CN117879115B - Intelligent power supply buffer module with high energy conversion efficiency and implementation method - Google Patents
Intelligent power supply buffer module with high energy conversion efficiency and implementation method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/00032—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries characterised by data exchange
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/0071—Regulation of charging or discharging current or voltage with a programmable schedule
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
- H02J7/00714—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery charging or discharging current
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
- H02J7/007182—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters in response to battery voltage
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
- H02J7/007192—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature
- H02J7/007194—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters in response to temperature of the battery
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4278—Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller
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Abstract
The invention relates to the field of electric energy storage, in particular to an intelligent power supply buffer module with high energy conversion efficiency and an implementation method. Comprising the following steps: constructing an energy flow control model, and adjusting parameters of the energy flow control model in real time based on a self-adaptive learning network; and combining the output of the energy flow control model with time sequence analysis, a state space model and a deep neural network to perform energy prediction to obtain a comprehensively predicted battery energy value, and dynamically adjusting a charging and discharging strategy by comprehensive heat evaluation and safety risk evaluation. The problem that the prior art does not realize real-time monitoring of the heat and the safety condition of the battery, so that the battery is overheated or excessively charged and discharged is solved; the lack of an adaptive learning mechanism can not adjust parameters in real time to cope with changing input and output requirements; the lack of detailed assessment of battery energy stability limits the performance of conventional power buffer modules in terms of energy conversion efficiency, battery performance and life, adaptability, intelligence, and decision support.
Description
Technical Field
The invention relates to the field of electric energy storage, in particular to an intelligent power supply buffer module with high energy conversion efficiency and an implementation method.
Background
With the global energy structure transformation and the improvement of environmental protection requirements, the utilization rate of renewable energy sources such as solar energy and wind energy is continuously increased. This energy conversion places higher demands on the power supply buffering technology, especially in terms of improving energy storage efficiency and management intelligence. In addition, the popularity of electric vehicles has also accelerated the need for efficient power buffer technology.
Traditional power supply buffering technology is often limited by the problems of low energy conversion efficiency, limited battery performance and service life, insufficient adaptability, low intelligent degree, lack of effective data driving decision support and the like. These limitations prevent the performance of power buffer systems in terms of high-efficiency energy management and long-term stable operation. Accordingly, developing an intelligent power buffer module that overcomes these limitations, provides higher energy conversion, better battery performance, and greater system flexibility, is an important need and challenge in the art.
Chinese patent application number: CN202210891251.8, publication date: 2022.10.04 discloses a mobile power supply energy storage battery based on high-efficiency energy conversion, which comprises an energy storage battery main body, wherein a T-shaped plate is fixedly connected to the lower surface of the energy storage battery main body, and a connecting cover is movably sleeved on the outer wall of the T-shaped plate. The invention enables the mobile power supply energy storage battery to have the function of low temperature and high temperature automatic protection, enables the mobile power supply energy storage battery to have high-efficiency and stable energy conversion capability under severe environment, avoids excessive attenuation of battery electric quantity, improves the reliability and effect of the use of the mobile power supply energy storage battery, reduces aging speed, prolongs the service life of the mobile power supply energy storage battery, and can prevent the condition that diaphragm damage is caused by low temperature to generate metallic lithium to the greatest extent so as to cause short circuit thermal runaway, improves the use safety of the mobile power supply energy storage battery, and prevents the capability of the mobile power supply energy storage battery to automatically extinguish fire to the greatest extent so as to prevent the greater loss caused by the expansion of fire.
However, the above technology has at least the following technical problems: the prior art does not realize the real-time monitoring of the heat and the safety condition of the battery, which may cause the overheat or the overcharge and the discharge of the battery, increase the safety risk and reduce the performance and the service life of the battery; the lack of a highly intelligent self-adaptive learning mechanism can not adjust parameters in real time to cope with changing input and output requirements, so that the flexibility and the efficiency of the system are limited; lack of detailed assessment of battery energy stability results in an insufficiently accurate or effective management strategy; the performance of the traditional power buffer module in the aspects of energy conversion efficiency, battery performance and service life, adaptability, intelligence and data driving decision support are limited.
Disclosure of Invention
The invention provides an intelligent power supply buffer module with high energy conversion efficiency and an implementation method thereof, which solve the problems that the prior art does not realize the real-time monitoring of the heat and the safety condition of a battery, and the battery is possibly overheated or excessively charged and discharged, so that the safety risk is increased, and the performance and the service life of the battery are reduced; the lack of a highly intelligent self-adaptive learning mechanism can not adjust parameters in real time to cope with changing input and output requirements, so that the flexibility and the efficiency of the system are limited; lack of detailed assessment of battery energy stability results in an insufficiently accurate or effective management strategy; the performance of the traditional power buffer module in the aspects of energy conversion efficiency, battery performance and service life, adaptability, intelligence and data driving decision support are limited. The high energy conversion efficiency of the intelligent power supply buffer module is realized, the charge and discharge strategy of the battery is optimized through the comprehensive control model and algorithm, and the efficiency and the safety of energy management are improved.
The invention relates to an intelligent power supply buffer module with high energy conversion efficiency and an implementation method thereof, which concretely comprise the following technical scheme:
An intelligent power buffer module with high energy conversion efficiency, comprising the following parts:
a battery system, a sensor array, a microprocessor/controller unit, and a communication interface;
The battery system, the core of the power buffer module, is responsible for storing and releasing electric energy;
the sensor array comprises a temperature sensor, a current sensor and a voltage sensor, is used for monitoring the state of the battery in real time and comprises charging/discharging current, voltage and temperature of the battery, and transmits battery state data to the microprocessor/controller unit;
the microprocessor/controller unit is used for processing battery state data, executing an energy flow control model, carrying out energy prediction and adjusting a charge-discharge strategy;
The communication interface is used for data exchange between the power supply buffer module and an external system, and the external system comprises a power grid and a management system.
The implementation method of the intelligent power supply buffer module with high energy conversion efficiency comprises the following steps:
S1, constructing an energy flow control model, and adjusting parameters of the energy flow control model in real time based on a self-adaptive learning network;
s2, carrying out energy prediction by combining the output of the energy flow control model with time sequence analysis, a state space model and a deep neural network to obtain a comprehensively predicted battery energy value;
And S3, dynamically adjusting a charging and discharging strategy based on the comprehensively predicted battery energy value, comprehensive heat evaluation and safety risk evaluation.
Preferably, the S1 specifically includes:
An energy flow control model is constructed using nonlinear differential equations to describe the dynamic change in battery energy flow.
Preferably, the S1 further includes:
And (3) evaluating the energy stability of the battery in different states, collecting battery energy data, and calculating the average value, variance and autocorrelation function of the battery energy data to be used as an index for measuring the energy stability.
Preferably, the S1 further includes:
Defining a parameter adjustment formula of an energy flow control model; the parameter adjustment formula is based on a gradient descent method, and a second derivative term is introduced.
Preferably, the S2 specifically includes:
And linear trend of the energy data is extracted by adopting time sequence analysis, the energy demand is predicted, and the charge and discharge period of the battery is optimized.
Preferably, the S2 further includes:
Dynamic changes in battery energy flow are captured by a state space model and incorporated into the observed data in conjunction with a kalman filter.
Preferably, the S2 further includes:
Simulating an energy flow pattern through a deep neural network; and comprehensively predicting the energy values predicted by the time sequence analysis, the state space model and the deep neural network to obtain the comprehensively predicted battery energy value.
Preferably, the S3 specifically includes:
Dynamically adjusting a charging and discharging strategy by combining a heat management method and a safety evaluation model; and defining a heat evaluation formula and a security risk evaluation formula to obtain a heat evaluation score and a security risk score.
Preferably, the step S3 further includes:
Defining a comprehensive strategy formula based on the heat assessment score and the security risk score to obtain a heat-security score; and judging whether the battery is in an overheated state or has potential safety hazards based on the heat-safety score and a preset threshold value, so as to determine whether the charge-discharge strategy needs to be adjusted.
The technical scheme of the invention has the beneficial effects that:
1. The energy flow of the battery is accurately controlled and predicted, and the charge and discharge period of the battery is optimized, so that the storage and conversion efficiency of electric energy is improved, and particularly, the dynamic change of the energy of the battery can be more accurately described by using a nonlinear differential equation in an energy flow control model, and the effective utilization of the energy is ensured; the comprehensive heat-safety scoring mechanism helps the system monitor the heat and the safety condition of the battery in real time, prevents overheating or overdischarge, improves the safety of the battery, prolongs the service life of the battery, and reduces the maintenance cost;
2. By utilizing time sequence analysis, a state space model and a neural network and combining battery chemical characteristics and environmental factors, the model can adapt to various complex use scenes, and the power supply buffer module can keep high-efficiency operation under different environmental conditions and load demands; the self-adaptive learning network based on the deep neural network structure enables the model to adjust parameters thereof in real time so as to cope with changing input and output requirements, thereby reducing the burden of operators and improving the overall operation efficiency of the system; by collecting and analyzing the battery energy data and calculating the statistical physical indexes such as the average value, variance, autocorrelation function and the like, the data support can be provided for operation decision, the accurate assessment of the energy stability of the battery is facilitated, and scientific basis is provided for battery management and maintenance.
Drawings
FIG. 1 is a block diagram of an intelligent power buffer module with high energy conversion efficiency according to an embodiment of the present invention;
fig. 2 is a flowchart of an implementation method of an intelligent power buffer module with high energy conversion efficiency according to an embodiment of the present invention.
Detailed Description
In order to further illustrate the technical means and effects adopted by the present invention to achieve the preset purpose, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent power supply buffer module with high energy conversion efficiency and the implementation method thereof provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, there is shown a block diagram of an intelligent power buffer module with high energy conversion efficiency according to an embodiment of the present invention, the module includes the following parts:
a battery system, a sensor array, a microprocessor/controller unit, and a communication interface;
The battery system, the core of the power buffer module, is responsible for storing and releasing the electric energy;
The sensor array comprises a temperature sensor, a current sensor and a voltage sensor and is used for monitoring the state of the battery in real time, such as the charging/discharging current, the voltage and the temperature of the battery, and the sensor array transmits the battery state data monitored in real time to the microprocessor/controller unit;
a microprocessor/controller unit for processing battery state data, executing an energy flow control model, performing energy prediction and adjusting charge-discharge strategies;
And the communication interface is used for data exchange between the power supply buffer module and an external system (such as a power grid, a management system and the like).
Referring to fig. 2, a flowchart of a method for implementing a high-energy-conversion-efficiency intelligent power buffer module according to an embodiment of the present invention is shown, where the method includes the following steps:
S1, constructing an energy flow control model, and adjusting parameters of the energy flow control model in real time based on a self-adaptive learning network;
In order to accurately control and predict the energy flow of the battery, an energy flow control model is constructed, and according to the basic energy input and output of the battery, the influence of the chemical characteristics and environmental factors of the battery is integrated, and a nonlinear differential equation is used for describing the dynamic change of the energy flow of the battery. The specific formula of the energy flow control model is as follows:
，
Wherein, Representing time/>Battery energy value of/>And/>Representing input and output currents, parameters/>, respectivelyAnd/>Is an adjustment parameter representing the degree of influence of input and output on battery energy,/>Taking into account the periodic influence of environmental factors (such as temperature changes) on battery performance,/>Is amplitude,/>Is the angular frequency.
The energy stability of the battery in different states is evaluated by using a statistical physical method, battery energy data in a period is collected, and the average value, variance and autocorrelation function of the battery energy data are calculated and used as indexes for measuring the energy stability, wherein the specific formula is as follows:
，
Wherein, Representing the variance of the battery energy for measuring energy stability,/>Is the number of samples in a period,/>Is the average of the battery energy,/>Is time/>Taking into account the time dependence of the energy variation, the autocorrelation function of/>Is the time delay used to calculate the autocorrelation.
In order to adjust the parameters of the energy flow control model in real time, the parameters of the energy flow control model are adjusted in real time by utilizing an adaptive learning network based on a deep neural network structure and combining an energy stability evaluation result so as to adapt to the changing input and output requirements. The parameter adjustment formula is based on a gradient descent method, and a second derivative term is introduced to increase the stability and convergence speed of the optimization process. The parameter adjustment formula is as follows:
，
Wherein, Representing the adjusted parameters,/>Parameters representing the pre-tuning energy flow control model, including parameters/>、/>、/>、/>，/>Is the learning rate used to adjust the amplitude of the adjustment,/>Is the prediction error of the energy flow control model, i.e. the predicted energy value and the actual observed energy value of the energy flow control model,/>Is a gradient term,/>Is a momentum term coefficient, increases the stability of the optimization process,/>Is a Hessian matrix for taking into account the effect of error curvature on the learning process,/>Is a weight factor that introduces the energy stability assessment results.
S2, carrying out energy prediction by combining the output of the energy flow control model with time sequence analysis, a state space model and a deep neural network to obtain a comprehensively predicted battery energy value;
the energy flow control model captures the basic dynamic characteristics of battery energy flow, and combines the output of the energy flow control model with time series analysis, a state space model and a deep neural network to conduct energy prediction. Time series analysis can reveal linear trends in energy data, state space models are used to capture nonlinear dynamics, and deep neural networks can model energy flow patterns. The advantages of various models are utilized, the possible deviation caused by any single model is reduced, the integrated comprehensive predicted value is formed, and the specific implementation process is as follows:
and linear trend of the energy data is extracted by adopting time sequence analysis, the energy demand in a short period is predicted, and the charge and discharge period of the battery is optimized, so that the storage efficiency of the electric energy is improved. The formula is as follows:
，
Wherein, Representing the predicted energy value of the autoregressive moving average model at time t +1,Is at time/>Battery energy value of/>Is at time/>Prediction error term,/>Is/>The coefficients of the autoregressive portion represent the effect of previous observations in the time series on the current value prediction,/>Is/>Coefficients of the moving average part affect the effect of past error terms on the current prediction,/>Is/>Coefficients of individual seasonal autoregressive terms for handling seasonal influences,/>Is/>Coefficients of individual seasonal moving average terms, for handling seasonal influences,Is the length of the seasonal period.
The dynamic change of the battery energy flow under the changing load and the environmental condition is captured by using a state space model, the evolution of the energy flow state is described, and the observed data is incorporated by combining a Kalman filter, so that the prediction accuracy is improved. The formula is as follows:
，
，
Wherein, Is the original prediction of the state space model,/>Is a corrected predicted value after considering the observed data,/>Is a state transition matrix describing how the energy flow state transitions from one point in time to the next,/>Is a control input matrix representing the effect of external control inputs on the energy flow state,/>Is a control input at time t,/>Is process noise, representing a random disturbance of the energy flow state,/>Is Kalman gain,/>Is an observation matrix mapping the energy flow state to the observation space,/>Is the actual observation at time t.
The deep neural network analyzes and simulates the energy flow mode, so that the prediction can adapt to changeable power supply use situations, and the deep neural network improves the accuracy of long-term prediction by learning the historical data mode. The formula is as follows:
，
Wherein, Is the predicted energy value of the deep neural network at time t+1,/>Is an activation function,/>Is the weight matrix of the h hidden layer,/>Is a weight matrix for input of historical energy data,/>Is historical energy data at time t,/>Is a weight matrix for processing recursive inputs of a neural network,/>Is a bias term.
By weighing the results of different prediction methods, the accuracy and reliability of overall prediction are improved, and the comprehensive prediction formula is as follows:
，
Wherein, Is a battery energy value comprehensively predicted at time t+1,/>、/>、/>Is a weight parameter for each item.
And S3, dynamically adjusting a charging and discharging strategy based on the comprehensively predicted battery energy value, comprehensive heat evaluation and safety risk evaluation.
And the thermal efficiency and the safety performance of the power supply buffer module are optimized by dynamically adjusting the charging and discharging strategy by combining a thermal management method and a safety risk assessment model. The heat evaluation is to evaluate the heat change in the battery system, namely the difference between the predicted temperature and the reference temperature, and to quantify the irreversibility of the heat flow by combining the entropy variable to adjust the measures in time so as to prevent overheating and protect the service life and the performance of the battery, and the heat evaluation formula is as follows:
，
Wherein, Is the heat assessment score at time t+1,/>Is ambient temperature/>Is a coefficient representing the temperature change per unit energy increase,/>Is the reference temperature/>Is a reference energy level, e.g. nominal energy capacity,/>Is an adjustment coefficient,/>Is a constant that is related to the thermodynamic characteristics of the battery system.
The safety risk assessment is to ensure the safety of the system while high-efficiency energy storage is ensured by predicting the potential risk of the battery system, and the uncertainty of the state of the battery system is assessed by combining the information entropy, so that the assessment accuracy of the safety risk is improved. The security risk assessment formula is as follows:
，
Wherein, Is the security risk score at time t+1,/>Is a coefficient indicating the effect of energy level on the state of the safety parameter,/>Is/>Energy threshold of individual safety parameters,/>And/>Is/>The coefficient of the security parameter reflects the/>Response characteristics of individual safety parameters as a function of energy.
And the heat management and the safety are balanced by integrating the heat evaluation and the safety risk evaluation, so that the aims of optimizing the battery performance and prolonging the service life are achieved. The comprehensive strategy formula is as follows:
，
Wherein, Is a comprehensive caloric-safety score,/>And/>Is a parameter that adjusts the weights of the caloric assessment and the security risk assessment in the final score.
The integrated heat-safety score reflects the heat and safety conditions of the current battery system, helping to determine whether charge-discharge strategies need to be adjusted to protect the battery and optimize performance. Judging whether the battery is in an overheat state or has potential safety hazards according to a preset threshold value set based on an expert experience method, so as to determine whether a charging and discharging strategy needs to be adjusted. If the score shows that the heat is too high or potential safety hazards exist, the charging rate is reduced or the discharging mode is temporarily switched; if the score is normal, the charge rate may be increased or the charge time may be prolonged. The dynamic charge-discharge strategy adjustment can maximize the energy conversion efficiency of the power supply buffer module on the premise of ensuring safety, optimize the storage and use of electric energy, and adapt to various running conditions and environmental changes.
In summary, an intelligent power supply buffer module with high energy conversion efficiency and an implementation method are completed.
The sequence of the embodiments of the invention is merely for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (6)
1. The implementation method of the intelligent power supply buffer module with high energy conversion efficiency is characterized by comprising the following steps of:
S1, constructing an energy flow control model, and describing dynamic change of battery energy flow by using a nonlinear differential equation; the specific formula is as follows:
，
Wherein, Representing time/>Is a battery energy value of (a); /(I)And/>Representing input and output currents, respectively; parameter/>And/>Is an adjustment parameter representing the degree of influence of input and output on battery energy; /(I)Is the amplitude; /(I)Is the angular frequency;
Adjusting parameters of the energy flow control model in real time based on the adaptive learning network; defining a parameter adjustment formula of an energy flow control model; the parameter adjustment formula is based on a gradient descent method, and a second derivative term is introduced; the parameter adjustment formula is as follows:
，
Wherein, Representing the adjusted parameters,/>Parameters representing a pre-tuning energy flow control model, including parameters、/>、/>、/>；/>Is the learning rate; /(I)Is the prediction error of the energy flow control model; /(I)Is a gradient term; /(I)Is a momentum term coefficient; /(I)Is a weight factor for introducing the energy stability evaluation result; /(I)Is a Hessian matrix; /(I)Representing the variance of the battery energy;
S2, carrying out energy prediction by combining the output of the energy flow control model with time sequence analysis, a state space model and a deep neural network to obtain a comprehensively predicted battery energy value; simulating an energy flow pattern through a deep neural network; the formula for time series analysis is as follows:
，
Wherein, Representing the predicted energy value of the autoregressive moving average model at time t+1; /(I)Is at time/>Is a battery energy value of (a); /(I)Is at time/>Is used for predicting the error term of the (a); /(I)Is/>Coefficients of the individual autoregressive portions; /(I)Is/>Coefficients of the moving average portions; /(I)Is/>Coefficients of the individual seasonal autoregressive term; /(I)Is/>Coefficients of individual seasonal moving average term; /(I)Is the length of the seasonal period;
the state space model formula is as follows:
，
，
Wherein, Is the original prediction of the state space model; /(I)The correction predicted value is obtained by considering the observed data; /(I)Is a state transition matrix; /(I)Is a control input matrix; /(I)Is a control input at time t; /(I)Is process noise; /(I)Is the kalman gain; /(I)Is an observation matrix; /(I)Is the actual observation at time t;
S3, dynamically adjusting a charge-discharge strategy by combining a thermal management method and a safety evaluation model based on the comprehensively predicted battery energy value; defining a heat evaluation formula and a security risk evaluation formula to obtain a heat evaluation score and a security risk score; the heat evaluation formula is as follows:
，
Wherein, Is the heat assessment score at time t+1; /(I)Is ambient temperature; /(I)Is a coefficient representing a temperature change caused by an increase in energy per unit; /(I)Is a reference temperature; /(I)Is a reference energy level; /(I)Is an adjustment coefficient; /(I)Is a constant, related to the thermodynamic characteristics of the battery system; /(I)Is a battery energy value comprehensively predicted at time t+1;
The security risk assessment formula is as follows:
，
Wherein, Is the security risk score at time t+1; /(I)Is a coefficient indicating the effect of the energy level on the safety parameter status; /(I)Is/>An energy threshold for the individual security parameters; /(I)And/>Is/>Coefficients of the individual security parameters;
and defining a comprehensive strategy formula based on the heat evaluation score and the security risk score to obtain a heat-security score.
2. The method for implementing the intelligent power supply buffer module with high energy conversion efficiency according to claim 1, wherein the step S1 specifically includes:
The energy stability of the battery in different states is evaluated, battery energy data are collected, and the average value, variance and autocorrelation function of the battery energy data are calculated and used as indexes for measuring the energy stability; the specific formula is as follows:
,
Wherein, Representing the variance of the battery energy; /(I)Is the number of samples in a time period; /(I)Is the average value of the battery energy; is time/> Is a function of the autocorrelation of (a); /(I)Is a time delay.
3. The method for implementing the intelligent power supply buffer module with high energy conversion efficiency according to claim 1, wherein the step S2 specifically includes:
And linear trend of the energy data is extracted by adopting time sequence analysis, the energy demand is predicted, and the charge and discharge period of the battery is optimized.
4. The method for implementing the intelligent power buffer module with high energy conversion efficiency according to claim 1, wherein S2 further comprises:
Dynamic changes in battery energy flow are captured by a state space model and incorporated into the observed data in conjunction with a kalman filter.
5. The method for implementing the intelligent power supply buffer module with high energy conversion efficiency according to claim 1, wherein the step S3 specifically includes:
and judging whether the battery is in an overheated state or has potential safety hazards based on the heat-safety score and a preset threshold value, so as to determine whether the charge-discharge strategy needs to be adjusted.
6. The high-energy-conversion-efficiency intelligent power supply buffer module is applied to the implementation method of the high-energy-conversion-efficiency intelligent power supply buffer module as claimed in claim 1, and is characterized by comprising the following parts:
a battery system, a sensor array, a microprocessor/controller unit, and a communication interface;
The battery system, the core of the power buffer module, is responsible for storing and releasing electric energy;
the sensor array comprises a temperature sensor, a current sensor and a voltage sensor, is used for monitoring the state of the battery in real time and comprises charging/discharging current, voltage and temperature of the battery, and transmits battery state data to the microprocessor/controller unit;
the microprocessor/controller unit is used for processing battery state data, executing an energy flow control model, carrying out energy prediction and adjusting a charge-discharge strategy;
The communication interface is used for data exchange between the power supply buffer module and an external system, and the external system comprises a power grid and a management system.
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