Electric vehicle air conditioner refrigerating circuit state estimation method based on data driving modeling and extended Kalman filtering algorithm

Abstract

The invention relates to an electric vehicle air conditioner refrigerating circuit state estimation method based on data-driven modeling and an extended Kalman filtering algorithm, and belongs to the field of electric vehicle air conditioner system refrigerating circuit state monitoring. The method comprises the following steps: establishing a continuous state space equation based on a moving boundary method, a momentum law, a mass conservation law and an energy conservation law; collecting real vehicle data, and identifying system parameters of the system state equation based on a least square search method; discretizing the continuous state space equation; and estimating the refrigerant loop state by adopting an extended Kalman filtering method according to the discretized state-space equation and the parameters of the identification system. According to the method, the grey box model and the moving boundary method are adopted, the least square algorithm and the extended Kalman filtering are combined, and efficient modeling and accurate state monitoring of the refrigerant loop system are achieved. According to the method, modeling complexity is simplified, real-time performance and precision are improved, and important support is provided for performance improvement of an automobile air conditioning system.

Description

Technical Field

[0001] The invention belongs to the field of refrigeration circuit state monitoring of electric vehicle air-conditioning systems, and relates to an electric vehicle air-conditioning refrigeration circuit state estimation method based on data-driven modeling and an extended Kalman filter algorithm. Background Art

[0002] In the development of modern trams, the importance of the air-conditioning system cannot be ignored, because it directly affects the tram's endurance, service life, and the safety of passengers and equipment. Therefore, it is particularly important to monitor the refrigerant circuit status in the air-conditioning system in real time and effectively, especially for the monitoring of the compressor's inlet superheat and subcooling. The current mainstream monitoring method is usually to install temperature and pressure sensors on the wall of the refrigerant pipeline for detection. However, due to the complex position of the refrigeration circuit in the vehicle body, the acquired data often has a certain delay and instability, and under certain special working conditions, such as the critical state of the ratio of the two-phase liquid in the evaporator and condenser, it is difficult to obtain accurate estimates through direct measurement.

[0003] The refrigeration circuit structure of the air conditioning system is as follows Figure 1 As shown in the figure, the refrigerant circuit presents a highly complex, nonlinear multi-input and multi-output system characteristic. Traditional modeling methods cannot adapt to the complex dynamic characteristics of the system, so they often show problems such as low modeling accuracy and difficulty in state estimation in practical applications. One of the existing solutions is to establish a mathematical model of the refrigerant circuit through the moving boundary method, but this method has the problem of extremely complex modeling process and difficulty in universal application. Therefore, how to effectively establish a mathematical model of the refrigerant circuit has become an important research topic to improve the monitoring accuracy and real-time performance of the system.

[0004] In the traditional automotive industry, the acquisition of compressor inlet status usually relies on installing temperature and pressure sensors at the compressor inlet to directly measure the temperature and pressure of the refrigerant. However, this direct measurement method has many limitations. First, the status collected by the sensor does not truly reflect the real-time status of the refrigerant, because these parameters need to be transmitted through the compressor and the pipe wall, resulting in a certain lag in the measurement data. Secondly, since the working environment of the compressor is usually quite harsh, the data obtained by the temperature and pressure sensors will inevitably be disturbed by the environment, resulting in certain errors, which poses a huge challenge to the accurate estimation of the system status. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a state estimation method for the refrigeration circuit of electric vehicle air conditioning based on data-driven modeling and extended Kalman filter algorithm. This method not only retains the theoretical interpretability of mathematical modeling, but also greatly reduces the complexity and workload of modeling. By introducing data-driven system identification technology, actual measurement data can be used to improve the accuracy of the model and avoid some difficulties in traditional modeling methods. In addition, thanks to the unique advantages of the moving boundary method, in this method, the estimation of the two-phase liquid ratio in the evaporator and condenser can be achieved to a certain extent, thereby improving the model's ability to describe complex states.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for estimating a state of a tram air conditioning refrigeration circuit based on data-driven modeling and an extended Kalman filter algorithm, the method comprising the following steps:

[0008] S1. Establish the continuous state space equation based on the moving boundary method, momentum law, mass conservation law and energy conservation law;

[0009] S2. Collect real vehicle data, identify system parameters of the system state equation based on the least squares search method, and discretize the continuous state space equation;

[0010] S3. Based on the discretized state space equation and the identification of system parameters, the extended Kalman filter method is used to estimate the refrigerant circuit state.

[0011] Further, in step S1, the following steps are specifically included:

[0012] S11. Based on the law of conservation of mass and the law of momentum, a mathematical model of the compressor regarding mass flow is established:

[0013]

[0014] In the formula, is the compressor mass flow rate, N comp Indicates the compressor speed, V d is the compressor displacement, ρ e is the density of refrigerant at the compressor inlet, η vol is the volumetric efficiency;

[0015] S12. Based on the law of conservation of mass and the law of conservation of energy, ignoring the heat exchange in the supercooling section, a mathematical model is established;

[0016]

[0017] In the formula, ρ lc Represents the density of the liquid refrigerant in the condenser, hlgc Indicates the latent enthalpy released when the refrigerant changes from gas to liquid in the condenser, A c Refers to the cross-sectional area of ​​the refrigerant side of the condenser pipe. It represents the average steam ratio of the gas-liquid two-phase region during the condensation process, l c Represents the length of the two-phase heat exchange section during the condensation process, h cond,in and h cond,out are the enthalpy values ​​of the refrigerant at the inlet and outlet of the two-phase section of the condenser, α ic is the convective heat transfer coefficient between the condenser wall and the refrigerant on the refrigerant side, a is the diameter of the refrigerant side pipe, T wc and T wc Corresponding to the temperature of the condenser wall and the refrigerant, L c represents the length of the condenser flat tube, ρ gc Represents the density of the gaseous refrigerant in the condenser, P c Indicates the refrigerant pressure at the condenser outlet, C p Refers to the specific heat capacity of the condenser wall, m represents the mass of the condenser wall, a ish It refers to the convection heat transfer coefficient between the refrigerant in the superheat section and the condenser wall, T ic Represents the temperature of the refrigerant at the condenser inlet, a oc is the convective heat transfer coefficient on the air side of the condenser, A oc is the heat exchange area on the air side of the condenser, T ac is the temperature of the air at the inlet of the condenser air side;

[0018] S13. Ignoring the heat exchange between the expansion valve and the outside world, a mathematical model of the expansion valve regarding mass flow is established based on the law of conservation of mass and the law of momentum:

[0019]

[0020] In the formula is the mass flow rate of refrigerant at the outlet of the expansion valve, C q is the flow coefficient, A v is the area at the current opening of the expansion valve, ρ v is the density of the refrigerant at the inlet of the expansion valve, P c and P e are the refrigerant pressures at the condenser outlet and the evaporator inlet respectively;

[0021] S14. Based on the moving boundary method, a mathematical model of the evaporator regarding mass flow, temperature, pressure and two-phase segment is established:

[0022]

[0023] ρ leRepresents the density of the refrigerant in the evaporator, h lge It is the latent heat value required for the refrigerant to change from liquid to gas during evaporation. e is the cross-sectional area of ​​the refrigerant-side pipe in the evaporator, is the average vapor ratio of the gas-liquid two-phase region during evaporation, l e is the length of the two-phase heat exchange section during the evaporation process, is the mass flow rate at the evaporator inlet, h ie and h ge are the enthalpy values ​​of the refrigerant at the inlet and outlet of the two-phase evaporator, respectively, and a ie is the convection heat transfer coefficient between the evaporator wall and the refrigerant on the refrigerant side, D ie is the refrigerant side pipe diameter, T we and T re are the temperatures of the evaporator wall and the refrigerant, L e represents the total length of the evaporator pipe, ρ ge Refers to the density of the gaseous refrigerant in the evaporator at the current pressure, P e Indicates the pressure of the refrigerant in the evaporator. Refers to the mass flow rate at the evaporator outlet, C p represents the specific heat capacity of the evaporator wall, m refers to the total mass of the evaporator, a ish Refers to the convection heat transfer coefficient between the refrigerant in the superheated section and the evaporator wall, T ie Represents the temperature of the refrigerant at the evaporator inlet, a oe is the convective heat transfer coefficient on the air side of the evaporator, A oe is the heat exchange area on the air side of the evaporator, T ae is the temperature of the air at the inlet of the evaporator air side.

[0024] Furthermore, based on the established mathematical model, the continuous state space equation is established:

[0025]

[0026] Wherein, A1, A2, A3, B1, B2, B3, B4, C1, C2, C3, C4, C5, C6, D1, D2, D3, E1, E2, E3, E4, E5, E6 represent the system parameters of the refrigeration circuit state equation respectively, e Indicates the length of the two-phase section of the evaporator, T wfe represents the evaporator wall temperature, A v Indicates the expansion valve opening area, P c ,P e Respectively represent the condenser and evaporator pressures; α oe Represents the evaporator heat transfer coefficient, which is related to the evaporator fan speed, Nefan Indicates the evaporator fan speed; T wfc Indicates the condenser wall temperature; l c represents the length of the two-phase section of the condenser, α oc Represents the condenser heat transfer coefficient, which is related to the condenser fan speed, N cfan Indicates condenser;

[0027] u=[N comp N efan N cfan A v ] T

[0028] x=[l e P e T wfe P c T wfc ] T

[0029] u is the input quantity of the state equation; x is the state quantity of the state equation;

[0030] The continuous state-space equations are based on the following assumptions:

[0031] The expansion valve mass flow is assumed to be the same as the evaporator inlet mass flow and the condenser outlet mass flow; the compressor mass flow is assumed to be the same as the condenser inlet mass flow and the evaporator outlet mass flow; the evaporator inlet enthalpy is assumed to be the same as the expansion valve outlet enthalpy; the evaporator outlet enthalpy is assumed to be the same as the compressor inlet enthalpy; the compressor outlet enthalpy is assumed to be the same as the condenser inlet enthalpy; the condenser outlet enthalpy is assumed to be the same as the expansion valve inlet enthalpy.

[0032] Further, in step S2, the following steps are specifically included:

[0033] S21, collecting compressor speed, heat exchanger fan speed, expansion valve opening, evaporator and condenser outlet temperature and pressure, selecting data of the refrigeration system in the stable stage, and matching all the data one by one at the same time;

[0034] S22, selecting compressor speed, heat exchanger fan speed, and expansion valve opening data as system input, and evaporator and condenser outlet temperature and pressure data as output;

[0035] S23, based on the Levenberg-Marquardt least squares search method, calculate the coefficient P;

[0036] P=[A1 A2 A3 B1…E5 E6]

[0037] S24. Continuous state space equations after discretization parameter identification.

[0038] Further, in step S23, the Levenberg-Marquardt least squares search method expression is:

[0039]

[0040] Where f(·) represents the residual, J(·) represents the Jacobian matrix, Δx represents the parameter update amount, and λ is the damping parameter.

[0041] Further, in step S23, the search process of the Levenberg-Marquardt least squares search method is:

[0042] S231, input state space equation Real vehicle observation data x, initial parameter estimation value; set the minimum error threshold minS and the maximum number of iterations maxIt;

[0043] S232. Calculate the output value of the current discretized state space equation:

[0044] y=(x,a)

[0045] Then calculate the error, the formula is:

[0046]

[0047] In the formula, y i Indicates the actual value, Represents the predicted value, which is the sum of the squared differences between the predicted value and the actual value of each data point;

[0048] S233, calculate the parameter adjustment step size h by Levenberg-Marquardt method LM , the calculation formula is:

[0049]

[0050] In the formula, is the derivative of the current state;

[0051] S234, using the step size to update the last parameter:

[0052]

[0053] Calculate the output value and the new error using the updated parameters;

[0054] S235. In each round, determine whether the termination condition is met. If the error is less than the preset minimum error threshold or the maximum number of iterations has been reached, stop the iteration and output the final parameters and error values ​​to finally obtain the complete state equation; otherwise, perform the next round of parameter update.

[0055] Furthermore, the discretized state space equation is expressed as:

[0056]

[0057] In the formula, subscripts k and k-1 represent the current moment and the previous moment respectively.

[0058] Further, in step S3, the extended Kalman filter method is used to estimate the refrigerant circuit state x=[l e P e T wfe P c T wfc ] T , where the extended Kalman filter process includes:

[0059] S31, initialize the covariance matrix P0 and design the initial value x0 of the system state quantity;

[0060] S32, based on the posterior estimation value of the previous moment k-1, the prior estimation state of the current moment k is calculated by discrete state equation;

[0061]

[0062] In the formula, φ k-1 Represents the Jacobian matrix of the previous moment. The Jacobian matrix is ​​used to calculate the Kalman filter gain and update the state. represents the posterior estimate at the previous moment;

[0063] S33, use the posterior estimated covariance of the previous moment k-1 to calculate the prior estimated covariance P of the current moment k k / k-1 ;

[0064]

[0065] Among them, Q k-1 represents the process noise covariance matrix;

[0066] S34, Kalman gain K corrected based on the prior estimated covariance of the current time k k ;

[0067]

[0068] Among them, H k is the observation matrix, R is the observation noise covariance matrix;

[0069] S35, fusing the predicted value and the measured value of the Kalman filter according to the ratio of the Kalman gain to obtain a posterior estimation value;

[0070]

[0071] Among them, y k Output for the state equation;

[0072] S36. Calculate the posterior estimation covariance of the Kalman filter for use in the next round of calculation:

[0073] P k =P k / k-1 -K k H k P k / k-1

[0074] The subscript k / k-1 represents a priori estimate.

[0075] Further, in step S32, the Jacobian matrix φ is:

[0076]

[0077] The Jacobian matrix is ​​used to calculate the Kalman filter gain and update the state.

[0078] The beneficial effects of the present invention are:

[0079] The present invention adopts a gray box model system identification concept based on the moving boundary method and establishes a continuous state space equation. The gray box model can combine the physical characteristics of the system with the data-driven characteristics, which can effectively simplify the complexity of refrigerant circuit system modeling and retain the rationality of the model in physical interpretation. Using the moving boundary method, the refrigerants in different phases in the system can be dynamically modeled, overcoming the problem that traditional models are difficult to accurately describe the dynamic changes of multiphase liquids.

[0080] The present invention obtains the characteristic parameters of the refrigerant circuit by combining the system identification with the Levenberg-Marquardt least squares search algorithm, and uses the extended Kalman filter to estimate the state variables of the system. This method has significant advantages in refrigerant circuit system modeling and state monitoring, especially in achieving higher accuracy and real-time performance in modern automobile air conditioning systems.

[0081] The present invention introduces an extended Kalman filter algorithm. As a nonlinear filtering method, the extended Kalman filter can realize real-time tracking and estimation of the refrigerant working medium state by linearizing the nonlinear characteristics of the system. By combining the Kalman filter with the moving boundary method, not only the real-time acquisition of the refrigerant system state is improved, but also the system's tolerance to the temperature and pressure sensor errors is significantly improved, thereby improving the overall monitoring accuracy and reliability.

[0082] The present invention combines the Levenberg-Marquardt least squares search algorithm and the extended Kalman filter method to achieve efficient identification of characteristic parameters of the refrigerant circuit system and accurate estimation of state variables, solving the problems of difficult refrigerant system modeling, insufficient real-time performance and large measurement errors in traditional methods. This method provides a new technical path for state monitoring and control of the refrigerant system, and also provides important support for improving the performance and stability of automobile air-conditioning systems.

[0083] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0085] Figure 1 It is a schematic diagram of the refrigeration circuit structure of the air-conditioning system;

[0086] Figure 2 It is a schematic diagram of the overall architecture of the electric vehicle air conditioning refrigeration circuit state estimation method based on data-driven modeling and extended Kalman filter algorithm of the present invention;

[0087] Figure 3 A schematic diagram of assumed relationship of parameters in the electric vehicle air conditioning system of the present invention;

[0088] Figure 4 A schematic diagram of the process of searching system parameters using the least square method of the present invention;

[0089] Figure 5 It is a schematic diagram of the system identification result based on the Levenberg-Marquardt least squares search method of the present invention.

[0090] Figure 6 Schematic diagram of the process of estimating the state of the refrigerant circuit using the extended Kalman filter algorithm of the present invention. DETAILED DESCRIPTION

[0091] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0092] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0093] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0094] See also Figure 2 to Figure 6 , which is a state estimation method for tram air-conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm.

[0095] Example

[0096] This embodiment provides a detailed implementation process of a method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and an extended Kalman filter algorithm. The overall process is as follows: Figure 2 As shown, the specific steps include:

[0097] S1. Based on the moving boundary method, momentum law, mass conservation law and energy conservation law, a grey box model of the air conditioning system is established. The specific implementation steps are as follows:

[0098] S11. The function of the compressor is to convert the high-temperature, low-pressure gaseous refrigerant into high-temperature, high-pressure gas through compression, and send it to the condenser for condensation. Based on the law of conservation of mass and the law of momentum, a mathematical model of the compressor regarding mass flow is established:

[0099]

[0100] in is the compressor mass flow rate, V d is the compressor displacement, ρ e is the density of refrigerant at the compressor inlet, η vol is the volumetric efficiency;

[0101] S12. In the condenser, the refrigerant is converted from high-temperature and high-pressure steam to saturated steam, then from saturated steam to two-phase liquid, from two-phase liquid to saturated liquid, and finally from saturated liquid to supercooled liquid. Based on the law of conservation of mass and energy, ignoring the heat exchange in the supercooling section, a mathematical model is established:

[0102]

[0103] Where ρ lc Represents the density of the liquid refrigerant in the condenser, h lgc Indicates the latent enthalpy released when the refrigerant changes from gas to liquid in the condenser, A c Refers to the cross-sectional area of ​​the refrigerant side of the condenser pipe. It represents the average steam ratio of the gas-liquid two-phase region during the condensation process, l c Represents the length of the two-phase heat exchange section during the condensation process, h cond,in and h cond,out are the enthalpy values ​​of the refrigerant at the inlet and outlet of the two-phase section of the condenser, α ic is the convective heat transfer coefficient between the condenser wall and the refrigerant on the refrigerant side, a is the diameter of the refrigerant side pipe, T wc and T wc Corresponding to the temperature of the condenser wall and the refrigerant, L c represents the length of the condenser flat tube, ρ gc represents the density of the gaseous refrigerant R134a in the condenser, and dρ gc / dP c From experiments, it can be found that it is close to a constant, P c represents the pressure of the refrigerant in the condenser, C2 refers to the specific heat capacity of the condenser wall, m represents the mass of the condenser wall, and a ish It refers to the convection heat transfer coefficient between the refrigerant in the superheat section and the condenser wall, T ic Represents the temperature of the refrigerant at the condenser inlet, a oc is the convective heat transfer coefficient on the air side of the condenser, A oc is the heat exchange area on the air side of the condenser, T ac is the temperature of the air at the inlet of the condenser air side;

[0104] S13. Ignoring the heat exchange between the expansion valve and the outside world, a mathematical model of the expansion valve regarding mass flow is established based on the law of conservation of mass and the law of momentum; the model is as follows:

[0105]

[0106] In the formula is the mass flow rate of refrigerant R134a at the outlet of the expansion valve, C q is the flow coefficient, A v is the area at the current opening of the expansion valve, ρ v is the density of the refrigerant at the inlet of the expansion valve, P c and P e are the refrigerant pressures at the condenser outlet and the evaporator inlet respectively;

[0107] S14, the inlet section of the evaporator is a two-phase liquid section, which gradually changes into a saturated gas state after passing through the evaporator. Based on the moving boundary method, a mathematical model of the evaporator regarding mass flow, temperature, pressure and two-phase section is established; the model is as follows:

[0108]

[0109] ρ le Represents the density of refrigerant R134a in the evaporator, h lge It is the latent heat value required for the refrigerant to change from liquid to gas during evaporation. e is the cross-sectional area of ​​the refrigerant-side pipe in the evaporator, is the average vapor ratio of the gas-liquid two-phase region during evaporation, l e is the length of the two-phase heat exchange section during the evaporation process, is the mass flow rate at the evaporator inlet, h ie and h ge are the enthalpy values ​​of the refrigerant at the inlet and outlet of the two-phase evaporator, respectively. These values ​​are related to the current pressure of the refrigerant and can be obtained from the thermodynamic properties of the refrigerant. ie is the convection heat transfer coefficient between the evaporator wall and the refrigerant on the refrigerant side, D ie is the refrigerant side pipe diameter, T we and T re are the temperatures of the evaporator wall and the refrigerant, L e represents the total length of the evaporator pipe, ρ ge Refers to the density of the gaseous refrigerant R134a in the evaporator at the current pressure, according to the experimental and thermodynamic properties dρ ge / dP e Close to constant, P e represents the pressure of the refrigerant in the evaporator, and It refers to the mass flow rate at the evaporator outlet, C prepresents the specific heat capacity of the evaporator wall, m refers to the total mass of the evaporator, a is Refers to the convection heat transfer coefficient between the refrigerant in the superheated section and the evaporator wall, T ie Represents the temperature of the refrigerant at the evaporator inlet, a oe is the convective heat transfer coefficient on the air side of the evaporator, A oe is the heat exchange area on the air side of the evaporator, and T ae is the temperature of the air at the inlet of the evaporator air side;

[0110] S15. Establish the continuous state space equation based on the following assumptions:

[0111] The mass flow rate of the expansion valve is assumed to be the same as the mass flow rate of the evaporator inlet and the mass flow rate of the condenser outlet; the mass flow rate of the compressor is assumed to be the same as the mass flow rate of the condenser inlet and the mass flow rate of the evaporator outlet; the enthalpy value of the evaporator inlet is assumed to be the same as the enthalpy value of the expansion valve outlet; the enthalpy value of the evaporator outlet is assumed to be the same as the enthalpy value of the compressor inlet; the enthalpy value of the compressor outlet is assumed to be the same as the enthalpy value of the condenser inlet; the enthalpy value of the condenser outlet is assumed to be the same as the enthalpy value of the expansion valve inlet. The above assumptions are as follows Figure 3 shown.

[0112] Under the above assumptions and simplified parameters, the state space equation of the continuous system is established as follows:

[0113]

[0114] Wherein, A1, A2, A3, B1, B2, B3, B4, C1, C2, C3, C4, C5, C6, D1, D2, D3, E1, E2, E3, E4, E5, E6 represent the system parameters of the state equation of the refrigeration circuit respectively, e Indicates the length of the two-phase section of the evaporator, T wfe represents the evaporator wall temperature, A v Indicates the expansion valve opening area, P c ,P e Respectively represent the condenser and evaporator pressure; N comp Indicates the compressor speed; α oe Represents the evaporator heat transfer coefficient, which is related to the evaporator fan speed, N efan Indicates the evaporator fan speed; T wfc Indicates the condenser wall temperature; l c represents the length of the two-phase section of the condenser, α oc Represents the condenser heat transfer coefficient, which is related to the condenser fan speed, N cfan Indicates condenser;

[0115] u=[N comp N efan Ncfan A v ] T

[0116] x=[l e P e T wfe P c T wfc ] T

[0117] u is the input quantity of the state equation; x is the state quantity of the state equation;

[0118] The present invention adopts a gray box model system identification concept based on the moving boundary method and establishes a continuous state space equation. The gray box model can combine the physical characteristics of the system with the data-driven characteristics, which can effectively simplify the complexity of refrigerant circuit system modeling and retain the rationality of the model in physical interpretation. Using the moving boundary method, the refrigerants in different phases in the system can be dynamically modeled, overcoming the problem that traditional models are difficult to accurately describe the dynamic changes of multiphase liquids.

[0119] S2. Based on the actual vehicle data, the unknown coefficients of the above state equation are identified by using the system identification method. The specific implementation steps are as follows:

[0120] S21, collecting compressor speed, heat exchanger fan speed, expansion valve opening, evaporator and condenser outlet temperature and pressure, selecting data of the refrigeration system in the stable stage, and ensuring that all data correspond to each other at the same time;

[0121] S22. A gray box system is constructed based on the above state space equation for parameter identification. The compressor speed, heat exchanger fan speed, and expansion valve opening data are selected as system inputs. Since the two-phase gas gap is difficult to measure and estimate, the system output only uses the evaporator and condenser outlet temperature and pressure data;

[0122] S23, based on the Levenberg-Marquardt least squares search method, calculate the coefficient P;

[0123] P=[A1 A2 A3 B1…E5 E6]

[0124] The expression of the Levenberg-Marquardt least squares search method is:

[0125]

[0126] Δx=-(J T J+λI) -1 J T f

[0127] Where f(·) represents the residual, J(·) represents the Jacobian matrix, Δx represents the parameter update amount, and λ is the damping parameter.

[0128] The specific process is as follows Figure 4 , the search process is to estimate all coefficients at once, and the specific steps include:

[0129] S231, input state space equation Actual vehicle observation data x, initial parameter estimation value, setting minimum requirement minS (minimum error threshold), and maximum number of iterations maxIt.

[0130] S232. Calculate the output value of the current model, which is equal to the output value of the model function state equation:

[0131] y=(x,a)

[0132] Then calculate the error, the formula is:

[0133]

[0134] In the formula, y i Indicates the actual value, Represents the predicted value, which is the sum of the squared differences between the predicted value and the actual value of each data point.

[0135] S233, calculate the parameter adjustment step size h by Levenberg-Marquardt method LM , the calculation formula is:

[0136]

[0137] In the formula, is the state quantity derivative;

[0138] S234, using the step size to update the last parameter:

[0139]

[0140] Then the new error is calculated using the new parameters, and the error calculation formula is the same as the original error formula.

[0141] S235. In each round, determine whether the termination condition is met. If the error is less than the preset minimum error threshold or the maximum number of iterations has been reached, stop the iteration and output the final parameters and error values ​​to finally obtain the complete state equation; otherwise, perform the next round of parameter update.

[0142] like Figure 5The figure shows the system identification results based on the Levenberg-Marquardt least squares search method. The dotted line in the figure is the verification data, and the solid line is the estimated data. After identification, the system response result is close to the verification data.

[0143] S24, the state equation obtained by discretization, the specific steps of discretization are as follows:

[0144]

[0145] in is the state equation, Ts is the sampling time, x k With x k-1 are the state quantities at the current moment and the previous moment respectively; the discretized state equation is obtained:

[0146]

[0147] In the formula, subscripts k and k-1 represent the current moment and the previous moment respectively.

[0148] The present invention obtains the characteristic parameters of the refrigerant circuit by combining the system identification with the Levenberg-Marquardt least squares search algorithm, and uses the extended Kalman filter to estimate the state variables of the system. This method has significant advantages in refrigerant circuit system modeling and state monitoring, especially in achieving higher accuracy and real-time performance in modern automobile air conditioning systems.

[0149] S3. Based on the obtained discretized state equation and identification coefficient, in order to obtain the outlet temperature and pressure of the evaporator and condenser of the refrigeration circuit more accurately and stably and estimate the two-phase section ratio, the extended Kalman filter method is used to estimate the refrigerant circuit state x = [l e P e T wfe P c T wfc ] T , the extended Kalman filter process is as follows Figure 6 , the specific steps are as follows:

[0150] S31, initialize the covariance matrix P0 and design the initial value x0 of the system state quantity;

[0151] S32, based on the posterior estimation value of the previous moment k-1, the prior estimation state of the current moment k is calculated by discrete state equation;

[0152]

[0153] The Jacobian matrix φ is:

[0154]

[0155] The Jacobian matrix is ​​used to calculate the Kalman filter gain and update the state.

[0156] S33, use the posterior estimated covariance of the previous moment k-1 to calculate the prior estimated covariance P of the current moment k k / k-1 ;

[0157]

[0158] Among them, Q k-1 represents the process noise covariance matrix;

[0159] S34, Kalman gain K corrected based on the prior estimated covariance of the current time k k ;

[0160]

[0161] H k is the observation matrix, R is the observation noise covariance matrix;

[0162] S35, fusing the predicted value and the measured value of the Kalman filter according to the ratio of the Kalman gain to obtain a posterior estimation value;

[0163]

[0164] where y k Output for the state equation;

[0165] S36. Calculate the posterior estimation covariance of the Kalman filter for use in the next round of calculations.

[0166] P k =P k / k-1 -k k H k P k / k-1

[0167] The subscript k / k-1 represents a priori estimate.

[0168] The present invention introduces an extended Kalman filter algorithm. As a nonlinear filtering method, the extended Kalman filter can realize real-time tracking and estimation of the refrigerant working medium state by linearizing the nonlinear characteristics of the system. By combining the Kalman filter with the moving boundary method, not only the real-time acquisition of the refrigerant system state is improved, but also the system's tolerance to the temperature and pressure sensor errors is significantly improved, thereby improving the overall monitoring accuracy and reliability.

[0169] In this embodiment, since the constructed model belongs to a highly coupled complex nonlinear system, there are certain challenges in real-time state estimation. To this end, the present invention uses Kalman filtering technology to estimate the compressor inlet temperature and pressure. Kalman filtering has strong real-time and noise suppression capabilities. By fusing sensor data and model prediction results, it can significantly improve the accuracy of sensor measurement values ​​and reduce delays and fluctuations in sensor measurements. This method can provide more stable and reliable estimation results when the system state is difficult to measure directly, thereby further improving the operating efficiency and safety of the entire air-conditioning system.

[0170] In summary, the data-driven modeling method based on the Levenberg-Marquardt least squares search algorithm proposed in the present invention achieves the multiple effects of model interpretability, model simplification and real-time enhancement through the application of the moving boundary method state estimation and Kalman filtering. This not only provides a new solution for the refrigerant circuit state monitoring of the electric vehicle air-conditioning system, but also provides a useful reference for the modeling and control of similar complex nonlinear systems. The present invention combines the Levenberg-Marquardt least squares search algorithm and the extended Kalman filtering method to achieve efficient identification of the characteristic parameters of the refrigerant circuit system and accurate estimation of the state variables, solving the problems of difficult modeling of the refrigerant system, insufficient real-time performance and large measurement errors in the traditional method. This method provides a new technical path for the state monitoring and control of the refrigerant system, and also provides important support for improving the performance and stability of the automobile air-conditioning system.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and an extended Kalman filter algorithm, characterized in that: The method comprises the following steps: S1. Establish the continuous state space equation based on the moving boundary method, momentum law, mass conservation law and energy conservation law; S2. Collect real vehicle data, identify system parameters of the system state equation based on the least squares search method, and discretize the continuous state space equation; S3. Based on the discretized state space equation and the identification of system parameters, the extended Kalman filter method is used to estimate the refrigerant circuit state.

2. The method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm according to claim 1 is characterized in that: In step S1, the following steps are specifically included: S11. Based on the law of conservation of mass and the law of momentum, a mathematical model of the compressor regarding mass flow is established: In the formula, is the compressor mass flow rate, N comp Indicates the compressor speed, V d is the compressor displacement, ρ e is the refrigerant density at the compressor inlet, η vol is the volumetric efficiency; S12. Based on the law of conservation of mass and the law of conservation of energy, ignoring the heat exchange in the supercooling section, a mathematical model is established; In the formula, ρ lc Represents the density of the liquid refrigerant in the condenser, h lgc Indicates the latent enthalpy released when the refrigerant changes from gas to liquid in the condenser, A c Refers to the cross-sectional area of ​​the refrigerant side of the condenser pipe. It represents the average steam ratio of the gas-liquid two-phase region during the condensation process, l c Represents the length of the two-phase heat exchange section during the condensation process, h cond,in and h cond,out are the enthalpy values ​​of the refrigerant at the inlet and outlet of the two-phase section of the condenser, α ic is the convective heat transfer coefficient between the condenser wall and the refrigerant on the refrigerant side, a is the diameter of the refrigerant side pipe, T wc and T wc Corresponding to the temperature of the condenser wall and the refrigerant, L c represents the length of the condenser flat tube, ρ gc Represents the density of the gaseous refrigerant in the condenser, P c Indicates the refrigerant pressure at the condenser outlet, C p Refers to the specific heat capacity of the condenser wall, m represents the mass of the condenser wall, a ish It refers to the convection heat transfer coefficient between the refrigerant in the superheat section and the condenser wall, T ic Represents the temperature of the refrigerant at the condenser inlet, a oc is the convective heat transfer coefficient on the air side of the condenser, A oc is the heat exchange area on the air side of the condenser, T ac is the temperature of the air at the inlet of the condenser air side; S13. Ignoring the heat exchange between the expansion valve and the outside world, a mathematical model of the expansion valve regarding mass flow is established based on the law of conservation of mass and the law of momentum: In the formula is the mass flow rate of refrigerant at the outlet of the expansion valve, C q is the flow coefficient, A v is the area at the current opening of the expansion valve, ρ v is the density of the refrigerant at the inlet of the expansion valve, P c and P e are the refrigerant pressures at the condenser outlet and the evaporator inlet respectively; S14. Based on the moving boundary method, a mathematical model of the evaporator regarding mass flow, temperature, pressure and two-phase segment is established: ρ le Represents the density of the refrigerant in the evaporator, h lge It is the latent heat value required for the refrigerant to change from liquid to gas during evaporation. e is the cross-sectional area of ​​the refrigerant-side pipe in the evaporator, is the average vapor ratio of the gas-liquid two-phase region during evaporation, l e is the length of the two-phase heat exchange section during the evaporation process, is the mass flow rate at the evaporator inlet, h ie and h ge are the enthalpy values ​​of the refrigerant at the inlet and outlet of the two-phase evaporator, respectively, and a ie is the convection heat transfer coefficient between the evaporator wall and the refrigerant on the refrigerant side, D ie is the refrigerant side pipe diameter, T we and T re are the temperatures of the evaporator wall and the refrigerant, L e represents the total length of the evaporator pipe, ρ ge Refers to the density of the gaseous refrigerant in the evaporator at the current pressure, P e Indicates the pressure of the refrigerant in the evaporator. Refers to the mass flow rate at the evaporator outlet, C p represents the specific heat capacity of the evaporator wall, m refers to the total mass of the evaporator, a ish Refers to the convection heat transfer coefficient between the refrigerant in the superheated section and the evaporator wall, T ie Represents the temperature of the refrigerant at the evaporator inlet, a oe is the convective heat transfer coefficient on the air side of the evaporator, A oe is the heat exchange area on the air side of the evaporator, T ae is the temperature of the air at the inlet of the evaporator air side.

3. The method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm according to claim 2 is characterized in that: Based on the established mathematical model, the continuous state space equation is established: Wherein, A1, A2, A3, B1, B2, B3, B4, C1, C2, C3, C4, C5, C6, D1, D2, D3, E1, E2, E3, E4, E5, E6 represent the system parameters of the refrigeration circuit state equation respectively, e Indicates the length of the two-phase section of the evaporator, T wfe represents the evaporator wall temperature, A v Indicates the expansion valve opening area, P c ,P e Respectively represent the condenser and evaporator pressures; α oe Represents the evaporator heat transfer coefficient, which is related to the evaporator fan speed, N efan Indicates the evaporator fan speed; T wfc Indicates the condenser wall temperature; l c represents the length of the two-phase section of the condenser, α oc Represents the condenser heat transfer coefficient, which is related to the condenser fan speed, N cfan It indicates a condenser; u=[N comp N efan N cfan A v ] T x=[l e P e T wfe P c T wfc ] T u is the input quantity of the state equation; x is the state quantity of the state equation; The continuous state-space equations are based on the following assumptions: The expansion valve mass flow is assumed to be the same as the evaporator inlet mass flow and the condenser outlet mass flow; the compressor mass flow is assumed to be the same as the condenser inlet mass flow and the evaporator outlet mass flow; the evaporator inlet enthalpy is assumed to be the same as the expansion valve outlet enthalpy; the evaporator outlet enthalpy is assumed to be the same as the compressor inlet enthalpy; the compressor outlet enthalpy is assumed to be the same as the condenser inlet enthalpy; the condenser outlet enthalpy is assumed to be the same as the expansion valve inlet enthalpy.

4. The method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm according to claim 3 is characterized in that: In step S2, the following steps are specifically included: S21, collecting compressor speed, heat exchanger fan speed, expansion valve opening, evaporator and condenser outlet temperature and pressure, selecting data of the refrigeration system in the stable stage, and matching all the data one by one at the same time; S22, selecting compressor speed, heat exchanger fan speed, and expansion valve opening data as system input, and evaporator and condenser outlet temperature and pressure data as output; S23, based on the Levenberg-Marquardt least squares search method, calculate the coefficient P; P=[A1 A2 A3 B1…E5 E6] S24. Continuous state space equations after discretization parameter identification.

5. The method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm according to claim 4 is characterized in that: In step S23, the Levenberg-Marquardt least squares search method expression is: Where f(·) represents the residual, J(·) represents the Jacobian matrix, Δx represents the parameter update amount, and λ is the damping parameter.

6. The method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm according to claim 5, characterized in that: In step S23, the search process of the Levenberg-Marquardt least squares search method is: S231, input state space equation Real vehicle observation data x, initial parameter estimation value; set the minimum error threshold minS and the maximum number of iterations maxIt; S232. Calculate the output value of the current discretized state space equation: y=(x,a) Then calculate the error, the formula is: In the formula, y i Indicates the actual value, Represents the predicted value, which is the sum of the squared differences between the predicted value and the actual value of each data point; S233, calculate the parameter adjustment step size h by Levenberg-Marquardt method LM , the calculation formula is: In the formula, is the derivative of the current state; S234, using the step size to update the last parameter: a jn+1 =a jn -h LM Calculate the output value and the new error using the updated parameters; S235. In each round, determine whether the termination condition is met. If the error is less than the preset minimum error threshold or the maximum number of iterations has been reached, stop the iteration and output the final parameters and error values ​​to finally obtain the complete state equation; otherwise, perform the next round of parameter update.

7. The method for estimating the state of a refrigeration circuit of an electric vehicle air conditioner based on data-driven modeling and an extended Kalman filter algorithm according to claim 4, characterized in that: The discretized state space equation is expressed as: In the formula, subscripts k and k-1 represent the current moment and the previous moment respectively.

8. The method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm according to claim 7, characterized in that: In step S3, the extended Kalman filter method is used to estimate the refrigerant circuit state x = [l e P e T wfe P c T wfc ] T , where the extended Kalman filter process includes: S31, initialize the covariance matrix P0 and design the initial value x0 of the system state quantity; S32, based on the posterior estimation value of the previous moment k-1, the prior estimation state of the current moment k is calculated by discrete state equation; In the formula, φ k-1 Represents the Jacobian matrix of the previous moment. The Jacobian matrix is ​​used to calculate the Kalman filter gain and update the state. represents the posterior estimate at the previous moment; S33, use the posterior estimated covariance of the previous moment k-1 to calculate the prior estimated covariance P of the current moment k k / k-1 ; Among them, Q k-1 represents the process noise covariance matrix; S34, Kalman gain K corrected based on the prior estimated covariance of the current time k k ; Among them, H k is the observation matrix, R is the observation noise covariance matrix; S35, fusing the predicted value and the measured value of the Kalman filter according to the ratio of the Kalman gain to obtain a posterior estimation value; Among them, y k Output for the state equation; S36. Calculate the posterior estimation covariance of the Kalman filter for use in the next round of calculation: P k =P k / k-1 -K k H k P k / k-1 The subscript k / k-1 represents a priori estimate.

9. The method for estimating the state of a tram air conditioning refrigeration circuit based on data-driven modeling and extended Kalman filter algorithm according to claim 8, characterized in that: In step S32, the Jacobian matrix φ is: The Jacobian matrix is ​​used to calculate the Kalman filter gain and update the state.

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