CN116107209A - Real-time optimizing system for working point of drying control system after coating of new energy battery - Google Patents
Real-time optimizing system for working point of drying control system after coating of new energy battery Download PDFInfo
- Publication number
- CN116107209A CN116107209A CN202211701216.1A CN202211701216A CN116107209A CN 116107209 A CN116107209 A CN 116107209A CN 202211701216 A CN202211701216 A CN 202211701216A CN 116107209 A CN116107209 A CN 116107209A
- Authority
- CN
- China
- Prior art keywords
- frequency
- model
- optimization
- nmp
- fan
- 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.)
- Pending
Links
- 238000001035 drying Methods 0.000 title claims abstract description 47
- 239000011248 coating agent Substances 0.000 title claims abstract description 30
- 238000000576 coating method Methods 0.000 title claims abstract description 30
- 238000005457 optimization Methods 0.000 claims abstract description 94
- 238000000034 method Methods 0.000 claims abstract description 33
- 238000005265 energy consumption Methods 0.000 claims abstract description 20
- 230000006870 function Effects 0.000 claims description 38
- 230000008569 process Effects 0.000 claims description 16
- 238000010438 heat treatment Methods 0.000 claims description 11
- 238000005070 sampling Methods 0.000 claims description 11
- 230000008859 change Effects 0.000 claims description 10
- 238000005485 electric heating Methods 0.000 claims description 9
- 230000004044 response Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012804 iterative process Methods 0.000 claims description 3
- 238000012886 linear function Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 description 16
- 230000001276 controlling effect Effects 0.000 description 6
- 239000007789 gas Substances 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000001105 regulatory effect Effects 0.000 description 4
- 238000001816 cooling Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000002904 solvent Substances 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000010981 drying operation Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000002341 toxic gas Substances 0.000 description 1
- 238000009423 ventilation 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Battery Electrode And Active Subsutance (AREA)
Abstract
The invention relates to a real-time optimizing system for a working point of a drying control system after coating a new energy battery, which constructs a multi-objective optimizing function based on energy consumption and NMP concentration indexes, and determines a control target of NMP concentration, an inner circulating fan frequency and a minimum frequency of an outer circulating fan by solving an optimizing problem, wherein the constructed optimizing problem objective function is realized by solving a linear optimizing method with constraint. And when the RTO optimization control module is called each time, the optimal NMP concentration control target, the internal circulation fan target and the external circulation fan frequency lower limit protection value in the next calling period are obtained by solving the optimization problem, so that the total circulation air quantity of the system is reduced as much as possible on the premise that the NMP concentration is not out of standard, and the aim of energy-saving economic operation is achieved on the premise that the NMP concentration is not out of standard.
Description
Technical Field
The invention belongs to the field of drying control after battery coating, and particularly relates to a real-time optimizing system for a working point of a drying control system after new energy battery coating.
Background
The drying process after the battery production line coating is an important link in the production of new energy batteries, and is a process of drying a wet film after the battery coating, and the drying operation is carried out on the coated coating in a drying chamber by using circulated high-temperature hot air. Since the NMP solvent is used for the battery cathode coating, the solvent is vaporized during the drying process to generate a weak toxic gas, and when the gas concentration reaches a certain strength, the risk of explosion exists, so that the NMP gas concentration needs to be strictly controlled within a safety threshold in the battery drying process. The battery coating and drying system adopts a cascade design of a plurality of ovens, the coated pole piece uniformly passes through a plurality of ovens at a set vehicle speed, the temperatures and NMP concentrations in different ovens are different, and the quality of the pole piece leaving the drying procedure and the drying process of the plurality of ovens have strong coupling relations, so that the battery coating and drying system is a typical multivariable, nonlinear and strong coupling complex control system with a certain time delay.
The battery coating and drying system needs to ensure that the temperature and pressure in each oven can meet the control target of drying stably, and simultaneously avoid the concentration of NMP exceeding the standard, so the design of the control system is a typical multi-target control problem. In the existing actual operation process, a plurality of independent single-loop control designs are adopted under most conditions, and part of loops are fixed on a set value for a long time, so that real-time adjustment is difficult to be carried out according to the changes of the temperature, air and NMP concentration in an actual oven, and therefore, in order to prevent the NMP from exceeding the standard, the NMP is always in overshoot control for a long time, and the NMP concentration is reduced, and meanwhile, the waste of energy consumption is brought.
In addition, battery pole pieces of different specifications correspond to different gains and time delays of dynamic response of a battery coating and drying system, NMP gas evaporation capacity generated in an oven is also different, further temperature and pressure changes in the oven are also different, optimal working points of different pole pieces are different, in the process of switching products and standby to production switching, a real-time controller is absent, the existing manual operation is often used for ensuring that NMP concentration reaches the standard, NMP concentration is greatly reduced by greatly increasing circulating air quantity in the switching process, and excessive low-temperature return air is heated before entering a drying chamber in a high-ventilation and quick-circulation mode, so that more electric energy is consumed, and a large amount of energy is wasted.
In a word, the existing automatic control of the battery coating and drying system often uses fixed working point control, lacks the automatic switching of the working points under different working conditions, easily generates the deviation of the working points from the actual demands, and causes unnecessary heating energy consumption and energy consumption waste of fan transmission.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention aims to provide a real-time optimizing system for the working point of a drying control system after coating a new energy battery, which converts the problem of dynamic optimizing of the working point of an oven in the drying system after coating the battery into a linear programming problem with constraint adjustment and achieves the aim of energy-saving and economic operation on the premise that the concentration of NMP does not exceed the standard.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the real-time optimizing system for the working point of the drying control system after coating the new energy battery comprises m sections of ovens, n sections of total air supply pipes and total air return pipes, wherein each section of oven comprises an electric air return valve, an internal circulating fan, an electric heating bag and an exhaust fan; controlling the return air quantity by adjusting the frequency of m internal circulating fans and the opening degree of m return air valves; controlling the temperature in the oven by adjusting the power of the m electric heating packs; the change of the pressure in the oven and the change of the NMP concentration are compensated by adjusting the frequency of m exhaust fans; controlling the NMP concentration and the total NMP concentration of each section of oven by adjusting the frequency of n external circulating fans; wherein n is less than m;
the optimization system uses T RTO The optimization system comprises an RTO optimization control module; the RTO optimization control module constructs a multi-objective optimization function based on energy consumption and NMP concentration indexes, determines a control target of NMP concentration, an inner circulating fan frequency and a minimum frequency of an outer circulating fan by solving the objective optimization function, and the constructed objective optimization function is realized by solving a linear optimization method with constraint; the energy consumption index is established and the heating quantity is mathematically expressed through the fan frequency and the temperature difference; the NMP concentration index is based on a model from the frequency of the internal circulation fan to the NMP concentration, and average NMP concentration in a future period is calculated in a predictive mode; the model for predicting NMP concentration is a model which is identified on line in real time according to the need in the process of initializing a given model or calling an RTO optimization module; each time an RTO optimization control module is called, an optimal NMP concentration control target, an inner circulating fan frequency and an outer circulating fan frequency lower limit in the next calling period are obtained by solving an optimization objective functionThe protection value can reduce the total circulating air quantity of the system as much as possible on the premise that the NMP concentration is not out of standard.
The target optimization function of the RTO optimization control module is as follows:
s.t.
C i ＜C i,max i＝1,…,m (2)
F in,min ≤f in,i ≤F in,max ,i＝1,···,m (3)
in the formula (1), J 2 Is an objective function, Q i (f in,i )、
The index is used for representing the heat of the i-th section oven and the NMP concentration respectively; wherein q is i And r i The heat index of the ith section of oven and the weight coefficient of the NMP index are respectively, and the larger the weight coefficient is, the more strict the constraint of the corresponding item i in the optimization process is; m represents the number of ovens, F in,min And F in,max The minimum value and the maximum value constraint conditions of the frequency of the internal circulation fan are respectively; c (C) i,max Is the maximum safe value of NMP concentration; h c The method is based on a prediction period of the influence of the frequency of an internal circulating fan on the concentration of NMP, and the basic time unit of model prediction is the same as the sampling time of a system; summing the NMP concentration and dividing the NMP concentration by the predicted period time, and representing future average NMP concentrations corresponding to different internal circulation fan frequency setting values in one predicted period;
Q i the method is a representation of external input heat in the drying process, and the external heat input needs energy consumption as an index for measuring economy; q (Q) i And f in The relationship of (2) can be expressed by the formula (4-6);
Q i ＝V i *ΔT i (4)
ΔT i ＝T i,tgt -T i,ret (5)
as can be seen from formula (4), Q i Is the circulation air volume V in the ith section of oven i And oven temperature delta T i Because the return air temperature is smaller than the target temperature of the oven in the normal drying process, the temperature difference is positive, and the air quantity is also non-negative, so Q i The value is positive; wherein T is i,tgt Is the target temperature of the section i oven, T i,ret Is the return air temperature of the ith section of oven; wherein, the circulating air volume V i Calculated by equation (6), where f i The fan frequency; p is the number of motor pole pairs; r is the radius of the fan blade rotor; l is the width of the fan blade rotor; lambda (lambda) v Is the volume coefficient of the fan; the circulating air volume can be approximately a linear function of the fan frequency; according to the return air temperature at the RTO optimization moment, the frequency of an internal circulating fan is utilized
Calculating the consumed heat quantity Q of the corresponding heating bag 1 ,…,Q m ]；
C NMP ＝[C 1 ,…C m ]For each oven NMP concentration, C i Is characterized by NMP concentration, is used as a variable related to a process safety index and is also related to the frequency f of an internal circulating fan in Related to; considering the dynamic response of NMP and internal recycle blower frequency, at each dynamic operating point, oven NMP concentrations [ C ] were identified 1 ,…C m ]With internal circulation fan frequency
ARX model in between:
A i (z -1 )C i (k)＝z -d B i (z -1 )f in,i (k)+ξ i (k) i＝1,2,…,m (7)
wherein A, B is a model parameter, f in,i (k) The frequency of the circulating fan in the ith section at the k moment; c (C) i (k) Drying for the ith section at k timeNMP concentration of the tank; zeta type toy i (k) The uncertainty disturbance at the moment k;
calculating the frequency of the internal circulation fan according to the model of (7)
Corresponding NMP concentration [ C 1 ,…C m ]And (3) converting the time response of the fan frequency to the NMP concentration model in continuous time, and substituting the model into the RTO optimization model (1) to solve the optimization problem.
Before the RTO optimization control module is put into use, manually setting a frequency step test of the internal circulation fan, acquiring test data, and then carrying out model identification, wherein the identified model is stored in an RTO model to be used as an initial model for storage;
when an RTO optimization control module is adopted for optimization, an initial model is started in the first period, and then each new RTO period is judged according to the change condition of an internal circulation fan, and whether the model needs to be updated or not;
if the absolute value of the frequency of the internal circulation fan after optimization and the variation before optimization is smaller than a threshold value at the kth RTO optimization moment, continuously using the original model; if the frequency variation of the internal circulation fan exceeds a threshold value after RTO optimization, setting a model identification data acquisition signal to be started until the time of the (k+1) RTO period, and ending model identification data recording; the data recording time interval during the period uses the fastest sampling period supported by the system, which is at least less than 1/10 of the RTO period, so that enough sampling points are ensured in one period; at the time of the (k+1) RTO period, after optimizing output, performing model identification from the frequency of the internal circulation fan to the NMP concentration once; updating the model when the accuracy of the identified model is higher than that of the previous model; if the model accuracy is lower than the previous model, the model is not updated.
The specific calculation steps of the optimal solving problem of the optimization objective function of the RTO module are as follows:
H(t)＝L[x,u,t]+λ T f[x,u,t] (8)
In the formula (8), the first term is a performance index function, lambda in the second term is a Lagrange operator, f [ x, u, t ] is a constraint equation, namely a state equation of a model between the concentration of the oven NMP and the frequency of the internal circulating fan in the formula (7), x is the concentration of the oven NMP, and u is the frequency of the internal circulating fan; according to the principle of minima:
because the internal circulation fan frequency u has control constraint, the internal circulation fan frequency u is not necessarily equal to 0, namely the conditional equation (12) is not necessarily solved; considering that if u makes the state vector or the Hamiltonian function to obtain the minimum value, equation (12) may be replaced, so that the Hamiltonian function is minimized, where u is the solution of optimal control, and the corresponding Hamiltonian function is:
minH[x * ,λ * ,u,t]＝H[x * ,λ * ,u * ,t] (13)
step 2, given an initial input variable value and a learning rate, iteratively solving according to the following method:
given an initial control variable u (0), an initial step length (learning rate) eta (0), an iteration cut-off condition epsilon and an initial iteration count k=0, the real-time optimization of the control parameters is completed through the following steps (iterative process):
(1) Calculating the gradient of each step:
(2) If k=0, jump to (3); otherwise, u (k) is substitutedObjective function J 2 If |J 2 (k)-J 2 (k+1) |ε, terminate the iteration and output u (k), if |J 2 (k)-J 2 (k+1) | > ε, then calculate
Wherein Δu (k-1) =u (k) -u (k-1), and +.>
(3) Calculation of
(4) Returning to (1) continuing the next iteration;
the frequency u (t) of the circulating fan in the control parameter and the NMP concentration index of each section of oven can be optimized in real time through the steps
Step 3, inputting the optimal target function of the APC optimization control module based on the optimal internal circulation fan frequency and the NMP concentration target value calculated in the step 2;
step 4, calculating total air quantity and corresponding outer circulation fan frequency lower limit protection based on the optimal inner circulation fan frequency obtained in the step 2;
considering the air quantity loss of the return air pipeline, the total circulating air quantity in the return air pipe is as follows:
wherein V is in,i Is the circulation air quantity of the ith internal circulation fan, V η For total pipe air loss, V out,total The total air quantity generated by each internal circulating fan in the return air pipe and the total air quantity loss are added;
in order to ensure that the concentration of NMP does not exceed the standard, enough return air must enter the oven, namely the total air volume generated by each external circulation fan cannot be smaller than the total circulation air volume:
according to the relation between the wind quantity and the fan frequency in the formula (6), the reference frequency of the external circulation fan corresponding to the minimum circulation wind quantity can be calculated, namely the minimum value of the external circulation fan frequency under the current working condition is used as the lower limit constraint condition of the external circulation fan frequency control.
The model updating method for predicting the NMP concentration comprises the following steps:
based on the optimal internal circulation fan frequency obtained in the step 2, checking whether a model identification data acquisition signal is started in the last period, if so, performing model identification on the internal circulation fan frequency to NMP concentration for one time according to the complete data of one period, and setting the model identification acquisition signal to be ended;
based on the optimal internal circulation fan frequency obtained in the step 2, comparing the optimal internal circulation fan frequency with the internal circulation fan frequency value before optimization, if the absolute value is larger than the threshold value, setting a model identification data acquisition signal to be started, and starting to record data of a complete RTO period; when the accuracy of the identified model is higher than that of the previous model, updating the model for the next period; if the model accuracy is lower than the previous model, the model is not updated.
The model for predicting the NMP concentration is simplified into an initialization model in different working intervals:
dividing a main operating frequency range of an internal circulation fan into a plurality of working points; identifying the models of a plurality of working points offline, and storing the models into an RTO module; and during optimization, according to the range of the working point where the frequency value of the current internal circulation fan is located, starting a corresponding model to predict the NMP concentration.
After the scheme is adopted, a multi-objective optimization function is constructed based on the energy consumption and the NMP concentration index, the control objective of the NMP concentration and the lowest frequency of the external circulation fan are determined by solving the optimization problem, and the constructed optimization problem objective function is realized by solving a linear optimization method with constraint; and when the RTO optimization control module is called each time, the optimal NMP concentration control target and the frequency lower limit protection value of the external circulation fan in the next calling period are obtained by solving the optimization problem, so that the total circulation air volume of the system is reduced as much as possible on the premise that the NMP concentration is not out of standard, and the aim of energy-saving and economic operation is achieved on the premise that the NMP concentration is not out of standard.
Drawings
Fig. 1 is a schematic structural view of a battery coating and drying system;
FIG. 2 is a schematic diagram of the components of the optimization system of the present invention.
Detailed Description
As shown in fig. 1, the battery coating and drying system comprises a plurality of sections of ovens, an air supply pipe and an air return pipe, wherein each section of oven comprises an electric air return valve, an internal circulation fan, an electric heating bag and an exhaust fan. NMP-containing gas exhausted by the multi-section oven enters the air supply pipe through each exhaust pipe, and then the high-temperature high-NMP-concentration gas is sent into the cooling absorption device, and NMP is liquefied and separated out through heat exchange and cooling. The cooled circulating air with low NMP concentration is blown to the oven through the air return pipe and a plurality of external circulating fans. In the process of air return, an air return valve is arranged at the inlet of each section of oven to control the air return quantity of each section of oven. Meanwhile, in order to ensure the temperature stability in the oven, circulating return air needs to enter the oven after being preheated by the heat exchanger, and the heat and air consumed by exhaust are continuously supplemented.
The battery coating and drying system is set to comprise m sections of ovens and n sections of total air supply pipes and total air return pipes which are matched, and in order to ensure the temperature and air flow balance in the ovens, a main control loop of the drying system is considered to comprise: the air return quantity is regulated by controlling the frequency of m internal circulation fans and the opening degree of m return air valves, the temperature in the oven is regulated by controlling the power of m electric heating bags, the NMP concentration is regulated by controlling the frequency of n external circulation fans, and the influence of other executors on the change of the pressure in the oven is compensated by regulating the frequency of m exhaust fans, so that the working condition in the oven is kept stable. Wherein, the value of n can be equal to m or less than m, and the practical application often uses several sections of nearby drying boxes to share an external circulation air pipe and an external circulation fan so as to reduce the cost.
For each section of oven, the Controlled Variable (CV) is the temperature inside the oven, the pressure inside the oven, the NMP concentration at the exhaust outlet of the oven, and the Manipulated Variable (MV) is the frequency of the exhaust fan, the opening of the return air valve, the frequency of the internal circulation fan, the power of the electric heating bag, the frequency of the external circulation fan, and the like. The Disturbance Variable (DV) is the speed of the coater and the temperature of the return circulating air after cooling, which are usually changed according to the type and the production of the battery drying pole piece, and the corresponding internal temperature of the oven and the frequency of the lowest external circulating fan are also changed according to the difference of the coating procedure.
The main energy consumption of the drying process comprises: the motor energy consumption of the exhaust fan and the internal circulation fan of each drying box, the heating electric energy of the electric heating bag and the motor energy consumption of the external circulation fan on the return air pipe. The energy balance of the whole process link can be characterized by the change of heat in unit time. The pole piece entering the oven is at a relatively low temperature, the temperature of the pole piece leaving the oven is the temperature of the end oven, meanwhile, the air quantity entering the exhaust pipe is cooled and separated to cause heat loss of liquid NMP, and finally, the temperature in the oven is balanced by heating the heating bag to compensate a part of heat.
Referring to FIG. 2, the invention designs a real-time optimizing system for the working point of a drying control system after coating a new energy battery, which uses T as the energy RTO Updated for periodic timing. The optimizing system comprises an RTO optimizing control module, wherein the RTO optimizing control module constructs a multi-objective optimizing function based on energy consumption and NMP concentration indexes, a control objective of NMP concentration, an inner circulating fan frequency and a lowest frequency of an outer circulating fan are determined by solving the objective optimizing function, and the constructed objective optimizing function is realized by solving a linear optimizing method with constraint; the energy consumption index is established and the heating quantity is mathematically expressed through the fan frequency and the temperature difference; the NMP concentration index is used for predicting and calculating the average NMP concentration of a future period of time based on a model from the internal circulation fan to the NMP concentration; model for predicting NMP concentration real-time on-line according to need in initializing given model or calling RTO optimization moduleAnd identifying the model. And when the optimization system is called each time, the optimal NMP control target and the frequency lower limit protection value of the external circulation fan in the next calling period are obtained by solving the optimization problem, so that the total circulation air volume of the system is reduced as much as possible on the premise that the NMP concentration does not exceed the standard, and the total circulation air volume and the total power consumption of the system are positively correlated, and therefore, the optimization target can embody the index of reducing the energy consumption.
Before the RTO optimization control module is put into use, manually setting a frequency step test of the internal circulation fan, acquiring test data, and then carrying out model identification, wherein the identified model is stored in an RTO model to be used as an initial model for storage;
when an RTO optimization control module is adopted for optimization, an initial model is started in the first period, and then each new RTO period is judged according to the change condition of an internal circulation fan, and whether the model needs to be updated or not;
if the absolute value of the frequency of the internal circulation fan after optimization and the variation before optimization is smaller than a threshold value at the kth RTO optimization moment, continuously using the original model; if the frequency variation of the internal circulation fan exceeds a threshold value after RTO optimization, setting a model identification data acquisition signal to be started until the time of the (k+1) RTO period, and ending model identification data recording; the data recording time interval during the period uses the fastest sampling period supported by the system, which is at least less than 1/10 of the RTO period, so that enough sampling points are ensured in one period; at the time of the (k+1) RTO period, after optimizing output, performing model identification from the frequency of the internal circulation fan to the NMP concentration once; updating the model when the accuracy of the identified model is higher than that of the previous model; if the model accuracy is lower than the previous model, the model is not updated.
The optimization system disclosed by the invention converts the dynamic optimizing problem of the working point of the battery coating oven into a linear programming problem with constraint adjustment, and achieves the aim of energy-saving and economic operation on the premise that the concentration of NMP does not exceed the standard.
Aiming at the process characteristics and energy balance relation of electric heating hot air circulation drying, a multi-objective optimization function J based on energy consumption and NMP concentration indexes is determined 2 Finally solving the linear optimization problem with constraint,the total circulating air volume of the system is minimized as much as possible under the condition that the NMP concentration is not out of standard. Taking an m-section oven as an example, the frequency of an internal circulation fan is
The optimization model of the RTO optimization control module in the battery coating and drying process is as follows:
s.t.
C i ＜C i,max i＝1,…,m (2)
F in,min ≤f in ≤F in,max (3)
wherein J is 2 Is an objective function, Q i (f in,i )、
The index is used for representing the heat of the i-th section oven and the NMP concentration respectively; wherein q is i And r i The weight coefficients of the heat index and the NMP index are respectively, and the larger the weight coefficient is, the more strict the constraint of the corresponding item i in the optimization process is; m represents the number of ovens, F in,min And F in,max The minimum and maximum constraints of the internal circulation fan frequency are respectively. C (C) i,max Is the maximum safe value for NMP concentration. Hc is a predicted period based on the effect of the internal circulation fan frequency as input on the concentration of NMP, the length of the period being determined by the model response time. The sum of NMP concentrations divided by the prediction time characterizes future average NMP concentrations for different internal circulation fan frequency settings over a prediction period.
Q i The method is a representation of external input heat in the drying process, and the external heat input needs energy consumption and can be used as an index for measuring economy; q (Q) i And f in The relationship of (2) can be expressed by the formula (4-6);
Q i ＝V i *ΔT i (4)
ΔT i ＝T i,tgt -T i,ret (5)
as can be seen from formula (4), Q i Is the circulation air volume V in the ith section of oven i And oven temperature delta T i Because the return air temperature is smaller than the target temperature of the oven in the normal drying process, the temperature difference is positive, and the air quantity is also non-negative, so Q i The value is positive. Wherein T is i,tgt Is the target temperature of the section i oven, T i,ret Is the return air temperature of the ith section of oven; wherein, the circulating air volume V i Can be calculated by the formula (6), wherein f i Fan frequency; p is the number of motor pole pairs; r is the radius of the fan blade rotor; l is the width of the fan blade rotor; lambda (lambda) v Is the volume coefficient of the fan; the amount of circulated air may be approximately a linear function of fan frequency. According to the return air temperature at the RTO optimization moment, the frequency of an internal circulating fan can be utilized
Calculating the consumed heat quantity Q of the corresponding heating bag 1 ,…,Q m ]。
C NMP ＝[C 1 ,…C m ]For each oven NMP concentration, C i Is characterized by NMP concentration, is used as a variable related to the safety performance of the process and is also related to the frequency f of an internal circulating fan in Related to the following. Considering the dynamic response of NMP and internal recycle blower frequency, at each dynamic operating point, oven NMP concentrations [ C ] were identified 1 ,…C m ]With internal circulation fan frequency
ARX model in between:
A i (z -1 )C i (k)＝z -d B i (z -1 )f in,i (k)+ξ i (k) i＝1,2,…,m (7)
wherein A, B is a model parameter, f in,i (k) The frequency of the circulating fan in the ith section at the k moment; c (C) i (k) NMP concentration for section i oven at time k; zeta type toy i (k) The uncertainty disturbance at the moment k;
the frequency of the internal circulation fan can be calculated according to the formula (7)
Corresponding NMP concentration [ C 1 ,…C m ]Is converted into a continuous time model to be substituted into the RTO optimization model (1) to solve the optimization problem. According to the RTO optimization model (1), as the frequency of the internal circulation fan is reduced, the circulation air quantity of the oven is reduced, so that the cold-heat exchange between circulation air is reduced, and the total heat consumed by each section of heating package in the oven is reduced, namely the first term is reduced; at the same time, the total NMP concentration in each section in the oven is increased due to the reduction of the air return quantity, namely the second item is increased, so that the frequency target F of the internal circulation fan at the optimal working point can be calculated due to the mutual offset between the reduction of the total heat consumption and the increase of the total NMP concentration in,tgt And calculating an optimal NMP concentration control target for each section by a model (7)>
As the optimal control target y of CV under the current working condition TGT And at each sampling instant a scroll optimization is performed, eventually taking only the first control input to act on the system. />
The specific calculation steps of the optimal solving problem of the optimization objective function of the RTO optimization control module are as follows:
H(t)＝L[x,u,t]+λ T f[x,u,t] (8)
In the formula (8), the first term is a performance index function, lambda in the second term is a Lagrange operator, f [ x, u, t ] is a constraint equation, namely a state equation of a model between the concentration of the oven NMP and the frequency of the internal circulating fan in the formula (7), x is the concentration of the oven NMP, and u is the frequency of the internal circulating fan; according to the principle of minima:
because the internal circulation fan frequency u has control constraint, the internal circulation fan frequency u is not necessarily equal to 0, namely the conditional equation (12) is not necessarily solved; considering that if u makes the state vector or the Hamiltonian function to obtain the minimum value, equation (12) may be replaced, so that the Hamiltonian function is minimized, where u is the solution of optimal control, and the corresponding Hamiltonian function is:
step 2, given the initial input variable value and step length, iteratively solving according to the following method
Given an initial control variable u (0), an initial step length (learning rate) eta (0), an iteration cut-off condition epsilon and an initial iteration count k=0, the real-time optimization of the control parameters is completed through the following steps (iterative process):
(1) Calculating the gradient of each step:
(2) If k=0, jump to (3); otherwise, u (k) is substituted into the objective function J 2 If |J (k) -J (k+1) |ε, the iteration is terminated and u (k) is output, and if |J (k) -J (k+1) | > ε, then the iteration is calculated
Wherein delta isu(k-1)＝u(k)-u(k-1)，
(3) Calculation of
(4) Returning to (1) continuing the next iteration;
the frequency u (t) of the circulating fan in the control parameter and the NMP concentration index of each section of oven can be optimized in real time through the steps
And 3, outputting the optimal internal circulation fan frequency and the NMP concentration target value calculated in the step 2 to other controllers or operators as control target values.
And 4, calculating total air quantity and corresponding outer circulation fan frequency lower limit protection based on the optimal inner circulation fan frequency obtained in the step 2.
Considering the air quantity loss of the return air pipeline, the total circulating air quantity in the return air pipe is as follows:
wherein V is in,i Is the circulation air quantity of the ith internal circulation fan, V η For total pipe air loss, V out,total The total air quantity generated by each internal circulating fan in the return air pipe and the total air quantity loss are added;
in order to ensure that the concentration of NMP does not exceed the standard, enough return air must enter the oven, namely the total air volume generated by each external circulation fan cannot be smaller than the total circulation air volume:
according to the relation between the wind quantity and the fan frequency in the formula (6), the reference frequency of the external circulation fan corresponding to the minimum circulation wind quantity can be calculated, namely the minimum value of the external circulation fan frequency under the current working condition is used as the lower limit constraint condition of the external circulation fan frequency control.
And 5, checking whether the model identification data acquisition signal is started or not according to the optimal internal circulation fan frequency obtained in the step 2, and if the model identification data acquisition signal is started in the previous period, performing primary model identification on the internal circulation fan frequency and the NMP concentration according to the complete data of the previous period, and setting the model identification acquisition signal to be finished.
And 6, comparing the optimal internal circulation fan frequency obtained in the step 2 with the internal circulation fan frequency value before optimization, if the change is significant (the absolute value is greater than a threshold value, such as 2 Hz), setting a model identification data acquisition signal to be started, and starting to record data of a complete RTO period, wherein the period of data recording is at least 1/10 or less of the RTO period based on the period of actual acquisition (the actual use is 5 seconds of the sampling period, and the RTO period is 3 minutes and 60 times of the sampling period). When the accuracy of the identified model is higher than that of the previous model, updating the model for the next period; if the model accuracy is lower than the previous model, the model is not updated.
The model used in the prediction of NMP concentration in the RTO optimization process can be used in the strategy of updating the NMP concentration in real time in the steps 5 and 6, or can be simplified into the automatic selection of the initialization model in different working range intervals. The main operating frequency range [ 18-46 ] Hz of the internal circulation fan is divided into 7 operating points, each of which covers a range of 4 Hz. And identifying the models of 7 working points offline, and storing the models into an RTO module. And during optimization, according to the range of the working point where the frequency value of the current internal circulation fan is located, starting a corresponding model to predict the NMP concentration.
In practical application, the real-time optimization system can dynamically output control target values of different working points of the drying system based on comprehensive indexes of energy consumption and NMP concentration, the output control target values not only can provide guidance for manual adjustment of operators, and the operators can determine whether to accept issuing or not, but also can automatically issue the control target values to a traditional single-loop PID controller as control target set values to realize energy saving under the dynamic targets of the system; the method can also be combined with other multivariable controllers to form double-layer optimal control, thereby further improving control precision and saving energy.
The foregoing embodiments of the present invention are not intended to limit the technical scope of the present invention, and therefore, any minor modifications, equivalent variations and modifications made to the above embodiments according to the technical principles of the present invention still fall within the scope of the technical proposal of the present invention.
Claims (6)
1. The real-time optimizing system for the working point of the drying control system after coating the new energy battery comprises m sections of ovens, n sections of total air supply pipes and total air return pipes, wherein each section of oven comprises an electric air return valve, an internal circulating fan, an electric heating bag and an exhaust fan; the method is characterized in that: controlling the return air quantity by adjusting the frequency of m internal circulating fans and the opening degree of m return air valves; controlling the temperature in the oven by adjusting the power of the m electric heating packs; the change of the pressure in the oven and the change of the NMP concentration are compensated by adjusting the frequency of m exhaust fans; controlling the NMP concentration and the total NMP concentration of each section of oven by adjusting the frequency of n external circulating fans; wherein n is less than m;
the optimization system uses T RTO The optimization system comprises an RTO optimization control module; the RTO optimization control module constructs a multi-objective optimization function based on energy consumption and NMP concentration indexes, determines a control target of NMP concentration, an inner circulating fan frequency and a minimum frequency of an outer circulating fan by solving the objective optimization function, and the constructed objective optimization function is realized by solving a linear optimization method with constraint; the energy consumption index is established and the heating quantity is mathematically expressed through the fan frequency and the temperature difference; the NMP concentration index is used for predicting and calculating the average NMP concentration of a future period of time based on a model from the internal circulation fan to the NMP concentration; the model for predicting NMP concentration is a model which is identified on line in real time according to the need in the process of initializing a given model or calling an RTO optimization module; each time call RAnd when the TO optimizing control module is used, an optimal NMP concentration control target in the next calling period, the frequency of the internal circulating fan and the frequency lower limit protection value of the external circulating fan are obtained by solving an optimizing objective function, so that the total circulating air volume of the system is reduced as much as possible on the premise that the NMP concentration does not exceed the standard.
2. The real-time optimizing system for the operating point of the post-coating drying control system for the new energy battery according to claim 1, wherein the system is characterized in that: the target optimization function of the RTO optimization control module is as follows:
s.t.
C i ＜C i,max i＝1,…,m (2)
F in,min ≤f in,i ≤F in,max ,i＝1,···,m (3)
in the formula (1), J 2 Is an objective function, Q i (f in,i )、
The index is used for representing the heat of the i-th section oven and the NMP concentration respectively; wherein q is i And r i The heat index of the ith section of oven and the weight coefficient of the NMP index are respectively, and the larger the weight coefficient is, the more strict the constraint of the corresponding item i in the optimization process is; m represents the number of ovens, F in,min And F in,max The minimum value and the maximum value constraint conditions of the frequency of the internal circulation fan are respectively; c (C) i,max Is the maximum safe value of NMP concentration; h c The method is based on a prediction period of the influence of the frequency of an internal circulating fan on the concentration of NMP, and the basic time unit of model prediction is the same as the sampling time of a system; summing the NMP concentration and dividing the NMP concentration by the predicted period time, and representing future average NMP concentrations corresponding to different internal circulation fan frequency setting values in one predicted period;
Q i is a meter for externally inputting heat in the drying processThe energy consumption is required by external heat input and is used as an index for measuring economy; q (Q) i And f in The relationship of (2) can be expressed by the formula (4-6);
Q i ＝V i *ΔT i (4)
ΔT i ＝T i,tgt -T i,ret (5)
as can be seen from formula (4), Q i Is the circulation air volume V in the ith section of oven i And oven temperature delta T i Because the return air temperature is smaller than the target temperature of the oven in the normal drying process, the temperature difference is positive, and the air quantity is also non-negative, so Q i The value is positive; wherein T is i,tgt Is the target temperature of the section i oven, T i,ret Is the return air temperature of the ith section of oven; wherein, the circulating air volume V i Calculated by equation (6), where f i The fan frequency; p is the number of motor pole pairs; r is the radius of the fan blade rotor; l is the width of the fan blade rotor; lambda (lambda) v Is the volume coefficient of the fan; the circulating air volume can be approximately a linear function of the fan frequency; according to the return air temperature at the RTO optimization moment, the frequency of an internal circulating fan is utilized
Calculating the consumed heat quantity Q of the corresponding heating bag 1 ,…,Q m ]；
C NMP ＝[C 1 ,…C m ]For each oven NMP concentration, C i Is characterized by NMP concentration, is used as a variable related to a process safety index and is also related to the frequency f of an internal circulating fan in Related to; considering the dynamic response of NMP and internal recycle blower frequency, at each dynamic operating point, oven NMP concentrations [ C ] were identified 1 ,…C m ]With internal circulation fan frequency
ARX model in between:
A i (z -1 )C i (k)＝z -d B i (z -1 )f in,i (k)+ξ i (k) i＝1,2,…,m (7)
wherein A, B is a model parameter, f in,i (k) The frequency of the circulating fan in the ith section at the k moment; c (C) i (k) NMP concentration for section i oven at time k; zeta type toy i (k) The uncertainty disturbance at the moment k;
calculating the frequency of the internal circulation fan according to the model of (7)
Corresponding NMP concentration [ C 1 ,…C m ]And (3) converting the time response of the fan frequency to the NMP concentration model in continuous time, and substituting the model into the RTO optimization model (1) to solve the optimization problem.
3. The real-time optimizing system for the operating point of the post-coating drying control system for the new energy battery according to claim 2, wherein: before the RTO optimization control module is put into use, manually setting a frequency step test of the internal circulation fan, acquiring test data, and then carrying out model identification, wherein the identified model is stored in an RTO model to be used as an initial model for storage;
when an RTO optimization control module is adopted for optimization, an initial model is started in the first period, and then each new RTO period is judged according to the change condition of an internal circulation fan, and whether the model needs to be updated or not;
if the absolute value of the frequency of the internal circulation fan after optimization and the variation before optimization is smaller than a threshold value at the kth RTO optimization moment, continuously using the original model; if the frequency variation of the internal circulation fan exceeds a threshold value after RTO optimization, setting a model identification data acquisition signal to be started until the time of the (k+1) RTO period, and ending model identification data recording; the data recording time interval during the period uses the fastest sampling period supported by the system, which is at least less than 1/10 of the RTO period, so that enough sampling points are ensured in one period; at the time of the (k+1) RTO period, after optimizing output, performing model identification from the frequency of the internal circulation fan to the NMP concentration once; updating the model when the accuracy of the identified model is higher than that of the previous model; if the model accuracy is lower than the previous model, the model is not updated.
4. The real-time optimizing system for the operating point of the post-coating drying control system for the new energy battery according to claim 3, wherein: the specific calculation steps of the optimal solving problem of the optimization objective function of the RTO module are as follows:
step 1, constructing a Hamiltonian H (t)
H(t)＝L[x,u,t]+λ T f[x,u,t] (8)
In the formula (8), the first term is a performance index function, lambda in the second term is a Lagrange operator, f [ x, u, t ] is a constraint equation, namely a state equation of a model between the concentration of the oven NMP and the frequency of the internal circulating fan in the formula (7), x is the concentration of the oven NMP, and u is the frequency of the internal circulating fan; according to the principle of minima:
because the internal circulation fan frequency u has control constraint, the internal circulation fan frequency u is not necessarily equal to 0, namely the conditional equation (12) is not necessarily solved; considering that if u makes the state vector or the Hamiltonian function to obtain the minimum value, equation (12) may be replaced, so that the Hamiltonian function is minimized, where u is the solution of optimal control, and the corresponding Hamiltonian function is:
minH[x * ,λ * ,u,t]＝H[x * ,λ * ,u * ,t] (13)
step 2, given an initial input variable value and a learning rate, iteratively solving according to the following method:
given an initial control variable u (0), an initial step length (learning rate) eta (0), an iteration cut-off condition epsilon and an initial iteration count k=0, the real-time optimization of the control parameters is completed through the following steps (iterative process):
(1) Calculating the gradient of each step:
(2) If k=0, jump to (3); otherwise, u (k) is substituted into the objective function J 2 If |J 2 (k)-J 2 (k+1) |ε, terminate the iteration and output u (k), if |J 2 (k)-J 2 (k+1) | > ε, then calculate
Wherein Δu (k-1) =u (k) -u (k-1), and +.>
(3) Calculation of
(4) Returning to (1) continuing the next iteration;
the frequency u (t) of the circulating fan in the control parameter and the NMP concentration index of each section of oven can be optimized in real time through the steps
Step 3, inputting the optimal target function of the APC optimization control module based on the optimal internal circulation fan frequency and the NMP concentration target value calculated in the step 2;
step 4, calculating total air quantity and corresponding outer circulation fan frequency lower limit protection based on the optimal inner circulation fan frequency obtained in the step 2;
considering the air quantity loss of the return air pipeline, the total circulating air quantity in the return air pipe is as follows:
wherein V is in,i Is the circulation air quantity of the ith internal circulation fan, V η For total pipe air loss, V out,total The total air quantity generated by each internal circulating fan in the return air pipe and the total air quantity loss are added;
in order to ensure that the concentration of NMP does not exceed the standard, enough return air must enter the oven, namely the total air volume generated by each external circulation fan cannot be smaller than the total circulation air volume:
according to the relation between the wind quantity and the fan frequency in the formula (6), the reference frequency of the external circulation fan corresponding to the minimum circulation wind quantity can be calculated, namely the minimum value of the external circulation fan frequency under the current working condition is used as the lower limit constraint condition of the external circulation fan frequency control.
5. The real-time optimizing system for the operating point of the post-coating drying control system of the new energy battery according to claim 4, wherein: the model updating method for predicting the NMP concentration comprises the following steps:
based on the optimal internal circulation fan frequency obtained in the step 2, checking whether a model identification data acquisition signal is started in the last period, if so, performing model identification on the internal circulation fan frequency to NMP concentration for one time according to the complete data of one period, and setting the model identification acquisition signal to be ended;
based on the optimal internal circulation fan frequency obtained in the step 2, comparing the optimal internal circulation fan frequency with the internal circulation fan frequency value before optimization, if the absolute value is larger than the threshold value, setting a model identification data acquisition signal to be started, and starting to record data of a complete RTO period; when the accuracy of the identified model is higher than that of the previous model, updating the model for the next period; if the model accuracy is lower than the previous model, the model is not updated.
6. The real-time optimizing system for the operating point of the post-coating drying control system of the new energy battery according to claim 4, wherein: the model for predicting the NMP concentration is simplified into an initialization model in different working intervals:
dividing a main operating frequency range of an internal circulation fan into a plurality of working points; identifying the models of a plurality of working points offline, and storing the models into an RTO module; and during optimization, according to the range of the working point where the frequency value of the current internal circulation fan is located, starting a corresponding model to predict the NMP concentration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211701216.1A CN116107209A (en) | 2022-12-28 | 2022-12-28 | Real-time optimizing system for working point of drying control system after coating of new energy battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211701216.1A CN116107209A (en) | 2022-12-28 | 2022-12-28 | Real-time optimizing system for working point of drying control system after coating of new energy battery |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116107209A true CN116107209A (en) | 2023-05-12 |
Family
ID=86253620
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211701216.1A Pending CN116107209A (en) | 2022-12-28 | 2022-12-28 | Real-time optimizing system for working point of drying control system after coating of new energy battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116107209A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117313403A (en) * | 2023-10-16 | 2023-12-29 | 东莞市鹏锦机械科技有限公司 | Monitoring method, equipment and storage medium for heat recovery device of coating machine oven |
-
2022
- 2022-12-28 CN CN202211701216.1A patent/CN116107209A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117313403A (en) * | 2023-10-16 | 2023-12-29 | 东莞市鹏锦机械科技有限公司 | Monitoring method, equipment and storage medium for heat recovery device of coating machine oven |
| CN117313403B (en) * | 2023-10-16 | 2024-04-26 | 东莞市鹏锦机械科技有限公司 | Monitoring method, equipment and storage medium for heat recovery device of coating machine oven |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103225799B (en) | Method for controlling main steam temperature in thermal power plant | |
| CN110257577B (en) | Burning process control method and system for ball type hot blast stove | |
| CN105676647B (en) | A kind of cold, heat and power triple supply system forecast Control Algorithm based on state observer | |
| CN104019520B (en) | Data drive control method for minimum energy consumption of refrigerating system on basis of SPSA | |
| CN104699064B (en) | A kind of ature of coal self-tuning model method of fired power generating unit coordinated control system | |
| CN108151253B (en) | Automatic compensation method for air supply temperature of variable air volume air conditioner | |
| Gwerder et al. | Predictive control for integrated room automation | |
| CN109004251B (en) | Intelligent control method of solid oxide fuel cell system | |
| CN103134046B (en) | Superheated steam temperature two-stage coordination, prediction and control method of thermal power generating unit | |
| CN112490540A (en) | Power battery system, temperature control method and device thereof, medium and processor | |
| CN102401371A (en) | Reheated gas temperature optimization control method based on multi-variable predictive control | |
| CN116107209A (en) | Real-time optimizing system for working point of drying control system after coating of new energy battery | |
| CN117970986B (en) | Temperature and humidity control method, device and medium of cold and hot system | |
| Li et al. | Model parameter identification of indoor temperature lag characteristic based on hysteresis relay feedback control in VAV systems | |
| CN111950158A (en) | Central air conditioner energy consumption optimization method based on sequence least square programming | |
| CN116165888A (en) | Multi-layer optimization and multivariable control system for drying new energy battery after coating | |
| CN114371619B (en) | MGT-CCHP variable working condition dynamic energy efficiency optimization control method | |
| CN109539359B (en) | Phase-change electric heat storage heating system and method based on different working conditions PID + adaptive feedforward compensation | |
| CN113835342B (en) | Disturbance rejection predictive control method for overheat steam temperature system | |
| CN115860445A (en) | Multi-level cooperative scheduling method for uranium enrichment refrigerating unit and closed cooling tower | |
| CN111473408A (en) | Control method of heat supply control system based on climate compensation | |
| CN107621781A (en) | Oil field heating furnace constant temperature heats and thermal efficiency Self-Optimizing Control Approach and system | |
| CN116300566A (en) | New energy battery post-coating drying multivariable model predictive control system | |
| CN112556258A (en) | Heat pump intelligent control method for compensating time delay | |
| CN111193261A (en) | Day-ahead optimization method of multi-energy flow system based on building equivalent heat energy storage |
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 |