting in reduced energy efficiency. This is particularly true during the luffing process, where the dynamic energy demand and the supercapacitor's charge and discharge characteristics are poorly matched, further exacerbating system energy losses. Furthermore, efficiently storing regenerative braking energy during the energy recovery phase is a challenge in current technology.

[0004] Therefore, there is an urgent need for a comprehensive energy management method based on supercapacitors, which can achieve efficient management of energy flow through real-time power distribution, dynamic grouping and intelligent control, and effectively extend the service life of the supercapacitor group, thereby improving the energy utilization efficiency during the luffing process of the lifting machinery. Summary of the Invention

[0005] The embodiments of the present invention provide a method and system for managing energy during the luffing process of a lifting machinery based on a supercapacitor, which can solve the problems in the prior art.

[0006] According to a first aspect of the embodiments of the present invention,

[0007] Provided is a method for energy management during the luffing process of a lifting machinery based on a supercapacitor, comprising:

[0008] A neural network prediction model is used to analyze the luffing angle rate and hoisting weight of the lifting machinery, and an instantaneous power curve of the luffing process is calculated. The integral value of the instantaneous power curve on the time axis is used as the theoretical energy demand of the luffing process. Supercapacitors are grouped based on the theoretical energy demand and an initial grouping coefficient is calculated. The ratio of the initial grouping coefficient to the historical number of cycles is determined as the capacity decay rate. The remaining life coefficient of each group of supercapacitors is calculated based on the capacity decay rate. The dynamic grouping coefficient is calculated based on the calculation of the initial grouping coefficient and the remaining life coefficient.

[0009] A fuzzy controller based on the dynamic grouping coefficient is established, with the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors as inputs of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped into charge and discharge power correction values by the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient. The power allocation coefficient is used as the upper limit of the charge and discharge power of each group of supercapacitors. The charge and discharge current of each group of supercapacitors is adjusted by an adaptive PI controller, wherein the control parameters of the adaptive PI controller are adaptively related to the power allocation coefficient and the terminal voltage deviation.

[0010] During the process of decreasing the amplitude angle, the regenerative braking power is compared with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged according to the proportion of the power distribution coefficient of each group of supercapacitors. During the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio between the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an active disturbance rejection controller to suppress load power fluctuations to ensure the stability of the system voltage.

[0011] In an optional embodiment,

[0012] A neural network prediction model is used to analyze the luffing angle rate and hoisting weight of the lifting machinery, and the instantaneous power curve of the luffing process is calculated. The integral value of the instantaneous power curve on the time axis is used as the theoretical energy demand of the luffing process. Based on the theoretical energy demand, supercapacitors are grouped and the initial grouping coefficients are calculated, including:

[0013] A neural network prediction model is constructed, in which the input layer receives the amplitude variation angle rate signal and the hoisting weight signal, and the output layer outputs the instantaneous power curve of the amplitude variation process;

[0014] The amplitude variation angle rate signal and the hoist weight signal are sampled to obtain a discrete data sequence, time domain noise reduction and feature extraction are performed on the discrete data sequence to obtain time domain features, the extracted time domain features are normalized according to the maximum and minimum method to obtain training samples, a weighted loss function including an instantaneous power prediction error term and a model complexity penalty term is constructed, and the neural network prediction model is iteratively trained using a momentum-based adaptive optimization method until the weighted loss function converges to a preset threshold, thereby obtaining a trained neural network prediction model;

[0015] The instantaneous power curve output by the trained neural network prediction model is segmented and integrated on the time axis. The time window length of the integration interval is adaptively adjusted according to the changing trend of the amplitude variation angle rate signal. The time window length is positively correlated with the rate of change of the amplitude variation angle rate signal. A dual-loop state observer is designed to filter the integration result to obtain the theoretical energy demand of the amplitude variation process.

[0016] The theoretical energy demand is divided by the rated energy storage capacity of a single group of supercapacitors to obtain a ratio, the basic group number is calculated according to the ratio, and a redundancy coefficient is set based on the system redundancy requirement, and the basic group number is multiplied by the redundancy coefficient and rounded up to obtain the actual group number;

[0017] The terminal voltage signals of each group of supercapacitors are collected in real time. At the same time, the surface temperature signals of each group of supercapacitors are collected using a temperature sensor array. A Coulomb counting model is established based on the terminal voltage signals to calculate the state of charge of each group of supercapacitors.

[0018] A fuzzy inferencer based on a triangular membership function is constructed. The inter-group deviation value of the terminal voltage signal, the maximum temperature difference value of the temperature signal, and the imbalance degree of the charge state are used as input variables of the fuzzy inferencer. An inference mechanism including fuzzification, rule reasoning, and defuzzification is established. The fuzzy inferencer outputs a grouping coefficient correction value, and a baseline grouping coefficient is determined according to the initial operating state of the system. The grouping coefficient correction value is added to the baseline grouping coefficient to obtain the initial grouping coefficient.

[0019] In an optional embodiment,

[0020] The ratio of the initial grouping coefficient to the historical cycle number is determined as the capacity decay rate, and the remaining life coefficient of each group of supercapacitors is calculated according to the capacity decay rate. The dynamic grouping coefficient obtained based on the calculation of the initial grouping coefficient and the remaining life coefficient includes:

[0021] Collecting a charge and discharge cycle count value of the supercapacitor as a historical cycle number, determining a ratio of the initial grouping coefficient to the historical cycle number as a benchmark capacity decay rate, and adaptively adjusting a sliding time window length according to a numerical value of the benchmark capacity decay rate, wherein the sliding time window length decreases as the benchmark capacity decay rate increases;

[0022] continuously collecting a real-time terminal voltage monitoring signal and a real-time temperature monitoring signal of the supercapacitor within the sliding time window length, calculating a real-time terminal voltage deviation matrix based on the real-time terminal voltage monitoring signal, calculating a real-time temperature distribution uniformity coefficient based on the real-time temperature monitoring signal, calculating a real-time capacity decay rate according to the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient, and determining a ratio of the real-time capacity decay rate to a reference capacity decay rate as a decay rate correction coefficient;

[0023] A Weibull distribution life prediction model is established based on the decay rate correction coefficient, and a characteristic life parameter is calculated. The characteristic life parameter is proportional to the inverse of the decay rate correction coefficient, and the proportional coefficient is adaptively adjusted according to the degree of fluctuation of the decay rate correction coefficient; and a maximum likelihood estimation method is used to calculate the shape parameter of the Weibull distribution life prediction model.

[0024] Inputting the characteristic life parameter and shape parameter into a preset Weibull distribution prediction equation to calculate the remaining life coefficient, and calculating the product of the initial grouping coefficient and the remaining life coefficient to obtain an intermediate dynamic grouping coefficient;

[0025] The difference in real-time capacity decay rates at adjacent sampling moments is calculated to obtain the capacity decay change rate. The normalized value of the capacity decay change rate is used as a smoothing coefficient. The intermediate dynamic grouping coefficient is subjected to a first-order low-pass filtering process using the smoothing coefficient. The time constant of the filtering process is proportional to the smoothing coefficient. The result after filtering is output as the final dynamic grouping coefficient.

[0026] In an optional embodiment,

[0027] A fuzzy controller based on the dynamic grouping coefficient is established, and the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors are used as inputs of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped to charge and discharge power correction values by the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient, including:

[0028] The terminal voltage signal of each group of supercapacitors is collected and digitally filtered to calculate the system average terminal voltage. The difference between the terminal voltage of each group of supercapacitors and the system average terminal voltage is used as the terminal voltage deviation. The terminal voltage deviation is subjected to sliding average filtering to obtain a filtered terminal voltage deviation. The filtered rear-end voltage deviations at adjacent sampling moments are differentially calculated and combined with the sampling period to obtain a terminal voltage change rate. The terminal voltage change rate is clipped to obtain a clipped terminal voltage change rate.

[0029] The filtered terminal voltage deviation is divided into preset intervals to establish a first linguistic variable set, and a dynamically adjustable first fuzzy subset boundary is set for each interval. At the same time, the terminal voltage change rate after clipping is divided into preset intervals to establish a second linguistic variable set, and the boundary value is adaptively adjusted according to the change trend of the terminal voltage deviation to obtain a second fuzzy subset boundary;

[0030] A triangular membership function with optimized overlap is used to perform fuzzy processing on the terminal voltage deviation after filtering to obtain a first membership value, and an asymmetric trapezoidal membership function with configurable slope is used to perform fuzzy processing on the terminal voltage change rate after limiting to obtain a second membership value;

[0031] A fuzzy rule base is constructed based on the first membership value, the second membership value, the first language variable set, and the second language variable set, and the rules are optimized online using a variable structure fuzzy control strategy to obtain an optimized fuzzy rule set. Fuzzy reasoning is performed on the optimized fuzzy rule set using a fuzzy sub-rule parallel reasoning method, and the reasoning results of each sub-rule are fuzzily synthesized using adaptive weights to obtain a synthesized fuzzy output.

[0032] Defuzzifying the integrated fuzzy output using a centroid method to obtain an initial power correction value, and dynamically limiting the initial power correction value in combination with system state parameters to obtain a charge and discharge power correction value;

[0033] A recursive compensation model is constructed by taking the dynamic grouping coefficient as the initial value and the charge and discharge power correction value as the target value. The recursive compensation model dynamically adjusts the iteration step size according to the deviation between the compensation target value and the current power coefficient. The recursive compensation model is used to perform iterative calculations. When the change in the power coefficient between two adjacent iterations is less than a preset change threshold, the iteration is stopped to obtain the compensated grouping coefficient. The values of the compensated grouping coefficients that are less than the dead zone threshold are set to zero, and the values that are greater than the dead zone threshold are normalized to obtain the power allocation coefficient.

[0034] In an optional embodiment,

[0035] The integrated fuzzy output is defuzzified using an improved centroid method to obtain an initial power correction value, and the initial power correction value is dynamically limited in combination with the system state parameter to obtain a charge and discharge power correction value, including:

[0036] The fuzzy control output is defuzzified by calculating the membership degree of each rule, the discrete points of the output universe, and the area weight of the output membership function at the discrete points, and performing weighted summation based on the product of the membership degree and the area weight and the discrete point value to obtain an initial power correction value; wherein the area weight is calculated using piecewise linear approximation, and for a trapezoidal area, the product of the mean of the membership degrees of adjacent discrete points and the distance between the discrete points is used, and for a rectangular area, the product of the membership degree of the discrete point and the distance between the discrete points is used;

[0037] Collecting system operating state parameters, including state of charge, terminal voltage, and temperature, calculating a deviation between the state of charge and a desired state of charge to obtain a state of charge deviation, calculating a deviation between the terminal voltage and a rated terminal voltage to obtain a terminal voltage deviation, and calculating a difference between the temperature and a temperature threshold to obtain a temperature deviation;

[0038] establishing a first clipping function based on the state of charge deviation, establishing a second clipping function based on the terminal voltage deviation, and establishing a third clipping function based on the temperature deviation, and taking the product of the initial power correction value and the first clipping function, the second clipping function, and the third clipping function as an intermediate power correction value;

[0039] The first limiting function, the second limiting function and the third limiting function are subjected to piecewise linearization processing, and the product of the limiting function after the piecewise linearization processing and the initial power correction value is used as the final charge and discharge power correction value for adjusting the charge and discharge power of the system.

[0040] In an optional embodiment,

[0041] During the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio between the main power supply and each group of supercapacitors. The sliding mode controller is used to achieve power coordination control between each group of supercapacitors and the main power supply. The active disturbance rejection controller is used to suppress load power fluctuations to ensure the stability of the system voltage.

[0042] Obtain the real-time state of charge, terminal voltage, and bus voltage of each group of supercapacitors during the process of increasing the amplitude angle, as well as the power distribution coefficient corresponding to each group of supercapacitors;

[0043] Establishing an intelligent power distributor based on the power distribution coefficient, taking the real-time state of charge, terminal voltage and bus voltage as system state variables, and inputting the main power supply output power and the output power of each group of supercapacitors as control variables into the intelligent power distributor;

[0044] Constructing an objective function in the intelligent power distributor, solving the objective function using a dynamic programming algorithm, and obtaining an output power ratio between the main power supply and each group of supercapacitors;

[0045] Constructing a sliding surface according to the deviation between the target value and the actual value of the output power ratio, and designing a control law of the sliding mode controller based on the sliding surface, wherein the control law includes an equivalent control term consisting of a system dynamic estimation term and a state tracking deviation term, and a switching control term determined by a reaching law parameter;

[0046] Outputting control instructions for the main power supply and each group of supercapacitors through the sliding mode controller, and collecting load power fluctuation information after executing the control instructions, obtaining system output and tracking error through an extended state observer based on the load power fluctuation information, and constructing a state observation equation for the active disturbance rejection controller based on the system output and tracking error;

[0047] The output value of the state observation equation is compared with the reference input to obtain a compensation control quantity, and the control instructions of the main power supply and each group of supercapacitors are corrected according to the compensation control quantity to suppress load power fluctuations. The corrected control instructions are applied to the main power supply and each group of supercapacitors to achieve power collaborative control and ensure system voltage stability. At the same time, the controlled system state is input into the intelligent power distributor as a new state variable for iterative optimization.

[0048] In an optional embodiment,

[0049] The formula for constructing the objective function in the intelligent power divider is as follows:

[0050]

[0051] Where J represents the objective function, T represents the total time, w1 represents the weight of the bus voltage deviation term, V bus(t) Represents the bus voltage, that is, the actual voltage value of the system at time t, V ref(t) represents the bus reference voltage, w2 represents the weight of the supercapacitor power distribution deviation term, N represents the number of supercapacitor groups, P SC，i(t) represents the output power of the i-th group of supercapacitors, k i represents the power allocation coefficient of the i-th group of supercapacitors, w3 represents the weight of the main power supply power utilization deviation term, P main(t) Indicates the actual power value provided by the main power supply at time t, Pmain，max Indicates the maximum output power of the main power supply.

[0052] According to a second aspect of the embodiments of the present invention,

[0053] Provided is a supercapacitor-based energy management system for the luffing process of a lifting machinery, comprising:

[0054] The first unit is used to analyze the luffing angle rate and the lifting weight of the lifting machinery using a neural network prediction model, calculate an instantaneous power curve of the luffing process, use the integral value of the instantaneous power curve on the time axis as the theoretical energy demand of the luffing process, group supercapacitors based on the theoretical energy demand and calculate an initial grouping coefficient, determine the ratio of the initial grouping coefficient to the historical cycle number as the capacity decay rate, calculate the remaining life coefficient of each group of supercapacitors based on the capacity decay rate, and obtain a dynamic grouping coefficient based on the calculation of the initial grouping coefficient and the remaining life coefficient;

[0055] a second unit for establishing a fuzzy controller based on the dynamic grouping coefficient, using the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors as inputs of the fuzzy controller, mapping different combinations of the terminal voltage deviation and the terminal voltage change rate into charge and discharge power correction values through the fuzzy controller, establishing a recursive compensation model based on the charge and discharge power correction values to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient, using the power allocation coefficient as the upper limit of the charge and discharge power of each group of supercapacitors, and adjusting the charge and discharge current of each group of supercapacitors through an adaptive PI controller, wherein the control parameters of the adaptive PI controller are adaptively related to the power allocation coefficient and the terminal voltage deviation;

[0056] The third unit is used to compare the regenerative braking power with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient during the process of decreasing the amplitude angle. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged according to the proportion of the power distribution coefficient of each group of supercapacitors; during the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an active interference rejection controller to suppress load power fluctuations to ensure the stability of the system voltage.

[0057] According to a third aspect of the embodiments of the present invention,

[0058] An electronic device is provided, comprising:

[0059] processor;

[0060] a memory for storing processor-executable instructions;

[0061] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0062] According to a fourth aspect of the embodiments of the present invention,

[0063] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0064] In this embodiment, by analyzing the crane's operating conditions and the aging characteristics of supercapacitors, the charge and discharge power distribution of each supercapacitor group is dynamically adjusted to avoid overcharging and overdischarging, balance the losses of each supercapacitor group, and thus extend the overall service life. During the luffing angle reduction process, regenerative braking energy is preferentially stored in the supercapacitors. Any energy exceeding the supercapacitor's charge capacity is stored in the high-temperature superconducting energy storage inductor. Energy from the high-temperature superconducting energy storage inductor is then replenished to the supercapacitor at the appropriate time, maximizing regenerative braking energy recovery. During the luffing angle increase process, the output power of the main power supply and supercapacitors is intelligently distributed. Sliding mode control and active disturbance rejection control technologies are used to suppress load power fluctuations, ensure system voltage stability, and improve crane operation reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of the flow of a method for energy management during the luffing process of a lifting machinery based on supercapacitors according to an embodiment of the present invention;

[0066] Figure 2 The figure is a structural diagram of an energy management system for the luffing process of a lifting machinery based on supercapacitors according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 making creative efforts shall fall within the scope of protection of the present invention.

[0068] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0069] Figure 1 FIG. 1 is a flow chart of an energy management method for a lifting machinery luffing process based on a supercapacitor according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0070] S101. Analyze the luffing angle rate and load weight of the lifting machinery using a neural network prediction model, calculate an instantaneous power curve during the luffing process, use the integral of the instantaneous power curve on the time axis as the theoretical energy demand for the luffing process, group supercapacitors based on the theoretical energy demand and calculate an initial grouping coefficient. Determine the capacity decay rate as the ratio of the initial grouping coefficient to the historical number of cycles. Calculate the remaining life coefficient of each group of supercapacitors based on the capacity decay rate, and calculate a dynamic grouping coefficient based on the initial grouping coefficient and the remaining life coefficient.

[0071] S102. Establishing a fuzzy controller based on the dynamic grouping coefficients, using the terminal voltage deviations and terminal voltage change rates of each group of supercapacitors as inputs to the fuzzy controller. Using the fuzzy controller, different combinations of the terminal voltage deviations and terminal voltage change rates are mapped into charge and discharge power correction values. A recursive compensation model is established based on the charge and discharge power correction values to perform real-time correction on the dynamic grouping coefficients to obtain power allocation coefficients. The power allocation coefficients are used as the upper limits of the charge and discharge power of each group of supercapacitors. An adaptive PI controller is used to adjust the charge and discharge current of each group of supercapacitors, wherein the control parameters of the adaptive PI controller are adaptively related to the power allocation coefficients and the terminal voltage deviations.

[0072] S103. During the process of decreasing the amplitude angle, the regenerative braking power is compared with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged in proportion to the power distribution coefficient of each group of supercapacitors; during the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio between the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an active interference rejection controller to suppress load power fluctuations to ensure the stability of the system voltage.

[0073] During the reduction of the amplitude angle, the regenerative braking power is monitored in real time and compared with the rechargeable power of the supercapacitor bank, calculated based on the power distribution coefficient. When the regenerative braking power exceeds the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor. The high-temperature superconducting energy storage inductor is a highly efficient energy storage device used to temporarily store energy that cannot be immediately absorbed by the supercapacitor. Simultaneously, the power distribution coefficient of the supercapacitor bank is monitored in real time. If the rechargeable power of a particular supercapacitor bank is detected to be higher than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed to each supercapacitor bank in proportion to the power distribution coefficient.

[0074] In an optional embodiment, a neural network prediction model is used to analyze the luffing angle rate and the hoisting weight of the lifting machinery, and an instantaneous power curve of the luffing process is calculated. The integral value of the instantaneous power curve on the time axis is used as the theoretical energy demand of the luffing process. The supercapacitors are grouped based on the theoretical energy demand and the initial grouping coefficients are calculated, including:

[0075] A neural network prediction model is constructed, in which the input layer receives the amplitude variation angle rate signal and the hoisting weight signal, and the output layer outputs the instantaneous power curve of the amplitude variation process;

[0076] The amplitude variation angle rate signal and the hoist weight signal are sampled to obtain a discrete data sequence, time domain noise reduction and feature extraction are performed on the discrete data sequence to obtain time domain features, the extracted time domain features are normalized according to the maximum and minimum method to obtain training samples, a weighted loss function including an instantaneous power prediction error term and a model complexity penalty term is constructed, and the neural network prediction model is iteratively trained using a momentum-based adaptive optimization method until the weighted loss function converges to a preset threshold, thereby obtaining a trained neural network prediction model;

[0077] The instantaneous power curve output by the trained neural network prediction model is segmented and integrated on the time axis. The time window length of the integration interval is adaptively adjusted according to the changing trend of the amplitude variation angle rate signal. The time window length is positively correlated with the rate of change of the amplitude variation angle rate signal. A dual-loop state observer is designed to filter the integration result to obtain the theoretical energy demand of the amplitude variation process.

[0078] The theoretical energy demand is divided by the rated energy storage capacity of a single group of supercapacitors to obtain a ratio, the basic group number is calculated according to the ratio, and a redundancy coefficient is set based on the system redundancy requirement, and the basic group number is multiplied by the redundancy coefficient and rounded up to obtain the actual group number;

[0079] The terminal voltage signals of each group of supercapacitors are collected in real time. At the same time, the surface temperature signals of each group of supercapacitors are collected using a temperature sensor array. A Coulomb counting model is established based on the terminal voltage signals to calculate the state of charge of each group of supercapacitors.

[0080] A fuzzy inferencer based on a triangular membership function is constructed. The inter-group deviation value of the terminal voltage signal, the maximum temperature difference value of the temperature signal, and the imbalance degree of the charge state are used as input variables of the fuzzy inferencer. An inference mechanism including fuzzification, rule reasoning, and defuzzification is established. The fuzzy inferencer outputs a grouping coefficient correction value, and a baseline grouping coefficient is determined according to the initial operating state of the system. The grouping coefficient correction value is added to the baseline grouping coefficient to obtain the initial grouping coefficient.

[0081] For example, a neural network model is first constructed to predict instantaneous power during the luffing process. The model's inputs are the luffing angle rate and the load weight, and its output is an instantaneous power curve. To train this model, a large amount of luffing angle rate and load weight data must be collected, for example, every 0.1 seconds for 10 minutes. This data constitutes a discrete data sequence.

[0082] The collected discrete data sequence is then preprocessed. This involves removing noise and extracting features. For example, a sliding average filter can be used to remove high-frequency noise and extract time-domain features such as mean, variance, and peak. To eliminate the influence of different feature dimensions, the extracted features are normalized, for example, using the maximum-minimum method to scale the feature values to between 0 and 1. This processed data serves as training samples for the neural network model.

[0083] To optimize the performance of the neural network model, a weighted loss function is designed that incorporates the instantaneous power prediction error and a model complexity penalty. For example, mean squared error can be used as the prediction error term, and L2 regularization can be used as the model complexity penalty term. A momentum-based adaptive optimization method, such as the Adam optimizer, is used to iteratively train the neural network model until the weighted loss function converges to a preset threshold, such as 0.01.

[0084] The trained neural network model can be used to predict the instantaneous power curve of the amplitude variation process. To calculate the theoretical energy demand of the amplitude variation process, the instantaneous power curve needs to be integrated on the time axis. The time window length of the integration interval needs to be adaptively adjusted according to the changing trend of the amplitude variation angle rate. For example, when the amplitude variation angle rate changes rapidly, a shorter time window, such as 0.5 seconds, is used; when the amplitude variation angle rate changes slowly, a longer time window, such as 2 seconds, is used. To improve the accuracy and stability of the integration result, a dual-loop state observer is designed to filter the integration result.

[0085] Based on the calculated theoretical energy demand and the rated energy storage capacity of a single supercapacitor bank, the number of basic supercapacitor groups can be calculated. If the theoretical energy demand is 1000 joules and the rated energy storage capacity of a single supercapacitor bank is 200 joules, the number of basic supercapacitor groups is 5. Considering system redundancy requirements, a redundancy factor, such as 1.2, needs to be set. Multiplying the number of basic supercapacitor groups by the redundancy factor and rounding up yields an actual number of groups of 6.

[0086] To monitor the state of supercapacitors in real time, it is necessary to collect terminal voltage and temperature signals from each group of supercapacitors. For example, the terminal voltage signal can be collected every 1 second, and the temperature signal every 5 seconds. Based on the terminal voltage signals, a coulomb counting model can be established to calculate the state of charge of each group of supercapacitors.

[0087] To further optimize the grouping of supercapacitors, a fuzzy inference engine based on a triangular membership function was constructed. The input variables of this fuzzy inference engine include the inter-group deviation of the terminal voltage signal, the maximum temperature difference of the temperature signal, and the state-of-charge imbalance. If the inter-group deviation of the terminal voltage signal is 1 volt, the maximum temperature difference of the temperature signal is 5 degrees Celsius, and the state-of-charge imbalance is 0.1, the fuzzy inference engine will output a grouping coefficient correction value of 0.2. The baseline grouping coefficient in the initial operating state of the system, for example, 1, is added to the grouping coefficient correction value to obtain an initial grouping coefficient of 1.2.

[0088] In this embodiment, by accurately predicting energy demand and adaptively adjusting grouping, energy waste can be minimized and energy utilization efficiency can be improved. Redundant design and status monitoring can effectively avoid single points of failure, improving system reliability and stability. Balancing the load across each group of supercapacitors can prevent overcharging or over-discharging of individual supercapacitors, thereby extending their service life.

[0089] In an optional embodiment, the ratio of the initial grouping coefficient to the historical cycle number is determined as the capacity decay rate, and the remaining life coefficient of each group of supercapacitors is calculated according to the capacity decay rate. The dynamic grouping coefficient is obtained based on the calculation of the initial grouping coefficient and the remaining life coefficient, including:

[0090] Collecting a charge and discharge cycle count value of the supercapacitor as a historical cycle number, determining a ratio of the initial grouping coefficient to the historical cycle number as a benchmark capacity decay rate, and adaptively adjusting a sliding time window length according to a numerical value of the benchmark capacity decay rate, wherein the sliding time window length decreases as the benchmark capacity decay rate increases;

[0091] continuously collecting a real-time terminal voltage monitoring signal and a real-time temperature monitoring signal of the supercapacitor within the sliding time window length, calculating a real-time terminal voltage deviation matrix based on the real-time terminal voltage monitoring signal, calculating a real-time temperature distribution uniformity coefficient based on the real-time temperature monitoring signal, calculating a real-time capacity decay rate according to the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient, and determining a ratio of the real-time capacity decay rate to a reference capacity decay rate as a decay rate correction coefficient;

[0092] A Weibull distribution life prediction model is established based on the decay rate correction coefficient, and a characteristic life parameter is calculated. The characteristic life parameter is proportional to the inverse of the decay rate correction coefficient, and the proportional coefficient is adaptively adjusted according to the degree of fluctuation of the decay rate correction coefficient; and a maximum likelihood estimation method is used to calculate the shape parameter of the Weibull distribution life prediction model.

[0093] Inputting the characteristic life parameter and shape parameter into a preset Weibull distribution prediction equation to calculate the remaining life coefficient, and calculating the product of the initial grouping coefficient and the remaining life coefficient to obtain an intermediate dynamic grouping coefficient;

[0094] The difference in real-time capacity decay rates at adjacent sampling moments is calculated to obtain the capacity decay change rate. The normalized value of the capacity decay change rate is used as a smoothing coefficient. The intermediate dynamic grouping coefficient is subjected to a first-order low-pass filtering process using the smoothing coefficient. The time constant of the filtering process is proportional to the smoothing coefficient. The result after filtering is output as the final dynamic grouping coefficient.

[0095] For example, first, the charge and discharge cycle count value of the supercapacitor is collected. For example, the historical cycle count of the supercapacitor is obtained by reading the counter chip built into the supercapacitor or by using an external counter to record the number of charge and discharge cycles. Assume that the historical cycle count of a supercapacitor is 1000 times.

[0096] The ratio of the initial grouping coefficient to the historical cycle count is then used as the baseline capacity decay rate. The initial grouping coefficient is used to group supercapacitors based on their initial performance indicators and can be set based on actual conditions. Assuming the initial grouping coefficient is 1, the baseline capacity decay rate is 1 / 1000 = 0.001.

[0097] The sliding time window length is adaptively adjusted based on the baseline capacity decay rate. A higher baseline capacity decay rate indicates a more advanced supercapacitor aging process and requires more frequent monitoring, so the sliding time window length should be shorter. For example, when the baseline capacity decay rate is 0.001, the sliding time window length is set to 10 minutes; when the baseline capacity decay rate is 0.01, the sliding time window length is set to 1 minute.

[0098] The real-time terminal voltage and temperature monitoring signals of the supercapacitor are continuously collected within the sliding time window. For example, the terminal voltage and temperature data are collected every 1 second for 10 minutes.

[0099] The real-time terminal voltage deviation matrix is calculated based on the real-time terminal voltage monitoring signal. For example, the difference between the terminal voltage at each moment and the average terminal voltage is calculated to form a deviation matrix.

[0100] The real-time temperature distribution uniformity coefficient is calculated based on the real-time temperature monitoring signal. For example, the ratio of the difference between the highest temperature and the lowest temperature to the average temperature is calculated as the temperature distribution uniformity coefficient.

[0101] The real-time capacity decay rate is calculated based on the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient. For example, the real-time capacity decay rate is obtained by taking the weighted sum of the root mean square value of the terminal voltage deviation matrix and the temperature distribution uniformity coefficient. Assume that the calculated real-time capacity decay rate is 0.0012.

[0102] The ratio of the real-time capacity decay rate to the reference capacity decay rate is determined as the decay rate correction factor. In this example, the decay rate correction factor is 0.0012 / 0.001=1.2.

[0103] A Weibull distribution life prediction model is established based on the decay rate correction coefficient to calculate the characteristic life parameter. The characteristic life parameter is proportional to the inverse of the decay rate correction coefficient, and the proportionality factor is adaptively adjusted based on the degree of fluctuation of the decay rate correction coefficient. For example, when the decay rate correction coefficient fluctuates greatly, the proportionality factor is smaller; when the decay rate correction coefficient fluctuates less, the proportionality factor is larger. Assume that the calculated characteristic life parameter is 833.

[0104] At the same time, the maximum likelihood estimation method is used to calculate the shape parameter of the Weibull distribution life prediction model. Assume that the calculated shape parameter is 2.

[0105] The characteristic life parameters and shape parameters are input into the preset Weibull distribution prediction equation to calculate the remaining life coefficient. Assume that the calculated remaining life coefficient is 0.9.

[0106] The intermediate dynamic grouping coefficient is calculated by multiplying the initial grouping coefficient and the remaining life coefficient. In this example, the intermediate dynamic grouping coefficient is 1×0.9=0.9.

[0107] The capacity decay rate of change is calculated by calculating the difference between the real-time capacity decay rates at adjacent sampling moments. For example, if the current real-time capacity decay rate is 0.0012 and the previous real-time capacity decay rate is 0.0011, the capacity decay rate of change is 0.0001.

[0108] The normalized value of the capacity decay rate is used as the smoothing coefficient. For example, the capacity decay rate is divided by the maximum capacity decay rate to obtain the normalized value, which is used as the smoothing coefficient. Assume that the smoothing coefficient is 0.1.

[0109] Apply a first-order low-pass filter to the intermediate dynamic grouping coefficients using a smoothing coefficient. The filtering time constant is proportional to the smoothing coefficient. For example, the time constant can be set to 10 times the smoothing coefficient. The filtered result is output as the final dynamic grouping coefficient. Assume the final dynamic grouping coefficient is 0.89.

[0110] In this embodiment, dynamic grouping can be used to avoid combining supercapacitors with different degrees of aging, thereby preventing premature failure of supercapacitors with less severe aging and extending the overall service life of the supercapacitors. Dynamic grouping can promptly detect and isolate failed supercapacitors to prevent them from affecting the operation of the entire system, thereby improving system reliability. Dynamic grouping can also be used to combine supercapacitors with similar performance, thereby improving the overall performance of the system, such as increasing the system's charge and discharge efficiency and power density.

[0111] In an optional embodiment, a fuzzy controller based on the dynamic grouping coefficient is established, and the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors are used as inputs of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped to charge and discharge power correction values by the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient, including:

[0112] The terminal voltage signal of each group of supercapacitors is collected and digitally filtered to calculate the system average terminal voltage. The difference between the terminal voltage of each group of supercapacitors and the system average terminal voltage is used as the terminal voltage deviation. The terminal voltage deviation is subjected to sliding average filtering to obtain a filtered terminal voltage deviation. The filtered rear-end voltage deviations at adjacent sampling moments are differentially calculated and combined with the sampling period to obtain a terminal voltage change rate. The terminal voltage change rate is clipped to obtain a clipped terminal voltage change rate.

[0113] The filtered terminal voltage deviation is divided into preset intervals to establish a first linguistic variable set, and a dynamically adjustable first fuzzy subset boundary is set for each interval. At the same time, the terminal voltage change rate after clipping is divided into preset intervals to establish a second linguistic variable set, and the boundary value is adaptively adjusted according to the change trend of the terminal voltage deviation to obtain a second fuzzy subset boundary;

[0114] A triangular membership function with optimized overlap is used to perform fuzzy processing on the terminal voltage deviation after filtering to obtain a first membership value, and an asymmetric trapezoidal membership function with configurable slope is used to perform fuzzy processing on the terminal voltage change rate after limiting to obtain a second membership value;

[0115] A fuzzy rule base is constructed based on the first membership value, the second membership value, the first language variable set, and the second language variable set, and the rules are optimized online using a variable structure fuzzy control strategy to obtain an optimized fuzzy rule set. Fuzzy reasoning is performed on the optimized fuzzy rule set using a fuzzy sub-rule parallel reasoning method, and the reasoning results of each sub-rule are fuzzily synthesized using adaptive weights to obtain a synthesized fuzzy output.

[0116] Defuzzifying the integrated fuzzy output using a centroid method to obtain an initial power correction value, and dynamically limiting the initial power correction value in combination with system state parameters to obtain a charge and discharge power correction value;

[0117] A recursive compensation model is constructed by taking the dynamic grouping coefficient as the initial value and the charge and discharge power correction value as the target value. The recursive compensation model dynamically adjusts the iteration step size according to the deviation between the compensation target value and the current power coefficient. The recursive compensation model is used to perform iterative calculations. When the change in the power coefficient between two adjacent iterations is less than a preset change threshold, the iteration is stopped to obtain the compensated grouping coefficient. The values of the compensated grouping coefficients that are less than the dead zone threshold are set to zero, and the values that are greater than the dead zone threshold are normalized to obtain the power allocation coefficient.

[0118] Exemplarily, the terminal voltage signal of each group of supercapacitors is first collected, and a digital filter (finite impulse response filter or infinite impulse response filter) is used to remove noise and interference. Specifically, a second-order Butterworth low-pass filter with a cutoff frequency of 10Hz is used to filter the collected voltage signal. The average value of the terminal voltage of all supercapacitor groups is calculated as the system average terminal voltage. The terminal voltage of each group of supercapacitors is compared with the calculated system average terminal voltage to obtain a difference, which is the terminal voltage deviation. In order to further smooth the terminal voltage deviation, a sliding average filter is performed on it. For example, a sliding average filter with a window width of 5 is used to filter the terminal voltage deviation. The difference between the filtered terminal voltage deviations of two adjacent sampling moments is calculated, and the difference is divided by the sampling period (0.1 seconds) to obtain the terminal voltage change rate. In order to avoid the influence of outliers, the terminal voltage change rate is limited. The limit range is set to -10V / s to 10V / s. If the calculated terminal voltage change rate exceeds this range, it is limited to this range.

[0119] The filtered terminal voltage deviation is divided into several intervals, such as [-3V, -1V], [-1V, 1V], and [1V, 3V], and a first linguistic variable set is established, such as {negative large, negative small, zero, positive small, positive large}. Dynamically adjustable first fuzzy subset boundaries are set for each interval. For example, the boundary of the "negative large" fuzzy subset can be set to [-4, -2], the boundary of the "negative small" fuzzy subset can be set to [-2, 0], and so on. These boundary values can be dynamically adjusted based on the system operating status. Simultaneously, the terminal voltage change rate after clipping is also divided into several intervals, such as [-10V / s, -5V / s], [-5V / s, 0V / s], [0V / s, 5V / s], and [5V / s, 10V / s], and a second linguistic variable set is established, such as {negative large, negative small, zero, positive small, positive large}. The boundary value of the second fuzzy subset is adaptively adjusted based on the changing trend of the terminal voltage deviation (e.g., increasing or decreasing). For example, if the terminal voltage deviation continues to increase, the boundary value of the "positive" fuzzy subset can be adjusted to a larger value, such as [8V / s, 10V / s].

[0120] The filtered terminal voltage deviation is fuzzified using a triangular membership function with optimized overlap to obtain a first membership value. For example, if the filtered terminal voltage deviation is 2V, the boundary of the "positive small" fuzzy subset is [1, 3], then its membership value is (3-2) / (3-1)=0.5. The slope-configurable asymmetric trapezoidal membership function is used to fuzzify the terminal voltage change rate after clipping to obtain a second membership value. For example, if the terminal voltage change rate after clipping is 6V / s, the boundary of the "positive large" fuzzy subset is [5, 10], the left slope is 1, and the right slope is 0.5, then its membership value is (10-6) / (10-5)×0.5=0.4

[0121] A fuzzy rule base is constructed based on the first membership value, the second membership value, the first language variable set, and the second language variable set. For example, a rule may be: if the terminal voltage deviation is "positive and large" and the terminal voltage change rate is "positive and large", then the power correction value is "negative and large". The variable structure fuzzy control strategy is used to optimize the rules online, for example, the weights of the rules are adjusted or new rules are added according to the system operation status. The optimized fuzzy rule set is obtained. The fuzzy sub-rule parallel reasoning method is used to perform fuzzy reasoning on the optimized fuzzy rule set, for example, the Mamdani reasoning method is used. The reasoning results of each sub-rule are fuzzy-synthesized by adaptive weights to obtain a synthesized fuzzy output. For example, the weight of each sub-rule is dynamically adjusted according to its activation strength.

[0122] The integrated fuzzy output is defuzzified using the centroid method to obtain an initial power correction value. For example, if the fuzzy output is a triangular fuzzy number (0, 0.5, 1), its defuzzified value is (0 + 0.5 + 1) / 3 = 0.5. The initial power correction value is dynamically limited based on system state parameters (e.g., the supercapacitor's state of charge) to obtain the charge and discharge power correction value. For example, if the initial power correction value is 0.6, but the supercapacitor's state of charge is already high, the power correction value can be limited to 0.4.

[0123] A recursive compensation model is constructed using the dynamic grouping coefficient as the initial value and the charge / discharge power correction value as the target value. This model dynamically adjusts the iteration step size based on the deviation between the compensation target value and the current power coefficient. For example, if the deviation is large, a larger iteration step size is used; if the deviation is small, a smaller iteration step size is used. This model is used for iterative calculations, and the iteration is terminated when the change in the power coefficient between two consecutive iterations is less than a preset change threshold (0.01). The compensated grouping coefficients are obtained by setting values less than the dead zone threshold (0.05) to zero, and normalizing values greater than the dead zone threshold to obtain the power allocation coefficients. If the compensated grouping coefficients are [0.1, 0.2, 0.3], the normalized power allocation coefficients are [0.1 / 0.6, 0.2 / 0.6, 0.3 / 0.6] = [0.167, 0.333, 0.5].

[0124] In this embodiment, by dynamically adjusting the power distribution coefficient, the terminal voltages of each supercapacitor group can be balanced more quickly, shortening the balancing time. The use of fuzzy control and a recursive compensation model allows for more precise control of power distribution, thereby improving the accuracy of terminal voltage balancing. By balancing the terminal voltages of each supercapacitor group, overcharging or over-discharging of individual supercapacitors can be avoided, thereby extending the service life of the entire supercapacitor group.

[0125] In an optional embodiment, defuzzifying the integrated fuzzy output using an improved center of gravity method to obtain an initial power correction value, and dynamically limiting the initial power correction value in combination with system state parameters to obtain a charge and discharge power correction value includes:

[0126] The fuzzy control output is defuzzified by calculating the membership degree of each rule, the discrete points of the output universe, and the area weight of the output membership function at the discrete points, and performing weighted summation based on the product of the membership degree and the area weight and the discrete point value to obtain an initial power correction value; wherein the area weight is calculated using piecewise linear approximation, and for a trapezoidal area, the product of the mean of the membership degrees of adjacent discrete points and the distance between the discrete points is used, and for a rectangular area, the product of the membership degree of the discrete point and the distance between the discrete points is used;

[0127] Collecting system operating state parameters, including state of charge, terminal voltage, and temperature, calculating a deviation between the state of charge and a desired state of charge to obtain a state of charge deviation, calculating a deviation between the terminal voltage and a rated terminal voltage to obtain a terminal voltage deviation, and calculating a difference between the temperature and a temperature threshold to obtain a temperature deviation;

[0128] establishing a first clipping function based on the state of charge deviation, establishing a second clipping function based on the terminal voltage deviation, and establishing a third clipping function based on the temperature deviation, and taking the product of the initial power correction value and the first clipping function, the second clipping function, and the third clipping function as an intermediate power correction value;

[0129] The first limiting function, the second limiting function and the third limiting function are subjected to piecewise linearization processing, and the product of the limiting function after the piecewise linearization processing and the initial power correction value is used as the final charge and discharge power correction value for adjusting the charge and discharge power of the system.

[0130] For example, a fuzzy controller is first constructed. Its inputs are factors that affect battery charge and discharge power, such as state-of-charge deviation and temperature deviation. Its output is a correction to the charge and discharge power. The fuzzy controller's rule base is based on expert experience or experimental data. For example, when the state-of-charge deviation is large and the temperature is high, the charging power should be reduced.

[0131] Next, the fuzzy controller's output is defuzzified to obtain the initial power correction value. Specifically, the output domain is divided into a number of discrete points, and the membership degree of each rule under the current input is calculated, as well as the area weight of the output membership function at each discrete point. The area weight is calculated using a piecewise linear approximation method: for trapezoidal areas, the product of the mean of the membership degrees of adjacent discrete points and the distance between the discrete points is used; for rectangular areas, the product of the membership degrees of discrete points and the distance between the discrete points is used. The initial power correction value is then obtained by weighted summing the product of the membership degrees and the area weight, along with the discrete point values.

[0132] For example, suppose the output domain is [-1, 1], and the discrete points are -1, -0.5, 0, 0.5, and 1. The output membership function of a rule is a triangle with a center point of 0.5 and a base width of 1. The membership degree of this rule under the current input is 0.8. Therefore, the area weight between the discrete points 0 and 0.5 is (0 + 0.8) / 2 × 0.5 = 0.2, and the area weight between the discrete points 0.5 and 1 is (0.8 + 0) / 2 × 0.5 = 0.2.

[0133] Then, the system operating parameters, including state of charge, terminal voltage, and temperature, are collected. The deviation between the state of charge and the desired state of charge, the deviation between the terminal voltage and the rated terminal voltage, and the difference between the temperature and the temperature threshold are calculated.

[0134] Assuming the current SOC is 80% and the desired SOC is 90%, the SOC deviation is -10%. If the front-end voltage is 4.1V and the rated terminal voltage is 4.2V, the terminal voltage deviation is -0.1V. If the current temperature is 30°C and the temperature threshold is 40°C, the temperature deviation is -10°C.

[0135] Next, based on the state of charge deviation, terminal voltage deviation, and temperature deviation, a limiting function is established to map the deviation to a limited range, such as [0, 1].

[0136] Assume that the limiting function of the state of charge deviation is: when the deviation is less than -20%, the output is 0; when the deviation is greater than 20%, the output is 1; between -20% and 20%, the output changes linearly.

[0137] The product of the initial power correction value and the three limiting functions is used as the intermediate power correction value. The three limiting functions are piecewise linearized, and the product of the piecewise linearized limiting function and the initial power correction value is used as the final charge and discharge power correction value, which is used to adjust the system's charge and discharge power.

[0138] Assuming the initial power correction value is 0.5, the state of charge deviation limit function output is 0.8, the terminal voltage deviation limit function output is 0.9, and the temperature deviation limit function output is 0.7, the intermediate power correction value is 0.5×0.8×0.9×0.7=0.252.

[0139] In this embodiment, dynamic limiting can prevent battery damage from overcharging, over-discharging, or overheating, thereby extending the battery's service life. Fuzzy control allows for precise power regulation based on the battery's real-time status, improving battery charge and discharge efficiency. The fuzzy controller's rule base is easy to understand and modify, eliminating the need for complex mathematical models and reducing the complexity of control system development.

[0140] In an optional embodiment, during the increase of the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, and realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller. An active disturbance rejection controller is used to suppress load power fluctuations to ensure the stability of the system voltage.

[0141] Obtain the real-time state of charge, terminal voltage, and bus voltage of each group of supercapacitors during the process of increasing the amplitude angle, as well as the power distribution coefficient corresponding to each group of supercapacitors;

[0142] Establishing an intelligent power distributor based on the power distribution coefficient, taking the real-time state of charge, terminal voltage and bus voltage as system state variables, and inputting the main power supply output power and the output power of each group of supercapacitors as control variables into the intelligent power distributor;

[0143] Constructing an objective function in the intelligent power distributor, solving the objective function using a dynamic programming algorithm, and obtaining an output power ratio between the main power supply and each group of supercapacitors;

[0144] Constructing a sliding surface according to the deviation between the target value and the actual value of the output power ratio, and designing a control law of the sliding mode controller based on the sliding surface, wherein the control law includes an equivalent control term consisting of a system dynamic estimation term and a state tracking deviation term, and a switching control term determined by a reaching law parameter;

[0145] Outputting control instructions for the main power supply and each group of supercapacitors through the sliding mode controller, and collecting load power fluctuation information after executing the control instructions, obtaining system output and tracking error through an extended state observer based on the load power fluctuation information, and constructing a state observation equation for the active disturbance rejection controller based on the system output and tracking error;

[0146] The output value of the state observation equation is compared with the reference input to obtain a compensation control quantity, and the control instructions of the main power supply and each group of supercapacitors are corrected according to the compensation control quantity to suppress load power fluctuations. The corrected control instructions are applied to the main power supply and each group of supercapacitors to achieve power collaborative control and ensure system voltage stability. At the same time, the controlled system state is input into the intelligent power distributor as a new state variable for iterative optimization.

[0147] For example, the real-time state of charge, terminal voltage, and bus voltage of each supercapacitor group are first acquired during the increase in the amplitude angle, along with the corresponding power allocation coefficient for each supercapacitor group. Assume there are two supercapacitor groups, with states of charge of 80% and 70%, terminal voltages of 2.5V and 2.4V, and a bus voltage of 2.6V. The power allocation coefficient for the first supercapacitor group is 0.6, and for the second group is 0.4. This data can be measured in real time by sensors and transmitted to the intelligent power distributor.

[0148] Next, an intelligent power distributor is established based on the acquired power allocation coefficients. The real-time state of charge, terminal voltage, and bus voltage of the supercapacitors are used as system state variables, and the main power supply output power and the output power of each supercapacitor group are input into the intelligent power distributor as control variables.

[0149] The objective function is constructed in the intelligent power distributor. The design goal of the objective function is to minimize the output power of the main power supply while meeting the requirements of system voltage stability and taking into account the charge state balance of each group of supercapacitors.

[0150] A dynamic programming algorithm is used to solve the objective function. This algorithm breaks the problem down into multiple subproblems and finds the optimal solution to the overall problem by finding the optimal solution to each subproblem. For example, time can be discretized to find the optimal output power ratio between the main power supply and each set of supercapacitors within each time step. Suppose the dynamic programming algorithm calculates that the main power supply output power is 10kW, the first set of supercapacitors output power is 6kW, and the second set of supercapacitors output power is 4kW.

[0151] A sliding surface is constructed based on the deviation between the target and actual output power ratios. The sliding surface is a function that describes the deviation in the system state. The goal of the sliding mode controller is to make the system state slide along the sliding surface to the desired state. For example, if the actual output power of the first supercapacitor bank is 5 kW, a deviation of 1 kW from the target value of 6 kW, the value on the sliding surface will reflect this deviation.

[0152] The control law of the sliding mode controller is designed based on the sliding surface. The control law consists of two components: an equivalent control term and a switching control term. The equivalent control term compensates for the effects of system dynamics, while the state tracking deviation term eliminates state deviations. The switching control term ensures that the system state moves along the sliding surface. The reaching law parameters determine how quickly the system state approaches the sliding surface.

[0153] The sliding mode controller outputs control commands for the main power supply and each set of supercapacitors. For example, the control command might be to increase the main power supply output by 1kW, the first set of supercapacitors by 0.6kW, and the second set of supercapacitors by 0.4kW.

[0154] Collect load power fluctuation information after executing control instructions. Assume that the load power fluctuation is 2kW.

[0155] Based on the load power fluctuation information, the system output and tracking error are obtained through the extended state observer. The extended state observer can estimate the unmodeled dynamics and disturbances in the system.

[0156] The state observation equation of the ADRC is constructed based on the system output and tracking error. The state observation equation describes the evolution of the system state.

[0157] The output value of the state observation equation is compared with the reference input to obtain the compensation control variable. The compensation control variable is used to offset the impact of load power fluctuations on the system.

[0158] The control instructions for the main power supply and each set of supercapacitors are modified based on the compensation control amount. For example, if the compensation control amount is -1kW, the control instructions for the main power supply are modified to decrease by 1kW.

[0159] The modified control instructions are applied to the main power supply and each set of supercapacitors to achieve coordinated power control and ensure system voltage stability. The controlled system state is input as a new state variable into the intelligent power distributor for iterative optimization, continuously adjusting the power distribution strategy to adapt to changing load demands and amplitude modulation angles.

[0160] In this embodiment, an auto-disturbance rejection controller suppresses load power fluctuations, ensuring system voltage stability and effectively preventing the destabilizing effects of voltage fluctuations on the system, thereby improving system reliability and safety. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio between the main power supply and each group of supercapacitors, achieving efficient energy utilization, reducing energy consumption, and extending the service life of the supercapacitors. The fast response characteristics of the sliding mode controller enable rapid tracking of target power distribution, enabling the system to quickly adapt to changes in the amplitude angle, thereby improving the system's dynamic performance and adaptability.

[0161] In an optional implementation, the formula for constructing the objective function in the intelligent power divider is as follows:

[0162]

[0163] Where J represents the objective function, T represents the total time, w1 represents the weight of the bus voltage deviation term, V bus(t) Represents the bus voltage, that is, the actual voltage value of the system at time t, V ref(t) represents the bus reference voltage, w2 represents the weight of the supercapacitor power distribution deviation term, N represents the number of supercapacitor groups, P SC，i(t) represents the output power of the i-th group of supercapacitors, k i represents the power allocation coefficient of the i-th group of supercapacitors, w3 represents the weight of the main power supply power utilization deviation term, P main(t) Indicates the actual power value provided by the main power supply at time t, P main，max Indicates the maximum output power of the main power supply.

[0164] The goal of an intelligent power divider is to optimize the output of each power source in the system to maintain a stable bus voltage and efficiently utilize each power source. To achieve this goal, an objective function needs to be constructed to evaluate the advantages and disadvantages of different power allocation strategies.

[0165] First, you need to determine the type and characteristics of each power source in the system. For example, the system may include a main power supply, a supercapacitor bank, and so on. For the main power supply, you need to determine its maximum output power. For the supercapacitor bank, you need to determine the capacity, charge and discharge rate, and other parameters of each supercapacitor bank. Suppose the system consists of a main power supply and two supercapacitor banks. The main power supply has a maximum output power of 100kW, and the capacities of the two supercapacitor banks are 10kWh and 20kWh, respectively.

[0166] Next, we need to determine the reference value of the bus voltage. The bus voltage is the supply voltage for each load in the system and must be maintained within a certain range to ensure normal operation of the loads. Assume the bus reference voltage is 500V.

[0167] Next, we need to determine the weights for each term in the objective function. An objective function typically contains multiple terms, such as bus voltage deviation, supercapacitor power allocation deviation, and main power supply power utilization deviation. The weight of each term reflects its importance in the objective function. Assume that the bus voltage deviation has a weight of 0.5, the supercapacitor power allocation deviation has a weight of 0.3, and the main power supply power utilization deviation has a weight of 0.2.

[0168] The objective function value can then be calculated based on the determined power supply characteristics, bus voltage reference value, and objective function weights. For example, at a certain moment, the system bus voltage is 490V, the output power of the two supercapacitor groups is 10kW and 20kW, respectively, and the main power supply output power is 70kW. The bus voltage deviation is 10V, and the supercapacitor power allocation deviation is calculated based on the pre-set power allocation coefficient, assuming it is 1kW and 2kW, respectively. The main power supply power utilization deviation is 30kW. Based on the set weights, the objective function value can be calculated.

[0169] Finally, an optimization algorithm, such as a particle swarm optimization algorithm or a genetic algorithm, is used to find a power allocation strategy that minimizes the objective function. For example, an optimization algorithm can be used to find a power allocation strategy that minimizes the bus voltage deviation, the supercapacitor power allocation deviation, and the main power supply power utilization deviation.

[0170] Through the above steps, an intelligent power distributor can be constructed to achieve optimized control of each power source in the system.

[0171] In this embodiment, by optimizing and controlling the output of each power supply, the bus voltage can be effectively maintained stable, thereby ensuring the normal operation of the load. For example, when the load changes, the intelligent power divider can automatically adjust the output of each power supply to maintain the stability of the bus voltage. By optimizing the power distribution strategy, the utilization rate of the main power supply can be improved and the service life of the supercapacitor can be extended. For example, when the load is low, the intelligent power divider can reduce the output of the main power supply and use supercapacitors for power supply, thereby improving the utilization rate of the main power supply. Through intelligent power distribution, the reliability and stability of the system can be improved. For example, when a power supply fails, the intelligent power divider can automatically switch to another power supply to ensure the normal operation of the system.

[0172] Figure 2 FIG. 1 is a structural diagram of an energy management system for a lifting machinery luffing process based on a supercapacitor according to an embodiment of the present invention. Figure 2 As shown, the system includes:

[0173] The first unit is used to analyze the luffing angle rate and the lifting weight of the lifting machinery using a neural network prediction model, calculate an instantaneous power curve of the luffing process, use the integral value of the instantaneous power curve on the time axis as the theoretical energy demand of the luffing process, group supercapacitors based on the theoretical energy demand and calculate an initial grouping coefficient, determine the ratio of the initial grouping coefficient to the historical cycle number as the capacity decay rate, calculate the remaining life coefficient of each group of supercapacitors based on the capacity decay rate, and obtain a dynamic grouping coefficient based on the calculation of the initial grouping coefficient and the remaining life coefficient;

[0174] a second unit for establishing a fuzzy controller based on the dynamic grouping coefficient, using the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors as inputs of the fuzzy controller, mapping different combinations of the terminal voltage deviation and the terminal voltage change rate into charge and discharge power correction values through the fuzzy controller, establishing a recursive compensation model based on the charge and discharge power correction values to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient, using the power allocation coefficient as the upper limit of the charge and discharge power of each group of supercapacitors, and adjusting the charge and discharge current of each group of supercapacitors through an adaptive PI controller, wherein the control parameters of the adaptive PI controller are adaptively related to the power allocation coefficient and the terminal voltage deviation;

[0175] The third unit is used to compare the regenerative braking power with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient during the process of decreasing the amplitude angle. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged according to the proportion of the power distribution coefficient of each group of supercapacitors; during the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an active interference rejection controller to suppress load power fluctuations to ensure the stability of the system voltage.

[0176] According to a third aspect of the embodiments of the present invention,

[0177] An electronic device is provided, comprising:

[0178] processor;

[0179] a memory for storing processor-executable instructions;

[0180] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0181] According to a fourth aspect of the embodiments of the present invention,

[0182] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0183] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy management of the luffing process of a lifting machinery based on supercapacitors, characterized in that: include: A neural network prediction model is used to analyze the luffing angle rate and hoisting weight of the lifting machinery, and an instantaneous power curve of the luffing process is calculated. The integral value of the instantaneous power curve on the time axis is used as the theoretical energy demand of the luffing process. Supercapacitors are grouped based on the theoretical energy demand and an initial grouping coefficient is calculated. The ratio of the initial grouping coefficient to the historical number of cycles is determined as the capacity decay rate. The remaining life coefficient of each group of supercapacitors is calculated based on the capacity decay rate. The dynamic grouping coefficient is calculated based on the calculation of the initial grouping coefficient and the remaining life coefficient. A fuzzy controller based on the dynamic grouping coefficient is established, with the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors as inputs of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped into charge and discharge power correction values by the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient. The power allocation coefficient is used as the upper limit of the charge and discharge power of each group of supercapacitors. The charge and discharge current of each group of supercapacitors is adjusted by an adaptive PI controller, wherein the control parameters of the adaptive PI controller are adaptively related to the power allocation coefficient and the terminal voltage deviation. During the process of decreasing the amplitude angle, the regenerative braking power is compared with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged according to the proportion of the power distribution coefficient of each group of supercapacitors; during the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an active disturbance rejection controller to suppress load power fluctuations to ensure the stability of the system voltage; During the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor uses a dynamic programming algorithm to optimize the output power ratio between the main power supply and each group of supercapacitors. The sliding mode controller is used to achieve power coordination control between each group of supercapacitors and the main power supply. The active disturbance rejection controller is used to suppress load power fluctuations to ensure the stability of the system voltage. Obtain the real-time state of charge, terminal voltage, and bus voltage of each group of supercapacitors during the process of increasing the amplitude angle, as well as the power distribution coefficient corresponding to each group of supercapacitors; Establishing an intelligent power distributor based on the power distribution coefficient, taking the real-time state of charge, terminal voltage and bus voltage as system state variables, and inputting the main power supply output power and the output power of each group of supercapacitors as control variables into the intelligent power distributor; Constructing an objective function in the intelligent power distributor, solving the objective function using a dynamic programming algorithm, and obtaining an output power ratio between the main power supply and each group of supercapacitors; Constructing a sliding surface according to the deviation between the target value and the actual value of the output power ratio, and designing a control law of the sliding mode controller based on the sliding surface, wherein the control law includes an equivalent control term consisting of a system dynamic estimation term and a state tracking deviation term, and a switching control term determined by a reaching law parameter; Outputting control instructions for the main power supply and each group of supercapacitors through the sliding mode controller, and collecting load power fluctuation information after executing the control instructions, obtaining system output and tracking error through an extended state observer based on the load power fluctuation information, and constructing a state observation equation for the active disturbance rejection controller based on the system output and tracking error; The output value of the state observation equation is compared with the reference input to obtain a compensation control variable, and the control instructions of the main power supply and each group of supercapacitors are corrected according to the compensation control variable to suppress load power fluctuations. The corrected control instructions are applied to the main power supply and each group of supercapacitors to achieve power coordinated control and ensure system voltage stability. At the same time, the controlled system state is input into the intelligent power distributor as a new state variable for iterative optimization; The formula for constructing the objective function in the intelligent power divider is as follows: Where J represents the objective function, T represents the total time, w1 represents the weight of the bus voltage deviation term, V bus(t) Represents the bus voltage, that is, the actual voltage value of the system at time t, V ref(t) represents the bus reference voltage, w2 represents the weight of the supercapacitor power distribution deviation term, N represents the number of supercapacitor groups, P SC，i(t) represents the output power of the i-th group of supercapacitors, k i represents the power allocation coefficient of the i-th group of supercapacitors, w3 represents the weight of the main power supply power utilization deviation term, P main(t) Indicates the actual power value provided by the main power supply at time t, P main，max Indicates the maximum output power of the main power supply.

2. The method according to claim 1, characterized in that A neural network prediction model is used to analyze the luffing angle rate and hoisting weight of the lifting machinery, and the instantaneous power curve of the luffing process is calculated. The integral value of the instantaneous power curve on the time axis is used as the theoretical energy demand of the luffing process. Based on the theoretical energy demand, supercapacitors are grouped and the initial grouping coefficients are calculated, including: A neural network prediction model is constructed, in which the input layer receives the amplitude variation angle rate signal and the hoisting weight signal, and the output layer outputs the instantaneous power curve of the amplitude variation process; The amplitude variation angle rate signal and the hoist weight signal are sampled to obtain a discrete data sequence, time domain noise reduction and feature extraction are performed on the discrete data sequence to obtain time domain features, the extracted time domain features are normalized according to the maximum and minimum method to obtain training samples, a weighted loss function including an instantaneous power prediction error term and a model complexity penalty term is constructed, and the neural network prediction model is iteratively trained using a momentum-based adaptive optimization method until the weighted loss function converges to a preset threshold, thereby obtaining a trained neural network prediction model; The instantaneous power curve output by the trained neural network prediction model is segmented and integrated on the time axis. The time window length of the integration interval is adaptively adjusted according to the changing trend of the amplitude variation angle rate signal. The time window length is positively correlated with the rate of change of the amplitude variation angle rate signal. A dual-loop state observer is designed to filter the integration result to obtain the theoretical energy demand of the amplitude variation process. The theoretical energy demand is divided by the rated energy storage capacity of a single group of supercapacitors to obtain a ratio, the basic group number is calculated according to the ratio, and a redundancy coefficient is set based on the system redundancy requirement, and the basic group number is multiplied by the redundancy coefficient and rounded up to obtain the actual group number; The terminal voltage signals of each group of supercapacitors are collected in real time. At the same time, the surface temperature signals of each group of supercapacitors are collected using a temperature sensor array. A Coulomb counting model is established based on the terminal voltage signals to calculate the state of charge of each group of supercapacitors. A fuzzy inferencer based on a triangular membership function is constructed. The inter-group deviation value of the terminal voltage signal, the maximum temperature difference value of the temperature signal, and the imbalance degree of the charge state are used as input variables of the fuzzy inferencer. An inference mechanism including fuzzification, rule reasoning, and defuzzification is established. The fuzzy inferencer outputs a grouping coefficient correction value, and a baseline grouping coefficient is determined according to the initial operating state of the system. The grouping coefficient correction value is added to the baseline grouping coefficient to obtain the initial grouping coefficient.

3. The method according to claim 1, characterized in that The ratio of the initial grouping coefficient to the historical cycle number is determined as the capacity decay rate, and the remaining life coefficient of each group of supercapacitors is calculated according to the capacity decay rate. The dynamic grouping coefficient obtained based on the calculation of the initial grouping coefficient and the remaining life coefficient includes: Collecting a charge and discharge cycle count value of the supercapacitor as a historical cycle number, determining a ratio of the initial grouping coefficient to the historical cycle number as a benchmark capacity decay rate, and adaptively adjusting a sliding time window length according to a numerical value of the benchmark capacity decay rate, wherein the sliding time window length decreases as the benchmark capacity decay rate increases; continuously collecting a real-time terminal voltage monitoring signal and a real-time temperature monitoring signal of the supercapacitor within the sliding time window length, calculating a real-time terminal voltage deviation matrix based on the real-time terminal voltage monitoring signal, calculating a real-time temperature distribution uniformity coefficient based on the real-time temperature monitoring signal, calculating a real-time capacity decay rate according to the real-time terminal voltage deviation matrix and the real-time temperature distribution uniformity coefficient, and determining a ratio of the real-time capacity decay rate to a reference capacity decay rate as a decay rate correction coefficient; A Weibull distribution life prediction model is established based on the decay rate correction coefficient, and a characteristic life parameter is calculated. The characteristic life parameter is proportional to the inverse of the decay rate correction coefficient, and the proportional coefficient is adaptively adjusted according to the degree of fluctuation of the decay rate correction coefficient; and a maximum likelihood estimation method is used to calculate the shape parameter of the Weibull distribution life prediction model. Inputting the characteristic life parameter and shape parameter into a preset Weibull distribution prediction equation to calculate the remaining life coefficient, and calculating the product of the initial grouping coefficient and the remaining life coefficient to obtain an intermediate dynamic grouping coefficient; The difference in real-time capacity decay rates at adjacent sampling moments is calculated to obtain the capacity decay change rate. The normalized value of the capacity decay change rate is used as a smoothing coefficient. The intermediate dynamic grouping coefficient is subjected to a first-order low-pass filtering process using the smoothing coefficient. The time constant of the filtering process is proportional to the smoothing coefficient. The result after filtering is output as the final dynamic grouping coefficient.

4. The method according to claim 1, wherein A fuzzy controller based on the dynamic grouping coefficient is established, and the terminal voltage deviation and terminal voltage change rate of each group of supercapacitors are used as inputs of the fuzzy controller. Different combinations of the terminal voltage deviation and the terminal voltage change rate are mapped to charge and discharge power correction values by the fuzzy controller. A recursive compensation model is established based on the charge and discharge power correction value to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient, including: The terminal voltage signal of each group of supercapacitors is collected and digitally filtered to calculate the system average terminal voltage. The difference between the terminal voltage of each group of supercapacitors and the system average terminal voltage is used as the terminal voltage deviation. The terminal voltage deviation is subjected to sliding average filtering to obtain a filtered terminal voltage deviation. The filtered rear-end voltage deviations at adjacent sampling moments are differentially calculated and combined with the sampling period to obtain a terminal voltage change rate. The terminal voltage change rate is clipped to obtain a clipped terminal voltage change rate. The filtered terminal voltage deviation is divided into preset intervals to establish a first linguistic variable set, and a dynamically adjustable first fuzzy subset boundary is set for each interval. At the same time, the terminal voltage change rate after clipping is divided into preset intervals to establish a second linguistic variable set, and the boundary value is adaptively adjusted according to the change trend of the terminal voltage deviation to obtain a second fuzzy subset boundary; A triangular membership function with optimized overlap is used to perform fuzzy processing on the terminal voltage deviation after filtering to obtain a first membership value, and an asymmetric trapezoidal membership function with configurable slope is used to perform fuzzy processing on the terminal voltage change rate after limiting to obtain a second membership value; A fuzzy rule base is constructed based on the first membership value, the second membership value, the first language variable set, and the second language variable set, and the rules are optimized online using a variable structure fuzzy control strategy to obtain an optimized fuzzy rule set. Fuzzy reasoning is performed on the optimized fuzzy rule set using a fuzzy sub-rule parallel reasoning method, and the reasoning results of each sub-rule are fuzzily synthesized using adaptive weights to obtain a synthesized fuzzy output. Defuzzifying the integrated fuzzy output using a centroid method to obtain an initial power correction value, and dynamically limiting the initial power correction value in combination with system state parameters to obtain a charge and discharge power correction value; A recursive compensation model is constructed by taking the dynamic grouping coefficient as the initial value and the charge and discharge power correction value as the target value. The recursive compensation model dynamically adjusts the iteration step size according to the deviation between the compensation target value and the current power coefficient. The recursive compensation model is used to perform iterative calculations. When the change in the power coefficient between two adjacent iterations is less than a preset change threshold, the iteration is stopped to obtain the compensated grouping coefficient. The values of the compensated grouping coefficients that are less than the dead zone threshold are set to zero, and the values that are greater than the dead zone threshold are normalized to obtain the power allocation coefficient.

5. The method according to claim 4, characterized in that The integrated fuzzy output is defuzzified using an improved centroid method to obtain an initial power correction value, and the initial power correction value is dynamically limited in combination with the system state parameter to obtain a charge and discharge power correction value, including: The fuzzy control output is defuzzified by calculating the membership degree of each rule, the discrete points of the output universe, and the area weight of the output membership function at the discrete points, and performing weighted summation based on the product of the membership degree and the area weight and the discrete point value to obtain an initial power correction value; wherein the area weight is calculated using piecewise linear approximation, and for a trapezoidal area, the product of the mean of the membership degrees of adjacent discrete points and the distance between the discrete points is used, and for a rectangular area, the product of the membership degree of the discrete point and the distance between the discrete points is used; Collecting system operating state parameters, including state of charge, terminal voltage, and temperature, calculating a deviation between the state of charge and a desired state of charge to obtain a state of charge deviation, calculating a deviation between the terminal voltage and a rated terminal voltage to obtain a terminal voltage deviation, and calculating a difference between the temperature and a temperature threshold to obtain a temperature deviation; establishing a first clipping function based on the state of charge deviation, establishing a second clipping function based on the terminal voltage deviation, and establishing a third clipping function based on the temperature deviation, and taking the product of the initial power correction value and the first clipping function, the second clipping function, and the third clipping function as an intermediate power correction value; The first limiting function, the second limiting function and the third limiting function are subjected to piecewise linearization processing, and the product of the limiting function after the piecewise linearization processing and the initial power correction value is used as the final charge and discharge power correction value for adjusting the charge and discharge power of the system.

6. A supercapacitor-based energy management system for a lifting machinery luffing process, used to implement the method according to any one of claims 1 to 5, characterized in that: include: The first unit is used to analyze the luffing angle rate and the lifting weight of the lifting machinery using a neural network prediction model, calculate an instantaneous power curve of the luffing process, use the integral value of the instantaneous power curve on the time axis as the theoretical energy demand of the luffing process, group supercapacitors based on the theoretical energy demand and calculate an initial grouping coefficient, determine the ratio of the initial grouping coefficient to the historical cycle number as the capacity decay rate, calculate the remaining life coefficient of each group of supercapacitors based on the capacity decay rate, and obtain a dynamic grouping coefficient based on the calculation of the initial grouping coefficient and the remaining life coefficient; a second unit for establishing a fuzzy controller based on the dynamic grouping coefficient, using the terminal voltage deviation and the terminal voltage change rate of each group of supercapacitors as inputs of the fuzzy controller, mapping different combinations of the terminal voltage deviation and the terminal voltage change rate into charge and discharge power correction values through the fuzzy controller, establishing a recursive compensation model based on the charge and discharge power correction values to perform real-time correction on the dynamic grouping coefficient to obtain a power allocation coefficient, using the power allocation coefficient as the upper limit of the charge and discharge power of each group of supercapacitors, and adjusting the charge and discharge current of each group of supercapacitors through an adaptive PI controller, wherein the control parameters of the adaptive PI controller are adaptively related to the power allocation coefficient and the terminal voltage deviation; The third unit is used to compare the regenerative braking power with the rechargeable power of the supercapacitor group calculated by the power distribution coefficient during the process of decreasing the amplitude angle. When the regenerative braking power is greater than the rechargeable power, the excess energy is stored in the high-temperature superconducting energy storage inductor, and the power distribution coefficient of each group of supercapacitors is detected in real time. When it is detected that the rechargeable power corresponding to the power distribution coefficient of any group of supercapacitors is greater than the current charging power, the energy in the high-temperature superconducting energy storage inductor is distributed and charged according to the proportion of the power distribution coefficient of each group of supercapacitors; during the process of increasing the amplitude angle, an intelligent power distributor is established based on the power distribution coefficient of each group of supercapacitors. The intelligent power distributor adopts a dynamic programming algorithm to optimize the output power ratio of the main power supply and each group of supercapacitors, realizes the power coordinated control of each group of supercapacitors and the main power supply through a sliding mode controller, and adopts an active interference rejection controller to suppress load power fluctuations to ensure the stability of the system voltage.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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