CN114217530A - Hybrid energy storage control system based on lithium battery power estimation - Google Patents
Hybrid energy storage control system based on lithium battery power estimation Download PDFInfo
- Publication number
- CN114217530A CN114217530A CN202111518824.4A CN202111518824A CN114217530A CN 114217530 A CN114217530 A CN 114217530A CN 202111518824 A CN202111518824 A CN 202111518824A CN 114217530 A CN114217530 A CN 114217530A
- Authority
- CN
- China
- Prior art keywords
- power
- module
- lithium battery
- flushing device
- automatic flushing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 123
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 123
- 238000004146 energy storage Methods 0.000 title claims abstract description 65
- 238000011010 flushing procedure Methods 0.000 claims abstract description 99
- 230000006698 induction Effects 0.000 claims abstract description 86
- 238000012795 verification Methods 0.000 claims abstract description 80
- 239000003990 capacitor Substances 0.000 claims abstract description 41
- 238000005457 optimization Methods 0.000 claims abstract description 19
- 238000000034 method Methods 0.000 claims abstract description 14
- 230000001939 inductive effect Effects 0.000 claims abstract description 7
- 230000008859 change Effects 0.000 claims description 76
- 238000007599 discharging Methods 0.000 claims description 43
- 238000004088 simulation Methods 0.000 claims description 29
- 238000012937 correction Methods 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 15
- 238000005265 energy consumption Methods 0.000 claims description 13
- 238000009499 grossing Methods 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000010200 validation analysis Methods 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 10
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention provides a hybrid energy storage control system based on lithium battery power estimation, which comprises: the acquisition module is used for acquiring the operation parameters of the lithium battery of the induction automatic flushing device; an optimization module for optimizing a preset load demand prediction model of the inductive automatic flushing device based on the operating parameters; the distribution module is used for obtaining the optimal distribution power ratio of a lithium battery of the induction automatic flushing device and a super capacitor of the induction automatic flushing device based on the optimized load demand prediction model and a preset optimal energy storage management model; the verification module is used for judging whether the optimal distribution power ratio meets a verification condition; the method is used for obtaining the power distribution ratio between the lithium battery of the induction automatic flushing device and the super capacitor of the induction automatic flushing device based on the predicted load demand power and the preset optimal energy storage management model, so that the hybrid energy storage control system can operate efficiently and stably.
Description
Technical Field
The invention relates to the technical field of energy storage control, in particular to a hybrid energy storage control system based on lithium battery power estimation.
Background
At present, due to the problems of resource shortage, environmental pollution and the like, energy storage technology is receiving more and more attention in recent years. Lithium batteries are capable of storing energy in low frequency signals but still have some weaknesses such as poor low temperature operation, short cycle life, and low power density. The super capacitor has the characteristics of fast power response, high power density and strong circulation capacity, and is widely applied to high-power occasions. But the problem of low energy density needs to be further solved to realize the breakthrough development of the super capacitor.
And the hybrid energy storage control system combines two complementary energy storage components, so that the power density can be improved while the energy storage capacity is improved. In addition, the lithium battery can be protected from the influence of peak power and load fluctuation, the service life of the lithium battery is prolonged, and the storage efficiency of the system is further improved. Given the limited power and capacity of energy storage devices, they must be regulated by a controller. By placing a bidirectional DC/DC converter between each energy storage device in the energy storage system and the DC bus, energy can be regulated bidirectionally. When the energy storage system stores energy, the redundant energy of the direct current bus can be recovered, and fluctuation caused by energy accumulation of the direct current bus can be reduced and buffered. When the energy is released, the phenomenon of motor fault shutdown caused by too low direct current bus voltage can be compensated while the energy is supplemented to the load. However, this increases the complexity of the system architecture, and a reliable power management control strategy is needed for coordination or optimal management to achieve optimal charge and discharge efficiency. The basic principle is that according to the current, voltage and charge state of a super capacitor or a lithium battery, power distribution is carried out on all energy storage devices in the system according to the requirement of load change, so that the system can operate efficiently and stably.
Therefore, the invention provides a hybrid energy storage control system based on lithium battery power estimation.
Disclosure of Invention
The invention provides a hybrid energy storage control system based on lithium battery power estimation, which is used for obtaining a power distribution ratio between a lithium battery and a super capacitor based on predicted load demand power and a preset optimal energy storage management model, so that the hybrid energy storage control system can operate efficiently and stably.
The invention provides a hybrid energy storage control system based on lithium battery power estimation, which comprises:
the acquisition module is used for acquiring the operation parameters of the lithium battery of the induction automatic flushing device;
an optimization module for optimizing a preset load demand prediction model of the inductive automatic flushing device based on the operating parameters;
the distribution module is used for obtaining the optimal distribution power ratio of a lithium battery of the induction automatic flushing device and a super capacitor of the induction automatic flushing device based on the optimized load demand prediction model and a preset optimal energy storage management model;
and the verification module is used for judging whether the optimal distribution power ratio meets the verification condition.
Preferably, the hybrid energy storage control system based on lithium battery power estimation, the obtaining module includes:
the first acquisition module is used for acquiring the open-circuit voltage and the open-circuit current of the lithium battery of the induction automatic flushing device and acquiring the residual capacity of the lithium battery of the induction automatic flushing device according to a preset period;
the second acquisition module is used for acquiring the current operation condition index of the load of the induction automatic flushing device;
and the estimation module is used for estimating the maximum charge and discharge power of the lithium battery of the induction automatic flushing device based on the open-circuit voltage, the open-circuit current and the corresponding current residual capacity.
Preferably, the hybrid energy storage control system based on the power estimation of the lithium battery includes:
the first calculation module is used for calculating an optimization coefficient based on the difference between the current operation condition index of the load of the induction automatic flushing device and the corresponding standard operation condition index;
and the establishing submodule is used for optimizing the load demand prediction model based on the optimization coefficient.
Preferably, the distribution module includes:
a first prediction module for predicting a demand power function of a load of the inductive automatic flushing device based on the optimized load demand prediction model;
and the distribution submodule is used for obtaining the optimal distribution power ratio of the induction automatic flushing device lithium battery and the induction automatic flushing device super capacitor based on the demand power function and a preset optimal energy storage management model of the induction automatic flushing device.
Preferably, the hybrid energy storage control system based on the power estimation of the lithium battery, the verification module includes:
the verification submodule is used for judging whether the optimal distribution power ratio meets the verification condition;
the second calculation module is used for calculating the output current of a lithium battery of the induction automatic flushing device and the output current of a super capacitor of the induction automatic flushing device based on the optimal distribution power when the optimal distribution power meets the verification condition, and generating corresponding control signals;
and the first correction module is used for correcting the optimal energy storage management model based on the difference value of the corresponding parameter and the standard parameter in the unsatisfied verification condition when the optimal distribution power ratio does not meet the verification condition.
Preferably, the hybrid energy storage control system based on lithium battery power estimation includes:
the determining module is used for establishing an equivalent circuit model of the lithium battery of the induction automatic flushing device, determining a state space expression of the lithium battery of the induction automatic flushing device, and acquiring a terminal voltage of the equivalent circuit model, a first discharging resistor and a first charging resistor in a current preset period;
the third calculation module is used for calculating a discharging resistance gain coefficient adjustment value corresponding to the current preset period based on the difference between the terminal voltage and the open-circuit voltage, the open-circuit current of the current preset period and the first adjustment factor, and calculating a first discharging resistance gain coefficient of the next preset period based on the discharging resistance gain coefficient adjustment value and the first discharging resistance gain coefficient corresponding to the current preset period;
a fourth calculating module, configured to calculate a charging resistance gain coefficient adjustment value corresponding to a current preset period based on a difference between the terminal voltage and the open-circuit voltage, an open-circuit current in the current preset period, and a second adjustment factor, and calculate a second charging resistance gain coefficient in a next preset period based on the charging resistance gain coefficient adjustment value and a first charging resistance gain coefficient corresponding to the current preset period;
the fifth calculation module is used for obtaining a first discharging resistance change function and a second charging resistance change function based on the first discharging resistance, the first charging resistance, the first discharging resistance gain coefficient and the second charging resistance gain coefficient of the current preset period;
the second prediction module is used for obtaining a residual electric quantity prediction function of the lithium battery of the induction automatic flushing device based on the residual capacities of the lithium batteries of the induction automatic flushing device corresponding to different preset periods;
and the estimation submodule is used for calculating the maximum charging power and the maximum discharging power of the induction automatic flushing device based on the first discharging resistance change function, the second charging resistance change function, the open-circuit voltage and the residual capacity change function.
Preferably, the hybrid energy storage control system based on the power estimation of the lithium battery, the second prediction module includes:
the simulation module is used for simulating and generating a discrete curve for sensing the change of the residual capacity of the lithium battery of the automatic flushing device based on the residual capacity of the lithium battery of the automatic flushing device corresponding to different preset periods;
the smoothing module is used for smoothing the discrete curve by taking the residual capacity of the lithium battery of the induction automatic flushing device corresponding to different preset periods as a standard discrete point to obtain a first simulation curve;
the extension module is used for extending the discrete points based on an interpolation method to obtain first discrete points;
the second correction module is used for correcting the discrete curve of the residual capacity by taking the first discrete point as a standard point based on a preset correction model to obtain a second analog curve, and performing secondary smoothing treatment on the second analog curve by taking the standard discrete point and the first discrete point as standard points to obtain a third analog curve;
the first correction module is used for correcting the temperature deviation of the third simulation curve based on a preset temperature correction model to obtain a standard remaining power variation curve;
the first dividing module is used for dividing the standard remaining capacity variation curve into N simulation sub-line segments by taking all standard discrete points and the first discrete points as second discrete points;
the construction module is used for combining the N simulation sub-line segments to obtain N groups of basic data, and obtaining N prediction models based on the N groups of basic data and a preset modeling method;
the screening module is used for obtaining N residual capacity predicted values corresponding to the next preset period based on the N prediction models and obtaining a prediction model corresponding to the residual capacity predicted value with the minimum mean difference value as an optimal prediction model;
the third prediction module is used for obtaining a standard residual capacity prediction curve based on the standard residual capacity change curve and the optimal prediction model and converting the standard residual capacity prediction curve into a residual capacity prediction function corresponding to a lithium battery of the induction automatic flushing device;
the total number N of the simulation sub-line segments is equal to the total number of the second discrete points, and each simulation sub-line segment comprises one second discrete point.
Preferably, the hybrid energy storage control system based on lithium battery power estimation, the second acquisition module includes:
the acquisition submodule is used for acquiring load power in real time to obtain a load power change curve and reading an abrupt change point of the load power change curve;
the second dividing module is used for dividing the load power change curve into a high frequency band and a low frequency band based on the time intervals of all adjacent sudden change points;
a sixth calculating module, configured to obtain a first power average value based on load powers of all first abrupt change points included in each high frequency band, obtain a first frequency average value based on a time interval between the first abrupt change point included in each high frequency band and an adjacent first abrupt change point, obtain a second power average value based on load powers of all second abrupt change points included in each low frequency band, and obtain a second frequency average value based on a time interval between the second abrupt change point included in each low frequency band and an adjacent second abrupt change point;
the current operating condition index includes: the first power average value, the first frequency average value, the second power average value and the second frequency average value.
Preferably, the hybrid energy storage control system based on lithium battery power estimation, the distribution submodule includes:
the system comprises an invoking module, a first distribution factor and a second distribution factor, wherein the invoking module is used for invoking a current operation condition index of a load and a current residual capacity of a lithium battery of the induction automatic flushing device, invoking the first distribution factor in a preset correction list based on a difference between the current operation condition index and a corresponding standard operation condition index, and invoking the second distribution factor in the preset correction list based on the current residual capacity;
and the obtaining submodule is used for obtaining the optimal distribution power ratio of the induction automatic flushing device lithium battery and the induction automatic flushing device super capacitor based on the first distribution factor, the second distribution factor, the required power function and the optimal energy storage management model.
Preferably, the hybrid energy storage control system based on the power estimation of the lithium battery, the verification sub-module includes:
the seventh calculation module is used for calculating first power corresponding to a lithium battery of the induction automatic flushing device and second power corresponding to a super capacitor of the induction automatic flushing device based on the optimal distribution power ratio;
the first verification module is used for obtaining a first available power range corresponding to the lithium battery of the induction automatic flushing device based on a first available multiple threshold value and a first output power peak value preset by the lithium battery of the induction automatic flushing device, judging whether the first power is in the first available power range, if so, judging that the optimal distribution power ratio meets a first verification condition, and otherwise, judging that the optimal distribution power ratio does not meet the first verification condition;
the second verification module is used for obtaining a second available power range corresponding to the super capacitor based on a second available multiple threshold value preset by the super capacitor and a second output power peak value, judging whether the second power is in the second available power range, if so, judging that the optimal distribution power ratio meets a second verification condition, otherwise, judging that the optimal distribution power ratio does not meet the second verification condition;
the third verification module is used for determining the duration time of the current optimal distribution power ratio based on a demand power function of a load, obtaining a predicted value of the residual electric quantity of the lithium battery corresponding to the end of the duration time based on the duration time and a preset residual electric quantity prediction model, obtaining an energy consumption ratio corresponding to the optimal distribution power ratio based on the predicted value of the residual electric quantity of the lithium battery and the duration time, judging that the optimal distribution power ratio meets a third verification condition if the energy consumption ratio is within a preset energy consumption range, and otherwise, judging that the optimal distribution power ratio does not meet the third verification condition;
and the final verification module is used for judging that the optimal distribution power ratio meets the verification condition when judging that the optimal distribution power ratio simultaneously meets the first verification condition, the second verification condition and the third verification condition.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a structural diagram of a hybrid energy storage control system based on power estimation of a lithium battery according to an embodiment of the present invention;
FIG. 2 is a block diagram of an acquisition module according to an embodiment of the present invention;
FIG. 3 is a block diagram of an optimization module according to an embodiment of the present invention;
FIG. 4 is a block diagram of an allocation module according to an embodiment of the present invention;
FIG. 5 is a block diagram of a verification module in accordance with an embodiment of the present invention;
FIG. 6 is a block diagram of an estimation module in accordance with an embodiment of the present invention;
FIG. 7 is a block diagram of a second prediction module according to an embodiment of the present invention;
FIG. 8 is a block diagram of a second acquisition module according to an embodiment of the present invention;
FIG. 9 is a block diagram of an allocation submodule in an embodiment of the present invention;
FIG. 10 is a diagram of a verification sub-module in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1:
the invention provides a hybrid energy storage control system based on lithium battery power estimation, as shown in fig. 1, comprising:
the acquisition module is used for acquiring the operation parameters of the lithium battery of the induction automatic flushing device;
an optimization module for optimizing a preset load demand prediction model of the inductive automatic flushing device based on the operating parameters;
the distribution module is used for obtaining the optimal distribution power ratio of a lithium battery of the induction automatic flushing device and a super capacitor of the induction automatic flushing device based on the optimized load demand prediction model and a preset optimal energy storage management model;
and the verification module is used for judging whether the optimal distribution power ratio meets the verification condition.
In this embodiment, the operating parameters include: the lithium battery has the advantages of open-circuit voltage, open-circuit current, residual capacity, maximum charge-discharge power and current operation condition index of load.
In this embodiment, the preset load demand prediction model is used to predict the demand power of the load borne by the lithium battery.
In this embodiment, the preset optimal energy storage management model is used to obtain the optimal distribution power ratio of the lithium battery and the super capacitor.
In this embodiment, the optimal distribution power ratio is the power ratio of the lithium battery and the super capacitor in the optimal power distribution result of the hybrid energy storage control system.
In this embodiment, the verification conditions include: a first verification condition, a second verification condition, and a third verification condition.
In this embodiment, the hybrid energy storage control system combines the lithium battery and the super capacitor, and reasonably distributes the power of the lithium battery and the super capacitor, thereby realizing more stable and efficient power supply for the load.
The beneficial effects of the above technical scheme are: the reasonable power distribution of the lithium batteries and the super capacitors in the hybrid energy storage control system is realized based on the obtained operation parameters of the lithium batteries and the predicted river crossing required power, the high-efficiency and stable operation of the hybrid energy storage control system is realized, the energy storage capacity can be improved, the power density is improved, in addition, the lithium batteries can be protected from the influence of peak power and load fluctuation, the service life of the lithium batteries is prolonged, the storage efficiency of the system is further improved, and the optimal charge and discharge efficiency is achieved by coordination or optimized management.
Example 2:
based on embodiment 1, the obtaining module, as shown in fig. 2, includes:
the first acquisition module is used for acquiring the open-circuit voltage and the open-circuit current of the lithium battery of the induction automatic flushing device and acquiring the residual capacity of the lithium battery of the induction automatic flushing device according to a preset period;
the second acquisition module is used for acquiring the current operation condition index of the load of the induction automatic flushing device;
and the estimation module is used for estimating the maximum charge and discharge power of the lithium battery of the induction automatic flushing device based on the open-circuit voltage, the open-circuit current and the corresponding current residual capacity.
The beneficial effects of the above technical scheme are: and estimating the maximum charge and discharge power of the lithium battery based on the open-circuit voltage, the open-circuit current and the corresponding current residual capacity, so that the estimated maximum charge and discharge power takes the actually measured open-circuit voltage and open-circuit current into account, and also takes the influence of the residual capacity on the charge and discharge power into account, and the estimated maximum charge and discharge power of the lithium battery is more accurate.
Example 3:
based on embodiment 1, the optimization module, as shown in fig. 3, includes:
the first calculation module is used for calculating an optimization coefficient based on the difference between the current operation condition index of the load of the induction automatic flushing device and the corresponding standard operation condition index;
and the establishing submodule is used for optimizing the load demand prediction model based on the optimization coefficient.
In this embodiment, calculating the optimization coefficient based on the difference between the current operating condition index of the load and the corresponding standard operating condition index includes:
in the formula (I), the compound is shown in the specification,
in order to be able to optimize the coefficients,
in order to be the first optimization factor,
is a second optimization factor, P1Is the first power average value, P01Is a standard first power mean value, P2Is the second power average value, P02Is a standard second power average value, f1Is the first frequency average, f01Is a standard first frequency mean value, f2Is the second frequency average, f02Is a labelA quasi-second frequency mean;
for example: first optimization factor
Is 0.1, second optimization factor
Is 0.2, the first power mean value P1200, standard first power mean value P01Is 250, the second power mean value P2140, standard second power mean value P02Is 200, the first frequency mean value f140, standard first frequency mean value f01Is 50, the second frequency mean value f2Is 8, the standard second frequency mean value f02Is 10, then the coefficient is optimized
Is 0.13.
The beneficial effects of the above technical scheme are: and optimizing the load demand prediction model based on the optimization coefficient calculated based on the mean value of the load frequency peak value and the change frequency, so that the change of the load frequency is considered when the load demand prediction model predicts the load demand power, and the load demand prediction model is more accurate in predicting the load demand power.
Example 4:
based on embodiment 1, the allocation module, as shown in fig. 4, includes:
a first prediction module for predicting a demand power function of a load of the inductive automatic flushing device based on the optimized load demand prediction model;
and the distribution submodule is used for obtaining the optimal distribution power ratio of the induction automatic flushing device lithium battery and the induction automatic flushing device super capacitor based on the demand power function and a preset optimal energy storage management model of the induction automatic flushing device.
In this embodiment, the power demand function is a function reflecting the change of the power demand of the load.
The beneficial effects of the above technical scheme are: the optimal distribution power ratio of the lithium battery and the super capacitor is obtained based on the load demand power function obtained by predicting the optimized load demand prediction model and the preset optimal energy storage management model, and the influence of the load demand power change is considered, so that the obtained optimal distribution power ratio is more reasonable.
Example 5:
based on embodiment 1, the verification module, as shown in fig. 5, includes:
the verification submodule is used for judging whether the optimal distribution power ratio meets the verification condition;
the second calculation module is used for calculating the output current of a lithium battery of the induction automatic flushing device and the output current of a super capacitor of the induction automatic flushing device based on the optimal distribution power when the optimal distribution power meets the verification condition, and generating corresponding control signals;
and the first correction module is used for correcting the optimal energy storage management model based on the difference value of the corresponding parameter and the standard parameter in the unsatisfied verification condition when the optimal distribution power ratio does not meet the verification condition.
In this embodiment, the control signal is the current for controlling the lithium battery and the super capacitor to output according to the output current.
In this embodiment, the optimal energy storage management model is corrected based on the difference between the corresponding parameter and the standard parameter in the unsatisfied verification condition, for example, there are: and when the optimal distribution power does not meet the first verification condition, correcting the optimal energy storage management model based on the difference between the first power and the upper limit value or the lower limit value of the corresponding exceeded first available power range.
The beneficial effects of the above technical scheme are: and if the optimal distribution power ratio is judged not to meet the verification condition, correcting the optimal energy storage management model based on the difference value of the corresponding parameter and the standard parameter in the unsatisfied verification condition, correcting the prediction accuracy of the optimal energy storage management model, continuously training and correcting the optimal energy storage management model, and ensuring the accuracy of the subsequently output optimal distribution power ratio.
Example 6:
based on embodiment 2, the estimation module, as shown in fig. 6, includes:
the determining module is used for establishing an equivalent circuit model of the lithium battery of the induction automatic flushing device, determining a state space expression of the lithium battery of the induction automatic flushing device, and acquiring a terminal voltage of the equivalent circuit model, a first discharging resistor and a first charging resistor in a current preset period;
the third calculation module is used for calculating a discharging resistance gain coefficient adjustment value corresponding to the current preset period based on the difference between the terminal voltage and the open-circuit voltage, the open-circuit current of the current preset period and the first adjustment factor, and calculating a first discharging resistance gain coefficient of the next preset period based on the discharging resistance gain coefficient adjustment value and the first discharging resistance gain coefficient corresponding to the current preset period;
a fourth calculating module, configured to calculate a charging resistance gain coefficient adjustment value corresponding to a current preset period based on a difference between the terminal voltage and the open-circuit voltage, an open-circuit current in the current preset period, and a second adjustment factor, and calculate a second charging resistance gain coefficient in a next preset period based on the charging resistance gain coefficient adjustment value and a first charging resistance gain coefficient corresponding to the current preset period;
the fifth calculation module is used for obtaining a first discharging resistance change function and a second charging resistance change function based on the first discharging resistance, the first charging resistance, the first discharging resistance gain coefficient and the second charging resistance gain coefficient of the current preset period;
the second prediction module is used for obtaining a residual electric quantity prediction function of the lithium battery of the induction automatic flushing device based on the residual capacities of the lithium batteries of the induction automatic flushing device corresponding to different preset periods;
and the estimation submodule is used for calculating the maximum charging power and the maximum discharging power of the induction automatic flushing device based on the first discharging resistance change function, the second charging resistance change function, the open-circuit voltage and the residual capacity change function.
In the embodiment, the equivalent circuit model describes the external characteristics of the power battery by using a circuit network formed by traditional circuit elements such as resistors, capacitors and voltage sources. The model uses a voltage source to represent thermodynamic equilibrium potential of the power battery and uses an RC network to describe the dynamic characteristics of the power battery. The equivalent circuit model has better applicability to various working states of the power battery, and a state equation of the model can be deduced, so that the equivalent circuit model is convenient to analyze and apply.
In this embodiment, the state space expression is a formula in which an equivalent circuit model is dynamically expressed in a state space, and is composed of a state equation determined based on the equivalent circuit model and an output equation.
In this embodiment, the first discharging resistor and the first charging resistor are the discharging internal resistance and the charging internal resistance corresponding to the equivalent circuit model.
In this embodiment, calculating a discharging resistance gain coefficient adjustment value corresponding to a current preset period based on a difference between the terminal voltage and the open-circuit voltage, an open-circuit current of the current preset period, and a first adjustment factor, and calculating a first discharging resistance gain coefficient of a next preset period based on the discharging resistance gain coefficient adjustment value and a first discharging resistance gain coefficient corresponding to the current preset period, includes:
in the formula, alpha1A first discharge resistance gain coefficient, alpha, for the next predetermined period10A first discharge resistance gain coefficient, delta, corresponding to the current preset period1Is a first adjustment factor, R1Is a first discharge resistor, U is the terminal voltage of the equivalent circuit model, U0The current is an open-circuit voltage, and I is an open-circuit current of the current preset period;
for example, the first discharge resistance gain system corresponding to the current preset periodNumber alpha10Is 0.01, the first adjustment factor delta1Is 0.25, the first discharge resistance R1100, the terminal voltage U of the equivalent circuit model is 218, and the open circuit voltage U0220, the open circuit current I in the current preset period is 10, and the first discharge resistance gain coefficient α in the next preset period1Is 0.01005.
In this embodiment, calculating a charging resistance gain coefficient adjustment value corresponding to a current preset period based on a difference between the terminal voltage and the open-circuit voltage, an open-circuit current of the current preset period, and a second adjustment factor, and calculating a second charging resistance gain coefficient of a next preset period based on the charging resistance gain coefficient adjustment value and a first charging resistance gain coefficient corresponding to the current preset period, includes:
in the formula, alpha2A second charge resistance gain factor, alpha, for the next predetermined period20A second charge resistance gain coefficient, delta, corresponding to the current preset period2Is a second adjustment factor, R2Is a second charging resistor, U is the terminal voltage of the equivalent circuit model, U0The current is an open-circuit voltage, and I is an open-circuit current of the current preset period;
for example, the first discharge resistance gain coefficient α corresponding to the current preset period20Is 0.01, the second adjustment factor delta2Is 0.25, the second charging resistance R2100, the terminal voltage U of the equivalent circuit model is 218, and the open circuit voltage U0220, the open circuit current I in the current preset period is 10, and the first discharge resistance gain coefficient α in the next preset period1Is 0.01005.
In this embodiment, based on the first discharging resistor, the first charging resistor, the first discharging resistor gain coefficient, and the second charging resistor gain coefficient in the current preset period, the first discharging resistor change function and the second charging resistor change function are obtained as follows:
obtaining a first discharge resistance gain value based on the product of the first discharge resistance and the first discharge resistance gain coefficient, obtaining a discharge resistance of the next preset period based on the sum of the first discharge resistance and the corresponding gain value, and so on to obtain a first discharge resistance change function;
and obtaining a second charging resistance gain value based on the product of the second charging resistance and the second charging resistance gain coefficient, obtaining a charging resistance of the next preset period based on the sum of the second charging resistance and the corresponding gain value, and so on to obtain a second charging resistance change function.
In this embodiment, calculating the maximum charging power and the maximum discharging power based on the first discharging resistance variation function, the second charging resistance variation function, the open-circuit voltage, and the remaining capacity variation function includes:
determining a maximum discharge resistance value and a first period corresponding to the maximum discharge resistance value based on the first discharge resistance change function, determining a first residual capacity value corresponding to the first period based on the residual capacity change function, and determining a maximum discharge power based on the first residual capacity value and the maximum discharge resistance value;
and determining a maximum charging resistance value and a second period corresponding to the maximum charging resistance value based on the second charging resistance variation function, determining a second residual capacity value corresponding to the second period based on the residual capacity variation function, and determining the maximum charging power based on the second residual capacity value and the maximum charging resistance value.
The beneficial effects of the above technical scheme are: the maximum charging power and the maximum discharging power can be more accurately determined by considering the influence of resistance gain in the discharging process and the charging process and the influence of the residual capacity of the lithium battery on the output power.
Example 7:
based on embodiment 6, the second prediction module, as shown in fig. 7, includes:
the simulation module is used for simulating and generating a discrete curve for sensing the change of the residual capacity of the lithium battery of the automatic flushing device based on the residual capacity of the lithium battery of the automatic flushing device corresponding to different preset periods;
the smoothing module is used for smoothing the discrete curve by taking the residual capacity of the lithium battery of the induction automatic flushing device corresponding to different preset periods as a standard discrete point to obtain a first simulation curve;
the extension module is used for extending the discrete points based on an interpolation method to obtain first discrete points;
the second correction module is used for correcting the discrete curve of the residual capacity by taking the first discrete point as a standard point based on a preset correction model to obtain a second analog curve, and performing secondary smoothing treatment on the second analog curve by taking the standard discrete point and the first discrete point as standard points to obtain a third analog curve;
the first correction module is used for correcting the temperature deviation of the third simulation curve based on a preset temperature correction model to obtain a standard remaining power variation curve;
the first dividing module is used for dividing the standard remaining capacity variation curve into N simulation sub-line segments by taking all standard discrete points and the first discrete points as second discrete points;
the construction module is used for combining the N simulation sub-line segments to obtain N groups of basic data, and obtaining N prediction models based on the N groups of basic data and a preset modeling method;
the screening module is used for obtaining N residual capacity predicted values corresponding to the next preset period based on the N prediction models and obtaining a prediction model corresponding to the residual capacity predicted value with the minimum mean difference value as an optimal prediction model;
the third prediction module is used for obtaining a standard residual capacity prediction curve based on the standard residual capacity change curve and the optimal prediction model and converting the standard residual capacity prediction curve into a residual capacity prediction function corresponding to a lithium battery of the induction automatic flushing device;
the total number N of the simulation sub-line segments is equal to the total number of the second discrete points, and each simulation sub-line segment comprises one second discrete point.
In this embodiment, performing the second smoothing on the second analog curve by using the standard discrete points and the first discrete points as standard points is to smooth the second analog curve when the values of the standard points (that is, the standard discrete points and the first discrete points) on the second analog curve are not changed.
In this embodiment, the N simulated sub-line segments are combined to obtain N groups of basic data, that is: the first group of basic data is the Nth simulation sub-line segment, the second group of basic data is the sub-line segment composed of the Nth simulation sub-line segment and the (N-1) th simulation sub-line segment, the third group of basic data is the sub-line segment composed of the Nth simulation sub-line segment, the (N-1) th simulation sub-line segment and the (N-2) th simulation sub-line segment, and the rest is done in sequence until the Nth group of basic data is the sub-line segment composed of the 1 st simulation sub-line segment to the Nth simulation sub-line segment.
In this embodiment, the preset modeling method is, for example, a gaussian process regression modeling method.
In this embodiment, the mean difference is a difference between each remaining capacity prediction value and the mean of the N remaining capacity prediction values.
The beneficial effects of the above technical scheme are: the method comprises the steps of carrying out expansion on a first discrete point obtained on the basis of a residual electric quantity discrete point obtained by actual measurement based on a segmented cubic Hermite interpolation method to carry out correction, expanding actual measurement data capacity, carrying out temperature correction, ensuring the accuracy of a standard residual electric quantity change curve, screening out an optimal prediction model based on N groups of basic data obtained by the standard residual electric quantity change curve, ensuring the accuracy of a residual electric quantity prediction result and also ensuring the accuracy of an obtained residual electric quantity prediction function.
Example 8:
based on embodiment 2, the second acquisition module, as shown in fig. 8, includes:
the acquisition submodule is used for acquiring load power in real time to obtain a load power change curve and reading an abrupt change point of the load power change curve;
the second dividing module is used for dividing the load power change curve into a high frequency band and a low frequency band based on the time intervals of all adjacent sudden change points;
a sixth calculating module, configured to obtain a first power average value based on load powers of all first abrupt change points included in each high frequency band, obtain a first frequency average value based on a time interval between the first abrupt change point included in each high frequency band and an adjacent first abrupt change point, obtain a second power average value based on load powers of all second abrupt change points included in each low frequency band, and obtain a second frequency average value based on a time interval between the second abrupt change point included in each low frequency band and an adjacent second abrupt change point;
the current operating condition index includes: the first power average value, the first frequency average value, the second power average value and the second frequency average value.
In this embodiment, the first power average is a load power average of all the first abrupt change points included in the high frequency band.
In this embodiment, the first frequency average is an average value of time intervals between each first abrupt change point and adjacent first abrupt change points included in the high frequency band.
In this embodiment, the second power average is the average of the load power of all the second sudden change points included in the low frequency band.
In this embodiment, the second frequency average is an average value of time intervals between each second abrupt change point and adjacent second abrupt change points included in the low frequency band.
The beneficial effects of the above technical scheme are: and based on the high frequency band and the low frequency band determined by dividing the load power change curve and the corresponding peak power mean value and frequency mean value, optimizing the load demand prediction model subsequently, providing input data for the optimal energy storage management model, and accurately determining the optimal power distribution ratio.
Example 9:
based on embodiment 4, the allocating sub-module, as shown in fig. 9, includes:
the system comprises an invoking module, a first distribution factor and a second distribution factor, wherein the invoking module is used for invoking a current operation condition index of a load and a current residual capacity of a lithium battery of the induction automatic flushing device, invoking the first distribution factor in a preset correction list based on a difference between the current operation condition index and a corresponding standard operation condition index, and invoking the second distribution factor in the preset correction list based on the current residual capacity;
and the obtaining submodule is used for obtaining the optimal distribution power ratio of the induction automatic flushing device lithium battery and the induction automatic flushing device super capacitor based on the first distribution factor, the second distribution factor, the required power function and the optimal energy storage management model.
In this embodiment, the preset correction list includes a corresponding relationship between a difference between the current operating condition index and the corresponding standard operating condition index and the first distribution factor, and a corresponding relationship between the current remaining capacity and the second distribution factor.
The beneficial effects of the above technical scheme are: and acquiring a first distribution factor related to the current operating condition index of the load, a second distribution factor related to the current residual capacity of the lithium battery and a creep region power function as input data of the optimal energy storage management model, so that the acquired optimal distribution power ratio takes the operating condition of the load and the charge state of the lithium battery into consideration, the comprehensiveness of the power distribution ratio in determining the power distribution ratio is ensured, and the acquired power distribution ratio is more reasonable and feasible.
Example 10:
based on embodiment 5, the verification sub-module, as shown in fig. 10, includes:
the seventh calculation module is used for calculating first power corresponding to a lithium battery of the induction automatic flushing device and second power corresponding to a super capacitor of the induction automatic flushing device based on the optimal distribution power ratio;
the first verification module is used for obtaining a first available power range corresponding to the lithium battery of the induction automatic flushing device based on a first available multiple threshold value and a first output power peak value preset by the lithium battery of the induction automatic flushing device, judging whether the first power is in the first available power range, if so, judging that the optimal distribution power ratio meets a first verification condition, and otherwise, judging that the optimal distribution power ratio does not meet the first verification condition;
the second verification module is used for obtaining a second available power range corresponding to the super capacitor based on a second available multiple threshold value preset by the super capacitor and a second output power peak value, judging whether the second power is in the second available power range, if so, judging that the optimal distribution power ratio meets a second verification condition, otherwise, judging that the optimal distribution power ratio does not meet the second verification condition;
the third verification module is used for determining the duration time of the current optimal distribution power ratio based on a demand power function of a load, obtaining a predicted value of the residual electric quantity of the lithium battery corresponding to the end of the duration time based on the duration time and a preset residual electric quantity prediction model, obtaining an energy consumption ratio corresponding to the optimal distribution power ratio based on the predicted value of the residual electric quantity of the lithium battery and the duration time, judging that the optimal distribution power ratio meets a third verification condition if the energy consumption ratio is within a preset energy consumption range, and otherwise, judging that the optimal distribution power ratio does not meet the third verification condition;
and the final verification module is used for judging that the optimal distribution power ratio meets the verification condition when judging that the optimal distribution power ratio simultaneously meets the first verification condition, the second verification condition and the third verification condition.
In this embodiment, the first power is the output power of the lithium battery determined by the optimal power distribution ratio, that is, the product of the duty ratio of the lithium battery in the optimal power distribution ratio and the current required power of the load;
the second power is the output power of the super capacitor determined by the optimal power distribution ratio, namely the product of the duty ratio of the super capacitor in the optimal power distribution ratio and the current required power of the load.
In this embodiment, the first available multiple threshold is a ratio of a range of the output power of the lithium battery to the corresponding ideal output power;
the second available multiple threshold is the ratio of the range of the output power of the super capacitor to the corresponding ideal output power.
In this embodiment, the first output power peak value is a peak value of the actual output power of the lithium battery;
the second output power peak value is the peak value of the actually output power of the super capacitor.
In this embodiment, the first available power range is a range of the output power of the lithium battery, that is, a product of the first available multiple threshold and the first output power peak;
the second available power range is a range of power which can be output by the super capacitor, namely, a product of the second available multiple threshold and the second output power peak value.
In this embodiment, determining the duration of the current optimal distribution power ratio based on the demand power function of the load includes:
and determining the sudden change point of the required power of the load based on the required power function of the load, and determining the time interval between the current moment and the next sudden change point which occurs firstly as the duration.
In this embodiment, obtaining the energy consumption ratio corresponding to the optimal distribution power ratio based on the predicted value of the remaining power of the lithium battery and the duration includes:
and determining a first difference value between the predicted value of the residual electric quantity of the lithium battery and the current residual electric quantity, wherein the ratio of the first difference value to the duration is the energy consumption ratio.
The beneficial effects of the above technical scheme are: by verifying whether the power corresponding to the lithium battery and the super capacitor obtained based on the optimal distribution power ratio obtained in the previous step is in the corresponding available power range and judging whether the predicted obtained energy consumption ratio is in the preset energy consumption range, the reasonability and the feasibility of the obtained optimal distribution power ratio are further ensured.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A hybrid energy storage control system based on lithium battery power estimation, comprising:
the acquisition module is used for acquiring the operation parameters of the lithium battery of the induction automatic flushing device;
an optimization module for optimizing a preset load demand prediction model of the inductive automatic flushing device based on the operating parameters;
the distribution module is used for obtaining the optimal distribution power ratio of a lithium battery of the induction automatic flushing device and a super capacitor of the induction automatic flushing device based on the optimized load demand prediction model and a preset optimal energy storage management model;
and the verification module is used for judging whether the optimal distribution power ratio meets the verification condition.
2. The system of claim 1, wherein the means for obtaining comprises:
the first acquisition module is used for acquiring the open-circuit voltage and the open-circuit current of the lithium battery of the induction automatic flushing device and acquiring the residual capacity of the lithium battery of the induction automatic flushing device according to a preset period;
the second acquisition module is used for acquiring the current operation condition index of the load of the induction automatic flushing device;
and the estimation module is used for estimating the maximum charge and discharge power of the lithium battery of the induction automatic flushing device based on the open-circuit voltage, the open-circuit current and the corresponding current residual capacity.
3. The hybrid energy storage control system based on lithium battery power estimation of claim 1, the optimization module comprising:
the first calculation module is used for calculating an optimization coefficient based on the difference between the current operation condition index of the load of the induction automatic flushing device and the corresponding standard operation condition index;
and the establishing submodule is used for optimizing the load demand prediction model based on the optimization coefficient.
4. The system of claim 1, the distribution module comprising:
a first prediction module for predicting a demand power function of a load of the inductive automatic flushing device based on the optimized load demand prediction model;
and the distribution submodule is used for obtaining the optimal distribution power ratio of the induction automatic flushing device lithium battery and the induction automatic flushing device super capacitor based on the demand power function and a preset optimal energy storage management model of the induction automatic flushing device.
5. The system of claim 1, the validation module comprising:
the verification submodule is used for judging whether the optimal distribution power ratio meets the verification condition;
the second calculation module is used for calculating the output current of a lithium battery of the induction automatic flushing device and the output current of a super capacitor of the induction automatic flushing device based on the optimal distribution power when the optimal distribution power meets the verification condition, and generating corresponding control signals;
and the first correction module is used for correcting the optimal energy storage management model based on the difference value of the corresponding parameter and the standard parameter in the unsatisfied verification condition when the optimal distribution power ratio does not meet the verification condition.
6. The hybrid energy storage control system based on lithium battery power estimation of claim 2, the estimation module comprising:
the determining module is used for establishing an equivalent circuit model of the lithium battery of the induction automatic flushing device, determining a state space expression of the lithium battery of the induction automatic flushing device, and acquiring a terminal voltage of the equivalent circuit model, a first discharging resistor and a first charging resistor in a current preset period;
the third calculation module is used for calculating a discharging resistance gain coefficient adjustment value corresponding to the current preset period based on the difference between the terminal voltage and the open-circuit voltage, the open-circuit current of the current preset period and the first adjustment factor, and calculating a first discharging resistance gain coefficient of the next preset period based on the discharging resistance gain coefficient adjustment value and the first discharging resistance gain coefficient corresponding to the current preset period;
a fourth calculating module, configured to calculate a charging resistance gain coefficient adjustment value corresponding to a current preset period based on a difference between the terminal voltage and the open-circuit voltage, an open-circuit current in the current preset period, and a second adjustment factor, and calculate a second charging resistance gain coefficient in a next preset period based on the charging resistance gain coefficient adjustment value and a first charging resistance gain coefficient corresponding to the current preset period;
the fifth calculation module is used for obtaining a first discharging resistance change function and a second charging resistance change function based on the first discharging resistance, the first charging resistance, the first discharging resistance gain coefficient and the second charging resistance gain coefficient of the current preset period;
the second prediction module is used for obtaining a residual electric quantity prediction function of the lithium battery of the induction automatic flushing device based on the residual capacities of the lithium batteries of the induction automatic flushing device corresponding to different preset periods;
and the estimation submodule is used for calculating the maximum charging power and the maximum discharging power of the induction automatic flushing device based on the first discharging resistance change function, the second charging resistance change function, the open-circuit voltage and the residual capacity change function.
7. The hybrid energy storage control system based on lithium battery power estimation of claim 6, the second prediction module comprising:
the simulation module is used for simulating and generating a discrete curve for sensing the change of the residual capacity of the lithium battery of the automatic flushing device based on the residual capacity of the lithium battery of the automatic flushing device corresponding to different preset periods;
the smoothing module is used for smoothing the discrete curve by taking the residual capacity of the lithium battery of the induction automatic flushing device corresponding to different preset periods as a standard discrete point to obtain a first simulation curve;
the extension module is used for extending the discrete points based on an interpolation method to obtain first discrete points;
the second correction module is used for correcting the discrete curve of the residual capacity by taking the first discrete point as a standard point based on a preset correction model to obtain a second analog curve, and performing secondary smoothing treatment on the second analog curve by taking the standard discrete point and the first discrete point as standard points to obtain a third analog curve;
the first correction module is used for correcting the temperature deviation of the third simulation curve based on a preset temperature correction model to obtain a standard remaining power variation curve;
the first dividing module is used for dividing the standard remaining capacity variation curve into N simulation sub-line segments by taking all standard discrete points and the first discrete points as second discrete points;
the construction module is used for combining the N simulation sub-line segments to obtain N groups of basic data, and obtaining N prediction models based on the N groups of basic data and a preset modeling method;
the screening module is used for obtaining N residual capacity predicted values corresponding to the next preset period based on the N prediction models and obtaining a prediction model corresponding to the residual capacity predicted value with the minimum mean difference value as an optimal prediction model;
the third prediction module is used for obtaining a standard residual capacity prediction curve based on the standard residual capacity change curve and the optimal prediction model and converting the standard residual capacity prediction curve into a residual capacity prediction function corresponding to a lithium battery of the induction automatic flushing device;
the total number N of the simulation sub-line segments is equal to the total number of the second discrete points, and each simulation sub-line segment comprises one second discrete point.
8. The hybrid energy storage control system based on lithium battery power estimation according to claim 2, wherein the second acquisition module comprises:
the acquisition submodule is used for acquiring load power in real time to obtain a load power change curve and reading an abrupt change point of the load power change curve;
the second dividing module is used for dividing the load power change curve into a high frequency band and a low frequency band based on the time intervals of all adjacent sudden change points;
a sixth calculating module, configured to obtain a first power average value based on load powers of all first abrupt change points included in each high frequency band, obtain a first frequency average value based on a time interval between the first abrupt change point included in each high frequency band and an adjacent first abrupt change point, obtain a second power average value based on load powers of all second abrupt change points included in each low frequency band, and obtain a second frequency average value based on a time interval between the second abrupt change point included in each low frequency band and an adjacent second abrupt change point;
the current operating condition index includes: the first power average value, the first frequency average value, the second power average value and the second frequency average value.
9. The hybrid energy storage control system based on lithium battery power estimation of claim 4, the distribution submodule comprising:
the system comprises an invoking module, a first distribution factor and a second distribution factor, wherein the invoking module is used for invoking a current operation condition index of a load and a current residual capacity of a lithium battery of the induction automatic flushing device, invoking the first distribution factor in a preset correction list based on a difference between the current operation condition index and a corresponding standard operation condition index, and invoking the second distribution factor in the preset correction list based on the current residual capacity;
and the obtaining submodule is used for obtaining the optimal distribution power ratio of the induction automatic flushing device lithium battery and the induction automatic flushing device super capacitor based on the first distribution factor, the second distribution factor, the required power function and the optimal energy storage management model.
10. The hybrid energy storage control system based on lithium battery power estimation of claim 5, the verification sub-module comprising:
the seventh calculation module is used for calculating first power corresponding to a lithium battery of the induction automatic flushing device and second power corresponding to a super capacitor of the induction automatic flushing device based on the optimal distribution power ratio;
the first verification module is used for obtaining a first available power range corresponding to the lithium battery of the induction automatic flushing device based on a first available multiple threshold value and a first output power peak value preset by the lithium battery of the induction automatic flushing device, judging whether the first power is in the first available power range, if so, judging that the optimal distribution power ratio meets a first verification condition, and otherwise, judging that the optimal distribution power ratio does not meet the first verification condition;
the second verification module is used for obtaining a second available power range corresponding to the super capacitor based on a second available multiple threshold value preset by the super capacitor and a second output power peak value, judging whether the second power is in the second available power range, if so, judging that the optimal distribution power ratio meets a second verification condition, otherwise, judging that the optimal distribution power ratio does not meet the second verification condition;
the third verification module is used for determining the duration time of the current optimal distribution power ratio based on a demand power function of a load, obtaining a predicted value of the residual electric quantity of the lithium battery corresponding to the end of the duration time based on the duration time and a preset residual electric quantity prediction model, obtaining an energy consumption ratio corresponding to the optimal distribution power ratio based on the predicted value of the residual electric quantity of the lithium battery and the duration time, judging that the optimal distribution power ratio meets a third verification condition if the energy consumption ratio is within a preset energy consumption range, and otherwise, judging that the optimal distribution power ratio does not meet the third verification condition;
and the final verification module is used for judging that the optimal distribution power ratio meets the verification condition when judging that the optimal distribution power ratio simultaneously meets the first verification condition, the second verification condition and the third verification condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111518824.4A CN114217530B (en) | 2021-12-13 | 2021-12-13 | Hybrid energy storage control system based on lithium battery power estimation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111518824.4A CN114217530B (en) | 2021-12-13 | 2021-12-13 | Hybrid energy storage control system based on lithium battery power estimation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114217530A true CN114217530A (en) | 2022-03-22 |
| CN114217530B CN114217530B (en) | 2022-11-08 |
Family
ID=80701302
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111518824.4A Active CN114217530B (en) | 2021-12-13 | 2021-12-13 | Hybrid energy storage control system based on lithium battery power estimation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114217530B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090309416A1 (en) * | 2008-06-12 | 2009-12-17 | Bose Anima B | Active electrical power flow control system for optimization of power delivery in electric hybrid vehicles |
| CN105098807A (en) * | 2015-07-20 | 2015-11-25 | 安阳师范学院 | Complementary optimization control method among multiple hybrid energy storage devices in energy storage system |
| US20160105020A1 (en) * | 2014-10-09 | 2016-04-14 | Nec Laboratories America, Inc. | Modular multilvel converter and control framework for hybrid energy storage |
| CN106451508A (en) * | 2016-10-13 | 2017-02-22 | 深圳职业技术学院 | Configuration, charge and discharge method and device of distributed hybrid energy storage system |
| CN107294122A (en) * | 2017-04-17 | 2017-10-24 | 国网浙江省电力公司电力科学研究院 | A kind of mixed energy storage system is layered dynamic control method |
| CN108011437A (en) * | 2017-12-06 | 2018-05-08 | 国网辽宁省电力有限公司电力科学研究院 | Hybrid energy-storing power distribution system and method with super-charge super-discharge protective device |
| CN108539774A (en) * | 2018-04-26 | 2018-09-14 | 武汉理工大学 | Solar air power-driven light boat hybrid accumulator energy manager |
| CN110311396A (en) * | 2019-07-30 | 2019-10-08 | 太原理工大学 | A kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method |
| CN112260296A (en) * | 2020-10-10 | 2021-01-22 | 国网江苏省电力有限公司扬州供电分公司 | Hybrid energy storage system optimization method and device for stabilizing intermittent load |
| CN112486020A (en) * | 2020-12-01 | 2021-03-12 | 中国科学技术大学 | Hybrid energy storage system power distribution system and method based on adaptive model predictive control |
-
2021
- 2021-12-13 CN CN202111518824.4A patent/CN114217530B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090309416A1 (en) * | 2008-06-12 | 2009-12-17 | Bose Anima B | Active electrical power flow control system for optimization of power delivery in electric hybrid vehicles |
| US20160105020A1 (en) * | 2014-10-09 | 2016-04-14 | Nec Laboratories America, Inc. | Modular multilvel converter and control framework for hybrid energy storage |
| CN105098807A (en) * | 2015-07-20 | 2015-11-25 | 安阳师范学院 | Complementary optimization control method among multiple hybrid energy storage devices in energy storage system |
| CN106451508A (en) * | 2016-10-13 | 2017-02-22 | 深圳职业技术学院 | Configuration, charge and discharge method and device of distributed hybrid energy storage system |
| CN107294122A (en) * | 2017-04-17 | 2017-10-24 | 国网浙江省电力公司电力科学研究院 | A kind of mixed energy storage system is layered dynamic control method |
| CN108011437A (en) * | 2017-12-06 | 2018-05-08 | 国网辽宁省电力有限公司电力科学研究院 | Hybrid energy-storing power distribution system and method with super-charge super-discharge protective device |
| CN108539774A (en) * | 2018-04-26 | 2018-09-14 | 武汉理工大学 | Solar air power-driven light boat hybrid accumulator energy manager |
| CN110311396A (en) * | 2019-07-30 | 2019-10-08 | 太原理工大学 | A kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method |
| CN112260296A (en) * | 2020-10-10 | 2021-01-22 | 国网江苏省电力有限公司扬州供电分公司 | Hybrid energy storage system optimization method and device for stabilizing intermittent load |
| CN112486020A (en) * | 2020-12-01 | 2021-03-12 | 中国科学技术大学 | Hybrid energy storage system power distribution system and method based on adaptive model predictive control |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114217530B (en) | 2022-11-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Teleke et al. | Optimal control of battery energy storage for wind farm dispatching | |
| Purvins et al. | Application of battery-based storage systems in household-demand smoothening in electricity-distribution grids | |
| Qiu et al. | Heterogeneous energy storage optimization for microgrids | |
| CN102377248B (en) | Method for optimizing capacity of energy storage system in case of fluctuation of smooth and renewable energy sources electricity generation output | |
| CN107959301B (en) | Distributed energy storage optimal configuration and operation method for low-voltage distribution network containing photovoltaic | |
| CN111697578B (en) | Multi-target energy storage-containing regional power grid operation control method | |
| CN107528320B (en) | Power distribution network distributed power supply permeability evaluation method based on continuous power flow | |
| CN105990842B (en) | A kind of method and device of power peak regulation | |
| CN113988384A (en) | Energy storage capacity optimal configuration method for improving reliability of power distribution network | |
| US9871396B2 (en) | System and method for controlling charge of an energy storage device from a renewable energy source | |
| CN111009914A (en) | Active power distribution network-oriented energy storage device location and volume determination method | |
| CN111555356A (en) | Random variable analysis method for regional comprehensive energy system based on coupled Markov model | |
| CN115510677B (en) | Wind farm power generation capacity evaluation method and system | |
| CN111049170B (en) | Peak-shaving energy storage system considering temperature loss, operation optimization method and device | |
| KR102198040B1 (en) | Power management device and energy storage system including the same | |
| Taylor et al. | Optimal operation of grid-tied energy storage systems considering detailed device-level battery models | |
| Cui et al. | An optimal energy co-scheduling framework for smart buildings | |
| Akyurek et al. | Optimal distributed nonlinear battery control | |
| CN114217530B (en) | Hybrid energy storage control system based on lithium battery power estimation | |
| US20230148201A1 (en) | Method and system for supplying power to device, and related device | |
| Riar et al. | Energy management of a microgrid: Compensating for the difference between the real and predicted output power of photovoltaics | |
| CN115764936A (en) | Optimization method, device, equipment and storage medium for power grid energy storage configuration | |
| Aharon et al. | Premeditated generic energy storage model for sources rating selection in grid applications | |
| TW201815008A (en) | Intelligent fast battery balancer having a duty cycle fuzzy operation to quickly balance each cell of the battery pack | |
| Du et al. | Effects of battery degradation on economic viability of energy storage systems participating in regulation markets |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 528400, Fu Sha town, Guangdong City, Zhongshan province Fu Sha Industrial Park Patentee after: Guangdong Weili Electric Appliance Co.,Ltd. Address before: 528400, Fu Sha town, Guangdong City, Zhongshan province Fu Sha Industrial Park Patentee before: ZHONGSHAN DONGLIM WEILI ELECTRICAL APPLIANCES Co.,Ltd. |
|
| CP03 | Change of name, title or address |