Shipborne multi-source flexible DC looped network test bench and energy management method thereof

Abstract

The invention discloses a shipborne multi-source flexible direct current looped network test bench. Comprising a direct current looped network main system, a first hydrogen fuel cell power supply system, a second hydrogen fuel cell power supply system, a first composite energy storage system, a second composite energy storage system, a first load system, a second load system, a compact reconfigurable input / output CRIO controller, a lower computer and an upper computer. The direct-current ring network main system comprises a first direct-current bus, a second direct-current bus, a third direct-current bus and a fourth direct-current bus which are electrically connected end to end through four solid-state switches. The first hydrogen fuel cell power supply system is electrically connected with the second direct-current bus, the second hydrogen fuel cell power supply system is electrically connected with the fourth direct-current bus, the first composite energy storage system is electrically connected with the first direct-current bus, and the second composite energy storage system is electrically connected with the third direct-current bus. The technical problem that an existing rule type control method excessively depends on engineering experience and preset rules and is difficult to adapt to complex and changeable working conditions can be solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ship power systems, and more specifically, relates to a shipborne multi-source flexible DC ring network test bench and an energy management method thereof. Background Art

[0002] With the continuous improvement of ship electrification and intelligence, the shipboard flexible DC ring network system has gradually become an important development direction of ship power systems. In order to verify and optimize the performance of the system, it is particularly important to develop a professional shipboard multi-source flexible DC ring network test bench. The shipboard multi-source flexible DC ring network test bench can not only simulate actual navigation conditions, but also verify and optimize the system's energy management strategy to achieve efficient coordination and control between multiple energy sources. Among them, energy management, as the core of the system, is mainly responsible for coordinating the power distribution of various energy equipment to ensure the economy and reliability of the system.

[0003] At present, there are three main types of energy management for shipborne multi-source flexible DC ring network test benches, namely rule-based control method, dynamic programming (DP) method, and model predictive control (MPC) method: the rule-based control method distributes energy through preset logical rules, which is simple to implement; the dynamic programming method achieves optimal energy distribution through global optimization, but requires the full operating condition information to be known in advance; the model predictive control method achieves optimal control of the system in the prediction time domain through rolling optimization, which can effectively balance computational efficiency and control performance.

[0004] However, the energy management methods of the above-mentioned shipborne multi-source flexible DC ring network test benches all have some defects that cannot be ignored: first, the rule-based control method relies too much on engineering experience and preset rules, and it is difficult to adapt to complex and changeable working conditions; second, although the DP method can achieve global optimization, it has a large amount of calculation and requires the prediction of complete working condition information, making it difficult to achieve real-time control; third, the rule-based control method, DP method, and model predictive control method generally lack consideration of the degradation characteristics of hydrogen fuel cells and lithium batteries, and cannot adapt to the characteristics of equipment performance changing over time, which will lead to a decrease in control performance and aggravate equipment degradation. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a shipborne multi-source flexible DC ring network test bench and an energy management method thereof, which aims to solve the technical problems that the existing rule-based control method is overly dependent on engineering experience and preset rules and is difficult to adapt to complex and changeable working conditions, and the existing DP method has a large amount of calculation and requires the full knowledge of the working conditions, making it difficult to achieve real-time control. In addition, the existing rule-based control method, DP method and model predictive control method generally lack consideration of the degradation characteristics of hydrogen fuel cells and lithium batteries, and cannot adapt to the characteristics of equipment performance changing over time, thereby causing the control performance to decline and aggravating the equipment degradation.

[0006] To achieve the above-mentioned object, according to one aspect of the present invention, a shipboard multi-source flexible DC ring network test bench is provided, comprising a DC ring network main system, a first hydrogen fuel cell power supply system, a second hydrogen fuel cell power supply system, a first composite energy storage system, a second composite energy storage system, a first load system, a second load system, a compact reconfigurable input / output CRIO controller, a lower computer, and an upper computer, wherein the DC ring network main system comprises a first DC bus, a second DC bus, a third DC bus, and a fourth DC bus electrically connected end to end through four solid-state switches;

[0007] The first hydrogen fuel cell power supply system is electrically connected to the second DC bus, the second hydrogen fuel cell power supply system is electrically connected to the fourth DC bus, the first composite energy storage system is electrically connected to the first DC bus, and the second composite energy storage system is electrically connected to the third DC bus;

[0008] The first load system is electrically connected to the first DC bus, and the second load system is electrically connected to the third DC bus;

[0009] The CRIO controller is electrically connected to the first hydrogen fuel cell power supply system, the second hydrogen fuel cell power supply system, the first composite energy storage system, the second composite energy storage system, the first load system, and the second load system through weak current signal lines, and is also electrically connected to the first DC bus, the second DC bus, the third DC bus, and the fourth DC bus to monitor their working status in real time.

[0010] The lower computer is electrically connected to the second load system through a weak-point signal line.

[0011] The upper computer is electrically connected to the CRIO controller and the lower computer through a weak signal line.

[0012] Preferably, the first hydrogen fuel cell power supply system and the second hydrogen fuel cell power supply system are identical;

[0013] The first hydrogen fuel cell power supply system includes a hydrogen fuel cell, a first DC circuit breaker, a first DC fuse, a first unidirectional boost DC converter, a first current-voltage sensor, a second DC fuse, and a second DC circuit breaker;

[0014] The first hydrogen fuel cell is electrically connected to the second DC bus through the first DC circuit breaker, the first DC fuse, the first unidirectional boost DC converter, the first current-voltage sensor, the second DC fuse, and the second DC circuit breaker in sequence.

[0015] The rated power of the first hydrogen fuel cell is 40kW, and the output voltage range is 450 - 700V;

[0016] The rated voltages of the first DC circuit breaker and the second DC circuit breaker are 1000V, and the rated current is 63A;

[0017] The rated voltages of the first DC fuse and the second DC fuse are 1000V, and the rated current is 63A;

[0018] The first unidirectional boost DC converter is a unidirectional isolation type converter, with the input side voltage range of 400 - 850V, the output side voltage range of 400 - 850V, and the rated power of 40kW;

[0019] The current measurement range of the first voltage-current sensor is 0 - 100A, and the voltage measurement range is 0 - 1000V.

[0020] Preferably, the first composite energy storage system and the second composite energy storage system are exactly the same;

[0021] The first composite energy storage system includes a first lithium battery, a first super capacitor, a third DC circuit breaker, a third DC fuse, a first bidirectional boost DC converter, a second current-voltage sensor, a fourth DC fuse, a fourth DC circuit breaker, a fifth DC circuit breaker, a fifth DC fuse, a second bidirectional boost DC converter, a third current-voltage sensor, a sixth DC fuse, and a sixth DC circuit breaker;

[0022] The first lithium battery is electrically connected to the first DC bus through the third DC circuit breaker, the third DC fuse, the first bidirectional boost DC converter, the second current-voltage sensor, the fourth DC fuse, and the fourth DC circuit breaker in sequence;

[0023] The first super capacitor is electrically connected to the first DC bus through the fifth DC circuit breaker, the fifth DC fuse, the second bidirectional boost DC converter, the third current-voltage sensor, the sixth DC fuse, and the sixth DC circuit breaker in sequence.

[0024] The capacity of the first lithium battery is 65kWh, the nominal voltage is 633V, the working voltage range is 574 - 693V, and the maximum charge-discharge rate is 1C;

[0025] The capacitance of the first supercapacitor is 5.2 F, the rated power is 80 kW, and the operating voltage range is 200 - 800 V;

[0026] The rated voltages of the third, fourth, fifth, and sixth DC circuit breakers are 1000 V, and the rated current is 63 A;

[0027] The rated voltages of the third, fourth, fifth, and sixth DC fuses are 1000 V, and the rated current is 63 A;

[0028] The first and second bidirectional boost DC converters are bidirectional non - isolated converters. The voltage range on the A side is 400 - 850 V, the voltage range on the B side is 300 - 850 V, and the rated power is 60 kW.

[0029] The current measurement ranges of the second and third voltage - current sensors are 0 - 100 A, and the voltage measurement ranges are 0 - 1000 V.

[0030] Preferably, the first load system includes a first resistive load cabinet, a seventh DC fuse, a seventh DC circuit breaker, and a fourth voltage - current sensor;

[0031] The first resistive load cabinet is electrically connected to the first DC bus through the seventh DC fuse, the seventh DC circuit breaker, and the fourth voltage - current sensor in sequence.

[0032] The rated power of the first resistive load cabinet is 10 kW;

[0033] The rated voltage of the seventh DC fuse is 1000 V, and the rated current is 63 A;

[0034] The rated voltage of the seventh DC circuit breaker is 1000 V, and the rated current is 63 A;

[0035] The current measurement range of the fourth voltage - current sensor is 0 - 100 A, and the voltage measurement range is 0 - 1000 V.

[0036] The second load system includes a first dynamometer, a first reversible motor, a first DC / AC inverter, an eighth DC fuse, an eighth DC circuit breaker, and a fifth voltage - current sensor;

[0037] The first dynamometer is electrically connected to the third DC bus through the first reversible motor, the first DC / AC inverter, the eighth DC fuse, the eighth DC circuit breaker, and the fifth voltage - current sensor in sequence.

[0038] The first dynamometer is an eddy - current dynamometer, and its rated absorption power is 320 kW;

[0039] The rated power of the first reversible motor is 75 kW, its rated voltage is 380 V, and its rated speed is 1500 r / min;

[0040] The first DC / AC inverter outputs 380V three-phase alternating current.

[0041] The rated voltage of the eighth DC fuse is 1000V, and its rated current is 63A;

[0042] The rated voltage of the eighth DC circuit breaker is 1000V, and its rated current is 63A;

[0043] The current measurement range of the fifth voltage and current sensor is 0 - 100A, and the voltage measurement range is 0 - 1000V.

[0044] Generally speaking, compared with the prior art, the above technical solutions conceived by the shipborne multi-source flexible DC ring network test bench of the present invention can achieve the following beneficial effects:

[0045] (1) Integrating the first hydrogen fuel cell power supply system, the second hydrogen fuel cell power supply system, the first composite energy storage system, the second composite energy storage system, the first load system and the second load system, it realizes flexible power flow and reliable distribution through a four-section ring network structure, providing a complete test platform for the research of multi-source flexible DC ring networks;

[0046] (2) Adopting the design of connecting the four-section DC buses end to end with solid-state switches, it supports various topology structure transformations and operation mode switches, and can simulate various navigation conditions and fault scenarios;

[0047] (3) Precisely simulating ship propulsion and service loads through the first resistor load cabinet and the first dynamometer system, combined with high-precision voltage and current sensors and a hierarchical control architecture, it can realize the comprehensive verification and optimization of energy management strategies, and is particularly suitable for evaluating multi-energy coordinated control strategies.

[0048] According to another aspect of the present invention, there is provided an energy management method according to a shipborne multi-source flexible DC ring network test bench, characterized by including the following steps:

[0049] (1) The first hydrogen fuel cell power supply system and the second hydrogen fuel cell power supply system generate electrical energy and modulate and boost it to 690V, and are respectively connected to the second DC bus and the fourth DC bus. The first composite energy storage system and the second composite energy storage system are respectively connected to the first DC bus and the third DC bus;

[0050] (2) The upper computer loads the power data during the historical navigation of the ship and transmits this power data to the first load system and the second load system.

[0051] (3) The host computer obtains the voltage signals and current signals of the first hydrogen fuel cell power supply system, the second hydrogen fuel cell power supply system, the first lithium battery and the first supercapacitor in the first composite energy storage system, the voltage signals and current signals of the second lithium battery and the second supercapacitor in the second composite energy storage system, and the voltage signals and current signals of the first load system collected by the CRIO controller, generates corresponding power signals according to their respective voltage and current signals, obtains the internal resistance signals of the first hydrogen fuel cell in the first hydrogen fuel cell power supply system and the internal resistance signals of the second hydrogen fuel cell in the second hydrogen fuel cell power supply system collected by the CRIO controller, and obtains the rotational speed signal and torque signal of the first dynamometer in the second load system collected by the lower computer, and generates the power signal of the second load system according to the rotational speed signal and torque signal.

[0052] (4) The host computer adds the power signal of the first load system and the power signal of the second load system obtained in step (3) to obtain the total power signal, and inputs the total power signal into the pre-trained working condition recognition model to obtain the current typical operating condition of the ship.

[0053] (5) The host computer selects the corresponding power prediction model according to the current typical operating condition of the ship obtained in step (4) to perform power prediction on the total power signal obtained in step (4) to obtain load prediction data (specifically, the load prediction data 20 s after the current moment).

[0054] (6) The host computer obtains the reference power of the first hydrogen fuel cell, the reference power of the second hydrogen fuel cell, the reference power of the first lithium battery, the reference power of the second lithium battery, the reference power of the second supercapacitor, and the reference power of the second supercapacitor according to the total power signal obtained in step (4), the load prediction data obtained in step (5), the internal resistance signals of the first hydrogen fuel cell and the second hydrogen fuel cell obtained in step (3), the current signal of the first lithium battery in the first composite energy storage system, and the current signal of the second lithium battery in the second composite energy storage system.

[0055] (7) The CRIO controller obtains the reference power of the first hydrogen fuel cell, the reference power of the second hydrogen fuel cell, the reference power of the first lithium battery, the reference power of the second lithium battery, the reference power of the first supercapacitor, and the reference power of the second supercapacitor obtained from the host computer. It transfers the reference power of the first hydrogen fuel cell to the first unidirectional boost DC converter in the first hydrogen fuel cell power supply system, which uses power tracking control to keep the first hydrogen fuel cell near its reference power. It transfers the reference power of the second hydrogen fuel cell to the second unidirectional boost DC converter in the second hydrogen fuel cell power supply system, which uses power tracking control to keep the second hydrogen fuel cell near its reference power. It transfers the reference power of the first lithium battery to the first bidirectional boost DC converter in the first composite energy storage system, which uses power tracking control to keep the first lithium battery near its reference power. It transfers the reference power of the first supercapacitor to the second bidirectional boost DC converter in the first composite energy storage system, which uses power tracking control to keep the first supercapacitor near its reference power. It transfers the reference power of the second lithium battery to the third bidirectional boost DC converter in the second composite energy storage system, which uses power tracking control to keep the second lithium battery near its reference power. It transfers the reference power of the second supercapacitor to the fourth bidirectional boost DC converter in the second composite energy storage system, which uses power tracking control to keep the second supercapacitor near its reference power.

[0056] (8) The shipborne multi-source flexible DC ring network test bench maintains closed-loop operation, and the process ends.

[0057] Preferably, step (3) is specifically as follows: The host computer multiplies the voltage signal and the current signal of the first load system to obtain the power signal of the first load system, multiplies the voltage signal and the current signal of the first hydrogen fuel cell power supply system to obtain the output power signal of the first hydrogen fuel cell, multiplies the voltage signal and the current signal of the second hydrogen fuel cell power supply system to obtain the output power signal of the second hydrogen fuel cell, multiplies the voltage signal and the current signal of the first lithium battery in the first composite energy storage system to obtain the output power signal of the first lithium battery, multiplies the voltage signal and the current signal of the first supercapacitor in the first composite energy storage system to obtain the output power signal of the first supercapacitor, multiplies the voltage signal and the current signal of the second lithium battery in the second composite energy storage system to obtain the output power signal of the second lithium battery, multiplies the voltage signal and the current signal of the second supercapacitor in the second composite energy storage system to obtain the output power signal of the second supercapacitor, and obtains the power signal of the second load system according to the rotational speed signal and torque signal of the first dynamometer in the second load system and using the following formula.

[0058]

[0059] Wherein, T is the torque signal of the first dynamometer in the second load system, and n is the rotational speed signal of the first dynamometer in the second load system.

[0060] The working condition recognition model uses a support vector machine (SVM) combined with a rolling time window for working condition recognition.

[0061] During working condition recognition, the key features of 10 data points at the current moment and the previous 18 s are calculated to determine the working condition category of the current ship. The rolling window at time t contains the data within the time range of [t - 18 s, t]. The window slides and updates every 2 s. When sliding and updating, the earliest data point is removed, and the latest data point is added, always keeping 10 data points within the window.

[0062] Preferably, the working condition recognition model is obtained through the following steps:

[0063] (4-1) Obtain the original ship historical load power data set, and preprocess the original ship historical load power data set to obtain the preprocessed ship historical load power data set.

[0064] Specifically, for the missing data in the original ship historical load power data set, the linear interpolation method is used for filling, that is, linear estimation is performed based on the valid data points before and after the missing value to obtain a reasonable filling value. For the abnormal data in the original ship historical load power data, the Z-score method is first used to detect the outliers, that is, the Z-score of each data point in the original ship historical load power data is calculated, and the data points with the absolute value of the Z-score greater than 3 are marked as outliers, and then the moving median based on a 30-second window is used to correct all outliers.

[0065] (4-2) Extract multiple key features from the preprocessed ship historical load power data set obtained in step (4-1), which are used to characterize the operating state of the ship.

[0066] Specifically, in order to accurately characterize the operating state of the ship, a rolling time window is used to extract features from the preprocessed ship historical load power data set obtained in step (4-1). The window duration is 20 s, and the step size is 2 s. Within each rolling sliding time window, the mean f 1 of power fluctuations, the variance f 2 and the ratio of variance to mean f 3 are used as key features. Among them, the mean f 1 reflects the steady-state power level of the ship within the rolling time window, the variance f 2 characterizes the degree of power data deviation from the average value and depicts the severity of power fluctuations, while the ratio of variance to mean f 3It characterizes the power fluctuation intensity at the unit power level and reflects the dynamic change characteristics of power. The calculation formulas for the three are as follows:

[0067]

[0068]

[0069]

[0070] And there are:

[0071]

[0072] ΔP i =P i -P i-1 ,(i≥2)

[0073]

[0074] Where N is the number of sampling points within the time window, and P i is the i-th preprocessed ship historical load power data in the preprocessed ship historical load power dataset (where i ∈ [1, the total number of data in the preprocessed ship historical load power dataset]), is the variance of the change amount of the i-th preprocessed ship historical load power data, and ΔP i represents the difference between the preprocessed ship historical load data at the i-th moment and the previous moment, is the average value of the change amount of the i-th preprocessed ship historical load power data.

[0075] (4 - 3) Perform K-Means clustering analysis on all the key features obtained in step (4 - 2) to obtain various typical operating conditions of the ship and the corresponding multiple preprocessed ship historical load power data.

[0076] Specifically, in this step, the key features obtained in step (4 - 2) are input into the K-Means clustering algorithm for automatic classification. Based on the principle of minimizing the within-class distance and maximizing the between-class distance, the K-Means clustering algorithm can automatically cluster data points with similar features into the same category, thus obtaining 5 typical operating conditions of the ship, namely complex condition, power gradually increasing condition, power gradually decreasing condition, high-power steady state condition, and low-power steady state condition. After obtaining these typical operating conditions, the preprocessed ship historical load power data corresponding to each condition are integrated to obtain the preprocessed ship historical load power data for 5 typical operating conditions.

[0077] (4-4) Use the multiple preprocessed historical ship load power data and their key features corresponding to each typical operating condition obtained in step (4-3) to perform offline training on the condition recognition model, so as to obtain a trained condition recognition model;

[0078] Preferably, the power prediction models corresponding to each typical operating condition are CNN-LSTM models based on the attention mechanism and convolutional neural network-long short-term memory network, which are obtained through the following steps:

[0079] (5-1) Obtain the multiple preprocessed historical ship load power data corresponding to this typical operating condition obtained in step (4-3), and construct a training set for the power prediction model corresponding to this typical operating condition according to the historical ship load power data.

[0080] Specifically, in this step, first, obtain the volatility of each preprocessed historical ship load power data corresponding to this typical operating condition, and its calculation formula is:

[0081]

[0082] In the formula, P i is the i-th historical ship load power data among all preprocessed historical ship load power data corresponding to this typical operating condition, where i ∈ [2, the total number of all preprocessed historical ship load power data corresponding to this typical operating condition]. Then, select all preprocessed historical ship load power data corresponding to this typical operating condition and their volatilities to construct a training set for the power prediction model corresponding to this typical operating condition.

[0083] (5-2) Use the training set of the power prediction model corresponding to this typical operating condition obtained in step (5-1) to train the power prediction model, so as to obtain a trained power prediction model corresponding to this typical operating condition.

[0084] Preferably, step (6) specifically includes the following sub-steps:

[0085] (6-1) Use a low-pass filter controller to decompose the load data to obtain the reference power of the first supercapacitor, the reference power of the second supercapacitor, and the low-frequency power.

[0086] (6-2) According to the first hydrogen fuel cell internal resistance signal and the second hydrogen fuel cell internal resistance signal obtained in step (3), obtain the state of health (SOH) of the first hydrogen fuel cell and the SOH of the second hydrogen fuel cell.

[0087] (6-3) Obtain the hydrogen consumption rate curve of the first hydrogen fuel cell and the hydrogen consumption rate curve of the second hydrogen fuel cell respectively based on the SOH of the first hydrogen fuel cell and the SOH of the second hydrogen fuel cell obtained in step (6-2).

[0088] (6-4) Obtain the fitting equations of the hydrogen consumption rate and power of the first hydrogen fuel cell and the second hydrogen fuel cell respectively based on the hydrogen consumption rate data of the first hydrogen fuel cell and the hydrogen consumption rate data of the second hydrogen fuel cell obtained in step (6-3).

[0089] (6-5) Input the fitting equations of the hydrogen consumption rate and power of the first hydrogen fuel cell and the second hydrogen fuel cell obtained in step (6-4), the low-frequency power obtained in step (6-1), and the current signals of the first lithium battery and the second lithium battery obtained in step (3) into the MPC controller to obtain the reference powers of the first hydrogen fuel cell, the second hydrogen fuel cell, the first lithium battery, and the second lithium battery respectively.

[0090] Preferably, the expressions of the reference power and the low-frequency power of the supercapacitor in step (6-1) are respectively:

[0091]

[0092]

[0093] Among them, P load is the load data, s is the complex variable, T 1 is the filtering time constant, P SC / 2 is the reference power of the first supercapacitor, which is equal to the reference power of the second supercapacitor, and P low is the low-frequency power.

[0094] The SOH of the hydrogen fuel cell in step (6-2) is equal to:

[0095]

[0096] Among them, R end represents the ohmic internal resistance value / Ω at the end of the life of the first / second hydrogen fuel cell; R new represents the ohmic internal resistance value / Ω of the first / second hydrogen fuel cell when it is not in use; R represents the ohmic internal resistance value / Ω of the first / second hydrogen fuel cell in the current state.

[0097] The hydrogen consumption rate C fc of the hydrogen fuel cell in step (6-3) is calculated by the following formula:

[0098]

[0099] Among them, P fcis the output power of the hydrogen fuel cell after passing through the DC / DC converter, is the lower heating value of hydrogen. η fc is the system efficiency of the hydrogen fuel cell, η fc is defined as follows:

[0100] η fc = ε elec * ε conv * η init

[0101] where η init is the hydrogen fuel cell system efficiency at the initial state of the hydrogen fuel cell (SOH = 1). The coefficient of variation ε elec represents the degree of decrease in the electrical efficiency η elec and there is:

[0102]

[0103] where η DC / DC is the conversion efficiency of the DC / DC converter; E auz is the parasitic power of the auxiliary equipment; E stack is the stack output power; the coefficient of variation ε conv represents the degree of decrease in the conversion efficiency η conv and there is:

[0104] ε conv = 0.9 + 0.1 * SOH

[0105] In step (6-4), according to the hydrogen consumption rate data of the hydrogen fuel cell calculated in step (6-3), the quadratic polynomial fitting method is used to obtain the fitting equation of the hydrogen consumption rate C fc as follows:

[0106]

[0107] where a, b, and c are the fitting coefficients of the hydrogen fuel cell.

[0108] In step (6-5), the objective function and constraint conditions of the MPC controller are as follows:

[0109]

[0110]

[0111] where C fc1 and C fc2 are the hydrogen consumption rates of the first hydrogen fuel cell and the first hydrogen fuel cell respectively; C batt is the equivalent hydrogen consumption rate of the first / second lithium battery; k fc_deg and k Batt_degis the degradation weight, which is used to represent the optimization preference of the degradation of the hydrogen fuel cell and the lithium battery in the overall performance of the power system. Its value range is from 0 to 1, k fc_deg is preferably 0.6, k Batt_deg is preferably 0.4; C fc_deg is the equivalent hydrogen consumption of the degradation of the hydrogen fuel cell; C batt_deg is the degradation cost of the first / second lithium battery; P fc1 is the reference power of the first hydrogen fuel cell, P fc1 is the reference power of the second hydrogen fuel cell, P batt / 2 is the reference power of the first lithium battery, which is equal to the reference power of the second lithium battery.; SOC batt is the state of charge of the first / second lithium battery. d is the degradation factor, and its calculation formula is: d = α * SOH, where α is the energy efficiency optimization factor of the hydrogen fuel cell, and its value determines the sensitivity of the adjustment range of the output power of the hydrogen fuel cell to its degradation.

[0112] Generally speaking, compared with the prior art, the above technical solutions conceived by the energy management method of the shipborne multi-source flexible DC ring network test bench of the present invention can achieve the following beneficial effects:

[0113] (1) Since the present invention adopts steps (4) and (5), it identifies the working conditions through the support vector machine combined with the rolling time window, and selects the corresponding CNN-LSTM prediction model based on the attention mechanism for load power prediction according to the identified working conditions. Therefore, it can solve the technical problem that the existing rule-based control method relies too much on engineering experience and preset rules and is difficult to adapt to complex and changeable working conditions;

[0114] (2) Since the present invention adopts step (6), it decomposes the load data into high and low frequency parts and uses model predictive control for rolling optimization solution. Therefore, it can solve the technical problems that the existing dynamic programming method requires prior knowledge of complete working condition information, has a large amount of calculation and is difficult to achieve real-time control;

[0115] (3) Since the present invention adopts steps (6-2), (6-3) and (6-4), it obtains the health state by measuring the internal resistance of the hydrogen fuel cell, calculates the hydrogen consumption rate under different health states, and incorporates the degradation costs of the hydrogen fuel cell and the lithium battery into the optimization objective function. Therefore, it can solve the technical problem that the existing energy management methods generally lack consideration of the degradation characteristics of hydrogen fuel cells and lithium batteries. Description of the Drawings

[0116] Figure 1 is a schematic structural diagram of the shipborne multi-source flexible DC ring network test bench of the present invention;

[0117] Figure 2It is a schematic structural diagram of the first hydrogen fuel cell power supply system in the present invention;

[0118] Figure 3 It is a schematic structural diagram of the first composite energy storage system in the present invention;

[0119] Figure 4 It is a schematic structural diagram of the first load system in the present invention;

[0120] Figure 5 It is a schematic structural diagram of the second load system in the present invention;

[0121] Figure 6 It is a single-line diagram of the power supply system of the shipborne multi-source flexible DC ring network test bench in the present invention;

[0122] Figure 7 It is a schematic diagram of the energy management method of the power supply system of the shipborne multi-source flexible DC ring network test bench in the present invention;

[0123] Figure 8 It is a working condition identification window diagram provided by an embodiment of the present invention;

[0124] Figure 9 It is a curve graph of efficiency and hydrogen consumption rate under different SOH provided by an embodiment of the present invention. Detailed implementation manners

[0125] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0126] As Figure 1 shown, the present invention provides a shipborne multi-source flexible DC ring network test bench, including a DC ring network main system, a first hydrogen fuel cell power supply system 1, a second hydrogen fuel cell power supply system 2, a first composite energy storage system 3, a second composite energy storage system 4, a first load system 5, a second load system 6, a Compact Reconfigurable Input / Output (CompactRIO, abbreviated as CRIO) controller 12, a lower computer 13, and an upper computer 14.

[0127] The DC ring network main system includes a first DC bus 8, a second DC bus 9, a third DC bus 10, and a fourth DC bus 11 that are electrically connected end to end through four solid-state switches 7.

[0128] The first hydrogen fuel cell power supply system 1 is electrically connected to the second DC bus 9, the second hydrogen fuel cell power supply system 2 is electrically connected to the fourth DC bus 11, the first composite energy storage system 3 is electrically connected to the first DC bus 8, and the second composite energy storage system 4 is electrically connected to the third DC bus 10.

[0129] The first load system 5 is electrically connected to the first DC bus 8, and the second load system 6 is electrically connected to the third DC bus 10.

[0130] The CRIO controller 12 is electrically connected to the first hydrogen fuel cell power supply system 1, the second hydrogen fuel cell power supply system 2, the first composite energy storage system 3, the second composite energy storage system 4, the first load system 5, and the second load system 6 respectively through weak electrical signal lines (shown as dotted lines in the figure), and is simultaneously electrically connected to the first DC bus 8, the second DC bus 9, the third DC bus 10, and the fourth DC bus 11 to monitor their working states in real time.

[0131] The lower computer 13 is electrically connected to the second load system 6 through a weak electrical signal line (shown as a dotted line in the figure).

[0132] The upper computer 14 is electrically connected to the CRIO controller 12 and the lower computer 13 through weak electrical signal lines (shown as dotted lines in the figure).

[0133] The first hydrogen fuel cell power supply system 1 and the second hydrogen fuel cell power supply system 2 are exactly the same. As Figure 2 shown, the first hydrogen fuel cell power supply system 1 includes a hydrogen fuel cell 1-1, a first DC circuit breaker 1-2, a first DC fuse 1-3, a first unidirectional boost DC converter 1-4, a first current and voltage sensor 1-5, a second DC fuse 1-6, and a second DC circuit breaker 1-7. The first hydrogen fuel cell 1-1 is electrically connected to the second DC bus 9 in sequence through the first DC circuit breaker 1-2, the first DC fuse 1-3, the first unidirectional boost DC converter 1-4, the first current and voltage sensor 1-5, the second DC fuse 1-6, and the second DC circuit breaker 1-7.

[0134] Specifically, the rated power of the first hydrogen fuel cell 1-1 is 40kW, and the output voltage range is 450 - 700V; the rated voltages of the first DC circuit breaker 1-2 and the second DC circuit breaker 1-7 are 1000V, and the rated current is 63A; the rated voltages of the first DC fuse 1-3 and the second DC fuse 1-6 are 1000V, and the rated current is 63A; the first unidirectional boost DC converter 1-4 is a unidirectional isolation type converter, the input side voltage range is 400 - 850V, the output side voltage range is 400 - 850V, the rated power is 40kW, and the current measurement range of the first voltage and current sensor 1-5 is 0 - 100A, and the voltage measurement range is 0 - 1000V.

[0135] The first composite energy storage system 3 and the second composite energy storage system 4 are exactly the same. As Figure 3 shown, the first composite energy storage system 3 includes a first lithium battery 3-1, a first supercapacitor 3-2, a third DC circuit breaker 3-3, a third DC fuse 3-4, a first bidirectional boost DC converter 3-5, a second current-voltage sensor 3-6, a fourth DC fuse 3-7, a fourth DC circuit breaker 3-8, a fifth DC circuit breaker 3-9, a fifth DC fuse 3-10, a second bidirectional boost DC converter 3-11, a third current-voltage sensor 3-12, a sixth DC fuse 3-13, and a sixth DC circuit breaker 3-14. The first lithium battery 3-3 is electrically connected to the first DC bus 8 in sequence through the third DC circuit breaker 3-3, the third DC fuse 3-4, the first bidirectional boost DC converter 3-5, the second current-voltage sensor 3-6, the fourth DC fuse 3-7, and the fourth DC circuit breaker 3-8; the first supercapacitor 3-2 is electrically connected to the first DC bus 8 in sequence through the fifth DC circuit breaker 3-9, the fifth DC fuse 3-10, the second bidirectional boost DC converter 3-11, the third current-voltage sensor 3-12, the sixth DC fuse 3-13, and the sixth DC circuit breaker 3-14.

[0136] Specifically, the capacity of the first lithium battery 3-3 is 65 kWh, the nominal voltage is 633 V, the operating voltage range is 574 - 693 V, and the maximum charge-discharge rate is 1C; the capacity of the first supercapacitor 3-4 is 5.2 F, the rated power is 80 kW, and the operating voltage range is 200 - 800 V; the rated voltages of the third DC circuit breaker 3-3, the fourth DC circuit breaker 3-8, the fifth DC circuit breaker 3-9, and the sixth DC circuit breaker 3-14 are 1000 V, and the rated current is 63 A; the rated voltages of the third DC fuse 3-4, the fourth DC fuse 3-7, the fifth DC fuse 3-10, and the sixth DC fuse 3-13 are 1000 V, and the rated current is 63 A; the first bidirectional boost DC converter 3-5 and the second bidirectional boost DC converter 3-11 are bidirectional non-isolated converters, the voltage range of side A is 400 - 850 V, the voltage range of side B is 300 - 850 V, and the rated power is 60 kW. The current measurement range of the second voltage-current sensor 3-6 and the third voltage-current sensor 3-12 is 0 - 100 A, and the voltage measurement range is 0 - 1000 V.

[0137] As Figure 4 shown, the first load system 5 includes a first resistive load cabinet 5-1, a seventh DC fuse 5-2, a seventh DC circuit breaker 5-3, and a fourth voltage-current sensor 5-4. The first resistive load cabinet 5-1 is electrically connected to the first DC bus 8 in sequence through the seventh DC fuse 5-2, the seventh DC circuit breaker 5-3, and the fourth voltage-current sensor 5-4.

[0138] Specifically, the rated power of the first resistor load cabinet 5-1 is 10 kW, the rated voltage of the seventh DC fuse 5-2 is 1000 V, and the rated current is 63 A; the rated voltage of the seventh DC circuit breaker 5-3 is 1000 V, and the rated current is 63 A; the current measurement range of the fourth voltage and current sensor 5-4 is 0-100 A, and the voltage measurement range is 0-1000 V.

[0139] As Figure 5 As shown, the second load system 6 includes a first dynamometer 6-1, a first reversible motor 6-2, a first DC / AC inverter 6-3, an eighth DC fuse 6-4, an eighth DC circuit breaker 6-5, and a fifth voltage and current sensor 6-6. The first dynamometer 6-1 is sequentially connected to the third DC bus 10 through the first reversible motor 6-2, the first DC / AC inverter 6-3, the eighth DC fuse 6-4, the eighth DC circuit breaker 6-5, and the fifth voltage and current sensor 6-6.

[0140] Specifically, the first dynamometer 6-1 is an eddy current dynamometer with a rated absorption power of 320 kW; the rated power of the first reversible motor 6-2 is 75 kW, its rated voltage is 380 V, and its rated speed is 1500 r / min; the first DC / AC inverter 6-3 outputs 380 V three-phase AC power; the rated voltage of the eighth DC fuse 6-4 is 1000 V, and the rated current is 63 A; the rated voltage of the eighth DC circuit breaker 6-5 is 1000 V, and the rated current is 63 A; the current measurement range of the fifth voltage and current sensor 6-6 is 0-100 A, and the voltage measurement range is 0-1000 V.

[0141] As Figure 6 shown, it is a single-line diagram of the power supply system of the shipborne multi-source flexible DC loop network test bench of the present invention.

[0142] The working principle of the present invention is as follows:

[0143] First, the first hydrogen fuel cell power supply system and the second hydrogen fuel cell power supply system 2 generate electrical energy and modulate and boost it to 690V, and are respectively connected to the second DC bus 9 and the fourth DC bus 11. The first composite energy storage system 3 and the second composite energy storage system 4 are respectively connected to the first DC bus 8 and the third DC bus 10. The upper computer 14 implements corresponding control strategies based on the output voltages and output currents of each hydrogen fuel cell power supply system and each composite energy storage system, the voltages and currents of each DC bus, and the voltages and currents of the first load system 5 collected by the CRIO controller 12, and the actual load of the second load system 6 collected by the lower computer 13. At the same time, the upper computer 14 transmits the actual load and the daily load power command to the CRIO controller 12, and the CRIO controller 12 converts the actual power into speed and torque control commands and transmits them to the second load system 6; the CRIO controller 12 controls and transmits the daily load control command to the first load system 5.

[0144] As Figure 7 shown, the present invention also proposes an energy management method for the above-mentioned shipborne multi-source flexible DC loop network test bench, including the following steps:

[0145] (1) The first hydrogen fuel cell power supply system 1 and the second hydrogen fuel cell power supply system 2 generate electrical energy and modulate and boost it to 690V, and are respectively connected to the second DC bus 9 and the fourth DC bus 11. The first composite energy storage system 3 and the second composite energy storage system 4 are respectively connected to the first DC bus 8 and the third DC bus 10;

[0146] (2) The upper computer 14 loads the power data during the historical navigation of the ship and transmits the power data to the first load system 5 and the second load system 6.

[0147] Specifically, the power data during the historical navigation of the ship includes the driving load power and the service load power. The upper computer 14 transmits the loaded driving load power and service load power to the CRIO controller 12. The CRIO controller 12 converts the driving load power into a torque control command and the service load power into a power control command, and then transmits the torque control command to the first reversible motor in the second load system 6 and sends the power control command to the first resistor load cabinet in the first load system 5.

[0148] More specifically, the first reversible motor adjusts the motor output torque according to the torque control command of the CRIO controller 12 to simulate the change of the propeller load during ship navigation and realize the simulation of the ship propulsion load; the first resistor load cabinet automatically adjusts the internal resistance value according to the power control command transmitted by the CRIO controller 12, controls the power consumption by changing the resistance value, and realizes the simulation of the service power loads such as ship lighting and communication.

[0149] (3) The host computer 14 acquires the voltage signals and current signals of the first lithium battery and the first supercapacitor in the first hydrogen fuel cell power supply system 1, the second hydrogen fuel cell power supply system 2, and the first composite energy storage system 3, the voltage signals and current signals of the second lithium battery and the second supercapacitor in the second composite energy storage system 4, and the voltage signals and current signals of the first load system 5, generates corresponding power signals according to their respective voltage and current signals, acquires the internal resistance signals of the first hydrogen fuel cell in the first hydrogen fuel cell power supply system 1 and the internal resistance signals of the second hydrogen fuel cell in the second hydrogen fuel cell power supply system 2 collected by the CRIO controller 12, and acquires the rotational speed signal and torque signal of the first dynamometer in the second load system 6 collected by the lower computer 13, and generates the power signal of the second load system 6 according to the rotational speed signal and torque signal.

[0150] Specifically, the host computer 14 multiplies the voltage signal and current signal of the first load system 5 to obtain the power signal of the first load system 5, multiplies the voltage signal and current signal of the first hydrogen fuel cell power supply system 1 to obtain the output power signal of the first hydrogen fuel cell, multiplies the voltage signal and current signal of the second hydrogen fuel cell power supply system 2 to obtain the output power signal of the second hydrogen fuel cell, multiplies the voltage signal and current signal of the first lithium battery in the first composite energy storage system 3 to obtain the output power signal of the first lithium battery, multiplies the voltage signal and current signal of the first supercapacitor in the first composite energy storage system 3 to obtain the output power signal of the first supercapacitor, multiplies the voltage signal and current signal of the second lithium battery in the second composite energy storage system 4 to obtain the output power signal of the second lithium battery, multiplies the voltage signal and current signal of the second supercapacitor in the second composite energy storage system 4 to obtain the output power signal of the second supercapacitor, and obtains the power signal of the second load system 6 according to the rotational speed signal and torque signal of the first dynamometer in the second load system 6 and by using the following formula.

[0151]

[0152] Wherein, T is the torque signal of the first dynamometer in the second load system 6, and n is the rotational speed signal of the first dynamometer in the second load system 6.

[0153] More specifically, the first dynamometer generates a braking torque through the principle of electromagnetic induction and absorbs the mechanical energy output by the first reversible motor. When the first reversible motor drives the rotor of the first dynamometer to rotate, the excitation coil in the stator generates a magnetic field, and the rotor cuts the magnetic induction lines to induce eddy currents, thereby generating a braking torque related to the rotational speed. The first dynamometer acquires the output torque in real time through the built-in torque sensor and acquires the rotor rotational speed in real time through the magnetoelectric sensor.

[0154] (4) The upper computer 14 adds the power signal of the first load system 5 and the power signal of the second load system 6 obtained in step (3) to obtain the total power signal, and inputs the total power signal into a pre-trained operating condition recognition model to obtain the current typical operating condition of the ship.

[0155] Specifically, the operating condition recognition model of the present invention combines a support vector machine (SVM) with a rolling time window for operating condition recognition. Among them, the rolling time window intercepts and analyzes the power data during the historical navigation of the ship in segments in a dynamic manner according to the set time range, and improves the accuracy and reliability of operating condition recognition by continuously updating the power data within the time window.

[0156] More specifically, Figure 8 For the rolling time window of operating condition recognition, the time window T l = 20s, and t is the discrete time step, which is set to 2s in the present invention. That is, when recognizing the operating condition, the key features of 10 data points at the current moment and the previous 18s are calculated to determine the operating condition category of the current ship. The rolling window at time t contains the data within the time range of [t - 18s, t]. The window slides and updates every 2s. When sliding and updating, the earliest data point (i.e., the data point at t - 18s) is removed, and at the same time, the latest data point (t + 2 moment) is added, always keeping 10 data points within the window to ensure the continuity and real-time nature of the data.

[0157] The operating condition recognition model in this step is trained through the following steps:

[0158] (4 - 1) Obtain the original ship historical load power data set, and preprocess the original ship historical load power data set (the purpose is to remove anomalies and missing values caused by sensor failures, communication interferences or other factors) to obtain the preprocessed ship historical load power data set;

[0159] Specifically, for the missing data in the original ship historical load power data set, the linear interpolation method is used for filling, that is, linear estimation is performed based on the valid data points before and after the missing value to obtain a reasonable filling value; for the abnormal data in the original ship historical load power data, first use the Z - score method to detect the outliers, that is, calculate the Z - score of each data point in the original ship historical load power data (the difference between the current value and the mean divided by the standard deviation), and mark the data points with an absolute Z - score greater than 3 as outliers, and then use the moving median based on a 30 - second window to correct all outliers (specifically, taking the median of the data 15 seconds before and after the outlier point centered on the outlier point to replace the outlier).

[0160] (4-2) Extract multiple key features from the preprocessed historical ship load power dataset obtained in step (4-1), which are used to characterize the operating state of the ship.

[0161] Specifically, in order to accurately characterize the operating state of the ship, a rolling time window is used to extract features from the preprocessed historical ship load power dataset obtained in step (4-1). The window duration is 20 s and the step size is 2 s. Within each rolling sliding time window, the mean f of the power fluctuation 1 , variance f 2 and the ratio of variance to mean f 3 are selected as key features. Among them, the mean f 1 reflects the steady-state power level of the ship within the rolling time window, the variance f 2 characterizes the degree of fluctuation of the power data deviating from the average value and depicts the severity of the power fluctuation. The ratio of variance to mean f 3 represents the power fluctuation intensity under the unit power level and reflects the dynamic change characteristics of the power. The calculation formulas for the three are as follows:

[0162]

[0163]

[0164]

[0165] And there is:

[0166]

[0167] ΔP i = P i - P i-1 , (i ≥ 2)

[0168]

[0169] where N is the number of sampling points within the time window, P i is the i-th preprocessed historical ship load power data in the preprocessed historical ship load power dataset (where i ∈ [1, the total number of data in the preprocessed historical ship load power dataset]), is the variance of the change amount of the i-th preprocessed historical ship load power data, ΔP i represents the difference between the preprocessed historical ship load data at the i-th moment and the previous moment, is the average value of the change amount of the i-th preprocessed historical ship load power data.

[0170] (4-3) Perform K-Means clustering analysis on all the key features obtained in step (4-2) to obtain multiple typical operating conditions of the ship and their corresponding multiple preprocessed historical ship load power data.

[0171] Specifically, in this step, the key features obtained in step (4-2) are input into the K-Means clustering algorithm for automatic classification. Based on the principle of minimizing the within-class distance and maximizing the between-class distance, the K-Means clustering algorithm can automatically cluster data points with similar features into the same category, thus obtaining 5 typical operating conditions of the ship, namely complex condition, power gradually increasing condition, power gradually decreasing condition, high-power steady state condition, and low-power steady state condition. After obtaining these typical operating conditions, the preprocessed historical ship load power data corresponding to each condition are integrated to obtain the preprocessed historical ship load power data for 5 typical operating conditions.

[0172] (4-4) Use the multiple preprocessed historical ship load power data and their key features corresponding to each typical operating condition obtained in step (4-3) to perform offline training on the operating condition recognition model to obtain a trained operating condition recognition model.

[0173] Specifically, the preprocessed historical ship load power data and their key features corresponding to 5 typical operating conditions are constructed into a training set. The constructed training set is input into the SVM. Each sample in it is mapped to a high-dimensional feature space through a kernel function, and an optimal classification hyperplane is constructed in this high-dimensional feature space to realize the training of the operating condition recognition model, and finally a trained operating condition recognition model is obtained.

[0174] (5) The host computer 14 selects the corresponding power prediction model according to the current typical operating condition of the ship obtained in step (4) to perform power prediction on the total power signal obtained in step (4) to obtain load prediction data (specifically, the load prediction data 20 s after the current moment).

[0175] Specifically, the power prediction model of the present invention is a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model based on the attention mechanism, and this model can realize the prediction of the load power within the next 20 seconds.

[0176] The power prediction models corresponding to each typical operating condition of the present invention are obtained through the following steps of training:

[0177] (5-1) Obtain the multiple preprocessed historical ship load power data corresponding to the typical operating condition obtained in step (4-3), and construct a training set for the power prediction model corresponding to the typical operating condition according to the historical ship load power data.

[0178] Specifically, first, obtain the volatility of each preprocessed historical ship load power data corresponding to the typical operating condition, and its calculation formula is:

[0179]

[0180] In the formula, P i is the i-th historical ship load power data among all the preprocessed historical ship load power data corresponding to the typical operating condition, where i ∈ [2, the total number of all the preprocessed historical ship load power data corresponding to the typical operating condition]. Then, select all the preprocessed historical ship load power data corresponding to the typical operating condition and their volatilities to construct a training set for the power prediction model corresponding to the typical operating condition.

[0181] (5-2) Use the training set of the power prediction model corresponding to the typical operating condition obtained in step (5-1) to train the power prediction model, so as to obtain the trained power prediction model corresponding to the typical operating condition.

[0182] The advantages of the above steps (4) to (5) are that an innovative working condition identification and power prediction system is constructed: using multi-dimensional features such as mean and variance and K-Means clustering to automatically classify 5 typical working conditions such as complex working conditions and power gradually changing working conditions, real-time identification of ship operating conditions is carried out by the method of support vector machine combined with a rolling time window, and the CNN-LSTM prediction model based on the attention mechanism makes special power predictions for different working conditions, realizing accurate prediction of the load power in the next 20 seconds. This classification prediction method based on working condition identification not only improves the adaptability of the system to complex navigation environments, but also improves the prediction accuracy by considering features such as power fluctuations, providing reliable data support for energy management decisions.

[0183] (6) The host computer 14 obtains the reference power of the first hydrogen fuel cell, the reference power of the second hydrogen fuel cell, the reference power of the first lithium battery, the reference power of the second lithium battery, the reference power of the second super capacitor, and the reference power of the second super capacitor according to the total power signal obtained in step (4), the load prediction data obtained in step (5), the first hydrogen fuel cell internal resistance signal and the second hydrogen fuel cell internal resistance signal obtained in step (3), the current signal of the first lithium battery in the first composite energy storage system 3, and the current signal of the second lithium battery in the second composite energy storage system 4.

[0184] Specifically, in this step, first, the total power signal obtained in step (4) and the load prediction data obtained in step (5) are combined to obtain complete load data. Then, a low-pass filter controller is used to decompose the load data into high-frequency power and low-frequency power, where the high-frequency power is borne by the supercapacitor. Finally, the hydrogen consumption rate data of the hydrogen fuel cell, the internal resistance data of the hydrogen fuel cell, and the low-frequency power are input into a Model Predictive Control (MPC) controller. By setting a multi-objective function that includes minimizing the hydrogen consumption rate, maximizing the service life of the hydrogen fuel cell, and maximizing the service life of the lithium battery, the reference powers of the hydrogen fuel cell and the lithium battery are obtained.

[0185] This step (6) specifically includes the following sub-steps:

[0186] (6-1) Use a low-pass filter controller to decompose the load data to obtain the reference power of the first supercapacitor, the reference power of the second supercapacitor, and the low-frequency power.

[0187] Specifically, the expressions for the reference power of the supercapacitor and the low-frequency power are as follows:

[0188]

[0189]

[0190] Among them, P load is the load data, s is the complex variable, T 1 is the filtering time constant, P SC / 2 is the reference power of the first supercapacitor, which is equal to the reference power of the second supercapacitor, and P low is the low-frequency power.

[0191] (6-2) According to the first hydrogen fuel cell internal resistance signal and the second hydrogen fuel cell internal resistance signal obtained in step (3), obtain the State of Health (SOH) of the first hydrogen fuel cell and the SOH of the second hydrogen fuel cell.

[0192] Specifically, since the output power of the hydrogen fuel cell shows dynamic change characteristics under different operating conditions, the hydrogen fuel cell has different system efficiencies under different SOHs. The hydrogen fuel cell mostly uses the ohmic internal resistance as the characterization value of its health state, and its calculation formula is as follows:

[0193]

[0194] Among them, R end represents the ohmic internal resistance value / Ω at the end of the life of the first / second hydrogen fuel cell; R newRepresents the ohmic resistance value / Ω of the first / second hydrogen fuel cell when not in use; R represents the ohmic resistance value / Ω of the first / second hydrogen fuel cell in the current state.

[0195] (6-3) According to the SOH of the first hydrogen fuel cell and the SOH of the second hydrogen fuel cell obtained in step (6-2), respectively obtain the hydrogen consumption rate curve of the first hydrogen fuel cell and the hydrogen consumption rate curve of the second hydrogen fuel cell.

[0196] Specifically, the hydrogen consumption rate C of the hydrogen fuel cell fc is calculated by the following formula:

[0197]

[0198] where P fc is the output power of the hydrogen fuel cell after passing through the DC / DC converter, is the lower calorific value of hydrogen. η fc is the system efficiency of the hydrogen fuel cell, η fc is defined as follows:

[0199] η fc = ε elec * ε conv * η init

[0200] where η init is the hydrogen fuel cell system efficiency when the hydrogen fuel cell is in the initial state (SOH = 1). The coefficient of variation ε elec represents the degree of reduction of the electrical efficiency η elec (which is determined by the efficiency of the bidirectional boost DC converter and the parasitic power of the auxiliary equipment), and there is:

[0201]

[0202] where η DC / DC is the conversion efficiency of the DC / DC converter; E auz is the parasitic power of the auxiliary equipment; E stack is the stack output power; the coefficient of variation ε conv represents the degree of reduction of the conversion efficiency η conv (which is defined as the ratio of the electrical energy generated by the hydrogen fuel cell to the chemical energy of the hydrogen consumed by the hydrogen fuel cell and decreases as the performance of the hydrogen fuel cell degrades), and there is:

[0203] ε conv = 0.9 + 0.1 * SOH

[0204] More specifically, as Figure 9 shown, by calculating the hydrogen consumption rate of the hydrogen fuel cell at different SOHs, all the obtained hydrogen consumption rates are fitted to obtain the hydrogen consumption rate curve.

[0205] (6-4) Based on the hydrogen consumption rate data of the first hydrogen fuel cell and the hydrogen consumption rate data of the second hydrogen fuel cell obtained in step (6-3), respectively obtain the fitting equations of the hydrogen consumption rate and power of the first hydrogen fuel cell and the second hydrogen fuel cell.

[0206] Specifically, according to the hydrogen consumption rate data of the hydrogen fuel cell calculated in step (6-3), use the quadratic polynomial fitting method to obtain the hydrogen consumption rate C fc The fitting equation is:

[0207]

[0208] where a, b, and c are the fitting coefficients of the hydrogen fuel cell.

[0209] The advantages of the above sub-steps (6-2) to sub-step (6-3) are that an innovative hydrogen fuel cell performance monitoring and hydrogen consumption rate calculation system is constructed: by real-time monitoring the ohmic internal resistance of the hydrogen fuel cell to calculate the health state, and at the same time establishing a system efficiency model considering the efficiency of the bidirectional DC converter, the parasitic power of auxiliary equipment, and the performance degradation of the fuel cell stack, and using the quadratic polynomial fitting method to obtain the quantitative relationship between the hydrogen consumption rate and power. This complete evaluation chain from health state monitoring to hydrogen consumption rate fitting not only realizes the accurate quantification of the performance degradation of the hydrogen fuel cell, but also provides a reliable mathematical model for the subsequent energy management method, effectively supporting the economic dispatch decision based on performance degradation.

[0210] (6-5) Input the fitting equations of the hydrogen consumption rate and power of the first hydrogen fuel cell and the second hydrogen fuel cell obtained in step (6-4), the low-frequency power obtained in step (6-1), and the current signals of the first lithium battery and the second lithium battery obtained in step (3) into the MPC controller to respectively obtain the reference powers of the first hydrogen fuel cell, the second hydrogen fuel cell, the first lithium battery, and the second lithium battery.

[0211] Specifically, the optimization objective function of the MPC controller is to minimize the overall hydrogen consumption of the system while taking into account the performance degradation of the hydrogen fuel cell and the lithium battery.

[0212] The objective function and constraints of the MPC controller are as follows:

[0213]

[0214]

[0215] where C fc1 and C fc2 are respectively the hydrogen consumption rate of the first hydrogen fuel cell and the hydrogen consumption rate of the first hydrogen fuel cell; C battis the equivalent hydrogen consumption rate of the first / second lithium battery; k fc_deg and k Batt_deg are the degradation weights, used to represent the optimization preference of the degradation of the hydrogen fuel cell and the lithium battery in the overall performance of the power system. Its value range is 0 to 1, and k fc_deg is preferably 0.6, and k Batt_deg is preferably 0.4; C fc_deg is the equivalent hydrogen consumption of the degradation of the hydrogen fuel cell; C batt_deg is the degradation cost of the first / second lithium battery; P fc1 is the reference power of the first hydrogen fuel cell, P fc1 is the reference power of the second hydrogen fuel cell, P batt / 2 is the reference power of the first lithium battery, which is equal to the reference power of the second lithium battery.; SOC batt is the state of charge of the first / second lithium battery. d is the degradation factor, and its calculation formula is: d = α * SOH, where α is the energy efficiency optimization factor of the hydrogen fuel cell, and its value determines the sensitivity of the adjustment range of the output power of the hydrogen fuel cell affected by its degradation.

[0216] The advantage of this sub-step (6-5) is that it proposes a multi-objective optimized energy management strategy based on an MPC controller: by designing an objective function that includes minimizing the system hydrogen consumption, maximizing the hydrogen fuel cell life, and maximizing the lithium battery service life, and introducing a degradation weight coefficient to balance the performance degradation of different power sources, the collaborative optimization of the reference powers of the hydrogen fuel cell and the lithium battery is achieved. This strategy not only considers the economy of the system operation, but also realizes the active management of the performance decline of the power source through the introduction of the degradation factor, achieving the dual goals of improving the overall system efficiency and extending the service life of the equipment.

[0217] (7) The CRIO controller 12 obtains the reference power of the first hydrogen fuel cell, the reference power of the second hydrogen fuel cell, the reference power of the first lithium battery, the reference power of the second lithium battery, the reference power of the first supercapacitor, and the reference power of the second supercapacitor obtained from the host computer 14. It transmits the reference power of the first hydrogen fuel cell to the first unidirectional boost DC converter in the first hydrogen fuel cell power supply system, which uses power tracking control to keep the first hydrogen fuel cell near its reference power. It transmits the reference power of the second hydrogen fuel cell to the second unidirectional boost DC converter in the second hydrogen fuel cell power supply system, which uses power tracking control to keep the second hydrogen fuel cell near its reference power. It transmits the reference power of the first lithium battery to the first bidirectional boost DC converter in the first hybrid energy storage system, which uses power tracking control to keep the first lithium battery near its reference power. It transmits the reference power of the first supercapacitor to the second bidirectional boost DC converter in the first hybrid energy storage system, which uses power tracking control to keep the first supercapacitor near its reference power. It transmits the reference power of the second lithium battery to the third bidirectional boost DC converter in the second hybrid energy storage system, which uses power tracking control to keep the second lithium battery near its reference power. It transmits the reference power of the second supercapacitor to the fourth bidirectional boost DC converter in the second hybrid energy storage system, which uses power tracking control to keep the second supercapacitor near its reference power.

[0218] (8) The shipborne multi-source flexible DC ring network test bench remains in closed-loop operation, and the process ends.

[0219] It is easy for those skilled in the art to understand that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A shipborne multi-source flexible DC ring network test bench, comprising a DC ring network main system, a first hydrogen fuel cell power supply system, a second hydrogen fuel cell power supply system, a first composite energy storage system, a second composite energy storage system, a first load system, a second load system, a compact reconfigurable input / output CRIO controller, a lower computer, and an upper computer, characterized in that: The DC ring network main system includes a first DC bus, a second DC bus, a third DC bus, and a fourth DC bus electrically connected end to end through four solid-state switches; The first hydrogen fuel cell power supply system is electrically connected to the second DC bus, the second hydrogen fuel cell power supply system is electrically connected to the fourth DC bus, the first composite energy storage system is electrically connected to the first DC bus, and the second composite energy storage system is electrically connected to the third DC bus; The first load system is electrically connected to the first DC bus, and the second load system is electrically connected to the third DC bus; The CRIO controller is electrically connected to the first hydrogen fuel cell power supply system, the second hydrogen fuel cell power supply system, the first composite energy storage system, the second composite energy storage system, the first load system, and the second load system through weak current signal lines, and is also electrically connected to the first DC bus, the second DC bus, the third DC bus, and the fourth DC bus to monitor their working status in real time. The lower computer is electrically connected to the second load system through a weak-point signal line. The upper computer is electrically connected to the CRIO controller and the lower computer through a weak signal line.

2. The shipborne multi-source flexible DC ring network test bench according to claim 1 is characterized in that: The first hydrogen fuel cell power supply system and the second hydrogen fuel cell power supply system are exactly the same; The first hydrogen fuel cell power supply system includes a hydrogen fuel cell, a first DC circuit breaker, a first DC fuse, a first unidirectional step-up DC converter, a first current and voltage sensor, a second DC fuse, and a second DC circuit breaker; The first hydrogen fuel cell is electrically connected to the second DC busbar via the first DC circuit breaker, the first DC fuse, the first unidirectional step-up DC converter, the first current and voltage sensor, the second DC fuse, and the second DC circuit breaker in sequence. The first hydrogen fuel cell has a rated power of 40kW and an output voltage range of 450-700V; The rated voltage of the first DC circuit breaker and the second DC circuit breaker is 1000V, and the rated current is 63A; The rated voltage of the first DC fuse and the second DC fuse is 1000V, and the rated current is 63A; The first unidirectional boost DC converter is a unidirectional isolated converter with an input side voltage range of 400-850V, an output side voltage range of 400-850V, and a rated power of 40kW; The current measurement range of the first voltage and current sensor is 0-100A, and the voltage measurement range is 0-1000V.

3. The shipborne multi-source flexible DC ring network test bench according to claim 1 or 2, characterized in that: The first composite energy storage system and the second composite energy storage system are completely identical; The first composite energy storage system includes a first lithium battery, a first supercapacitor, a third DC circuit breaker, a third DC fuse, a first bidirectional boost DC converter, a second current and voltage sensor, a fourth DC fuse, a fourth DC circuit breaker, a fifth DC circuit breaker, a fifth DC fuse, a second bidirectional boost DC converter, a third current and voltage sensor, a sixth DC fuse, and a sixth DC circuit breaker; The first lithium battery is electrically connected to the first DC busbar via the third DC circuit breaker, the third DC fuse, the first bidirectional boost DC converter, the second current and voltage sensor, the fourth DC fuse, and the fourth DC circuit breaker in sequence; The first supercapacitor is electrically connected to the first DC busbar via the fifth DC circuit breaker, the fifth DC fuse, the second bidirectional step-up DC converter, the third current and voltage sensor, the sixth DC fuse, and the sixth DC circuit breaker in sequence. The first lithium battery has a capacity of 65kWh, a nominal voltage of 633V, an operating voltage range of 574-693V, and a maximum charge and discharge rate of 1C; The first supercapacitor has a capacity of 5.2F, a rated power of 80kW, and an operating voltage range of 200-800V; The rated voltage of the third DC circuit breaker, the fourth DC circuit breaker, the fifth DC circuit breaker and the sixth DC circuit breaker is 1000V and the rated current is 63A; The rated voltage of the third DC fuse, the fourth DC fuse, the fifth DC fuse and the sixth DC fuse is 1000V and the rated current is 63A; The first bidirectional boost DC converter and the second bidirectional boost DC converter are bidirectional non-isolated converters, with a voltage range of 400-850V on the A side, a voltage range of 300-850V on the B side, and a rated power of 60kW. The current measurement range of the second voltage and current sensor and the third voltage and current sensor is 0-100A, and the voltage measurement range is 0-1000V.

4. The shipborne multi-source flexible DC ring network test bench according to any one of claims 1 to 3, characterized in that: The first load system includes a first resistance load cabinet, a seventh DC fuse, a seventh DC circuit breaker, and a fourth voltage and current sensor; The first resistance load cabinet is electrically connected to the first DC bus through the seventh DC fuse, the seventh DC circuit breaker, and the fourth voltage and current sensor in sequence. The rated power of the first resistance load cabinet is 10kW; The rated voltage of the seventh DC fuse is 1000V and the rated current is 63A; The rated voltage of the seventh DC circuit breaker is 1000V and the rated current is 63A; The fourth voltage and current sensor has a current measurement range of 0-100A and a voltage measurement range of 0-1000V. The second load system includes a first dynamometer, a first reversible motor, a first DC / AC inverter, an eighth DC fuse, an eighth DC circuit breaker, and a fifth voltage and current sensor; The first dynamometer is electrically connected to the third DC bus through the first reversible motor, the first DC / AC inverter, the eighth DC fuse, the eighth DC circuit breaker, and the fifth voltage and current sensor in sequence. The first dynamometer is an eddy current dynamometer with a rated absorbed power of 320kW; The rated power of the first reversible motor is 75 kW, the rated voltage is 380 V, and the rated speed is 1500 r / min; The first DC / AC inverter outputs 380V AC three-phase electricity; The rated voltage of the eighth DC fuse is 1000V and the rated current is 63A; The rated voltage of the eighth DC circuit breaker is 1000V and the rated current is 63A; The fifth voltage and current sensor has a current measurement range of 0-100A and a voltage measurement range of 0-1000V.

5. An energy management method for a shipborne multi-source flexible DC ring network test bench according to any one of claims 1 to 4, characterized in that: The following steps are involved: (1) The first hydrogen fuel cell power supply system and the second hydrogen fuel cell power supply system generate electric energy and modulate and boost the voltage to 690V, and are respectively connected to the second DC bus and the fourth DC bus, and the first composite energy storage system and the second composite energy storage system are respectively connected to the first DC bus and the third DC bus; (2) The host computer loads the power data of the ship during its historical voyage and transmits the power data to the first load system and the second load system. (3) The upper computer obtains the voltage signal and current signal of the first hydrogen fuel cell power supply system, the second hydrogen fuel cell power supply system, the first lithium battery and the first super capacitor in the first composite energy storage system, the voltage signal and current signal of the second lithium battery and the second super capacitor in the second composite energy storage system, and the voltage signal and current signal of the first load system collected by the CRIO controller, generates corresponding power signals according to the respective voltage and current signals, obtains the first hydrogen fuel cell internal resistance signal of the first hydrogen fuel cell power supply system and the second hydrogen fuel cell internal resistance signal of the second hydrogen fuel cell power supply system collected by the CRIO controller, and obtains the speed signal and torque signal of the first dynamometer in the second load system collected by the lower computer, and generates the power signal of the second load system according to the speed signal and the torque signal. (4) The host computer adds the power signal of the first load system obtained in step (3) and the power signal of the second load system to obtain a total power signal, and inputs the total power signal into a pre-trained operating condition recognition model to obtain the current typical operating condition of the ship. (5) The host computer selects the corresponding power prediction model to perform power prediction on the total power signal obtained in step (4) according to the current typical operating conditions of the ship obtained in step (4) to obtain load prediction data (specifically, load prediction data 20 seconds after the current moment). (6) The host computer obtains the reference power of the first hydrogen fuel cell, the reference power of the second hydrogen fuel cell, the reference power of the first lithium battery, the reference power of the second lithium battery, the reference power of the second super capacitor, and the reference power of the second super capacitor based on the total power signal obtained in step (4), the load prediction data obtained in step (5), the internal resistance signal of the first hydrogen fuel cell and the internal resistance signal of the second hydrogen fuel cell obtained in step (3), the current signal of the first lithium battery in the first composite energy storage system, and the current signal of the second lithium battery in the second composite energy storage system. (7) The CRIO controller obtains the reference power of the first hydrogen fuel cell, the reference power of the second hydrogen fuel cell, the reference power of the first lithium battery, the reference power of the second lithium battery, the reference power of the first super capacitor, and the reference power of the second super capacitor obtained by the host computer, and transmits the reference power of the first hydrogen fuel cell to the first unidirectional boost DC converter in the first hydrogen fuel cell power supply system, which adopts a power tracking control method to keep the first hydrogen fuel cell near its reference power, and transmits the reference power of the second hydrogen fuel cell to the second unidirectional boost DC converter in the second hydrogen fuel cell power supply system, which adopts a power tracking control method to keep the second hydrogen fuel cell near its reference power, and transmits the reference power of the first lithium battery to the first composite storage The first bidirectional boost DC converter in the energy storage system adopts a power tracking control method to keep the first lithium battery therein near its reference power, and transmits the reference power of the first supercapacitor to the second bidirectional boost DC converter in the first composite energy storage system, and adopts a power tracking control method to keep the first supercapacitor therein near its reference power, and transmits the reference power of the second lithium battery to the third bidirectional boost DC converter in the second composite energy storage system, and adopts a power tracking control method to keep the second lithium battery therein near its reference power, and transmits the reference power of the second supercapacitor to the fourth bidirectional boost DC converter in the second composite energy storage system, and adopts a power tracking control method to keep the second supercapacitor therein near its reference power. (8) The shipborne multi-source flexible DC ring network test bench maintains closed-loop operation and the process ends.

6. The energy management method of the shipborne multi-source flexible DC ring network test bench according to claim 5 is characterized in that: Step (3) is specifically as follows: the host computer multiplies the voltage signal and the current signal of the first load system to obtain the power signal of the first load system, multiplies the voltage signal and the current signal of the first hydrogen fuel cell power supply system to obtain the output power signal of the first hydrogen fuel cell, multiplies the voltage signal and the current signal of the second hydrogen fuel cell power supply system to obtain the output power signal of the second hydrogen fuel cell, multiplies the voltage signal and the current signal of the first lithium battery in the first composite energy storage system to obtain the output power signal of the first lithium battery, multiplies the voltage signal and the current signal of the first super capacitor in the first composite energy storage system to obtain the output power signal of the first super capacitor, multiplies the voltage signal and the current signal of the second lithium battery in the second composite energy storage system to obtain the output power signal of the second lithium battery, multiplies the voltage signal and the current signal of the second super capacitor in the second composite energy storage system to obtain the output power signal of the second super capacitor, and obtains the power signal of the second load system according to the speed signal and the torque signal of the first dynamometer in the second load system and the following formula. Wherein, T is the torque signal of the first dynamometer in the second load system, and n is the speed signal of the first dynamometer in the second load system. The working condition identification model uses support vector machine SVM combined with rolling time window to identify working conditions; When identifying the working condition, the key features of the 10 data points at the current moment and the previous 18 seconds are calculated to determine the working condition category of the current ship. The rolling window at time t contains data in the time range of [t-18 seconds, t]. The window is updated every 2 seconds. During the sliding update, the earliest data point is removed and the latest data point is added, so that 10 data points are always maintained in the window.

7. The energy management method of the shipborne multi-source flexible DC ring network test bench according to claim 6 is characterized in that: The working condition identification model is trained through the following steps: (4-1) obtaining an original ship historical load power data set, and preprocessing the original ship historical load power data set to obtain a preprocessed ship historical load power data set; Specifically, for the missing data in the original ship historical load power data set, the linear interpolation method is used to fill in the missing data, that is, linear estimation is performed based on the valid data points before and after the missing value to obtain a reasonable filling value; for the abnormal data in the original ship historical load power data, the Z-score method is first used to detect the outliers, that is, the Z score of each data point in the original ship historical load power data is calculated, and the data points with an absolute value of the Z score greater than 3 are marked as outliers, and then all outliers are corrected using the moving median based on a 30-second window. (4-2) Extracting multiple key features from the preprocessed ship historical load power data set obtained in step (4-1), which are used to characterize the operating status of the ship. Specifically, in order to accurately characterize the operating status of the ship, a rolling time window is used to extract features from the preprocessed historical ship load power data set obtained in step (4-1), with a window length of 20s and a step length of 2s. In each rolling sliding time window, the mean f1, variance f2, and the ratio of variance to the mean f3 of the power fluctuation are selected as key features, where the mean f1 reflects the steady-state power level of the ship in the rolling time window, the variance f2 represents the degree of fluctuation of the power data from the mean, and depicts the severity of the power fluctuation, while the ratio of variance to the mean f3 represents the intensity of the power fluctuation at a unit power level, reflecting the dynamic change characteristics of the power. The calculation formulas for the three are: And there are: ΔP i =P i -P i-1 ,(i≥2) Where N is the number of sampling points in the time window, P i is the i-th preprocessed ship historical load power data in the preprocessed ship historical load power data set (where i∈[1, the total number of data in the preprocessed ship historical load power data set]), is the variance of the change in the i-th preprocessed historical load power data of the ship, ΔP i Represents the difference between the preprocessed historical ship load data at the i-th moment and the previous moment, is the average value of the change in the i-th preprocessed historical load power data of the ship. (4-3) Perform K-Means cluster analysis on all key features obtained in step (4-2) to obtain a variety of typical operating conditions of the ship and their corresponding multiple pre-processed historical load power data of the ship. This step is specifically to input the key features obtained in step (4-2) into the K-Means clustering algorithm for automatic classification. By minimizing the intra-class distance and maximizing the inter-class distance, the K-Means clustering algorithm can automatically cluster data points with similar features into the same category, thereby obtaining five typical operating conditions of the ship, namely, complex operating conditions, power gradually increasing operating conditions, power gradually decreasing operating conditions, high-power steady-state operating conditions, and low-power steady-state operating conditions. After obtaining these typical operating conditions, the pre-processed ship historical load power data corresponding to each operating condition are integrated to obtain the pre-processed ship historical load power data of the five typical operating conditions. (4-4) Using the multiple pre-processed ship historical load power data and their key features corresponding to each typical operating condition obtained in step (4-3), the operating condition identification model is offline trained to obtain a trained operating condition identification model.

8. The energy management method of the shipborne multi-source flexible DC ring network test bench according to claim 7 is characterized in that: The power prediction model corresponding to each typical operating condition adopts the convolutional neural network-long short-term memory network CNN-LSTM model based on the attention mechanism, which is trained through the following steps: (5-1) Obtain multiple pre-processed historical ship load power data corresponding to the typical operating condition obtained in step (4-3), and construct a training set of the power prediction model corresponding to the typical operating condition based on the historical ship load power data. Specifically, this step is as follows: first, the fluctuation rate of each pre-processed historical load power data of the ship corresponding to the typical operating condition is obtained, and the calculation formula is: Where P i is the i-th ship historical load power data among all preprocessed ship historical load power data corresponding to the typical operating condition, where i∈[2, the total number of all preprocessed ship historical load power data corresponding to the typical operating condition]. Then, all preprocessed ship historical load power data corresponding to the typical operating condition and their volatility are selected to construct a training set of the power prediction model corresponding to the typical operating condition. (5-2) Using the training set of the power prediction model corresponding to the typical operating condition obtained in step (5-1), the power prediction model is trained to obtain a trained power prediction model corresponding to the typical operating condition.

9. The energy management method of the shipborne multi-source flexible DC ring network test bench according to claim 8 is characterized in that: Step (6) specifically includes the following sub-steps: (6-1) The load data is decomposed by using a low-pass filter controller to obtain a reference power of the first supercapacitor, a reference power of the second supercapacitor, and a low-frequency power. (6-2) Obtain the state of health (SOH) of the first hydrogen fuel cell and the SOH of the second hydrogen fuel cell based on the internal resistance signal of the first hydrogen fuel cell and the internal resistance signal of the second hydrogen fuel cell obtained in step (3). (6-3) Based on the SOH of the first hydrogen fuel cell and the SOH of the second hydrogen fuel cell obtained in step (6-2), obtain the hydrogen consumption rate curve of the first hydrogen fuel cell and the hydrogen consumption rate curve of the second hydrogen fuel cell respectively. (6-4) Based on the hydrogen consumption rate data of the first hydrogen fuel cell and the hydrogen consumption rate data of the second hydrogen fuel cell obtained in step (6-3), the fitting equations of the hydrogen consumption rate and power of the first hydrogen fuel cell and the second hydrogen fuel cell are obtained respectively. (6-5) The fitting equations of the hydrogen consumption rate and power of the first hydrogen fuel cell and the second hydrogen fuel cell obtained in step (6-4), the low-frequency power obtained in step (6-1), and the current signal of the first lithium battery and the current signal of the second lithium battery obtained in step (3) are input into the MPC controller to respectively obtain the reference powers of the first hydrogen fuel cell, the second hydrogen fuel cell, the first lithium battery and the second lithium battery.

10. The energy management method of the shipborne multi-source flexible DC ring network test bench according to claim 9 is characterized in that: The expressions of the reference power and low-frequency power of the supercapacitor in step (6-1) are: Among them, P load is the load data, s is a complex variable, T1 is the filter time constant, P SC / 2 is the reference power of the first supercapacitor, which is equal to the reference power of the second supercapacitor. low For low frequency power. The SOH of the hydrogen fuel cell in step (6-2) is equal to: Where R end Indicates the ohmic internal resistance value of the first / second hydrogen fuel cell at the end of its life / Ω; R new represents the ohmic internal resistance value of the first / second hydrogen fuel cell when not in use / Ω; R represents the ohmic internal resistance value of the first / second hydrogen fuel cell in the current state / Ω. The hydrogen consumption rate C of the hydrogen fuel cell in step (6-3) fc It is calculated by the following formula: Where P fc is the output power of the hydrogen fuel cell after passing through the DC / DC converter, is the lower heating value of hydrogen. fc is the system efficiency of hydrogen fuel cells, η fc is defined as follows: or fc =e elec *e conv *or init where η init is the efficiency of the hydrogen fuel cell system in the initial state of the hydrogen fuel cell (SOH=1). Coefficient of variation ε elec Indicates electrical efficiency η elec of the reduction, and there are: where η DC / DC E is the conversion efficiency of the DC / DC converter; auz is the parasitic power of auxiliary equipment; E stack is the stack output power; coefficient of variation ε conv Indicates the conversion efficiency η conv of the reduction, and there are: ε conv =0.9+0.1*SOH In step (6-4), the hydrogen consumption rate C of the hydrogen fuel cell calculated in step (6-3) is obtained by using a quadratic polynomial fitting method. fc The fitting equation is: Where a, b, and c are the fitting coefficients of the hydrogen fuel cell. The objective function and constraints of the MPC controller in step (6-5) are as follows: Among them C fc1 and C fc2 are the hydrogen consumption rate of the first hydrogen fuel cell and the hydrogen consumption rate of the second hydrogen fuel cell respectively; C batt is the equivalent hydrogen consumption rate of the first / second lithium battery; k fc_deg and k Batt_deg is the degradation weight, which is used to indicate the optimization bias of hydrogen fuel cell and lithium battery degradation in the overall performance of the power system. Its value range is 0 to 1. fc_deg Preferably 0.6, k Batt_deg Preferably 0.4; C fc_deg is the equivalent hydrogen consumption of hydrogen fuel cell degradation; C batt_deg is the degradation cost of the first / second lithium battery; P fc1 is the first hydrogen fuel cell reference power, P fc1 is the second hydrogen fuel cell reference power, P batt / 2 is the reference power of the first lithium battery, which is equal to the reference power of the second lithium battery. ; SOC batt is the state of charge of the first / second lithium battery, d is the degradation factor, and its calculation formula is: d=α*SOH, where α is the energy efficiency optimization factor of the hydrogen fuel cell, and its value determines the sensitivity of the output power adjustment range of the hydrogen fuel cell to its degradation.

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