New energy ship energy storage battery system and control method

Abstract

The invention provides a new energy ship energy storage battery system and a control method, and the method comprises the steps: carrying out the electric quantity detection of a target capacitor bank, obtaining the first electric quantity rise time, and predicting the second electric quantity rise time of the target capacitor bank; extracting a power density characteristic in the charging power density sequence, and comparing the power density characteristic with a capacity rising characteristic of the target capacitor bank to obtain the battery convection energy efficiency of the energy storage battery system of the new energy ship; determining target required power of the energy storage battery system based on the current ship navigation state and the static load power; adjusting the discharge state of the energy storage battery pack according to the target demand power and the convection energy efficiency, and correcting a power control parameter when the target capacitor bank is charged according to the first electric quantity rise time and the second electric quantity rise time, so that the response speed of a power controller can be improved when the electric quantity rise speed of the target capacitor bank is relatively slow; and the dynamic performance of the ship is improved under the working condition of frequent load fluctuation.

Description

Technical Field

[0001] The present application relates to the field of battery energy storage technology, and more specifically, to a new energy ship energy storage battery system and a control method. Background Art

[0002] New energy ships refer to ships that are driven by new clean energy or high-efficiency energy-saving technologies, aiming to reduce the use of traditional fossil fuels, reduce greenhouse gas emissions, improve energy efficiency and reduce the impact on the environment. In recent years, with the gradual maturity of new energy power technologies such as lithium batteries and alcohol fuels, clean ship transformation has gradually become a promotion direction in the development of transportation and water transportation; ships that use new energy power such as lithium batteries have advantages such as less environmental pollution, low operating energy consumption, comfort and low noise, and are currently widely used around the world.

[0003] In the existing new energy ship energy storage battery system, a composite energy storage system is mainly composed of energy storage batteries and supercapacitors. The energy storage battery group provides electrical energy to the supercapacitor group by charging, so that the supercapacitor maintains a high-power standby state. When the ship load changes frequently or the instantaneous high power demand increases, the supercapacitor can discharge quickly to provide instant power response to the load. When the load demand is low, the energy storage battery group quickly replenishes the supercapacitor to ensure that the supercapacitor is always in a responsive state. However, in the prior art, since the energy storage battery has a slow response speed when charging the supercapacitor, the charge of the capacitor rises very slowly, which will affect the dynamic performance of the ship under conditions where the load fluctuates frequently. Summary of the invention

[0004] The present application provides a new energy ship energy storage battery system and control method, which can improve the response speed of the power controller when the charge of the target capacitor group increases slowly, thereby improving the dynamic performance of the ship under conditions where the load fluctuates frequently.

[0005] In a first aspect, the present application provides a power control method, which may be executed by a network device, or may also be executed by a chip configured in the network device, and the present application does not limit this.

[0006] Specifically, the method includes: The target capacitor group is charged through the energy storage battery group, and the charging power density sequence is detected during the charging process; Performing power detection on the target capacitor group to obtain a first power rise time, building a power density regression model through the charging power density sequence, and predicting a second power rise time of the target capacitor group through the power density regression model; Extracting the power density feature in the charging power density sequence and comparing it with the capacity increase feature of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; Obtain the current navigation status and static load power of the ship, and determine the target required power of the energy storage battery system based on the current navigation status and static load power of the ship; The discharge state of the energy storage battery group is adjusted according to the target required power and the convection energy efficiency, and the power control parameters when charging the target capacitor group are corrected according to the first power rise time and the second power rise time.

[0007] In combination with the first aspect, in certain implementations of the first aspect, constructing a power density regression model through the charging power density sequence specifically includes: Performing forward differentiation on each charging power value in the charging power density sequence to obtain a charging power ascending sequence; An autoregressive analysis is performed according to each charging power increase value in the charging power increase sequence and the corresponding time series to obtain a power density regression model.

[0008] In combination with the first aspect, in some implementations of the first aspect, predicting the second charge rise time of the target capacitor group by the power density regression model specifically includes: Obtaining the power density regression model and the charging time interval of the target capacitor group; During the charging time interval of the target capacitor group, power rise prediction is performed according to the power density regression model to obtain charging power rise values ​​corresponding to different prediction moments; The maximum value of all charging power increase values ​​is used as a characteristic increase value, and the second power increase time is determined based on a predicted time corresponding to the characteristic increase value.

[0009] In combination with the first aspect, in some implementations of the first aspect, extracting the power density feature in the charging power density sequence specifically includes: Normalizing the charging power density sequence to obtain a normalized charging power density sequence; Performing modal decomposition according to the normalized sequence of charging power density to obtain an eigenmode function of charging power density; Power density features are extracted based on the charging power density eigenmode function to obtain power density features corresponding to different moments.

[0010] In combination with the first aspect, in certain implementations of the first aspect, before extracting the power density feature in the charging power density sequence and comparing it with the capacity increase feature of the target capacitor group, it also includes: acquiring the capacity increase feature of the target capacitor group.

[0011] In combination with the first aspect, in some implementations of the first aspect, obtaining the capacity increase feature of the target capacitor group specifically includes: Performing power detection on the target capacitor group to obtain a power collection sequence; The power collection sequence is normalized and then forward-differentiated to obtain a capacity increase feature sequence. The capacity increase feature sequence is collected at equal intervals to obtain capacity increase features corresponding to different moments.

[0012] In combination with the first aspect, in certain implementations of the first aspect, determining the target required power of the energy storage battery system based on the current navigation state of the ship and the static load power specifically includes: Obtain the current ship's navigation status to determine the ship's propulsion power; The target required power of the energy storage battery system is determined according to the ship propulsion power and the static load power.

[0013] In a second aspect, the present application provides a new energy ship energy storage battery system, which includes a power control unit, and the power control unit includes: A data detection module, the detection module is used to charge the target capacitor group through the energy storage battery group, and detect and obtain a charging power density sequence during the charging process; A data processing module, the data processing module is used to detect the charge of the target capacitor group to obtain a first charge rise time, build a power density regression model through the charging power density sequence, and predict the second charge rise time of the target capacitor group through the power density regression model; The data processing module is also used to extract the power density characteristics in the charging power density sequence and compare it with the capacity increase characteristics of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; The data processing module is also used to obtain the current navigation status of the ship and the static load power, and determine the target required power of the energy storage battery system based on the current navigation status of the ship and the static load power; A power control module, the power control module is used to adjust the discharge state of the energy storage battery group according to the target required power and the convection energy efficiency, and to correct the power control parameters when charging the target capacitor group according to the first charge rise time and the second charge rise time.

[0014] In a third aspect, the present application provides a computer terminal device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned power control method.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned power control method.

[0016] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In a new energy ship energy storage battery system and control method provided by the present application, first, a target capacitor group is charged through an energy storage battery group, and a charging power density sequence is detected during the charging process; the target capacitor group is detected for power to obtain a first power rise time, a power density regression model is constructed through the charging power density sequence, and a second power rise time of the target capacitor group is predicted through the power density regression model; the power density characteristics in the charging power density sequence are extracted and compared with the capacity rise characteristics of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; the current ship navigation state and static load power are obtained, and the target required power of the energy storage battery system is determined based on the current ship navigation state and static load power; the discharge state of the energy storage battery group is adjusted according to the target required power and the convection energy efficiency, and the power control parameters when charging the target capacitor group are corrected according to the first power rise time and the second power rise time.

[0017] It can be seen that by detecting the charging power density sequence and establishing a power density regression model, the system of the present application can predict the rising trend of the power of the target capacitor group; and based on the second power rise time predicted by the power rise trend, the charging response of the target capacitor group is estimated. When it is found that the actual power rise time (the first power rise time) is slow and the prediction model indicates that the charging of the target capacitor group is lagging, the power control parameters when charging the target capacitor group are corrected so as to adjust the charging power in advance when the charging response is poor, so as to ensure that the system quickly adapts to the load demand; and in the charging process, the present application extracts the charging power density characteristics and compares them with the target capacitor group. By comparing the capacity increase characteristics of the capacitor group, the system can obtain the battery convection energy efficiency in real time. When the load fluctuates frequently, the change of the battery convection energy efficiency can directly affect the charging speed and discharge capacity, and then adopts multi-level discharge state control, that is, the discharge state of the energy storage battery group is adjusted according to the target demand power and the battery convection energy efficiency. When the load demand increases, the controller can immediately increase the discharge power to make up for the insufficient power of the capacitor group. In addition, by controlling the output of the energy storage battery group, the system can give priority to using the battery group to provide instantaneous power when the capacitor group charges slowly, thereby reducing the impact of load fluctuations on the ship's power system.

[0018] In summary, the present application performs multi-stage discharge state control through the target demand power and convection energy efficiency of the ship, and then corrects the power control parameters according to the first charge rise time and the second charge rise time. This can improve the response speed of the power controller when the charge rise speed of the target capacitor group is slow, and improve the dynamic performance of the ship under conditions where the load fluctuates frequently. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is an exemplary flow chart of a power control method according to some embodiments of the present application; Figure 2 is an exemplary process for determining the second power rise time in some embodiments of the present application; Figure 3 is a schematic diagram of the structure of a power control unit according to some embodiments of the present application; Figure 4 It is a structural schematic diagram of a computer terminal device for implementing a power control method according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] The present application charges a target capacitor group through an energy storage battery group, and detects a charging power density sequence during the charging process; performs power detection on the target capacitor group to obtain a first power rise time, constructs a power density regression model through the charging power density sequence, and predicts a second power rise time of the target capacitor group through the power density regression model; extracts the power density characteristics in the charging power density sequence and compares them with the capacity rise characteristics of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; obtains the current ship navigation state and static load power, and determines the target required power of the energy storage battery system based on the current ship navigation state and static load power; adjusts the discharge state of the energy storage battery group according to the target required power and the convection energy efficiency, and corrects the power control parameters when charging the target capacitor group according to the first power rise time and the second power rise time, so as to improve the response speed of the power controller when the power rise speed of the target capacitor group is slow, and improve the dynamic performance of the ship under the working condition of frequent load fluctuations.

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a power control method according to some embodiments of the present application, and the power control method 100 mainly includes the following steps: In step S101, the target capacitor group is charged by the energy storage battery group, and a charging power density sequence is detected during the charging process.

[0022] It should be noted that the energy storage battery pack described in the present application is a core device for storing electrical energy and providing power for ships. The energy storage battery pack is composed of multiple battery cells, and the cells are combined in series or parallel to meet the ship's requirements for energy density and power output. The energy storage battery pack stores electrical energy from shore power or shipboard power generation systems in the ship, and provides electricity as needed during navigation to support the operation of propulsion systems, living facilities and various equipment; the target capacitor group described in the present application refers to an energy storage device group with supercapacitors as the core. The target capacitor group usually forms a composite energy storage system together with the energy storage battery group, and its main function is to provide rapid power response when the ship load fluctuates frequently.

[0023] Optionally, in some embodiments, charging the target capacitor group through the energy storage battery group can be achieved by the following steps: obtaining a battery charging cycle, and charging the selected capacitor module in the target capacitor group through the energy storage battery group during the battery charging cycle. It should be noted that the battery charging cycle is preset to a constant time value according to demand, and the battery charging cycle is the interval time for charging the target capacitor group through the energy storage battery group. For example, in a specific implementation, the battery charging cycle can be preset to 1h and the charging time can be preset to 5min. Then, at the beginning of each battery charging cycle, the selected capacitor module in the target capacitor group is charged according to the preset charging time.

[0024] It should be noted that the charging power density sequence includes multiple charging power values ​​and corresponding collection times. The charging power density sequence is used to reflect the charging speeds corresponding to different times. Optionally, in some embodiments, the power detection can be performed at equal intervals by a power sensor located on the connection line between the output end of the energy storage battery group and the input end of the target capacitor group to obtain the charging power density sequence. In some other embodiments, other devices or equipment capable of power collection can also be used, which is not limited here.

[0025] In step S102, the target capacitor group is charged with electricity to obtain a first charge rise time, a power density regression model is constructed through the charging power density sequence, and a second charge rise time of the target capacitor group is predicted through the power density regression model.

[0026] It should be noted that, usually as the remaining power of the capacitor decreases, its internal resistance will usually increase. The present application calculates the power of the target capacitor group by measuring the internal resistance of the capacitor based on the relationship between the internal resistance of the capacitor and the remaining power of the battery. Optionally, in some embodiments, the power detection of the target capacitor group to obtain the first power rise time can be achieved by the following steps: performing multiple internal resistance tests on the target capacitor group at equal intervals, and determining the power values ​​of the target capacitor group at different test times according to the internal resistance values ​​corresponding to different test times according to a preset power mapping table, and taking the difference between the test time corresponding to the maximum power rise value and the charging start time as the first power rise time, and the charging start time is the start time of charging the target capacitor group through the energy storage battery group. In specific implementation, a temperature compensation unit can be added when the power detection of the target capacitor group is performed, that is, temperature compensation is added to the actual capacity detection. Since the capacity of the battery changes with temperature, the current temperature of the battery is obtained through a temperature sensor, and the capacity is corrected in combination with the temperature compensation model.

[0027] Optionally, in some embodiments, the voltage-charge characteristic curve of the target capacitor group can also be obtained. The voltage-charge characteristic curve is tested and calibrated according to the target capacitor group before leaving the factory, and the voltage between the capacitor plates of the target capacitor group is detected, so that a mathematical model between voltage and charge is established according to the voltage-charge characteristic curve to estimate the charge value, and the charge value at the different test times is obtained, and the difference between the test time corresponding to the maximum charge rise time and the charging start time is used as the first charge rise time.

[0028] Optionally, in some embodiments, constructing a power density regression model using the charging power density sequence may be implemented by the following steps: Performing forward differentiation on each charging power value in the charging power density sequence to obtain a charging power ascending sequence; An autoregressive analysis is performed according to each charging power increase value in the charging power increase sequence and the corresponding time series to obtain a power density regression model.

[0029] It should be noted that the function of the power density regression model is to predict the charge increase behavior of the target capacitor group under different power density conditions through the input charging power increase value data. In the process of constructing a power density regression model through the charging power increase value data, the regression model can use linear regression, nonlinear regression or machine learning regression algorithm (such as support vector machine regression or neural network) to fit the power density change trend. In specific implementation, a moving average autoregressive model can also be constructed through each charging power increase value in the charging power increase sequence and the corresponding time series as the power density regression model.

[0030] A preferred embodiment of the power density regression model obtained in this application is given below: First, the prediction period is preset to 1 minute. At this time, the charging power increase values ​​of the target capacitor group in the past 1 minute can be recorded respectively to obtain a charging power increase sequence. In other embodiments, the maintenance time period can also be preset to other time lengths; then, a time series graph of the charging power increase sequence can be drawn, and the horizontal axis of the time series graph corresponds to different moments, and then the time series graph of the charging power increase sequence can be exponentially converted to eliminate the trend of the variance in the time series graph changing with time.

[0031] Secondly, according to the time series diagram of the charging power rising sequence, draw the autocorrelation coefficient diagram of the charging power rising value, wherein the horizontal axis of the autocorrelation coefficient diagram is the number of lags and the vertical axis is the value of the autocorrelation coefficient. Draw the partial autocorrelation coefficient diagram of the charging power rising value, wherein the horizontal axis of the partial autocorrelation coefficient diagram is the number of lags and the vertical axis is the value of the partial autocorrelation coefficient.

[0032] According to the characteristics of the autocorrelation coefficient graph and the partial autocorrelation coefficient graph, the order of the model and the range of coefficient values ​​can be preliminarily determined. For example, the autocorrelation coefficient graph can be drawn to observe whether the autocorrelation coefficient shows truncation characteristics after a certain order. If the autocorrelation coefficient drops sharply after a certain order and remains near 0, the order of the autoregressive model can be preliminarily determined; the partial autocorrelation coefficient graph can be drawn to observe whether the partial autocorrelation coefficient shows truncation characteristics after a certain order. If the partial autocorrelation coefficient drops sharply after a certain order and remains near 0, the order of the moving average model can be preliminarily determined.

[0033] In specific implementation, the last significant autocorrelation coefficient can be found according to the autocorrelation coefficient graph, which is the order of the autocorrelation model. For example, if the last significant autocorrelation coefficient in the autocorrelation coefficient graph is at the 3rd order, the order of the autocorrelation model is 3; then, according to the partial autocorrelation coefficient graph, the last significant partial autocorrelation coefficient can be found, which is the order of the moving average model. For example, if the last significant partial autocorrelation coefficient in the partial autocorrelation coefficient graph is at the 2nd order, the order of the moving average model is 2; finally, according to the autocorrelation coefficient graph and the partial autocorrelation coefficient graph, the order (p, q) of the autoregressive moving average model is determined. For example, if the autocorrelation coefficient graph and the partial autocorrelation coefficient graph both decay to zero after the 3rd order, the order of the autoregressive moving average model is (3, 3); then, according to the order of the autoregressive moving average model, appropriate parameters are selected by the least squares method to establish an autoregressive moving average model of the charging power rising sequence, and the power density regression model is obtained.

[0034] In specific implementation, for example, the least squares method can be used to perform parameter estimation and significance test on the model autoregressive and moving average processes. In some embodiments, the significance test level is 0.05. Finally, the most suitable autoregressive moving average model parameters are selected according to the Schwarz Bayes criterion to determine the final autoregressive moving average model of the charging power rise value.

[0035] It should be noted that the second power rise time is used to reflect the characteristic response time of power rise prediction according to the power output of the energy storage battery group. Optionally, in some embodiments, reference Figure 2 As shown in FIG. 1 , this figure is an exemplary process for determining the second charge rise time in some embodiments of the present application. The second charge rise time of the target capacitor group predicted by the power density regression model can be implemented by the following steps: In step S1021, the power density regression model and the charging time interval of the target capacitor group are obtained; In step S1022, within the charging time interval of the target capacitor group, power increase prediction is performed according to the power density regression model to obtain charging power increase values ​​corresponding to different prediction moments; In step S1023, the maximum value of the charging power increase value is taken as the characteristic increase value, and the second power increase time is determined based on the predicted time corresponding to the characteristic increase value.

[0036] In specific implementation, the difference between the predicted moment corresponding to the characteristic rise value and the charging start time can be used as the second power rise time. It should be noted that the difference between the actually detected power rise time (first power rise time) and the predicted theoretical rise time (second power rise time) in this application is used to reflect the dynamic response lag of the battery pack in the new energy ship energy storage battery during the charging process, that is, the delay time of the system in responding to power changes under actual working conditions.

[0037] In step S103, the power density feature in the charging power density sequence is extracted and compared with the capacity increase feature of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system.

[0038] Optionally, in some embodiments, extracting the power density feature in the charging power density sequence may be achieved by using the following steps: Normalizing the charging power density sequence to obtain a normalized charging power density sequence; Performing modal decomposition according to the normalized sequence of charging power density to obtain an eigenmode function of charging power density; Power density features are extracted based on the charging power density eigenmode function to obtain power density features corresponding to different moments.

[0039] In specific implementation, the empirical mode decomposition method can be used to perform empirical mode decomposition on the charging power density normalized sequence to obtain a group of intrinsic mode function components, and then the mean of all intrinsic mode function components is used as the charging power density intrinsic mode function, wherein the empirical mode decomposition method can be used to perform empirical mode decomposition on the charging power density normalized sequence by the empirical mode decomposition method. The following steps are adopted to implement it: identifying local maximum and local minimum in the charging power density normalized sequence; obtaining the upper envelope and the lower envelope by interpolating the local extreme points, and averaging to generate an intermediate sequence; iteratively extracting the intrinsic mode function until the original sequence is decomposed into a number of intrinsic mode function components and a residual term.

[0040] Optionally, in some embodiments, the power density characteristics corresponding to different moments may be obtained by sampling the charging power density eigenmode function at equal intervals.

[0041] Optionally, in some embodiments, before extracting the power density feature in the charging power density sequence and comparing it with the capacity increase feature of the target capacitor group, the method further includes: acquiring the capacity increase feature of the target capacitor group.

[0042] The following steps may be used to obtain the capacity increase characteristics of the target capacitor group: Performing power detection on the target capacitor group to obtain a power collection sequence; The power collection sequence is normalized and then forward-differentiated to obtain a capacity increase feature sequence. The capacity increase feature sequence is collected at equal intervals to obtain capacity increase features corresponding to different moments.

[0043] In specific implementation, the voltage and current sensors can be connected to the target capacitor group, and the sensors collect sensor data according to the set sampling frequency. According to the measured voltage and current data, combined with the capacitance value of the supercapacitor, the charge change of the capacitor can be obtained through the built-in integration algorithm to obtain the charge collection sequence. The capacity increase characteristic sequence contains multiple capacity increase values, and the capacity increase value is the normalized charge difference value. The forward difference in this application refers to calculating the difference between two adjacent elements in the sequence to obtain a new differential sequence. This application can analyze the trend of changes in the sequence through forward difference.

[0044] It should be noted that the battery convection energy efficiency described in the present application is used to reflect the dynamic conversion efficiency of the battery of the new energy ship energy storage battery system when performing energy convection, wherein the power density characteristic represents the power characteristics of the energy storage battery group input, and the capacity increase characteristic represents the actual output response of the target capacitor group (i.e., energy absorption and storage efficiency). If there is a high linear correlation between the power density characteristic and the capacity increase characteristic, it can be explained that the change in battery power output can be well absorbed and converted by the battery, the system has a high convection energy efficiency, and the energy loss when charging the capacitor under complex working conditions is less. In some embodiments, the Pearson correlation coefficient between the capacity increase characteristic and the power density characteristic corresponding to different times can be used as the battery convection energy efficiency.

[0045] During the charging process, the system can obtain the battery convection energy efficiency in real time by extracting the charging power density characteristics and comparing them with the capacity rise characteristics of the target capacitor group. When the load fluctuates frequently, the change in battery convection energy efficiency can directly affect the charging speed and discharge capacity. Based on this feature, the system can optimize the discharge state and power control parameters in real time to maximize energy efficiency and reduce energy loss and charging delay during the charging process.

[0046] In step S104, the current navigation state and static load power of the ship are obtained, and the target required power of the energy storage battery system is determined based on the current navigation state and static load power of the ship.

[0047] It should be noted that the current navigation status of the ship includes the current operating conditions and mode of the ship, including information such as speed, acceleration, navigation direction, environmental conditions of the navigation area (such as wind speed), etc. The navigation status of the ship can be acquired in real time through a sensor system consisting of GPS, acceleration sensor, and speed sensor. The static load power represents the basic power demand of the ship in the current navigation state, including the basic power provided for the propulsion system, navigation system, living facilities, etc.

[0048] Optionally, in some embodiments, determining the target required power of the energy storage battery system based on the current navigation state of the ship and the static load power can be achieved by using the following steps: Obtain the current ship's navigation status to determine the ship's propulsion power; The target required power of the energy storage battery system is determined according to the ship propulsion power and the static load power.

[0049] The ship propulsion power can be calculated and determined in the following manner: the ship propulsion power = environmental impact power + k × ship speed ^ n, wherein k and n are constant coefficients calibrated according to the ship size and weight when calculating the ship propulsion power. The environmental impact power can be determined by a multivariate mapping function determined based on factors such as wind speed, wave height, and tidal current, that is, P 环境影响 =f(wind speed, wave height, tidal current).

[0050] In some embodiments, the target required power of the energy storage battery system can also be calculated and determined according to the following method: 目标 =P 推进 +P 辅助负载 +P 环境影响 −P 其他电力来源 , where P 推进 is the power requirement of the ship propulsion system; P 辅助负载 is the power requirement of other auxiliary equipment on board, such as lighting, communication and other equipment; P 环境影响 P is the change in power demand caused by environmental factors such as wind speed, wave height, and tidal current; 其他电力来源 It is the power provided by other power sources of the ship (such as diesel engines or fuel cells), wherein the power demand of the ship propulsion system, the auxiliary load power and the environmental impact power can be based on the said.

[0051] Optionally, in some embodiments, the target demand power of the energy storage battery system is the sum of the ship propulsion power and the static load power.

[0052] In step S105, the discharge state of the energy storage battery group is adjusted according to the target required power and the convection energy efficiency, and the power control parameters when charging the target capacitor group are corrected according to the first charge rise time and the second charge rise time.

[0053] It should be noted that in order to ensure that the energy storage system can fully cope with the load demand when the power demand of the ship is large, and to avoid insufficient power during the charging process of the target capacitor group and affect the power, the energy storage battery system in the present application realizes fine control of the discharge process through the setting of multi-stage discharge state; the multi-stage discharge state can be determined by mapping the threshold interval between the target demand power and the convection energy efficiency to determine the specific operations under different discharge states. Optionally, in some embodiments, the power demand can be divided into multiple threshold intervals based on the target demand power and the convection energy efficiency, and the corresponding discharge states are mapped through different intervals to control the discharge amount and discharge speed of the battery pack. In specific implementation, the energy storage battery pack has multiple discharge states, and different discharge states correspond to different numbers of batteries discharged at the same time, thereby avoiding insufficient charging power when charging the target capacitor group when the power demand of the ship is large, resulting in damage to the ship's power.

[0054] Optionally, in some embodiments, for example, the number of batteries and the discharge mode to be enabled are determined based on a combination of current power demand and energy efficiency: Low power demand range + high energy efficiency range: The system enters a low discharge state, and only a few battery packs may be required to participate in the discharge, meeting the basic load demand while avoiding energy waste.

[0055] Medium power demand range + medium energy efficiency range: The system enters a medium discharge state and enables a medium number of battery packs for discharge, ensuring that the system can follow the load demand and avoid power shortage.

[0056] High power demand range + low energy efficiency range: The system enters a high discharge state and enables more battery units to discharge to meet the high load demand of the ship. In addition, in order to avoid losses caused by low energy efficiency, some optimization measures can be taken, such as adjusting the battery output frequency or selecting battery modules with higher energy efficiency for priority discharge.

[0057] In some embodiments, the discharge state of the energy storage battery pack in the present application can be divided into multiple levels, each level corresponding to a different number of discharge batteries and output power settings, which are described as follows: Level 1 discharge state (low discharge): When the power demand is in a lower range, only a small number of battery cells participate in the discharge to maintain basic power supply. Level 2 discharge state (medium discharge): When the power demand reaches the medium range, the number of battery cells is increased to ensure the smooth operation of the load. This state is suitable for operation under general cruising or medium load conditions. Level 3 discharge state (high discharge): Under high load demand conditions, more battery cells are enabled, while ensuring that the charging power of the capacitor group is sufficient and does not affect the charging of the capacitor and the power output of the system. Level 4 discharge state (extreme discharge): When the power demand reaches the highest range and the battery convection efficiency is low, the system can enter the extreme discharge state and enable all battery cells to ensure sufficient power output under heavy load conditions, but it is limited to short-term use to prevent battery loss caused by excessive discharge.

[0058] Optionally, in some embodiments, the power control parameter when charging the target capacitor group is corrected according to the first charge rise time and the second charge rise time can be implemented by the following steps: Obtain the initial feedback gain of the PID controller when charging the target capacitor bank; Obtaining the first electrical quantity rise time, the second electrical quantity rise time, and a preset deviation standard value; The initial feedback gain of the PID controller is corrected according to the first electrical quantity rise time, the second electrical quantity rise time and a preset deviation standard value.

[0059] It should be noted that the feedback gain of the PID controller includes: proportional gain Kp, integral gain Ki and differential gain Kd. In specific implementation, the time deviation between the first electrical charge rise time and the second electrical charge rise time can be first obtained, and the ratio between the time deviation and the standard value of the deviation is used as the adjustment coefficient, and then the feedback gain of the PID controller is adjusted according to the adjustment coefficient. In some embodiments, the adjustment coefficient can be multiplied with the initial proportional gain Kp, integral gain Ki and differential gain Kd respectively to obtain the corrected feedback gain for power control of the charging of the target capacitor group. By correcting the power control parameters when charging the target capacitor group, the response speed of the controller can be improved when the electrical charge of the target capacitor group rises slowly, thereby avoiding affecting the dynamic performance of the ship under conditions where the load fluctuates frequently.

[0060] It should be noted that, since load fluctuations frequently affect the system power demand, when the rate of increase of the capacitor group power lags behind, the system will give priority to adjusting the energy storage battery group response demand by correcting the power control parameters when charging the target capacitor group. The control scheme continuously monitors the current ship load and power demand to ensure that even when the capacitor is charging slowly, the battery pack can flexibly supplement the load demand, thereby improving the overall dynamic response performance of the ship, and through the combined control of predictive models, feedback control, power density feature analysis and multi-stage discharge states, the present application can respond quickly when the capacitor group power rises slowly, thereby avoiding the impact of load fluctuations on the ship's power system. This method ensures that the system can respond flexibly under frequently fluctuating working conditions to maintain stable operation and efficient power output of the ship.

[0061] In addition, in another aspect of the present application, in some embodiments, the present application provides a new energy ship energy storage battery system, the system includes a power control unit, reference Figure 3 , which is a schematic diagram of the structure of a power control unit according to some embodiments of the present application, the power control unit 200 includes: a data detection module 201, a data processing module 202 and a power control module 203, which are described as follows: A data detection module 201, wherein the data detection module 201 is used to charge the target capacitor group through the energy storage battery group and detect and obtain a charging power density sequence during the charging process; A data processing module 202, the data processing module 202 is used to detect the charge of the target capacitor group to obtain a first charge rise time, build a power density regression model through the charging power density sequence, and predict the second charge rise time of the target capacitor group through the power density regression model; The data processing module 202 is also used to extract the power density characteristics in the charging power density sequence and compare it with the capacity increase characteristics of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; The data processing module 202 is also used to obtain the current navigation state of the ship and the static load power, and determine the target required power of the energy storage battery system based on the current navigation state of the ship and the static load power; A power control module 203 is used to adjust the discharge state of the energy storage battery group according to the target required power and the convection energy efficiency, and to correct the power control parameters when charging the target capacitor group according to the first charge rise time and the second charge rise time.

[0062] The above describes in detail an example of a new energy ship energy storage battery system and control method provided in an embodiment of the present application. It can be understood that in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to executing each function.

[0063] Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed in this document, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in hardware or in a computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Therefore, professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0064] In addition, the present application also provides a computer terminal device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned power control method.

[0065] In some embodiments, reference Figure 4 , which is a schematic diagram of the structure of a computer terminal device implementing a power control method according to some embodiments of the present application. A power control method in the above embodiment can be Figure 4 The computer terminal device 300 shown in the figure is implemented, and the computer terminal device 300 includes at least one communication bus 301, a communication interface 302, a processor 303 and a memory 304.

[0066] The processor 303 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of a power control method in the present application.

[0067] The communication bus 301 may include a path for transmitting information between the above-mentioned components.

[0068] The memory 304 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 304 may exist independently and be connected to the processor 303 via the communication bus 301. The memory 304 may also be integrated with the processor 303.

[0069] The memory 304 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 303. The processor 303 is used to execute the program code stored in the memory 304. The program code may include one or more software modules. The determination of the target demand power in the above embodiment can be implemented by the processor 303 and one or more software modules in the program code in the memory 304.

[0070] The communication interface 302 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0071] Optionally, the computer terminal device 300 may further include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0072] In a specific implementation, as an embodiment, a computer terminal device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0073] The above-mentioned computer terminal device may be a general-purpose computer terminal device or a dedicated computer terminal device. In a specific implementation, the computer terminal device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer terminal device.

[0074] In addition, in other aspects of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above power control method.

[0075] In summary, in a new energy ship energy storage battery system and control method disclosed in an embodiment of the present application, first, the target capacitor group is charged through the energy storage battery group, and a charging power density sequence is detected during the charging process; the target capacitor group is detected for power to obtain a first power rise time, a power density regression model is constructed through the charging power density sequence, and the second power rise time of the target capacitor group is predicted through the power density regression model; the power density characteristics in the charging power density sequence are extracted and compared with the capacity rise characteristics of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; the current ship navigation state and static load power are obtained, and the target required power of the energy storage battery system is determined based on the current ship navigation state and static load power; the discharge state of the energy storage battery group is adjusted according to the target required power and convection energy efficiency, and the power control parameters when charging the target capacitor group are corrected according to the first power rise time and the second power rise time, so that the response speed of the power controller can be improved when the power rise speed of the target capacitor group is slow, and the dynamic performance of the ship can be improved under the working condition of frequent load fluctuations.

[0076] The above is only an embodiment of the present application, and the common knowledge such as the specific technical scheme or characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical scheme of the present application, several modifications and improvements can be made, which should also be regarded as the scope of protection of the present application, and these will not affect the effect of the implementation of the present application and the practicality of the patent.

[0077] The scope of protection claimed by this application shall be based on the content of its claims. The specific implementation methods and other records in the specification can be used to interpret the content of the claims. Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include these modifications and variations.

Claims

1. A power control method for power control of a new energy ship energy storage battery system, characterized in that: The method comprises the following steps: The target capacitor group is charged through the energy storage battery group, and the charging power density sequence is detected during the charging process; Performing power detection on the target capacitor group to obtain a first power rise time, building a power density regression model through the charging power density sequence, and predicting a second power rise time of the target capacitor group through the power density regression model; Extracting the power density feature in the charging power density sequence and comparing it with the capacity increase feature of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; Obtain the current navigation status and static load power of the ship, and determine the target required power of the energy storage battery system based on the current navigation status and static load power of the ship; The discharge state of the energy storage battery group is adjusted according to the target required power and the convection energy efficiency, and the power control parameters when charging the target capacitor group are corrected according to the first power rise time and the second power rise time.

2. The method according to claim 1, characterized in that Constructing a power density regression model through the charging power density sequence specifically includes: Performing forward differentiation on each charging power value in the charging power density sequence to obtain a charging power ascending sequence; An autoregressive analysis is performed according to each charging power increase value in the charging power increase sequence and the corresponding time series to obtain a power density regression model.

3. The method according to claim 1, characterized in that Predicting the second charge rise time of the target capacitor group by the power density regression model specifically includes: Obtaining the power density regression model and the charging time interval of the target capacitor group; During the charging time interval of the target capacitor group, power rise prediction is performed according to the power density regression model to obtain charging power rise values ​​corresponding to different prediction moments; The maximum value of all charging power increase values ​​is used as a characteristic increase value, and the second power increase time is determined based on a predicted time corresponding to the characteristic increase value.

4. The method according to claim 1, characterized in that Extracting the power density feature in the charging power density sequence specifically includes: Normalizing the charging power density sequence to obtain a normalized charging power density sequence; Performing modal decomposition according to the normalized sequence of charging power density to obtain an eigenmode function of charging power density; Power density features are extracted based on the charging power density eigenmode function to obtain power density features corresponding to different moments.

5. The method according to claim 1, characterized in that Before extracting the power density feature in the charging power density sequence and comparing it with the capacity increase feature of the target capacitor group, the method further includes: acquiring the capacity increase feature of the target capacitor group.

6. The method according to claim 5, characterized in that Acquiring the capacity increase characteristic of the target capacitor group specifically includes: Performing power detection on the target capacitor group to obtain a power collection sequence; The power collection sequence is normalized and then forward-differentiated to obtain a capacity increase feature sequence. The capacity increase feature sequence is collected at equal intervals to obtain capacity increase features corresponding to different moments.

7. The method according to claim 1, characterized in that The target power requirement of the energy storage battery system is determined based on the current ship's navigation status and static load power. Specifically, it includes: Obtain the current ship's navigation status to determine the ship's propulsion power; The target required power of the energy storage battery system is determined according to the ship propulsion power and the static load power.

8. A new energy ship energy storage battery system, including a power control unit, characterized in that: The power control unit comprises: A data detection module, the data detection module is used to charge the target capacitor group through the energy storage battery group, and detect and obtain a charging power density sequence during the charging process; A data processing module, the data processing module is used to detect the charge of the target capacitor group to obtain a first charge rise time, build a power density regression model through the charging power density sequence, and predict the second charge rise time of the target capacitor group through the power density regression model; The data processing module is also used to extract the power density characteristics in the charging power density sequence and compare it with the capacity increase characteristics of the target capacitor group to obtain the battery convection energy efficiency of the new energy ship energy storage battery system; The data processing module is also used to obtain the current navigation status of the ship and the static load power, and determine the target required power of the energy storage battery system based on the current navigation status of the ship and the static load power; A power control module is used to adjust the discharge state of the energy storage battery group according to the target required power and the convection energy efficiency, and to correct the power control parameters when charging the target capacitor group according to the first power rise time and the second power rise time.

9. A computer terminal device, characterized in that: The computer terminal device comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the power control method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one computer program, characterized in that: The computer program is loaded and executed by a processor to implement the operations performed by the power control method according to any one of claims 1 to 7.

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