CN118199130A - Super capacitor energy storage capacity distribution method and system considering history error influence - Google Patents
Super capacitor energy storage capacity distribution method and system considering history error influence Download PDFInfo
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Abstract
The invention discloses a super capacitor energy storage capacity distribution method and a system considering the influence of historical errors, which relate to the technical field of energy storage and comprise the following steps: deploying monitoring equipment to collect monitoring data of the super capacitor; establishing an error model by utilizing equipment historical data to calibrate parameters; constructing a prediction model, evaluating the health state of the super capacitor, predicting the future performance, and optimizing the charge-discharge strategy; and predicting through a prediction model, constructing an objective function based on the output of the prediction model, adjusting an energy management strategy and adjusting the energy flow direction. According to the super-capacitor energy storage capacity distribution method considering the influence of the historical error, the performance of the super-capacitor is accurately predicted through the electrochemical model and the thermodynamic model, so that dynamic energy management is realized, a charge-discharge strategy is optimized, energy efficiency is improved, and waste is reduced. The service life of the capacitor is prolonged by reducing the load of equipment, the maintenance cost is reduced, and the adaptability to environment and performance changes is improved.
Description
Technical Field
The invention relates to the technical field of energy storage, in particular to a super capacitor energy storage capacity distribution method and system considering the influence of historical errors.
Background
Technical challenges continue to be faced in the energy storage systems of supercapacitors, mainly including performance decay prediction, real-time health monitoring, and optimization of energy management strategies.
Conventional management strategies typically rely on static parameters and empirical rules, resulting in a failure to accommodate rapid changes in the environment and device state in practical applications, thereby affecting the overall efficiency and security of the system.
During long-term operation, the performance of the super capacitor gradually decays due to the influence of physical aging, temperature change and cyclic load. Conventional methods often have difficulty accurately predicting performance degradation of the device, especially under complex operating conditions. Conventional systems lack an effective real-time monitoring mechanism and cannot discover and respond to performance degradation or potential faults in time. The traditional energy management method is not flexible enough and cannot be adjusted according to the real-time state of the capacitor and the change of the external environment.
Therefore, there is a need for a supercapacitor energy storage capacity allocation method considering the influence of historical errors, which improves energy efficiency and equipment life, and adapts to complex environments and operation conditions.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing energy management method has the problems of insufficient accuracy and slow response, and optimization of how to adjust the charge-discharge strategy in real time.
In order to solve the technical problems, the invention provides the following technical scheme: the super capacitor energy storage capacity distribution method considering the influence of the historical error comprises the following steps: deploying monitoring equipment to collect monitoring data of the super capacitor, wherein the monitoring data comprise equipment operation parameters, equipment historical data and electrochemical impedance spectrums; establishing an error model by utilizing equipment historical data, and performing equipment operation parameter calibration by analyzing model output; constructing a prediction model by using the calibrated equipment operation parameters and the electrochemical impedance spectrum, evaluating the health state of the super capacitor, predicting the future performance and optimizing the charge-discharge strategy; and after the optimized charge-discharge strategy is executed, collecting monitoring data of the super capacitor again, predicting through a prediction model, constructing an objective function based on the output of the prediction model, adjusting an energy management strategy and adjusting the energy flow direction.
As a preferable scheme of the super capacitor energy storage capacity distribution method considering the influence of the historical error, the invention comprises the following steps: the operating parameters include voltage, current and temperature.
As a preferable scheme of the super capacitor energy storage capacity distribution method considering the influence of the historical error, the invention comprises the following steps: the error model establishment comprises the steps of carrying out data preprocessing on equipment historical data and carrying out normalization processing; performing time sequence decomposition on the equipment historical data, and extracting trend, seasonal and residual error components; identifying abnormal points in the historical data by using a statistical abnormality detection method, and determining whether the data points deviate from a normal range by using a Z-score method of a moving window; extracting features from the decomposed time series data, wherein the features comprise statistical aggregation features, trend indexes and periodicity indexes; training a random forest model, and optimizing model parameters by using the extracted characteristics and the marked abnormal values as inputs; predicting errors in the equipment operation parameters by using the trained model, and adjusting the instant data value according to the prediction errors to calibrate;
The construction prediction model comprises an electrochemical model and a thermodynamic model.
As a preferable scheme of the super capacitor energy storage capacity distribution method considering the influence of the historical error, the invention comprises the following steps: the electrochemical model is represented as,
；
Wherein,Time/>Predicted voltage value at,/>Time/>Current at/>Representing static resistance,/>Representing the total capacity of the capacitor,/>Representing from time 0 to/>Represents the accumulated charge amount;
The thermodynamic model is represented as,
；
Wherein,Representing the rate of change of temperature over time,/>Time/>Current at/>Time of presentationResistance at/(I)Representing the heat generated by an internal heat source,/>Representing the convective heat transfer coefficient,/>Representing the surface area of the capacitor,/>Time/>Capacitance temperature at,/>Representing ambient temperature,/>Representing the mass of the capacitance,/>Representing the specific heat capacity of the capacitor.
As a preferable scheme of the super capacitor energy storage capacity distribution method considering the influence of the historical error, the invention comprises the following steps: the method comprises the steps of evaluating the health state of the super capacitor and predicting the future performance, wherein the method comprises the steps of carrying out numerical solution on a prediction model by using a fourth-order Dragon-grid-base tower method;
comparing the predicted data voltage V (T) and the temperature T (T) with the measured data, identifying whether the deviation exceeds a preset threshold value, if the voltage exceeds the threshold value, the temperature does not exceed the threshold value, judging whether the voltage is abnormal due to capacitance loss or circuit problems, immediately adjusting the charging rate and the charging depth, and reducing the voltage load;
If the temperature exceeds the threshold value and the voltage does not exceed the threshold value, judging whether the internal heat generation is abnormal or the temperature is abnormal due to insufficient heat dissipation, optimizing a heat dissipation strategy, adjusting a charge and discharge period and reducing heat generation;
If the voltage and the temperature exceed the threshold values, the running load is immediately reduced, the charge and discharge rate is reduced, and equipment inspection is performed.
As a preferable scheme of the super capacitor energy storage capacity distribution method considering the influence of the historical error, the invention comprises the following steps: the objective function is represented as a function of,
；
Wherein,Representing energy efficiency,/>Representing device lifetime,/>Representing environmental suitability,/>、/>And/>Representing the weight factor,/>And/>Respectively represent integral variable,/>Representing sensitivity parameters,/>Representing ambient temperature,/>Indicating the temperature range.
As a preferable scheme of the super capacitor energy storage capacity distribution method considering the influence of the historical error, the invention comprises the following steps: the adjusting the energy management strategy and adjusting the energy flow direction includes applying an optimization algorithm to maximize an objective function；
The adjustment energy management strategy comprises the following steps ofLess than the target efficiency threshold, reducing the charge rate and modifying the charge voltage, reducing energy loss, if/>The charging rate is increased on the premise of not influencing the service life of equipment and the environmental adaptability;
If it is The value is smaller than the environmental adaptability threshold, the capacity of the cooling system is increased, the cooling path is optimized, the charge-discharge period is adjusted, the operation of the equipment in the extreme environment is reduced, if/>The value is larger than or equal to an environmental adaptability threshold value, the environmental temperature range is improved, and the heat dissipation strategy is adjusted;
If it is If the service life of the device is smaller than the service life threshold value of the device, maintaining the current charge-discharge strategy and continuously monitoring, if/>The service life of the device is larger than or equal to the service life threshold value of the device, the charge and discharge period is prolonged, the charge and discharge depth is reduced, and the temperature change is slowed down;
The adjusting the energy flow direction includes recovering energy from the discharge process and redistributing the energy during execution of the adjusting energy management strategy.
It is another object of the present invention to provide a supercapacitor energy storage capacity distribution system considering the influence of the history error, which can solve the shortcomings of the conventional system in energy distribution accuracy and reaction speed by constructing the supercapacitor energy storage capacity distribution system considering the influence of the history error.
In order to solve the technical problems, the invention provides the following technical scheme: a supercapacitor energy storage capacity distribution system taking into account historical error effects, comprising: the system comprises a data acquisition module, an error analysis module, a charge and discharge optimization module and an energy management module; the data acquisition module is used for deploying monitoring equipment to collect monitoring data of the super capacitor, wherein the monitoring data comprises equipment operation parameters, equipment historical data and electrochemical impedance spectrums; the error analysis module is used for establishing an error model by utilizing the equipment history data, and analyzing the output of the model to calibrate the operation parameters of the equipment; the charge-discharge optimization module is used for constructing a prediction model by using the calibrated equipment operation parameters and the electrochemical impedance spectrum, evaluating the health state of the super capacitor, predicting the future performance and optimizing the charge-discharge strategy; the energy management module is used for collecting monitoring data of the super capacitor again after executing the optimized charge-discharge strategy, predicting the monitoring data through the prediction model, constructing an objective function based on the output of the prediction model, adjusting the energy management strategy and adjusting the energy flow direction.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the supercapacitor energy storage capacity allocation method taking into account the influence of historical errors as described above when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a supercapacitor energy storage capacity allocation method taking into account historic error effects as described above.
The invention has the beneficial effects that: the super-capacitor energy storage capacity distribution method considering the influence of the historical error realizes accurate monitoring and prediction of the performance and the health state of the super-capacitor through integrating the electrochemical model, the thermodynamic model, the real-time data analysis and the historical error correction.
The accuracy of prediction is improved, and the charging and discharging strategies are more reasonable. Meanwhile, a dynamic energy management strategy is introduced, and a charging and discharging strategy and an energy flow direction are adjusted according to the real-time state of the capacitor and external environmental conditions, so that the energy efficiency is effectively improved, and the energy waste is reduced.
By means of the energy management strategy, the pressure of equipment is reduced, the service life of the super capacitor is prolonged, and maintenance and replacement costs caused by performance attenuation are reduced. The adaptability and the reliability of environmental changes and equipment performance changes are improved, and the continuous efficient and safe operation of the super capacitor system under the continuously changing technology and market conditions is ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a supercapacitor energy storage capacity allocation method considering the influence of historical errors according to an embodiment of the present invention.
Fig. 2 is an overall structure diagram of a supercapacitor energy storage capacity distribution system according to a second embodiment of the present invention, in which the influence of historical errors is considered.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a supercapacitor energy storage capacity allocation method considering the influence of historical errors, including:
S1: deploying monitoring equipment to collect monitoring data of the super capacitor, wherein the monitoring data comprise equipment operation parameters, equipment historical data and electrochemical impedance spectrums;
S2: establishing an error model by utilizing equipment historical data, and performing equipment operation parameter calibration by analyzing model output;
S3: constructing a prediction model by using the calibrated equipment operation parameters and the electrochemical impedance spectrum, evaluating the health state of the super capacitor, predicting the future performance and optimizing the charge-discharge strategy;
s4: and after the optimized charge-discharge strategy is executed, collecting monitoring data of the super capacitor again, predicting through a prediction model, constructing an objective function based on the output of the prediction model, adjusting an energy management strategy and adjusting the energy flow direction.
And after the optimized charge-discharge strategy is executed, analyzing the super capacitor monitoring data collected again through a prediction model. The output of the model is utilized to adjust the overall energy management strategy to improve energy storage efficiency and prolong equipment life, and at the same time finely regulate energy flow direction to ensure that energy is distributed more effectively inside the capacitor system so as to maximize the performance of each component and the stability of the overall system. This includes, but is not limited to, adjusting charge and discharge parameters, and dynamically configuring the flow of energy between different capacitive cells to accommodate varying load demands and operating conditions.
The operating parameters include voltage, current, temperature, etc.
The electrochemical impedance spectroscopy data provides detailed information of the internal impedance of the capacitor, including the electrochemical stability and interface characteristics of the capacitor, and the resistance Rs and the capacitance C of the capacitor can be obtained by fitting the data.
The error model establishment comprises the steps of carrying out data preprocessing on equipment historical data and carrying out normalization processing; performing time sequence decomposition on the equipment historical data, and extracting trend, seasonal and residual error components; identifying abnormal points in the historical data by using a statistical abnormality detection method, and determining whether the data points deviate from a normal range by using a Z-score method of a moving window; extracting features from the decomposed time series data, wherein the features comprise statistical aggregation features, trend indexes and periodicity indexes; training a random forest model, using the extracted characteristic and the marked abnormal value as input, and optimizing model parameters to maximize prediction accuracy and reliability; predicting errors in the equipment operation parameters by using the trained model, and adjusting the instant data value according to the prediction errors to calibrate;
In the energy management and capacity allocation of supercapacitors, the accuracy of data is the most fundamental requirement. Historical errors, including sensor errors, operational errors, and environmental factors, can lead to misunderstanding of capacitance states if uncorrected, thereby affecting all decisions and operations based on these data. Through data calibration, the error model established by the historical data is used for adjusting the real-time monitoring data, so that the high quality and high accuracy of the obtained data are ensured. The real running state of the equipment can be reflected, and accurate input is provided for the establishment of the prediction model.
The calibrated data are used for optimizing the charging and discharging strategies of the super capacitor, so that the energy distribution is more accurate and efficient. The calibrated data provides unbiased reality for the model, so that the prediction of the model is more accurate and reliable.
Through continuous data calibration and dynamic prediction based on calibration data, not only can the current condition be better adapted, but also possible future changes can be foreseen and prepared, and the operation of the super capacitor is ensured to be always kept in an optimal state.
The construction prediction model comprises an electrochemical model and a thermodynamic model.
The electrochemical model is represented as,
；
Wherein,Time/>Predicted voltage value at,/>Time/>Current at/>Representing static resistance,/>Representing the total capacity of the capacitor,/>Representing from time 0 to/>Represents the accumulated charge amount;
The thermodynamic model is represented as,
；
Wherein,Representing the rate of change of temperature over time,/>Time/>Current at/>Time of presentationResistance at/(I)Representing the heat generated by an internal heat source,/>Representing the convective heat transfer coefficient,/>Representing the surface area of the capacitor,/>Time/>Capacitance temperature at,/>Representing ambient temperature,/>Representing the mass of the capacitance,/>Representing the specific heat capacity of the capacitor.
And according to the output of the prediction model about the future performance and the health state of the super capacitor, the parameters of charging and discharging are adjusted in real time. The adjustment can maximize the service efficiency of the capacitor, prolong the service life of the capacitor and avoid damage caused by excessive charge and discharge.
The evaluation of the health status of the super capacitor and the prediction of future performance comprise the use of a fourth-order Dragon-Gregory tower method to numerically solve the prediction model.
The fourth-order Dragon-Gerdostage method RK4 is a numerical solution method, providing more accurate results than the low-order numerical method. The RK4 method is more stable when dealing with complex dynamic systems that may lead to numerical instability, especially the thermodynamic and electrochemical models of the present invention. The accumulation of errors is relatively low in the long integration.
Comparing the predicted data voltage V (T) and the temperature T (T) with the measured data, identifying whether the deviation exceeds a preset threshold value, if the voltage exceeds the threshold value, the temperature does not exceed the threshold value, judging whether the voltage is abnormal due to capacitance loss or circuit problems, immediately adjusting the charging rate and the charging depth, and reducing the voltage load.
If the temperature exceeds the threshold value and the voltage does not exceed the threshold value, judging whether the internal heat generation is abnormal or the temperature is abnormal due to insufficient heat dissipation, optimizing a heat dissipation strategy, and adjusting the charge and discharge period to reduce heat generation.
If the voltage and the temperature exceed the threshold values, the running load is immediately reduced, the charge and discharge rate is reduced, and equipment inspection is performed.
And judging the reason of the voltage abnormality by comparing the voltage change trend with the historical data and the real-time data. An abnormal voltage may be associated with a particular pattern or rate of change, with a sudden drop in voltage or a sustained lower than normal range occurring.
The resistance, connectivity and the performance of the capacitor itself in the circuit are checked using a circuit testing tool to check whether the capacitor has a short circuit or leakage.
The capacitance tester is used to detect deviations between the actual capacitance value and the nominal value of the capacitor, and a drop in capacitance may indicate a degradation or loss of capacitance.
Judging the cause of the temperature abnormality uses a thermal imager to detect the thermal profile of the device, looking at whether there is a local overheating phenomenon, which may be direct evidence of an internal heat generation abnormality.
And comparing the data of the internal temperature sensor of the equipment with the data of the external environment temperature, and evaluating the efficiency and the reactivity of the heat dissipation system.
The integrity and efficiency of the heat dissipating system, including the cooling fan, cooling fins and coolant circulation system, were checked to ensure that all components were working properly and were free of clogging or damage.
By adapting to the charge-discharge strategy of the current equipment state, the energy utilization efficiency is maximized, and the overall performance of the equipment is improved. The equipment pressure caused by improper charge and discharge operation is reduced, so that the service life of the supercapacitor is prolonged. The voltage or temperature is prevented from exceeding a safety threshold, thereby avoiding a possible safety risk. And responding to the change of the environment or the self performance of the equipment, the charging and discharging strategy can flexibly cope with the change, and the equipment is kept to run in an optimal state.
The objective function is represented as a function of,
；
Wherein,Representing energy efficiency,/>Representing device lifetime,/>Representing environmental suitability,/>、/>And/>Representing the weight factor,/>And/>Respectively represent integral variable,/>Representing sensitivity parameters,/>Indicating the temperature of the environment and,Indicating the temperature range.
The adjusting the energy management strategy and adjusting the energy flow direction includes applying an optimization algorithm to maximize an objective function；
The adjustment energy management strategy comprises the following steps ofLess than the target efficiency threshold, reducing the charge rate and modifying the charge voltage, reducing energy loss, if/>The charging rate is increased on the premise of not influencing the service life of equipment and the environmental adaptability;
If it is The value is smaller than the environmental adaptability threshold, the capacity of the cooling system is increased, the cooling path is optimized, the charge-discharge period is adjusted, the operation of the equipment in the extreme environment is reduced, if/>The value is larger than or equal to an environmental adaptability threshold value, the environmental temperature range is improved, and the heat dissipation strategy is adjusted;
If it is If the service life of the device is smaller than the service life threshold value of the device, maintaining the current charge-discharge strategy and continuously monitoring, if/>The service life of the device is larger than or equal to the service life threshold value of the device, the charge and discharge period is prolonged, the charge and discharge depth is reduced, and the temperature change is slowed down;
The adjusting the energy flow direction includes recovering energy from the discharge process and redistributing the energy during execution of the adjusting energy management strategy.
By recovering and reusing energy, the system can reduce overall energy consumption and loss and improve energy utilization efficiency. In addition, the recovered energy can be stored for coping with sudden high load demands, further enhancing the coping capability of the system.
By testing new charge and discharge strategies under practical operating conditions, it is verified whether these strategies are valid and ensured that they actually improve the performance and efficiency of the device.
Based on the newly collected data, the charge-discharge strategies are further refined, and these strategies are optimized to better accommodate device characteristics and operating environments.
Supercapacitors may experience performance changes such as capacity fade, increased internal resistance, etc. over long term use. By re-evaluating and adjusting the energy management strategy, it can be ensured that the capacitor continues to operate in an optimal state.
Changes in environmental conditions (e.g., temperature, humidity) may also affect the performance of the capacitor. The steps allow the energy management system to adjust the strategy based on these changes, maintaining efficient and safe operation.
By precisely controlling how energy is distributed within the capacitor system, energy waste can be avoided and overall energy utilization can be improved. The pressure on the super capacitor can be reduced through reasonable charging and discharging strategies and energy management, the service life of the super capacitor is prolonged, and the maintenance and replacement cost is reduced. By continuously adjusting and optimizing the energy management strategy, it is ensured that the supercapacitor system can continue to operate under continuously changing technical and market conditions. The energy management prevents the problems of overcharge, overdischarge and the like and avoids extreme conditions leading to equipment failure.
Example 2
Referring to fig. 2, for one embodiment of the present invention, there is provided a supercapacitor energy storage capacity distribution system that considers the effects of historical errors, comprising:
the system comprises a data acquisition module, an error analysis module, a charge and discharge optimization module and an energy management module.
The data acquisition module is used for deploying monitoring equipment to collect monitoring data of the super capacitor, wherein the monitoring data comprises equipment operation parameters, equipment historical data and electrochemical impedance spectrums.
The error analysis module is used for establishing an error model by utilizing the equipment history data, and analyzing model output to calibrate equipment operation parameters.
The charge-discharge optimization module is used for constructing a prediction model by using the calibrated equipment operation parameters and the electrochemical impedance spectrum, evaluating the health state of the super capacitor, predicting the future performance and optimizing the charge-discharge strategy.
The energy management module is used for collecting monitoring data of the super capacitor again after executing the optimized charge-discharge strategy, predicting the monitoring data through the prediction model, constructing an objective function based on the output of the prediction model, adjusting the energy management strategy and adjusting the energy flow direction.
Example 3
One embodiment of the present invention, which is different from the first two embodiments, is:
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 4
For one embodiment of the invention, a super capacitor energy storage capacity distribution method considering the influence of historical errors is provided, and in order to verify the beneficial effects of the invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
And comparing the performance of the super capacitor energy storage capacity distribution method considering the influence of the historical error with the performance of the traditional charge-discharge strategy on energy efficiency, equipment health state maintenance and response environment change.
The experimental facilities include, supercapacitor group: two groups of super capacitors with the same specification, monitoring equipment and a control system.
The experimental group A uses the method to deploy an error model and an optimized charge-discharge strategy, and adjusts according to real-time data and historical errors. The control group B uses a fixed charge-discharge strategy using a conventional method, irrespective of the history error effects.
Experimental conditions included modeling different ambient temperatures (low, medium, high) and load variations.
Experimental period: each group of devices was operated for 500 charge-discharge cycles.
Two groups of experiments are arranged in the same experimental environment, so that all variables are consistent except for a charging and discharging strategy and an energy management strategy. And collecting real-time monitoring data of the two groups of super capacitors, including voltage, current, temperature, charge and discharge period and the like.
Experiment group a: and adjusting the charge-discharge strategy and the energy flow direction in real time according to the monitoring data. Control group B: and the operation is performed according to a preset charge-discharge strategy, and real-time adjustment is not performed.
The energy efficiency of the two groups was compared, the health status of the apparatus was evaluated, and the experimental results are shown in tables 1 and 2.
Table 1 energy efficiency comparison table
Table 2 comparison of health status indicators for devices
The experimental group A utilizes a dynamic charge-discharge strategy corrected according to the historical error, so that the energy efficiency under various environmental temperatures is improved, and the efficiency is improved more remarkably especially under the high-temperature condition. The charge and discharge parameters are adjusted in real time to adapt to the instant electrochemical reaction state and thermodynamic conditions, so that the energy conversion process is optimized, and the energy loss is reduced.
The energy efficiency of the experimental group A in the high-temperature test is higher than that of the control group B, and the effectiveness of the optimization strategy is verified by accurately controlling current and voltage parameters and optimizing energy input and output.
With a dynamically adjusted energy management strategy, the capacitors of experimental group a showed better health and slower performance decay rates at low and medium temperature conditions. Experimental group a had less internal resistance increase and capacity fade than the control group.
The result proves that the strategy of taking the historical data into consideration for error correction and environmental adaptability adjustment in the method can effectively relieve the stress caused by temperature fluctuation and charge-discharge cycle, thereby prolonging the service life of equipment and reducing maintenance requirements.
The environment feedback mechanism implemented in the invention allows the system to dynamically adjust the charging and discharging strategy according to the temperature and load changes monitored in real time, so that the experiment group A keeps stable performance under different environment conditions. Without this dynamic adjustment mechanism, control group B had significantly degraded performance when the environmental conditions were changed drastically. In contrast, experimental group a maintained higher efficiency and stability at high demand or high temperature by adjusting the energy output and optimizing the heat dissipation strategy.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The super capacitor energy storage capacity distribution method considering the influence of the historical error is characterized by comprising the following steps:
Deploying monitoring equipment to collect monitoring data of the super capacitor, wherein the monitoring data comprise equipment operation parameters, equipment historical data and electrochemical impedance spectrums;
establishing an error model by utilizing equipment historical data, and performing equipment operation parameter calibration by analyzing model output;
constructing a prediction model by using the calibrated equipment operation parameters and the electrochemical impedance spectrum, evaluating the health state of the super capacitor, predicting the future performance and optimizing the charge-discharge strategy;
And after the optimized charge-discharge strategy is executed, collecting monitoring data of the super capacitor again, predicting through a prediction model, constructing an objective function based on the output of the prediction model, adjusting an energy management strategy and adjusting the energy flow direction.
2. The supercapacitor energy storage capacity allocation method considering historical error effects according to claim 1, wherein: the operating parameters include voltage, current and temperature.
3. The supercapacitor energy storage capacity allocation method considering historical error effects according to claim 2, wherein: the error model establishment comprises the steps of carrying out data preprocessing on equipment historical data and carrying out normalization processing; performing time sequence decomposition on the equipment historical data, and extracting trend, seasonal and residual error components; identifying abnormal points in the historical data by using a statistical abnormality detection method, and determining whether the data points deviate from a normal range by using a Z-score method of a moving window; extracting features from the decomposed time series data, wherein the features comprise statistical aggregation features, trend indexes and periodicity indexes; training a random forest model, and optimizing model parameters by using the extracted characteristics and the marked abnormal values as inputs; predicting errors in the equipment operation parameters by using the trained model, and adjusting the instant data value according to the prediction errors to calibrate;
The construction prediction model comprises an electrochemical model and a thermodynamic model.
4. The supercapacitor energy storage capacity allocation method considering historical error effects according to claim 3, wherein: the electrochemical model is represented as,
；
Wherein,Time/>Predicted voltage value at,/>Time/>Current at/>Representing static resistance,/>Representing the total capacity of the capacitor,/>Representing from time 0 to/>Represents the accumulated charge amount;
The thermodynamic model is represented as,
；
Wherein,Representing the rate of change of temperature over time,/>Time/>Current at/>Time/>Resistance at/(I)Representing the heat generated by an internal heat source,/>Representing the convective heat transfer coefficient,/>Representing the surface area of the capacitor,/>Time/>Capacitance temperature at,/>Representing ambient temperature,/>Representing the mass of the capacitance,/>Representing the specific heat capacity of the capacitor.
5. The supercapacitor energy storage capacity allocation method considering historical error effects according to claim 4, wherein: the method comprises the steps of evaluating the health state of the super capacitor and predicting the future performance, wherein the method comprises the steps of carrying out numerical solution on a prediction model by using a fourth-order Dragon-grid-base tower method;
comparing the predicted data voltage V (T) and the temperature T (T) with the measured data, identifying whether the deviation exceeds a preset threshold value, if the voltage exceeds the threshold value, the temperature does not exceed the threshold value, judging whether the voltage is abnormal due to capacitance loss or circuit problems, immediately adjusting the charging rate and the charging depth, and reducing the voltage load;
If the temperature exceeds the threshold value and the voltage does not exceed the threshold value, judging whether the internal heat generation is abnormal or the temperature is abnormal due to insufficient heat dissipation, optimizing a heat dissipation strategy, adjusting a charge and discharge period and reducing heat generation;
If the voltage and the temperature exceed the threshold values, the running load is immediately reduced, the charge and discharge rate is reduced, and equipment inspection is performed.
6. The supercapacitor energy storage capacity allocation method considering historical error effects according to claim 5, wherein: the objective function is represented as a function of,
；
Wherein,Representing energy efficiency,/>Representing device lifetime,/>Representing environmental suitability,/>、/>And/>Representing the weight factor,/>And/>Respectively represent integral variable,/>Representing sensitivity parameters,/>Representing ambient temperature,/>Indicating the temperature range.
7. The supercapacitor energy storage capacity allocation method considering the influence of historical errors according to claim 6, wherein: the adjusting the energy management strategy and adjusting the energy flow direction includes applying an optimization algorithm to maximize an objective function；
The adjustment energy management strategy comprises the following steps ofLess than the target efficiency threshold, reducing the charge rate and modifying the charge voltage, reducing energy loss, if/>The charging rate is increased on the premise of not influencing the service life of equipment and the environmental adaptability;
If it is The value is smaller than the environmental adaptability threshold, the capacity of the cooling system is increased, the cooling path is optimized, the charge-discharge period is adjusted, the operation of the equipment in the extreme environment is reduced, if/>The value is larger than or equal to an environmental adaptability threshold value, the environmental temperature range is improved, and the heat dissipation strategy is adjusted;
If it is If the service life of the device is smaller than the service life threshold value of the device, maintaining the current charge-discharge strategy and continuously monitoring, if/>The service life of the device is larger than or equal to the service life threshold value of the device, the charge and discharge period is prolonged, the charge and discharge depth is reduced, and the temperature change is slowed down;
The adjusting the energy flow direction includes recovering energy from the discharge process and redistributing the energy during execution of the adjusting energy management strategy.
8. A system employing the supercapacitor energy storage capacity allocation method considering the influence of historical errors according to any one of claims 1 to 7, comprising:
The system comprises a data acquisition module, an error analysis module, a charge and discharge optimization module and an energy management module;
the data acquisition module is used for deploying monitoring equipment to collect monitoring data of the super capacitor, wherein the monitoring data comprises equipment operation parameters, equipment historical data and electrochemical impedance spectrums;
the error analysis module is used for establishing an error model by utilizing the equipment history data, and analyzing the output of the model to calibrate the operation parameters of the equipment;
The charge-discharge optimization module is used for constructing a prediction model by using the calibrated equipment operation parameters and the electrochemical impedance spectrum, evaluating the health state of the super capacitor, predicting the future performance and optimizing the charge-discharge strategy;
The energy management module is used for collecting monitoring data of the super capacitor again after executing the optimized charge-discharge strategy, predicting the monitoring data through the prediction model, constructing an objective function based on the output of the prediction model, adjusting the energy management strategy and adjusting the energy flow direction.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the supercapacitor energy storage capacity allocation method taking into account historical error effects of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the supercapacitor energy storage capacity allocation method taking into account historic error effects of any one of claims 1 to 7.
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