CN117895611A - Battery equalization control method and system based on fuzzy predictive control algorithm - Google Patents
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Abstract
The application discloses a battery equalization control method and system based on a fuzzy predictive control algorithm, and particularly relates to the technical field of battery equalization control. The method comprises the following steps: acquiring state values of an energy storage system, predicting a predicted value sequence of a plurality of moments in the future through a predicted control model, acquiring a first predicted value in the sequence, and recording the first predicted value as a model predicted value; calculating the model predicted value and a preset equalization control threshold value to obtain a first deviation value and a first deviation change rate; inputting the first deviation value and the first deviation change rate into a fuzzy controller to obtain an equalization control quantity; the equalization control amount is used to control the charging or discharging of the energy storage system. The method comprises the steps of predicting the variation of the voltage or the residual electric quantity state of a battery in the future, and obtaining a corresponding control strategy through a fuzzy controller to realize the advanced prejudgement of balanced control.
Description
Technical Field
The invention relates to the technical field of battery balance control, in particular to a battery balance control method and system based on a fuzzy prediction control algorithm.
Background
In recent years, green energy has become an important force leading to the future energy field, and among them, electric energy is particularly prominent and has penetrated the aspects of our lives. The battery, as a core power, is a basis for ensuring continuous and stable operation of a plurality of electronic and electric equipment. Currently, most battery systems require that the use requirements be met by connecting multiple batteries in series. However, due to the nature of the series connection, the total capacity of the energy storage system is often limited by the cell in which the capacity is the smallest. The performance of the battery is often affected by the environment and the frequency of use, resulting in inconsistent internal power levels within the energy storage system, which in turn may affect the life of the battery.
To solve this problem, battery equalization technology was introduced, which is an indispensable ring in modern energy storage systems. The basic principle is that advanced power electronic technology is utilized to ensure that the voltage deviation between the battery cells or the energy storage system is controlled within a preset range, so that each battery cell is ensured to keep the same state under normal working conditions, and the overcharge and overdischarge of the battery are effectively prevented. However, the conventional controller does not consider the relationship between the real-time performance and the prejudgement performance of the battery equalization control, and the problem of battery current control hysteresis may occur, so that the accuracy of the whole equalization system is reduced. The equalizing control system adopting the fuzzy controller also has the problems that the original fuzzy decision rule is difficult to apply and control lag is caused because the target value is too much changed.
Disclosure of Invention
The invention aims at: aiming at the problem of equalization control lag of the existing equalization control system, a battery equalization control method and system based on a fuzzy prediction control algorithm are provided, and an equalization scheme of an energy storage battery is provided by improving a battery equalization strategy through combination of fuzzy control and prediction control.
In order to achieve the above object, the present invention provides the following technical solutions: a battery equalization control method based on a fuzzy predictive control algorithm comprises the following steps:
Acquiring state values of an energy storage system, predicting a predicted value sequence of a plurality of moments in the future through a predicted control model, acquiring a first predicted value in the sequence, and recording the first predicted value as a model predicted value; the state value is a measured value of a voltage difference value between a first battery and a second battery in the energy storage system or a measured value of a residual electric quantity state difference value; the model predicted value is a predicted value of a voltage difference value or a predicted value of a residual electric quantity state difference value between a first battery and a second battery in the energy storage system; the first battery is the battery with the highest open circuit voltage or residual electric quantity state; the second battery is the battery with the lowest open circuit voltage or residual electric quantity state;
Calculating the model predicted value and a preset equalization control threshold value to obtain a first deviation value and a first deviation change rate;
Inputting the first deviation value and the first deviation change rate into a fuzzy controller to obtain an equalization control quantity; the balance control amount is used for controlling the charge or discharge of the energy storage system;
further, the predictive control model outputs predictive values at a plurality of future moments after applying an equalization control amount to the energy storage system based on the predictive value sequence at the previous moment.
Further, the step of correcting the model predicted value includes:
Collecting the state value of the energy storage system at the current moment and the model predicted value of the energy storage system at the current moment at the last moment, and comparing the state value with the model predicted value to obtain an error value;
and acquiring the predicted value sequence of the current moment to the next moment, correcting the predicted value sequence by using the error value, and acquiring a first predicted value in the predicted value sequence to obtain a corrected predicted value.
Further, according to the relation between the open-circuit voltage of the battery and the residual electric quantity state, different equalization control thresholds are output through a dynamic threshold module aiming at different stages of the residual electric quantity state.
Further, according to the relation between the open-circuit voltage of the battery and the residual electric quantity state, the correction predicted value is reduced when the equalization control threshold is fixed for different stages of the residual electric quantity state.
Further, the prediction function of the prediction control model is:
ym(k)＝y0(k)+aΔu(k)；
ym(k)＝[ym(k+1,k),…,ym(k+P)]T；
y0(k)＝[y0(k+1,k),…,y0(k+P)]T；
Δu(k)＝[Δu(k，k)，…，Δu(k+M-1，k)]；
Wherein Deltau (k) is the variation of the balanced current at the moment k and the moment k-1; (k+1, k) is a prediction of time k+1 at time k; y m (k) is a predicted value sequence of future P times predicted at time k; y 0 (k) is the initial value of the future P-time prediction; a is a predictive model vector of a controlled object based on step response, and is obtained by continuously optimizing historical data; p is the length of the predicted value sequence; m is the number of model predictors.
Further, the fuzzy controller uses fuzzy logic to make reasoning, and the steps include:
Constructing a first input fuzzy set according to the fuzzy universe of the first deviation value; constructing a second input fuzzy set according to the fuzzy universe of the first deviation change rate; constructing a first output fuzzy set according to the fuzzy universe of the balance control quantity,
Selecting a membership function, and fuzzifying the first deviation value to obtain a first input membership; fuzzifying the first deviation change rate to obtain a second input membership degree;
constructing a first fuzzy rule according to expert knowledge experience, mapping the first input membership degree and the second input membership degree into a first output membership degree through the first fuzzy rule, wherein the first output membership degree is the membership degree of the balance control quantity in the first output fuzzy set;
and performing defuzzification operation on the first output membership degree to obtain a determined value of the balance control quantity.
Further, the dynamic threshold module uses fuzzy logic to make reasoning, and the steps include:
Constructing a third input fuzzy set based on a fuzzy universe of the residual electric quantity state according to the relation between the open circuit voltage of the battery and the residual electric quantity state; constructing a second output fuzzy set according to the fuzzy universe of the balance control threshold;
selecting a membership function, and fuzzifying the state value to obtain a third input membership;
Constructing a second fuzzy rule of the relation between the residual electric quantity state and the balance control threshold value according to expert knowledge experience; mapping the third input membership degree into a second output membership degree through the second fuzzy rule; the second output membership degree is the membership degree of the equalization control threshold in the second fuzzy output set;
And performing defuzzification operation on the second output membership degree to obtain a determined value of the equalization control threshold.
Further, the battery equalization control system based on the fuzzy predictive control algorithm comprises:
The prediction module is used for predicting a predicted value sequence of a plurality of moments in the future according to the state value of the energy storage system, outputting a first predicted value in the predicted value sequence and recording the first predicted value as a model predicted value; inputting the model predictive value into a fuzzy controller to realize the advanced adjustment of the energy storage system; the state value is a measured value of a voltage difference value between a first battery and a second battery in the energy storage system or a measured value of a residual electric quantity state difference value; the model predicted value is a predicted value of a voltage difference value or a predicted value of a residual electric quantity state difference value between a first battery and a second battery in the energy storage system; the first battery is the battery with the highest open circuit voltage or residual electric quantity state; the second battery is the battery with the lowest open circuit voltage or residual electric quantity state;
The difference module is used for calculating a deviation value and a deviation change rate of the model predicted value at the moment k and the model predicted value at the moment k-1;
And the fuzzy control module is used for receiving the deviation value and the deviation change rate and outputting the determined value of the balance control quantity after fuzzy reasoning.
Compared with the prior art, the invention has the beneficial effects that:
1. The core of the fuzzy controller is fuzzy rules, which are determined in the design stage according to expert experience. When the target value changes, the original fuzzy rule may not be applicable any more, so that the performance of the controller is reduced; the system is supplemented by prediction control, and on the premise of meeting the control precision as a target, the input value of the next moment is predicted, so that the control quantity adjusted in future moment is predicted in advance, and the good dynamic stability of the system is maintained. By calculating the first deviation change rate, the change trend of the model predictive value relative to the equalization control threshold value at the future time can be predicted. If the deviation between the model predicted value and the equalization control threshold value is increased, the prediction accuracy is reduced, and the equalization control quantity is increased through the fuzzy controller, so that the prediction control model can optimize the predicted value sequence in advance, further expansion of the deviation value is avoided, and the stability of the energy storage system is improved.
2. Because the rolling optimization and the online correction can optimize the predicted value sequence at each moment, when the dynamic characteristics of the energy storage system change, the predicted control model can also adapt to the change of the energy storage system in real time, thereby reducing the delay caused by error accumulation and improving the control effect on the energy storage system. By repeatedly optimizing the predicted value sequence, errors generated in the control process can be corrected gradually, so that the predicted value sequence approaches the actual state value gradually, uncertainty of a predicted result caused by inaccuracy of the model is reduced, and influence of external interference on the energy storage system is reduced.
3. According to the OCV-SOC characteristic curve of the battery, when the battery is balanced at the end of charging and discharging, the influence of polarization voltage on open-circuit voltage is needed to be considered, and when the open-circuit voltage is changed greatly, the voltage deviation in the energy storage system can be increased, so that the voltage deviation can more easily reach a preset balance control threshold value, and the situation that the deviation of the state of the residual electric quantity of the battery is in a reasonable range, but the balance control system is still started can occur. Therefore, the equalization control threshold value needs to be calculated in real time, when the open-circuit voltage change is large, the equalization control threshold value is improved, the starting of the equalization control is reduced, and the electric quantity consumption is saved. When the open-circuit voltage changes are not obvious, the equalization control threshold is lowered, so that the equalization system can respond in time.
Description of the drawings:
FIG. 1 is a flow diagram of a fuzzy predictive control model;
FIG. 2 is a flow chart of online correction and dynamic blur threshold;
FIG. 3 is a fuzzy rule table of the fuzzy controller;
FIG. 4 is a graph of open circuit voltage versus state of charge of a battery;
FIG. 5 is a schematic illustration of a simulation of a first battery pack;
fig. 6 is a schematic diagram of a simulation of a second battery pack.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It will be apparent that the described embodiments are some, but not all, embodiments of the invention.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention, as claimed, but is merely representative of some embodiments of the invention. 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.
It should be noted that, under the condition of no conflict, the embodiments of the present invention and the features and technical solutions in the embodiments may be combined with each other.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, the terms "upper", "lower", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or an azimuth or a positional relationship conventionally put in use of the inventive product, or an azimuth or a positional relationship conventionally understood by those skilled in the art, such terms are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or element to be referred must have a specific azimuth, be constructed and operated in a specific azimuth, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Embodiment one: as can be seen in the view of figure 1,
The battery equalization control method based on the fuzzy predictive control algorithm provided by the embodiment comprises the following steps:
Acquiring state values of an energy storage system, predicting a predicted value sequence of a plurality of moments in the future through a predicted control model, acquiring a first predicted value in the sequence, and recording the first predicted value as a model predicted value; inputting the model predicted value into a fuzzy controller to realize the advanced regulation of the energy storage system; the state value is a measured value of a voltage difference value between a first battery and a second battery in the energy storage system or a measured value of a residual electric quantity state difference value; the model predicted value is a predicted value of a voltage difference value or a predicted value of a residual electric quantity state difference value between a first battery and a second battery in the energy storage system; the first battery is the battery with the highest open circuit voltage or residual electric quantity state; the second battery is the battery with the lowest open circuit voltage or residual electric quantity state;
Calculating the model predicted value and a preset equalization control threshold value to obtain a first deviation value and a first deviation change rate;
Inputting the first deviation value and the first deviation change rate into a fuzzy controller to obtain an equalization control quantity; the balance control amount is used for controlling the charge or discharge of the energy storage system;
Further, the predictive control model is a non-parametric model, and is mainly used for obtaining a predictive model vector based on step response characteristics by using the equilibrium control quantity and state value of the energy storage system, predicting the predicted value sequence of the energy storage system in the future by using the predictive model vector, and sending the obtained output to a fuzzy controller to achieve the purpose of advanced regulation. The non-parametric model is a vector model obtained by sampling experiment analysis of input and output of an actual energy storage system.
Further, the predictive model vector may be obtained by measuring a unit step response of the energy storage system. Starting to apply an equalization control quantity to the energy storage system at the time t, and starting to perform equalization control; meanwhile, sampling the state value of the energy storage system by taking S as a period to obtain a sampling value a iS (i=1, 2,3, … N); after a certain time t=ns, the state value of the energy storage system makes the sampling value ai S＝a(i+1)S (i > N); after the NS moment, the state value of the energy storage system tends to be stable along with the change of time, namely the energy storage system reaches an equilibrium state, and the application of the equilibrium control quantity is stopped. The sample value set [ a 1,a2,a3…aN ] obtained at this time is the prediction model vector, N is the model length, and in order to facilitate matrix operation, the prediction model vector is generally transposed to obtain [ a 1,a2,a3…aN]T.
Further, the function form of the predictive control model is as follows:
ym(k)＝y0(k)+aΔu(k)；
ym(k)＝[ym(k+1,k),…,ym(k+P)]T；
y0(k)＝[y0(k+1,k),…,y0(k+P)]T；
Δu(k)＝[Δu(k，k)，…，Δu(k+M-1，k)]；
Wherein Deltau (k) is the equilibrium control quantity at k time and k-1 time; (k+1, k) represents a prediction of time k+1 at time k; y m (k) is a predicted value sequence of future P times predicted at time k; y 0 (k) is a predicted value sequence of k-1 time to k time, and is taken as an initial value of predicting P times in future at k time; a is a predictive model vector of a controlled object based on step response; p is the prediction time domain, and in this embodiment, the number of elements in the prediction value sequence is equal; m is the control time domain, and in this embodiment, the number of control amounts is equal to the number of equalization control amounts, which is 1.
Further, the predictive control model receives an equalization control quantity Deltau (k) acting on the energy storage system at time k and the predicted value sequence y 0 (k) at time k-1 to time k; after applying 1 equalization control quantity Deltau (k) to the energy storage system, outputting the model predicted values of P times in the future from the k time to obtain a predicted value sequence y m (k), and obtaining the first element in the sequence as the model predicted value of the k time to the k+1 time. And then inputting the obtained model predicted value into a fuzzy controller to achieve the purpose of advanced adjustment.
Further, at the time k, the model predicted value and the equalization control threshold value are differenced to obtain a first deviation value; subtracting the first deviation value at the k-1 time from the first deviation value at the k time, and comparing the obtained difference with the time difference from the k time to the k-1 time to obtain a first deviation change rate. The first deviation value is used for reflecting the difference between the model predicted value and the equalization control threshold; the first deviation change rate describes the trend of the first deviation value with time, and can be used for reflecting the change direction of the model predicted value to the equalization control threshold. If the first deviation change rate is positive, indicating that the first deviation value is increasing, and the model predictive value is far from the equalization control threshold; if the first deviation change rate is negative, indicating that the first deviation value is decreasing, the model predictive value and the equalization control threshold are approaching.
The core of the fuzzy controller is fuzzy rules, which are determined in the design stage according to expert experience. When the target value changes, the original fuzzy rule may not be applicable any more, so that the performance of the controller is reduced; the system is supplemented by prediction control, and on the premise of meeting the control precision as a target, the input value of the next moment is predicted, so that the control quantity adjusted in future moment is predicted in advance, and the good dynamic stability of the system is maintained. By calculating the first deviation change rate, a change trend of the model predictive value with respect to the equalization control threshold at a future time can be predicted. If the deviation between the model predicted value and the equalization control threshold value is increased, the prediction accuracy is reduced, and the equalization control quantity is increased through the fuzzy controller, so that the prediction control model can optimize the predicted value sequence in advance, further expansion of the deviation value is avoided, and the stability of the equalization system is improved.
Further, the predictive control model outputs predictive values at a plurality of future moments after applying an equalization control amount to the energy storage system based on the predictive value sequence at the previous moment.
Further, the method further comprises the step of correcting the model predicted value, as shown in fig. 2, and the steps comprise:
Collecting the state value of the energy storage system at the current moment and the model predicted value of the energy storage system at the current moment at the last moment, and comparing the state value with the model predicted value to obtain an error value;
and acquiring the predicted value sequence of the current moment to the next moment, correcting the predicted value sequence by using the error value, and acquiring a first predicted value in the predicted value sequence to obtain a corrected predicted value.
Based on the fact that the current system is a complex time-varying system, known from the functional formula of the predictive control model,
ym(k)＝y0(k)+aΔu(k)；
Under the action of the balance control quantity delta u (k), the model predicted value and the actual state value have errors, so that the model predicted value is corrected in an online correction mode. And at the time k, firstly, collecting the state value of the energy storage system, comparing the state value with the model predicted value at the time k to obtain the error value, and adding the error value with each predicted value in the predicted value sequence to obtain a corrected predicted value sequence. And at the time k+1, taking the corrected predicted value sequence as an initial value used by the predictive control model to carry out new optimization. The calculation formula of the error is as follows:
e(k)＝y(k)-ym(k，k-1)；
Wherein e (k) is the error value at time k; y (k) is an actual state value of the energy storage system at the moment k; y m (k, k-1) is the model predictor for time k-1 versus time k.
Further, the correction function is:
yp(k)＝ym(k)+h·e(k)；
Wherein y p (k) is the corrected predicted value sequence at time k; y m (k) is a predicted value sequence at time k; h is a correction vector; e (k) is the error value at time k.
Because the rolling optimization and the online correction can optimize the predicted value sequence at each moment, when the dynamic characteristics of the energy storage system change, the predicted control model can also adapt to the energy storage system change in real time, thereby reducing delay caused by error accumulation and improving the control effect on the energy storage system. By repeatedly optimizing the predicted value sequence, errors generated in the control process can be corrected gradually, so that the predicted value sequence gradually approaches the state value, uncertainty of a predicted result caused by inaccuracy of the model is reduced, and interference caused by external interference to the energy storage system can be reduced.
Further, according to the relation between the open-circuit voltage of the battery and the residual electric quantity state, different equalization control thresholds are output by a dynamic threshold module through a fuzzy logic algorithm according to different stages of the residual electric quantity state.
Further, as shown in fig. 4, according to the OCV-SOC characteristic curve of the battery, the open circuit voltage U ocv does not change significantly in a wide area as the state of charge SOC changes; however, at the end of charge and discharge, U ocv changes significantly with the change in SOC. The reason is that polarization phenomenon exists inside the battery; in the process of charging or discharging the battery, when the state of residual electric quantity is more than or equal to 20% and less than or equal to 80%, the polarization voltage is smaller, and the open circuit voltage U ocv is not changed greatly; when the remaining state of charge is > 80% or < 20% during charge or discharge, the polarization voltage changes sharply in a short time, resulting in an increase in the change of the open circuit voltage U ocv. Therefore, the influence of the polarization voltage on the open circuit voltage needs to be considered when the battery is balanced at the end of the charge and discharge of the battery. The OCV-SOC characteristic, i.e. the relation between open circuit voltage and state of charge remaining, is usually measured practically in the laboratory.
Further, during charge and discharge, it is difficult to accurately characterize the inconsistency between batteries with unique equalization control variables. Therefore, the range of the battery SOC is segmented, and the voltage and the SOC are used as equalization control variables according to the battery OCV-SOC characteristic curve. When the SOC is 0-20% or 80-100%, adopting the voltage U as a detection value; and when the SOC is 20% -80%, selecting the state of charge SOC as a detection value.
Furthermore, based on the analysis, the embodiment adopts a fuzzy control algorithm to calculate the dynamic threshold value of the voltage U or the state of charge SOC in the equalization process, thereby realizing the adjustment and control of equalization judgment. The method comprises the following steps:
S1: based on a relation diagram of the open-circuit voltage and the residual electric quantity state of the battery, constructing a fuzzy set { S (small), M (medium), L (large) } aiming at the SOC, constructing a fuzzy set { R (decrease), I (increase) } aiming at the equalization control threshold, and selecting an isosceles triangle membership function.
S2: according to the relation between the SOC and the polarization voltage, two fuzzy rules are constructed: when the battery pack is in the stage of SOC < 20% and SOC > 80%, the polarization voltage is greatly increased, the variation amplitude of the battery open-circuit voltage is larger, and the equalization control threshold value is improved at the moment; when the battery pack is in the plateau period, that is, when the SOC is 20% or more and 80% or less, the open circuit voltage change of the battery is not significant, and the equalization control threshold should be lowered.
S3: acquiring a state of charge (SOC) of the energy storage system, and calculating a fuzzy conclusion of a single rule according to the fuzzy rule in the step S2; for example, when the SOC is 30%, the equalization control threshold should be lowered.
S4: the center of gravity method is used for defuzzification, i.e. one of the most representative equalization control thresholds is found in one output range. And selecting the center of gravity of the area enclosed by the isosceles triangle membership function and the abscissa axis as output, wherein the calculation formula is as follows:
Wherein Zi is the central value of the ith fuzzy subset, and phi (Z i) is the membership; when SOC is more than 20% or SOC is less than 80%, the gravity center value of the fuzzy set is the open circuit voltage U r; when the SOC is more than or equal to 20% and less than or equal to 80%, the gravity center value of the fuzzy set is the state of residual electric quantity SOC r.
According to the OCV-SOC characteristic curve of the battery, when the battery is balanced at the end of charging and discharging, the influence of the polarization voltage on the open-circuit voltage needs to be considered, and when the open-circuit voltage changes greatly, the voltage deviation in the energy storage system can be increased. Therefore, the voltage deviation can more easily reach the preset balance control threshold value, so that the situation that the deviation of the residual battery state of charge is in a reasonable range and the balance control system is still started can occur. Therefore, the equalization control threshold value needs to be calculated in real time, when the open-circuit voltage change is large, the equalization control threshold value is improved, the starting of the equalization control is reduced, and the electric quantity consumption is saved. When the open-circuit voltage changes are not obvious, the equalization control threshold is lowered, so that the equalization system can respond in time.
Further, according to the relation between the open-circuit voltage of the battery and the residual electric quantity state, the predictive control model reduces the model predictive value when the equalization control threshold is fixed for different phases of the residual electric quantity state. As shown in fig. 4, when the remaining charge state is less than 20% or the remaining charge state is greater than 80%, the battery open circuit voltage is greatly changed. If the equalization control threshold is a preset value, a situation may occur in which the remaining states of charge between the batteries differ less, but the open-circuit voltage of the batteries exceeds the equalization control threshold, so that the battery pack triggers unnecessary equalization operation, resulting in additional energy loss. Therefore, the predictive control model needs to reduce the model predictive value in advance, reducing the voltage deviation.
Further, the fuzzy controller is a two-dimensional fuzzy controller with double input and single output, and the step of fuzzy reasoning comprises the following steps:
constructing a first input fuzzy set { minimum (XS), minimum (S), medium (M), large (L), maximum (XL) } according to a fuzzy argument [0,0.5] of the first deviation value; constructing a second input fuzzy set { minimum (XS), minimum (S), medium (M), large (L), maximum (XL) } according to the fuzzy universe [0,1] of the first deviation change rate; and constructing a first output fuzzy set { minimum (XS), minimum (S), medium (M), large (L), maximum (XL) } according to fuzzy domains [0,5] of the equalization control quantity. The fuzzy arguments are the range of variation tested in the practice of the inventors.
Selecting an isosceles triangle as a membership function. Fuzzifying the first deviation value to obtain a first input membership degree; fuzzifying the first deviation change rate to obtain a second input membership degree;
As shown in fig. 3, a fuzzy rule table is constructed according to expert knowledge experience, the first input membership degree and the second input membership degree are mapped into first output membership degrees through the fuzzy rule table, and the first output membership degrees are membership degrees of the equalization control quantity in the first output fuzzy set;
the gravity center method is used for converting the first output membership degree into the determined balance control quantity, and the calculation formula is as follows:
Wherein I is the gravity center value of the fuzzy set, namely the determined value of the balance control quantity; y i is the central value of the ith fuzzy set, and μ (Y i) is the membership.
Further, as shown in fig. 5, the method provided in this embodiment is used to verify a battery pack in which 4 batteries with different residual charge states are connected in series, where the residual charge state sequence is [55 53 57 65]. From the graph, it can be seen that the battery pack reached an equilibrium state after about 160 seconds and was uniformly charged to 100%.
Further, as shown in fig. 6, the method provided in this embodiment is used to verify a battery pack in which 4 batteries with different residual charge states are connected in series, where the residual charge state sequence is [55 52 57 60]. From the graph, it can be seen that the battery pack reached an equilibrium state after about 125 seconds and was uniformly charged to 100%. The fuzzy predictive control algorithm adopted by the embodiment can realize the effect of battery balance control.
The embodiment also provides a battery equalization control system based on a fuzzy predictive control algorithm, which comprises:
The prediction module is used for predicting a predicted value sequence of a plurality of moments in the future according to the state value of the energy storage system, outputting a first predicted value in the predicted value sequence and recording the first predicted value as a model predicted value; inputting the model predictive value into a fuzzy controller to realize the advanced adjustment of the energy storage system; the state value is a measured value of a voltage difference value between a first battery and a second battery in the energy storage system or a measured value of a residual electric quantity state difference value; the model predicted value is a predicted value of a voltage difference value or a predicted value of a residual electric quantity state difference value between a first battery and a second battery in the energy storage system; the first battery is the battery with the highest open circuit voltage or residual electric quantity state; the second battery is the battery with the lowest open circuit voltage or residual electric quantity state;
The difference module is used for calculating a deviation value and a deviation change rate of the model predicted value at the moment k and the model predicted value at the moment k-1;
And the fuzzy control module is used for receiving the deviation value and the deviation change rate and outputting the determined value of the balance control quantity after fuzzy reasoning.
The above embodiments are only for illustrating the present invention and not for limiting the technical solutions described in the present invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above specific embodiments, and thus any modifications or equivalent substitutions are made to the present invention; all technical solutions and modifications thereof that do not depart from the spirit and scope of the invention are intended to be included in the scope of the appended claims.
Claims (8)
1. The battery equalization control method based on the fuzzy predictive control algorithm is characterized by comprising the following steps of:
Acquiring state values of an energy storage system, predicting a predicted value sequence of a plurality of moments in the future through a predicted control model, acquiring a first predicted value in the sequence, and recording the first predicted value as a model predicted value; the state value is a measured value of a voltage difference value between a first battery and a second battery in the energy storage system or a measured value of a residual electric quantity state difference value; the model predicted value is a predicted value of a voltage difference value or a predicted value of a residual electric quantity state difference value between a first battery and a second battery in the energy storage system; the first battery is the battery with the highest open circuit voltage or residual electric quantity state; the second battery is the battery with the lowest open circuit voltage or residual electric quantity state;
Calculating the model predicted value and a preset equalization control threshold value to obtain a first deviation value and a first deviation change rate;
Inputting the first deviation value and the first deviation change rate into a fuzzy controller to obtain an equalization control quantity; the equalization control amount is used to control the charging or discharging of the energy storage system.
2. The battery equalization control method based on a fuzzy predictive control algorithm of claim 1, wherein said predictive control model outputs predicted values at a plurality of future times after applying an equalization control amount to said energy storage system based on said predicted value sequence at a previous time at each time.
3. The battery equalization control method based on a fuzzy predictive control algorithm of claim 2, further comprising correcting said model predictive value, the step comprising:
Collecting the state value of the energy storage system at the current moment and the model predicted value of the energy storage system at the current moment at the last moment, and comparing the state value with the model predicted value to obtain an error value;
and acquiring the predicted value sequence of the current moment to the next moment, correcting the predicted value sequence by using the error value, and acquiring a first predicted value in the predicted value sequence to obtain a corrected predicted value.
4. The battery equalization control method based on a fuzzy predictive control algorithm of claim 1, further comprising: and outputting different equalization control thresholds through a dynamic threshold module according to the relation between the open-circuit voltage of the battery and the residual electric quantity state and aiming at different stages of the residual electric quantity state.
5. The battery equalization control method based on a fuzzy predictive control algorithm of claim 1, wherein the predictive function of the predictive control model is:
ym(k)＝y0(k)+aΔu(k)；
ym(k)＝[ym(k+1,k),…,ym(k+P)]T；
y0(k)＝[y0(k+1,k),…,y0(k+P)]T；
△u(k)＝[Δu(k，k)，…，Δu(k+M-1，k)]；
Wherein Deltau (k) is the variation of the balanced current at the time k and the time k-1; (k+1, k) is a prediction of time k+1 at time k; y m (k) is a predicted value sequence of future P times predicted at time k; y 0 (k) is the initial value of the future P-time prediction; a is a predictive model vector of a controlled object based on step response, and is obtained by continuously optimizing historical data; p is the length of the predicted value sequence; m is the number of model predictors.
6. The battery equalization control method based on a fuzzy predictive control algorithm of claim 1, wherein said fuzzy controller uses fuzzy logic for reasoning, the steps comprising:
Constructing a first input fuzzy set according to the fuzzy universe of the first deviation value; constructing a second input fuzzy set according to the fuzzy universe of the first deviation change rate; constructing a first output fuzzy set according to the fuzzy universe of the balance control quantity;
Selecting a membership function, and fuzzifying the first deviation value to obtain a first input membership; fuzzifying the first deviation change rate to obtain a second input membership degree;
constructing a first fuzzy rule according to expert knowledge experience, mapping the first input membership degree and the second input membership degree into a first output membership degree through the first fuzzy rule, wherein the first output membership degree is the membership degree of the balance control quantity in the first output fuzzy set;
and performing defuzzification operation on the first output membership degree to obtain a determined value of the balance control quantity.
7. The battery equalization control method based on a fuzzy predictive control algorithm of claim 4, wherein said dynamic threshold module uses fuzzy logic for reasoning, comprising the steps of:
Constructing a third input fuzzy set based on a fuzzy universe of the residual electric quantity state according to the relation between the open circuit voltage of the battery and the residual electric quantity state; constructing a second output fuzzy set according to the fuzzy universe of the balance control threshold;
selecting a membership function, and fuzzifying the state value to obtain a third input membership;
Constructing a second fuzzy rule of the relation between the residual electric quantity state and the balance control threshold value according to expert knowledge experience; mapping the third input membership degree into a second output membership degree through the second fuzzy rule; the second output membership degree is the membership degree of the equalization control threshold in the second fuzzy output set;
And performing defuzzification operation on the second output membership degree to obtain a determined value of the equalization control threshold.
8. The battery equalization control system based on the fuzzy predictive control algorithm is characterized by comprising the following components:
The prediction module is used for predicting a predicted value sequence of a plurality of moments in the future according to the state value of the energy storage system, outputting a first predicted value in the predicted value sequence and recording the first predicted value as a model predicted value; inputting the model predictive value into a fuzzy controller to realize the advanced adjustment of the energy storage system; the state value is a measured value of a voltage difference value between a first battery and a second battery in the energy storage system or a measured value of a residual electric quantity state difference value; the model predicted value is a predicted value of a voltage difference value or a predicted value of a residual electric quantity state difference value between a first battery and a second battery in the energy storage system; the first battery is the battery with the highest open circuit voltage or residual electric quantity state; the second battery is the battery with the lowest open circuit voltage or residual electric quantity state;
The difference module is used for calculating a deviation value and a deviation change rate of the model predicted value at the moment k and the model predicted value at the moment k-1;
And the fuzzy control module is used for receiving the deviation value and the deviation change rate and outputting the determined value of the balance control quantity after fuzzy reasoning.
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