CN111597723B - Intelligent control method for electric automobile air conditioning system based on improved intelligent model predictive control - Google Patents
Intelligent control method for electric automobile air conditioning system based on improved intelligent model predictive control Download PDFInfo
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Abstract
The invention relates to an intelligent control method of an air conditioning system of an electric vehicle based on improved intelligent model predictive control, which belongs to the technical field of whole vehicle heat management and comprises the following steps: s1: establishing an automobile air conditioning system-passenger cabin coupling thermal model; s2: establishing a model prediction controller matched with the vehicle air conditioning system-passenger cabin coupling model based on the vehicle air conditioning system-passenger cabin coupling model; s3: establishing a vehicle speed predictor based on a neural network, and predicting a future vehicle speed by utilizing a historical vehicle speed; s4: establishing an adaptive device aiming at different individual thermal habits based on PMV theory and an adaptive algorithm, and obtaining a target comfort temperature T comfort The method comprises the steps of carrying out a first treatment on the surface of the S5: and combining the speed prediction and the thermal comfort adaptation to establish a complete intelligent controller aiming at the automobile air conditioning system. The invention is based on model predictive control, is suitable for a multiple-input multiple-output controller system, is efficient and energy-saving, is more intelligent, and is suitable for an air conditioner control system for future intelligent and personalized driving.
Description
Technical Field
The invention belongs to the technical field of whole vehicle heat management, and relates to an intelligent control method of an air conditioning system of an electric vehicle based on improved intelligent model predictive control.
Background
Along with the increasing severity of world energy crisis and environmental pollution problems, people put higher demands on energy conservation and emission reduction performance of automobiles. The automobile industry is developed towards electric and intelligent technologies, and the development of the new technologies is favorable for energy conservation and emission reduction of the propulsion automobiles, but the development of the electric automobiles and intelligent automobiles also faces a plurality of problems to be solved urgently. It is known that energy consumption of an automobile heating ventilation air conditioning (HAVC) system accounts for a significant part of total energy consumption of an automobile, and for an electric automobile, the energy consumption of the air conditioning system has a significant influence on the endurance of the electric automobile, and related researches show that the energy consumption of the air conditioning system of the electric automobile can reduce the endurance of the electric automobile by 30% -40% on average. In order to improve the economy of an automobile during running, it is required to improve the efficiency performance of an air conditioner in various aspects. In the aspect of intelligence, due to the intelligent development requirement of automobiles, the automobile air conditioner needs to be more intelligent than the traditional air conditioner to improve the driving thermal comfort. Therefore, how to reduce the energy consumption of the air conditioning system of the electric vehicle and improve the comfort of passengers is one of the important points of research on the heat management system of the electric vehicle nowadays. In addition, the development of the intelligent air conditioning system of the automobile plays an important auxiliary role in the development work of unmanned automobiles.
The intelligent energy management decision device and the controller of the existing automobile air conditioning system can ensure the running efficiency and the intelligence of the automobile air conditioning system. Therefore, how to make control decisions is one of the important research points of automotive air conditioning systems. Compared with the traditional automobile, the electric automobile compressor mainly operates in a motor driven mode, and because the rotating speed control of the electric compressor can be accurate and is not influenced by the rotating speed of the engine, the rotating speed control of the compressor can be regulated according to real-time requirements and is not influenced by the working condition of the external automobile speed. The main flow control method of the current automobile air conditioning system is based on a regular switch controller and is controlled in a corresponding table look-up mode, or some traditional methods such as PID control and fuzzy control. These conventional control methods mainly utilize the difference between the target value and the feedback value to control and regulate according to the system feedback. This type of control method, while simple, is often limited in the effectiveness of the manner in which the feedback signal is relied upon alone, due to the variable operating conditions and rapid changes in the automotive system. In addition, the conventional pure feedback controller does not relate to an optimization algorithm, so that when facing a multi-input multi-output system, particularly like an automobile air conditioning system, each control amount is difficult to realize efficient and reasonable collocation, and the control effect is difficult to ensure. And when the working condition of the automobile changes rapidly, the feedback signal supports the system at the current time, but the state of the system changes rapidly, and when the controller finally acts on the system, the operation is often not optimal. Or even if the feedback signal is currently the best operation, it is not necessarily the best operation for the future in the long run. Therefore, in order to further improve the efficiency and intelligence of the controller and meet the requirement of the multiple input multiple output control of the air conditioning system of the automobile, a more predictive controller is needed.
Model Predictive Control (MPC) is a currently advanced control method, and by establishing a state model of a controlled system, predictive control can have a prediction function, and can predict future values of process output according to control input at the current moment of the system and history information of the process. By combining model predictive control with other working condition predictive methods (such as vehicle speed prediction), the future state of the automobile can be well predicted, and even if optimal control is given, the whole control process is more accurate and efficient, and the efficiency and the intelligence are improved. Meanwhile, by introducing the human comfort theory, the adaptation and analysis of the human body heat habit are introduced into the controller, so that the control system is more humanized. At present, a plurality of human body thermal comfort theory researches are carried out, wherein a more classical method is to represent human body thermal comfort evaluation by calculating PMV (Predicted Mean Vote) values, the values take the basic equation of human body thermal balance and the level of psychophysiology subjective thermal sensation as starting points, and the comprehensive evaluation indexes of a plurality of related factors of human body thermal comfort are considered. The index is from-3 to +3, and corresponds to the degree of human body from feeling cold to heat, and when pmv=0, the human body feels hot neutral, namely comfort value. In the control, model predictive control, vehicle speed prediction and human comfort response theory are combined, so that a controller can make a decision in advance to obtain the optimal control temperature and perform optimal control. The intelligent automobile control method has the advantages that the control effect and the energy-saving effect are improved, meanwhile, the heat habit problems of different individuals can be treated in a humanized manner, the intelligent automatic adjusting function is realized, and the technical method is a development technology expected by an intelligent automobile in the future.
At present, a control method of an automobile air conditioning system integrating model predictive control, vehicle speed prediction and human comfort adaptation does not appear.
Disclosure of Invention
Therefore, the invention aims to provide a method for automatically adjusting intelligent comfort temperature of an air conditioning system of an electric automobile with prediction capability by combining model prediction control and neural network prediction and a human body thermal comfort adaptation method.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent control method of an electric automobile air conditioning system based on improved intelligent model predictive control comprises the following steps:
s1: establishing an automobile air conditioning system-passenger cabin coupling thermal model;
s2: establishing a model prediction controller matched with the vehicle air conditioning system-passenger cabin coupling model based on the vehicle air conditioning system-passenger cabin coupling model;
s3: establishing a vehicle speed predictor based on a neural network, and predicting a future vehicle speed by utilizing a historical vehicle speed;
s4: establishing an adaptive device aiming at different individual thermal habits based on PMV theory and an adaptive algorithm, and obtaining a target comfort temperature T comfort ；
S5: and combining the speed prediction and the thermal comfort adaptation to establish a complete intelligent controller aiming at the automobile air conditioning system.
Further, the step S1 specifically includes the following steps:
s11: the method comprises the steps of establishing a one-dimensional dynamic thermal mathematical model of an automobile air conditioning system, wherein the automobile air conditioning system comprises a compressor, a condenser, an evaporator and an expansion valve, and the dynamic models of the evaporator and the condenser are established based on a moving boundary method;
s12: establishing a simplified dynamic thermal model of the one-dimensional passenger cabin system;
s13: and respectively coupling the air inlet and outlet ends of the evaporator in the air conditioning system with the air outlet and inlet ends of the passenger cabin to form air circulation.
Further, the dynamic thermal mathematical model of the one-dimensional automobile air conditioning system in step S11 includes:
1) A one-dimensional dynamic model of the refrigerant in the compressor is established, and the one-dimensional dynamic model is expressed as follows:
wherein,for mass flow rate, η of the compressor v For volumetric efficiency ρ r For the density of refrigerant, N comp For compressor speed, V d For compressor displacement, h c，o Is the enthalpy value of the outlet of the compressor, h c，i Is the enthalpy value of the inlet of the compressor, h is，o Is the isentropic outlet enthalpy value eta of the compressor is Isentropic efficiency;
2) For expansion valves, the expansion process in the expansion valve can be considered an adiabatic process, so that during its dynamic process, the refrigerant mass flow through the expansion valveThe relationship with its expansion valve pressure drop Δp is expressed by:
wherein C is q For the flow coefficient of the expansion valve ρ v For refrigerant density through expansion valve, A v Is the minimum flow area of the expansion valve;
3) According to the moving boundary method, the gas and liquid refrigerants in the evaporator satisfy the law of mass conservation, so the change in the length le of the two-phase region of the evaporator is obtained by:
pressure P in evaporator e The change over time is expressed as:
the change in evaporator wall temperature is then expressed in terms of energy conservation as:
wherein ρ is le Is the density of the liquid refrigerant in the evaporator, h lge A is the vaporization latent heat of the refrigerant in the evaporator e The total sectional area of the flat tube micro-channel of the evaporator,for average void fraction of evaporator two-phase region, h ge 、h le And h ie Enthalpy values, a, respectively representing the gas, liquid and inlet refrigerant in the evaporator at the present pressure ie Is the heat exchange coefficient between the inner wall of the evaporator and the refrigerant in the two-phase region, D ie Is the diameter of the inside of the flat tube of the evaporator, T we To evaporator wall temperature, T re Is the saturation temperature of the refrigerant at the current pressure of the evaporator, L e Is the total length of the flat tube of the evaporator, (C p m) we Represents the specific heat of the evaporator material and the mass of the evaporator, a o Is the heat exchange coefficient between the air and the evaporator wall surface, A oe T is the windward area of the evaporator ae Is the current temperature of the air surrounding the evaporator;
4) For the condenser, the heat exchange principle is similar to that of the evaporator, so there are:
if the air conditioning system refrigerant does not leak, the total mass of refrigerant in the system is unchanged, and the total mass of refrigerant in the evaporator and condenser is considered constant, so there is:
wherein ρ is lc Is the density of the liquid refrigerant in the condenser, h lgc A is the vaporization latent heat of the refrigerant in the condenser c Is the total sectional area of the flat tube micro-channel of the condenser,is the average void fraction of the two-phase region of the condenser, h gc ，h lc And h ic Respectively representing the enthalpy values of the gas, liquid and inlet refrigerant in the condenser at the current pressure, a ic Is the heat exchange coefficient between the inner wall of the condenser and the refrigerant in the two-phase zone, D ic Diameter T of condenser flat tube wc To condenser wall temperature, T rc Is the saturation temperature of the refrigerant at the current pressure of the condenser, L c Is the total length of the flat tube of the condenser, (C p m) wc Represents the specific heat of the condenser material and the mass of the condenser, a oc Is the heat exchange coefficient between the air and the condenser wall surface, A oc Is the windward area of the condenser, T ac Is the current temperature of the air surrounding the condenser, i.e. the ambient temperature, Σ represents a constant;
condenser air side heat exchange coefficient a oc Mainly influenced by the external wind speed, and during the running of the automobile, the external wind speed of the condenser is mainly influenced by the speed of the automobile, so a oc The relationship with vehicle speed was fitted experimentally and expressed as:
a oc ＝f p2 (V car )
vehicle speed V car Is determined by the driver and is not regulated by the air conditioning system controller, in which the vehicle speed is considered as a disturbance input.
Further, the one-dimensional passenger cabin system thermal model in step S12 includes:
total heat load of passenger compartment of automobileExpressed as:
during driving, convection heat is exchanged between the cabin and the outsideMainly driven by the speed V car And ambient temperature T ac Influence, and these two variables are input disturbances not controlled by the controller, < +_, in the heat exchange model>Calculated from the following formula:
wherein T is s The temperature of the structure surrounding the passenger compartment, based on energy conservation, the dynamic change of the temperature of the surrounding structure is expressed by the following formula:
from conservation of energy, the dynamic change in air temperature of the passenger compartment is expressed as:
wherein,for heat exchange of the body surface structure>For solar radiation heat load, +.>For the heat load caused by ventilation +.>For human body heat load->Thermal loads for machinery and instrumentation; t (T) cab For the temperature of the cabin of the motor vehicle, < > for>The refrigerating capacity of the air conditioning system introduced into the passenger cabin in unit time M a Is the air quality in the volume range of the automobile cabin, cp a Specific heat of air, h o Is the heat exchange coefficient between the outer side of the outer structure of the outer car cabin and the air side, is mainly determined by the speed of the car, and is the total surface area of the outer surface structure of the outer car cabin i Is the heat exchange coefficient between the inner surface of the automobile cabin and the air, M s And C ps The mass and specific heat of the vehicle cabin peripheral enclosed structure are respectively;
through the mathematical model, a coupling dynamic model of the passenger cabin and the air conditioning system is established, and state variables in the model are expressed as follows:
X＝[l e P e T we P c T wc T s T a ] T 。
further, the step S2 specifically includes the following steps:
s21: and (3) establishing a state estimator in model predictive control, wherein the system is expressed by the following state space expression after linear processing according to the established automobile air conditioner and passenger cabin model:
x(k+i│k)＝Ax(k+i-1│k)+Bu u (k+i-1│k)+B v v(k+i-1│k)
y(k+i│k)＝Cx(k+i-1│k)
in the above formula, x is a state variable matrix, u and v respectively represent a manipulated variable matrix and a disturbance input matrix, and are respectively:
u＝[N comp N fan ] T
s22: establishing an optimizer of a model predictive controller;
the optimizing device in the MPC controller comprises an optimizing algorithm, an optimal solution of the objective function is found, the minimum cost function is guaranteed, and the optimizing device has the main functions of finding a group of optimal solutions to meet the control accuracy and simultaneously reducing the control cost as much as possible;
first, the total cost function is represented by:
J(Z k )＝J y (Z k )+J u (Z k )+J Δu (Z k )
wherein J is y ，J u ，J Δu A cost function representing tracking error of control temperature, a cost function of control quantity and a cost function of control quantity change; for the current time k, if the control time domain is c, the three cost functions are respectively represented by the following formulas:
wherein i represents the calculation step of the time sequence and has i E [1, c ]]；i,c∈N；n y ，n u Respectively representing the number of control outputs and control inputs and outputs;the j-th control output of y, u, deltau represents the weight of the j-th control output at the i-th time step; z is Z k Representing a decision output sequence of a positive quadratic programming of the above objective function, the objective function and constraints of the quadratic programming optimization are represented by the following form:
min J(Z k )
s.t.0≤N comp ≤6000r/min
0≤N fan ≤3000r/min
0≤T we ≤10℃
obtaining Z when the control time domain is c by solving quadratic programming k In the form of:
in order to ensure the accuracy of the real-time control, the matrix Z k Only the first element is actually output by the MPC controller.
Further, the step S3 specifically includes the following:
the method of the neural network is utilized to predict the vehicle speed, in principle, the accuracy of the neural network prediction is continuously enhanced by changing each weight (training) in the neural network based on the error of the predicted vehicle speed target value and the actual value, and finally, a more accurate output value is achieved. For a vehicle speed prediction neural network structure, an input layer comprises a historical vehicle speed matrix, an average speed matrix with 0 removed, an average acceleration matrix and an average deceleration matrix; for the prediction time domain τ, five input matrices are sequentially noted as x 1，τ ，x 2，τ ，x 3，τ ，x 4，τ And x 5,τ The total input layer is expressed as:
X τ ＝[x 1,τ x 2,τ x 3,τ …x 5,τ ]
in the two-layer neural network used, the predicted vehicle speed v at the time of the next tau second is τ The relationship with the input is expressed as:
for the output layer, the future predicted vehicle speed is the only output, and for the predicted horizon τ, the vehicle speed predicted output layer is represented by:
V＝[v 1 v 2 v 3 …v τ ]
where i represents the number of input variables, w0 no And w1 n1 The weight coefficients of the n0 th neuron of the first layer neuron and the n1 st neuron of the second layer neuron are respectively represented, b0 n0 And b1 n1 Respectively representing the offset values of n0 neurons of the first layer neurons and n1 neurons of the second layer neurons;
the weight and the bias value of each neuron are continuously and automatically adjusted by the algorithm through repeated iteration of the neural network layer by layer, so that the neural network forecast can more accurately forecast the future vehicle speed through the current information.
Further, step S4 specifically includes the following:
an estimator of the current PMV value in the passenger cabin is established based on the human body thermal comfort theory, and the PMV value of the passenger cabin at the moment is calculated through external environment conditions, wherein the calculation formula of the PMV value is as follows:
PMV＝T s (M-φ 1 -φ 2 -φ 3 -φ 4 -φ 5 -φ 6 )
wherein T is S ＝0.303e -0.036M +0.028，
φ 1 ＝3.05e -3 +5733-6.99M-P w ，
φ 3 ＝1.7e -5 M(5867-P w )，
φ 4 ＝1.4e -3 M(34-T a )，
φ 5 ＝3.96e -8 f cl ((T cl +273) 4 -(T r +273) 4 )，
φ 6 ＝f cl h c (T cl -T a )，
T cl ＝35.7-0.028M-I cl (φ 5 +φ 6 )；
Wherein M is the metabolism rate of the passenger, P w Is the partial pressure of water vapor, T a For passenger compartment air temperature, T r For average radiation temperature in the cab, T cl For the surface temperature of the clothes, h c For the human body surface convection heat exchange coefficient, I cl For the thermal resistance of the passenger's clothing, V a Air flow rate for the passenger compartment;
after the obtained real-time PMV value, recording the PMV value when the driver controls to enter a steady state after each adjustment under the condition that the system control has reached the steady state, and recording the PMV value recorded at the ith time as the PMV i ；
By PMV i PMVa was calculated using the following formula:
where N represents the period of sampling;
the MPC controller allows the output of the controlled system to track the control target value, and the target cabin temperature, and in order to embed the adapted comfort temperature into the MPC controller, the comfort temperature calculator needs to calculate a sequence of comfort temperatures for the MPC to perform an optimal calculation. Thus, for control instant k, a continuous target comfort temperature sequence is calculated from:
in the above, phi 5 ,h c As the system state variables change, the system state variables, by definition, are changed, in order to reasonably simplify the calculation,other parameters are set as constants; by adapting the calculation, the resulting target comfort temperature is calculated by applying T rc (k+i|k) substitution step S2 in substitution step S2 objective function J y R (k+i|k) of (a) is accessed to the MPC optimizer.
The invention has the beneficial effects that: in the control, model predictive control, vehicle speed prediction and human comfort theory are combined, so that the controller can predict the future system state according to the current information under different running conditions of the electric vehicle, and the optimal control amount decision is made through an optimization algorithm. The method comprises the steps of obtaining the most probable automobile speed working condition in the future through a neural network prediction method, combining with a human comfort theory, adaptively obtaining the most comfortable temperature as a target control quantity in the future, and bringing the target control quantity into a model prediction controller to perform prediction control. The method can effectively solve the problems of untimely control and uncontrollable comfortable temperature, can save energy under time-varying working conditions and improves control effect. And moreover, the control efficiency and the control effect are improved, meanwhile, the heat habit problems of different individuals can be treated in a humanized way, and the intelligent automatic adjustment function is realized, so that the whole control process is more reasonable, efficient, humanized and intelligent. The method has the specific advantages that:
1) For the multi-input multi-output air conditioning system, the state of the control system is predicted by using model prediction control, and the system is optimally controlled by using an optimization algorithm, so that the control system can reasonably control the control amounts of the compressor and the fan according to the needs, better control effect and energy conservation are realized, the control is more timely, and the control output can be better changed due to rapid change of working conditions.
2) The vehicle speed condition is predicted by utilizing the neural network prediction, so that the system can obtain the most probable vehicle speed condition in the future in advance, the vehicle working condition information can be input into the controller in advance, the judgment of the system state by the controller is more accurate, the decision is better made, and the prediction capacity and the control effect of the controller are improved.
3) According to the invention, the comfort regulation of a driver is adapted by a simple algorithm, the most comfortable target temperature under the real-time working condition is calculated by using the adapted PMV index, and then the comfort temperature is led into the controller to serve as the target control temperature, so that the automobile air conditioning system can automatically regulate according to the past thermal regulation habit of an individual, thereby realizing the self-regulation and tracking of the real-time most comfortable target temperature of the automobile air conditioning system, and achieving the dual effects of comfort and energy conservation.
4) Compared with the traditional algorithm, the intelligent network-connected automobile heat management system is more intelligent, saves energy, is suitable for being applied to the intelligent network-connected automobile in the future, and improves the energy-saving and intelligent capabilities of the whole automobile heat management system of the intelligent network-connected automobile.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of the overall control logic of the intelligent controller of the present invention;
FIG. 2 is a schematic diagram of a one-dimensional thermal coupling model of an automotive dynamic air conditioning system and a passenger compartment structure;
FIG. 3 is a schematic illustration of heat exchange between the passenger compartment and ambient air and the air within the compartment;
FIG. 4 is a schematic diagram of a two-layer neural network vehicle speed prediction;
FIG. 5 is a flow chart of comfort temperature adaptation calculation and passenger compartment comfort temperature control;
FIG. 6 is a graph of the influence factors of PMV calculation.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to limit the invention; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc., that indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or suggesting that the referred device or element must have a specific azimuth, be constructed and operated in a specific azimuth, so that the terms describing the positional relationship in the drawings are merely for exemplary illustration and should not be construed as limiting the present invention, and that the specific meaning of the above terms may be understood by those of ordinary skill in the art according to the specific circumstances.
As shown in fig. 1, the intelligent control method of the air conditioning system of the electric vehicle based on model prediction control and vehicle speed prediction specifically comprises the following steps:
s1: the method for establishing the coupling thermal model of the automobile air conditioning system and the passenger cabin shown in fig. 2 specifically comprises the following steps:
s11: a dynamic thermal mathematical model of the one-dimensional automobile air conditioning system is built, wherein the dynamic thermal mathematical model comprises a compressor, a condenser, an evaporator and an expansion valve. The dynamic model of the heat exchanger is built based on a moving boundary method;
s12: simultaneously establishing a simplified dynamic thermal model of the one-dimensional passenger cabin system;
s13: the air inlet and outlet ends of an evaporator in the air conditioning system are respectively coupled with the air outlet and inlet ends of the passenger cabin to form air circulation;
a schematic diagram of the heat exchange between the passenger compartment and the ambient air and between the passenger compartment and the air in the compartment is shown in fig. 3.
The one-dimensional dynamic automobile air conditioning system thermal model comprises:
1) A one-dimensional dynamic mathematical model of the refrigerant in the compressor is expressed as follows:
wherein,for mass flow rate, η of the compressor v For volumetric efficiency ρ r For the density of refrigerant, N comp For compressor speed, V d For compressor displacement, h c,o Is the enthalpy value of the outlet of the compressor, h c,i Is the enthalpy value of the inlet of the compressor, h is,o Is the isentropic outlet enthalpy value eta of the compressor is Isentropic efficiency;
2) For expansion valves, the expansion process in the expansion valve can be considered an adiabatic process, so that during its dynamic process, the refrigerant mass flow through the expansion valveThe relationship with its expansion valve pressure drop Δp is expressed by:
wherein C is p For the flow coefficient of the expansion valve ρ v For refrigerant density through expansion valve, A v Is the minimum flow area of the expansion valve;
3) According to the moving boundary method, the gas and liquid refrigerants in the evaporator satisfy the law of mass conservation, so the change in length le of the two-phase region of the evaporator can be obtained by:
further, the pressure P in the evaporator e The change over time can be expressed as:
the change in evaporator wall temperature, again from conservation of energy, can be expressed as:
wherein ρ is le Is the density of the liquid refrigerant in the evaporator, h lge A is the vaporization latent heat of the refrigerant in the evaporator e The total sectional area of the flat tube micro-channel of the evaporator,average void fraction of two-phase region of evaporator. H ge ,h le And h ie The enthalpy values of the gas, liquid and inlet refrigerant in the evaporator at the present pressure are indicated, respectively, aie being the heat transfer coefficient between the inner evaporator wall and the refrigerant in the two-phase region, D ie Is the diameter of the inside of the flat tube of the evaporator, T we To evaporator wall temperature, T re Is the saturation temperature of the refrigerant at the current pressure of the evaporator, L e Is the total length of the flat tube of the evaporator, cp and m respectively represent the specific heat of the evaporator material and the mass of the evaporator. A o Is the heat exchange coefficient between the air and the evaporator wall. A is that oe Is the windward area of the evaporator, T ae Is the current temperature of the air surrounding the evaporator.
4) For the condenser, the heat exchange principle is similar to that of the evaporator, so there are:
assuming that the air conditioning system refrigerant does not leak, the total mass of refrigerant in the system is unchanged, so the total mass of refrigerant in the evaporator and condenser can be considered constant, so there is:
wherein ρ is lc Is the density of the liquid refrigerant in the condenser, h lgc A is the vaporization latent heat of the refrigerant in the condenser c Is the total sectional area of the flat tube micro-channel of the condenser,is the average void fraction of the two-phase region of the condenser. h is a gc ,h lc And h ic Respectively representing the enthalpy values of the gas, liquid and inlet refrigerant in the condenser, a, at the current pressure ic Is the heat exchange coefficient between the inner wall of the condenser and the refrigerant in the two-phase zone, D ic Diameter T of condenser flat tube wc To condenser wall temperature, T rc Is the saturation temperature of the refrigerant at the current pressure of the condenser, L c Is the total length of the condenser flat tube, cp and m represent the specific heat of the condenser material and the mass of the evaporator, respectively. a, a oc Is the heat exchange coefficient between the air and the condenser wall surface, A oc Is the windward area of the condenser, and T ac Is the current temperature of the air surrounding the condenser, i.e. the ambient temperature, Σ represents a constant.
In addition, the condenser air side heat exchange coefficient a oc Mainly influenced by external wind speedDuring the running of the automobile, the external wind speed of the condenser is mainly influenced by the speed of the automobile, so a oc The relationship with vehicle speed can be fitted experimentally, expressed as:
a oc ＝f p2 (V car )
in the present design, the vehicle speed V car Is primarily determined by the driver and is not regulated by the air conditioning system controller, so that in air conditioning system control, vehicle speed is considered as disturbance input.
S12: establishing a simplified one-dimensional passenger cabin system dynamic thermal model:
in particular, the total thermal load of the passenger compartment of a motor vehicleExpressed as:
during driving, convection heat is exchanged between the cabin and the outsideMainly driven by the speed V car And ambient temperature T ac And these two variables are input disturbances that are not controlled by the controller. In the heat exchange model,>can be calculated by the following formula:
wherein T is s Based on energy conservation, the dynamic change in the temperature of the peripheral structure can be expressed by the following formula:
further, from conservation of energy, the dynamic change in air temperature of the passenger compartment can be expressed as:
wherein,for heat exchange of the body surface structure>For solar radiation heat load, +.>For the heat load caused by ventilation +.>For human body heat load->Thermal loads for machinery and instrumentation; t (T) cab For the temperature of the cabin of the motor vehicle, < > for>The refrigerating capacity of the air conditioning system introduced into the passenger cabin in unit time M a Is the air quality in the volume range of the automobile cabin, cp a Is the specific heat of air. h is a o The heat exchange coefficient between the outer side of the outer vehicle cabin peripheral structure and the air side is mainly determined by the vehicle speed. S total surface area of the exterior surface Structure of the automobile cabin. H i Is the heat exchange coefficient between the inner surface of the automobile cabin and the air, M s And C ps The mass and specific heat of the vehicle cabin peripheral enclosed structure are respectively.
By the mathematical model, a coupled dynamic model of the passenger cabin and the air conditioning system is established. The state variables in the model can be expressed as:
X＝[l e P e T we P c T wc T s T a ] T
s2: the model prediction controller matched with the automobile air conditioning system-passenger cabin coupling model is established based on the automobile air conditioning system-passenger cabin coupling model, and specifically comprises the following steps:
s21: a state estimator in model predictive control is built. According to the established automobile air conditioner and passenger cabin model, after linear processing, the system can be expressed by the following state space expression:
x(k+i│k)＝Ax(k+i-1│k)+B u u(k+i-1│k)+B v v(k+i-1│k)
y(k+i│k)＝Cx(k+i-1│k)
in the above formula, x is a state variable matrix. u and v represent the manipulated variable matrix and the disturbance input matrix, respectively, as follows:
u＝[N comp N fan ] T
for MPC control parameters, the tuning parameters are shown in Table 1 below:
TABLE 1MPC control parameters
S22: and establishing an optimizer of the model predictive controller.
The optimizer in the MPC controller comprises an optimizing algorithm, so that the optimal solution of the objective function is found, and the minimum cost function is guaranteed. The main function of the optimizer is to find a group of optimal solutions to meet the control accuracy and reduce the control cost as much as possible. First, the total cost function is represented by the following equation:
J(Z k )＝J y (Z k )+J u (Z k )+J Δu (Z k )
wherein J is y ,J u ,J Δu And respectively controlling the cost function of the tracking error of the temperature, the cost function of the magnitude of the control quantity and the cost function of the magnitude of the change of the control quantity. For the current time instant k, if the control time domain is c, the three cost functions can be represented by the following equations, respectively:
wherein i represents the calculation step of the time sequence and has i E [1, c ]]；i,c∈N。n y ,n u The number of control outputs and control inputs and outputs are represented, respectively.The j-th control output of y, u, deltau represents the weight at the i-th time step, respectively. Z is Z k Representing a decision output sequence of a positive quadratic programming of the above objective function, the objective function and constraints of the quadratic programming optimization are represented by the following form:
min J(Z k )
s.t.0≤N comp ≤6000r/min
0≤N fan ≤3000r/min
0≤T we ≤10℃
by solving the quadratic programming, Z can be obtained when the control time domain is c k In the form of:
in order to ensure the accuracy of the real-time control, the matrix Z k Only the first element is actually output by the MPC controller.
S3: establishing a vehicle speed predictor based on the neural network as shown in fig. 4, and predicting the future vehicle speed by utilizing the historical vehicle speed specifically comprises:
the method of the neural network is utilized to predict the vehicle speed, in principle, the accuracy of the neural network prediction is continuously enhanced by changing each weight (training) in the neural network based on the error of the predicted vehicle speed target value and the actual value, and finally, a more accurate output value is achieved. For the vehicle speed prediction neural network structure, the input layer mainly consists of the other 4 of the historical vehicle speed matrixesThe eigenvalue matrix consists of average speed, average speed with 0 removed, average acceleration and average deceleration. For the prediction time domain τ, five input matrices are sequentially noted as x 1,τ ，x 2,τ ，x 3,τ ，x 4,τ And x 5,τ . The total input layer can be expressed as:
X τ ＝[x 1,τ x 2,τ x 3,τ …x 5,τ ]
in the two-layer neural network used in the invention, the predicted vehicle speed v at the time of the tau th second in the future τ The relationship with the input can be expressed as:
for the output layer, the future predicted vehicle speed is the only output, and for the predicted horizon τ, the vehicle speed predicted output layer may be represented by:
V＝[v 1 v 2 v 3 …v τ ]
in the above equation, i represents the number of input variables, w0 no And w1 n1 The weight coefficients of the n0 neurons of the first layer neurons and the n1 th neurons of the second layer neurons are represented, respectively. Similarly, b0 n0 And b1 n1 The offset values of the n0 neurons of the first layer neurons and the n1 th neurons of the second layer neurons are represented, respectively.
The weight and the bias value of each neuron are continuously and automatically adjusted by the algorithm through repeated iteration of the neural network layer by layer, so that the neural network forecast can more accurately forecast the future vehicle speed through the current information. The specific tuning parameters of the neural network of the present invention are shown in table 2 below:
TABLE 2 neural network predicted tuning parameters
S4: building an adaptation device aiming at different individual thermal habits based on PMV theory and adaptation algorithm, and according to the adaptationShould be calculated to obtain a target comfort temperature T comfort The control logic is shown in fig. 5, and specifically includes:
the estimator for establishing the current PMV value in the passenger cabin based on the human body thermal comfort theory is mainly influenced by 6 factors, and as shown in fig. 6, the PMV value of the passenger cabin at the moment can be calculated according to the external environment conditions, and the calculation formula of the PMV value is as follows:
PMV＝T s (M-φ 1 -φ 2 -φ 3 -φ 4 -φ 5 -φ 6 )
wherein T is S ＝0.303e -0.036M +0.028，
φ 1 ＝3.05e -3 +5733-6.99M-P w ，
φ 3 ＝1.7e -5 M(5867-P w )，
φ 4 ＝1.4e -3 M(34-T a )，
φ 5 ＝3.96e -8 f cl ((T cl +273) 4 -(T r +273) 4 )，
φ 6 ＝f cl h c (T cl -T a )，
T cl ＝35.7-0.028M-I cl (φ 5 +φ 6 )；
Wherein M is the metabolism rate of the passenger, P w Is the partial pressure of water vapor, T a For passenger compartment air temperature, T r Is in the cabAverage radiation temperature, T cl For the surface temperature of the clothes, h c For the human body surface convection heat exchange coefficient, I cl For the thermal resistance of the passenger's clothing, V a Is the passenger compartment air flow rate.
After the obtained real-time PMV value, recording the PMV value when the driver controls to enter a steady state after each adjustment under the condition that the system control has reached the steady state, and recording the PMV value recorded at the ith time as the PMV i ；
By PMV i PMVa was calculated using the following formula:
where N represents the period of the sample.
In this invention, the MPC controller causes the output of the controlled system to track the control target value, as well as the target cabin temperature. In order to embed the adapted comfort temperature into the MPC controller, the comfort temperature calculator needs to calculate a sequence of comfort temperature values for the MPC to perform an optimal calculation. Thus, for control instant k, the continuous target comfort temperature sequence can be calculated by:
in the above, phi 5 ,h c As system state variables change, other parameters may be set to constants by definition for reasonable simplicity of calculation. The details are shown in table 3 below:
TABLE 3 values of Table 3 PMV6 influencing factors
By adapting the calculation, the resulting target comfort temperature can be calculated by applying T rc (k+i|k) substitution of r in step 2 rc (k+i|k) access the MPC optimizer.
S5: and combining the speed prediction and the thermal comfort adaptation to establish a complete intelligent controller aiming at the automobile air conditioning system.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (6)
1. An intelligent control method of an electric automobile air conditioning system based on improved intelligent model predictive control is characterized by comprising the following steps: the method comprises the following steps:
s1: establishing an automobile air conditioning system-passenger cabin coupling thermal model;
s2: establishing a model prediction controller matched with the vehicle air conditioning system-passenger cabin coupling model based on the vehicle air conditioning system-passenger cabin coupling model;
s3: establishing a vehicle speed predictor based on a neural network, and predicting a future vehicle speed by utilizing a historical vehicle speed;
s4: establishing an adaptive device aiming at different individual thermal habits based on PMV theory and an adaptive algorithm, and obtaining a target comfort temperature T comfort ；
S5: combining speed prediction and thermal comfort adaptation to establish a complete intelligent controller for an automobile air conditioning system;
the step S2 specifically includes the following steps:
s21: and (3) establishing a state estimator in model predictive control, wherein the system is expressed by the following state space expression after linear processing according to the established automobile air conditioner and passenger cabin model:
x(k+i|k)＝Ax(k+i-1|k)+B u u(k+i-1|k)+B v v(k+i-1|k)
y(k+i|k)＝Cx(k+i-1|k)
in the above formula, x is a state variable matrix, u and v respectively represent a manipulated variable matrix and a disturbance input matrix, and are respectively:
u＝[N comp N fan ] T
s22: establishing an optimizer of a model predictive controller;
the optimizing device in the MPC controller comprises an optimizing algorithm, an optimal solution of the objective function is found, the minimum cost function is guaranteed, and the optimizing device has the main functions of finding a group of optimal solutions to meet the control accuracy and simultaneously reducing the control cost as much as possible;
first, the total cost function is represented by:
J(Z k )＝J y (Z k )+J u (Z k )+J Δu (Z k )
wherein J is y ，J u ，J Δu A cost function representing tracking error of control temperature, a cost function of control quantity and a cost function of control quantity change; for the current time k, if the control time domain is c, the three cost functions are respectively represented by the following formulas:
wherein i represents the calculation step of the time sequence and has i E [1, c ]]；i，c∈N；n y ，n u Respectively representing the number of control outputs and control inputs and outputs;the j-th control output of y, u, deltau represents the weight of the j-th control output at the i-th time step; z is Z k Representing a positive quadratic programming of the above objective functionThe objective function and constraints of the quadratic programming optimization are represented by the following forms:
min J(Z k )
s.t.0≤N comp ≤6000r/min
0≤N fan ≤3000r/min
0≤T we ≤10℃
obtaining Z when the control time domain is c by solving quadratic programming k In the form of:
in order to ensure the accuracy of the real-time control, the matrix Z k Only the first element is actually output by the MPC controller.
2. The intelligent control method for the electric automobile air conditioning system based on the improved intelligent model predictive control according to claim 1, wherein the method comprises the following steps: the step S1 specifically comprises the following steps:
s11: the method comprises the steps of establishing a one-dimensional dynamic thermal mathematical model of an automobile air conditioning system, wherein the automobile air conditioning system comprises a compressor, a condenser, an evaporator and an expansion valve, and the dynamic models of the evaporator and the condenser are established based on a moving boundary method;
s12: establishing a simplified dynamic thermal model of the one-dimensional passenger cabin system;
s13: and respectively coupling the air inlet and outlet ends of the evaporator in the air conditioning system with the air outlet and inlet ends of the passenger cabin to form air circulation.
3. The intelligent control method for the electric automobile air conditioning system based on the improved intelligent model predictive control according to claim 2, wherein the method comprises the following steps: the dynamic thermal mathematical model of the one-dimensional automobile air conditioning system in the step S11 comprises the following steps:
1) A one-dimensional dynamic model of the refrigerant in the compressor is established, and the one-dimensional dynamic model is expressed as follows:
wherein,for mass flow rate, η of the compressor v For volumetric efficiency ρ r For the density of refrigerant, N comp For compressor speed, V d For compressor displacement, h c，o Is the enthalpy value of the outlet of the compressor, h c，i Is the enthalpy value of the inlet of the compressor, h is，o Is the isentropic outlet enthalpy value eta of the compressor is Isentropic efficiency;
2) For expansion valves, during dynamic operation, refrigerant mass flow through the expansion valveThe relationship with its expansion valve pressure drop Δp is expressed by:
wherein C is q For the flow coefficient of the expansion valve ρ v For refrigerant density through expansion valve, A v Is the minimum flow area of the expansion valve;
3) According to the moving boundary method, the gas and liquid refrigerants in the evaporator satisfy the law of mass conservation, so the change in the length le of the two-phase region of the evaporator is obtained by:
pressure P in evaporator e The change over time is expressed as:
the change in evaporator wall temperature is then expressed in terms of energy conservation as:
wherein ρ is le Is the density of the liquid refrigerant in the evaporator, h lge A is the vaporization latent heat of the refrigerant in the evaporator e The total sectional area of the flat tube micro-channel of the evaporator,for average void fraction of evaporator two-phase region, h ge 、h le And h ie Enthalpy values, a, respectively representing the gas, liquid and inlet refrigerant in the evaporator at the present pressure ie Is the heat exchange coefficient between the inner wall of the evaporator and the refrigerant in the two-phase region, D ie Is the diameter of the inside of the flat tube of the evaporator, T we To evaporator wall temperature, T re Is the saturation temperature of the refrigerant at the current pressure of the evaporator, L e Is the total length of the flat tube of the evaporator, (C p m) we Represents the specific heat of the evaporator material and the mass of the evaporator, a oe Is the heat exchange coefficient between the air and the evaporator wall surface, A oe T is the windward area of the evaporator ae Is the current temperature of the air surrounding the evaporator;
4) For the condenser, the heat exchange principle is similar to that of the evaporator, so there are:
if the air conditioning system refrigerant does not leak, the total mass of refrigerant in the system is unchanged, and the total mass of refrigerant in the evaporator and condenser is considered constant, so there is:
wherein ρ is lc Is the density of the liquid refrigerant in the condenser, h lgc A is the vaporization latent heat of the refrigerant in the condenser c Is the total sectional area of the flat tube micro-channel of the condenser,is the average void fraction of the two-phase region of the condenser, h gc ，h lc And h ic Respectively representing the enthalpy values of the gas, liquid and inlet refrigerant in the condenser at the current pressure, a ic Is the heat exchange coefficient between the inner wall of the condenser and the refrigerant in the two-phase zone, D ic Diameter T of condenser flat tube wc To condenser wall temperature, T rc Is the saturation temperature of the refrigerant at the current pressure of the condenser, L c Is the total length of the flat tube of the condenser, (C p m) wc Represents the specific heat of the condenser material and the mass of the condenser, a oc Is the heat exchange coefficient between the air and the condenser wall surface, A oc Is the windward area of the condenser, T ac Is the current temperature of the air surrounding the condenser, i.e. the ambient temperature, Σ represents a constant;
condenser air side heat exchange coefficient a oc Mainly influenced by the external wind speed, and during the running of the automobile, the external wind speed of the condenser is mainly influenced by the speed of the automobile, so a oc The relationship with vehicle speed was fitted experimentally and expressed as:
a oc ＝f p2 (V car )
vehicle speed V car Is determined by the driver and not regulated by the air conditioning system controller, in the air conditioning system controlThe vehicle speed is considered as a disturbance input.
4. The intelligent control method for the electric automobile air conditioning system based on the improved intelligent model predictive control according to claim 2, wherein the method comprises the following steps: the one-dimensional passenger cabin system thermal model in step S12 includes:
total heat load of passenger compartment of automobileExpressed as:
during driving, convection heat is exchanged between the cabin and the outsideMainly driven by the speed V car And ambient temperature T ac Influence, and these two variables are input disturbances not controlled by the controller, < +_, in the heat exchange model>Calculated from the following formula:
wherein T is s The temperature of the structure surrounding the passenger compartment, based on energy conservation, the dynamic change of the temperature of the surrounding structure is expressed by the following formula:
from conservation of energy, the dynamic change in air temperature of the passenger compartment is expressed as:
wherein,for heat exchange of the body surface structure>For solar radiation heat load, +.>For the heat load caused by ventilation +.>For human body heat load->Thermal loads for machinery and instrumentation; t (T) cab For the temperature of the cabin of the motor vehicle, < > for>The refrigerating capacity of the air conditioning system introduced into the passenger cabin in unit time M a Is the air quality in the volume range of the automobile cabin, cp a Specific heat of air, h o Is the heat exchange coefficient between the outer side of the outer structure of the outer car cabin and the air side, is mainly determined by the speed of the car, and is the total surface area of the outer surface structure of the outer car cabin i Is the heat exchange coefficient between the inner surface of the automobile cabin and the air, M s And C ps The mass and specific heat of the vehicle cabin peripheral enclosed structure are respectively;
through the mathematical model, a coupling dynamic model of the passenger cabin and the air conditioning system is established, and state variables in the model are expressed as follows:
X＝[l e P e T we P c T wc T s T a ] T 。
5. the intelligent control method for the electric automobile air conditioning system based on the improved intelligent model predictive control according to claim 1, wherein the method comprises the following steps: the step S3 specifically includes the following:
for a vehicle speed prediction neural network structure, an input layer comprises a historical vehicle speed matrix, an average speed matrix with 0 removed, an average acceleration matrix and an average deceleration matrix; for the prediction time domain τ, five input matrices are sequentially noted as x 1，τ ，x 2，τ ，x 3，τ ，x 4,τ And x 5,τ The total input layer is expressed as:
X τ ＝[x 1，τ x 2，τ x 3，τ … x 5,τ ]
in the two-layer neural network used, the predicted vehicle speed v at the time of the next tau second is τ The relationship with the input is expressed as:
for the output layer, the future predicted vehicle speed is the only output, and for the predicted horizon τ, the vehicle speed predicted output layer is represented by:
V＝[v 1 v 2 v 3 … v τ ]
where i represents the number of input variables, w0 no And w1 n1 The weight coefficients of the n0 th neuron of the first layer neuron and the n1 st neuron of the second layer neuron are respectively represented, b0 n0 And b1 n1 The offset values of the n0 neurons of the first layer neurons and the n1 th neurons of the second layer neurons are represented, respectively.
6. The intelligent control method for the electric automobile air conditioning system based on the improved intelligent model predictive control according to claim 1, wherein the method comprises the following steps: the step S4 specifically includes the following:
an estimator of the current PMV value in the passenger cabin is established based on the human body thermal comfort theory, and the PMV value of the passenger cabin at the moment is calculated through external environment conditions, wherein the calculation formula of the PMV value is as follows:
PMV＝T s (M-φ 1 -φ 2 -φ 3 -φ 4 -φ 5 -φ 6 )
wherein T is S ＝0.303e -0.036M +0.028，
φ 1 ＝3.05e -3 +5733-6.99M-P w ，
φ 3 ＝1.7e - 5M(5867-P w )，
φ 4 ＝1.4e -3 M(34-T a )，
φ 5 ＝3.96e -8 f cl ((T cl +273) 4 -(T r +273) 4 )，
φ 6 ＝f cl h c (T cl -T a )，
T cl ＝35.7-0.028M-I cl (φ 5 +φ 6 )；
Wherein M is the metabolism rate of the passenger, P w Is the partial pressure of water vapor, T a For passenger compartment air temperature, T r For average radiation temperature in the cab, T cl For the surface temperature of the clothes, h c For the human body surface convection heat exchange coefficient, I cl For the thermal resistance of the passenger's clothing, V a Air flow rate for the passenger compartment;
after the obtained real-time PMV value, recording the PMV value when the driver controls to enter a steady state after each adjustment under the condition that the system control has reached the steady state, and recording the PMV value recorded at the ith time as the PMV i ；
By PMV i PMVa was calculated using the following formula：
Where N represents the period of sampling;
for control instant k, a continuous target comfort temperature sequence is calculated from the following equation:
in the above, phi 5 ,h c As the system state variables change, other parameters are set to constants; by adapting the calculation, the resulting target comfort temperature is calculated by applying T rc (k+i|k) substitution step S2 objective function J y R (k+i|k) of (a) is accessed to the MPC optimizer.
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