Air conditioner energy-saving control method based on future vehicle speed information pre-judgment

Abstract

The invention belongs to the technical field of air conditioner energy-saving control, and particularly relates to an air conditioner energy-saving control method based on future vehicle speed information prejudgment. The prediction model receives state information and speed information from a vehicle to select a suitable cabin set temperature Tzc, r. Then, the information is transmitted to a prediction model, and a state variable at a future moment is obtained through calculation. The optimal control quantity is calculated in the prediction step length through a cost function, so that the sum of the temperature tracking error and the energy consumption is minimum, the optimal control quantity is output, and rolling optimization is achieved; according to the method, the operation mode of the air conditioning system is dynamically adjusted through the network connection vehicle speed information, so that the energy efficiency and comfort are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field, and in particular relates to an air conditioning energy-saving control method based on prediction of future vehicle speed information. Background Art

[0002] The range of electric vehicles is significantly reduced due to the need for cabin cooling. Current air conditioning control strategies are often based on simple rules. When the external environment changes, it is not only difficult to maintain the thermal comfort of the cabin, but also has poor adaptability and may cause overcooling, resulting in energy waste. Model predictive control is a model-based control method that can effectively handle the multi-input and multi-output problems in air conditioning systems through accurate modeling and optimal control of multivariable systems. At the same time, networked information makes it possible to obtain future vehicle speed signals in real time, which provides strong support for the dynamic optimization control of air conditioning systems.

[0003] Therefore, there is an urgent need for an air-conditioning energy-saving control method based on the prediction of future vehicle speed information to achieve dynamic optimization control of the air-conditioning system under different working conditions. Summary of the invention

[0004] In order to overcome the above problems, the present invention provides an air-conditioning energy-saving control method based on the prediction of future vehicle speed information. The method dynamically adjusts the operation mode of the air-conditioning system through networked vehicle speed information to improve energy efficiency and comfort.

[0005] An air conditioning energy-saving control method based on prediction of future vehicle speed information includes the following contents:

[0006] 1. Establishment of prediction model:

[0007] A control-oriented prediction model for the air conditioning system is established. The model uses a polynomial fitting method to describe the cabin temperature T zc and evaporator wall temperature T e Dynamic behavior of the blower; the model is based on the blower mass flow rate M g , evaporator wall temperature setting value T es , Cabin set temperature T zc,r As input data, the evaporator wall temperature T e , Cabin air intake temperature T jq , Cabin temperature T zc , cabin shell temperature T kt 、Cabin internal temperature T cn , compressor power P ys , Blower power P gf is the output data; the specific formula is as follows:

[0008]

[0009] T e(k+1)=γ2+T e (k)+θ5T e (k)+θ6T es (k)+θ7M g (k)+θ8T e (k)M g (k)#(2)

[0010] where θ1-θ8 and γ1, γ2 represent the parameters to be identified, M g is the blower mass flow rate, T es is the set value of the evaporator wall temperature, k is the current moment;

[0011] Second, an energy consumption prediction model is established to estimate the power of the compressor and blower, the main energy-consuming components in the air-conditioning system. The mathematical form of the model is as follows:

[0012]

[0013]

[0014] Among them, σ1-σ5 and γ3 represent the parameters to be identified, P ys is the compressor power, P gf is the blower power;

[0015] Third, establish the following optimization problem. The optimization goal is to minimize the sum of the cabin temperature tracking error and the air conditioning system energy consumption in the prediction time domain. The formula is as follows:

[0016]

[0017] The cabin temperature T zc and evaporator wall temperature T e The dynamic equations and state constraints of the evaporator temperature are as follows:

[0018]

[0019] Where i represents the index of the prediction time, ranging from 0 to N p -1, where N p is the length of the prediction time domain, k is the current time index, indicating the current time point, (i|k) indicates the corresponding variable value at the i-th prediction time when the prediction is made at the current time k, T zc,r Set the cabin temperature, P gf is the blower power, Δt represents the time interval;

[0020] 4. Obtain the cabin temperature T within the prediction time domain zc , Cabin set temperature T zc,r , vehicle speed, compressor power P ys, Blower power P gf , solve the objective function, i.e., formula (5), to obtain the optimal control quantity: evaporator wall temperature setting value T es Blower mass flow rate M g ;

[0021] 5. Obtain the evaporator wall temperature setting value T es Blower mass flow rate M g The input is sent to the vehicle controller, which adjusts the speed of the compressor and the voltage of the blower according to the optimal control quantity.

[0022] The cabin setting temperature T zc,r Adjusts according to vehicle speed.

[0023] When the vehicle speed is higher than 60km / h, the cabin temperature is set to T zc,r When the vehicle speed is lower than 30 km / h, the cabin temperature is set to T zc,r The cabin temperature is set to 26°C at other vehicle speeds. zc,r is 25℃.

[0024] In the step 4, the objective function, i.e., formula (5), is solved using the fmincon toolbox in Matlab to obtain the optimal control quantity.

[0025] The T zc (i+1|k)=f Tzc (i|k): indicates the cabin temperature T at the i+1th prediction time when the prediction is made at the current time k zc is equal to the function of the cabin temperature at the current time k, i.e. the i-th prediction time; Indicates the evaporator wall temperature T at the i+1th prediction time when the prediction is made at the current time k e It is equal to the function of the evaporator wall temperature at the current time k, that is, the i-th prediction time.

[0026] The T zc，min ≤T zc (i|k)≤T zc，max : Indicates the cabin temperature T zc To be limited within the maximum and minimum cabin temperature;

[0027] T e，min ≤T e (i|k)≤T e，max : Indicates the evaporator wall temperature T e To limit the evaporator wall temperature T e between the maximum and minimum values.

[0028] The M g，min ≤M g (i|k)≤Mg，max : Indicates the blower mass flow rate M g To limit the blower mass flow rate between the maximum and minimum values;

[0029] T es，min ≤T es (i|k)≤T es，max : Indicates the evaporator wall temperature setting value T es To be limited between the maximum and minimum evaporator wall temperature set points.

[0030] The T zc (0|k) = T zc (k), T e (0|k) = T e (k): indicates the cabin temperature T at the first predicted moment when the prediction is made at the current moment k zc and evaporator wall temperature T e They are equal to the cabin temperature and evaporator temperature at the current moment respectively.

[0031] Beneficial effects of the present invention:

[0032] 1. The present invention proposes the vehicle speed sensitivity of the vehicle air-conditioning system, verifies the difference in air-conditioning energy efficiency at different vehicle speeds, and realizes the efficient use of the vehicle air-conditioning system.

[0033] 2. The present invention develops an ecological refrigeration strategy based on model predictive control, which dynamically changes the working mode of the air-conditioning system by utilizing the vehicle speed preview information provided in the networked environment to achieve low energy consumption of the air-conditioning system.

[0034] 3. The present invention proposes a simplified control-oriented prediction model for air conditioning systems and calibrates and verifies it based on a high-fidelity model.

[0035] 4. The present invention proposes an optimization problem that takes into account both cabin comfort and air conditioning energy consumption. By minimizing temperature tracking error and energy consumption, the control input is calculated to achieve precise control of the air conditioning system, thereby ensuring cabin thermal comfort while optimizing energy efficiency.

[0036] In summary, the present invention solves the problems of high energy consumption and insufficient comfort of existing electric vehicle air-conditioning systems; the present invention utilizes the efficiency sensitivity of the air-conditioning system to the vehicle speed and the heat storage capacity of the cabin to dynamically adjust the cabin temperature within a comfortable range, and achieves this goal by actively transferring the heat load of the air-conditioning system to a more efficient area under higher vehicle speed conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings used in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the contents of the embodiments of the present invention and these drawings without creative work.

[0038] Figure 1 This is a schematic diagram of the structure of an automobile air conditioning system according to the present invention.

[0039] Figure 2 This is an example of a high-fidelity model response of the rule-based temperature tracking controller of the present invention at different vehicle speeds.

[0040] Figure 3 Validation results for the control-oriented prediction model.

[0041] Figure 4 This is a technical block diagram of the present invention.

[0042] Figure 5 Providing future vehicle speed information to the vehicle networking system. The vehicle networking system predicts the vehicle speed changes in the future based on the information. DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0044] In the description of the present invention, unless otherwise clearly specified and limited, the terms "connected", "connected", and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0046] In the description of this embodiment, the terms "upper", "lower", "left", "right" and other directions or positional relationships are based on the directions or positional relationships shown in the drawings, and are only for the convenience of description and simplification of operation, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operate in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used to distinguish in the description and have no special meaning.

[0047] Example 1

[0048] An air conditioning energy-saving control method based on prediction of future vehicle speed information includes the following contents:

[0049] The automobile air-conditioning system to which the method is applied comprises a refrigerant circuit and a cabin air circuit, and system components comprise a compressor, a condenser, a thermal expansion valve, an evaporator, a blower and a cabin, wherein one end of the condenser is connected to one end of the thermal expansion valve, the other end of the thermal expansion valve is connected to an air inlet end of the evaporator, an air outlet end of the evaporator is connected to one end of the compressor, the other end of the compressor is connected to the other end of the condenser, a return air inlet of the cabin is simultaneously connected to an air inlet end of the evaporator, an air outlet end of the evaporator is simultaneously connected to an air inlet end of the blower, and an air outlet end of the blower is connected to an air inlet end of the cabin.

[0050] 1. Establishment of prediction model:

[0051] A control-oriented prediction model for the air conditioning system is established. The model uses a polynomial fitting method to describe the cabin temperature T zc and evaporator wall temperature T e Dynamic behavior of the blower; the model is based on the blower mass flow rate M g , evaporator wall temperature setting value T es , Cabin set temperature T zc,r As input data, the evaporator wall temperature T e , Cabin air intake temperature T jq , Cabin temperature T zc , cabin shell temperature T kt 、Cabin internal temperature T cn, compressor power P ys , Blower power P gf is the output data; the specific formula is as follows:

[0052]

[0053] T e (k+1)=γ2+T e (k)+θ5T e (k)+θ6T es (k)+θ7M g (k)+θ8T e (k)M g (k)#(2)

[0054] where θ1-θ8 and γ1, γ2 represent the parameters to be identified, M g is the blower mass flow rate, T es is the set value of the evaporator wall temperature, k is the current moment;

[0055] Second, an energy consumption prediction model is established to estimate the power of the compressor and blower, the main energy-consuming components in the air-conditioning system. The mathematical form of the model is as follows:

[0056]

[0057] Among them, σ1-σ5 and γ3 represent the parameters to be identified, P ys is the compressor power, P gf is the blower power;

[0058] Third, the cabin temperature tracking error and energy consumption are minimized by solving the following optimization problem. The following optimization problem is established. The optimization goal is to minimize the sum of the cabin temperature tracking error and the air conditioning system energy consumption in the prediction time domain. The formula is as follows:

[0059]

[0060] The cabin temperature T zc and evaporator wall temperature T e The dynamic equations and state constraints of the evaporator temperature are as follows:

[0061]

[0062] Where i represents the index of the prediction time, ranging from 0 to N p -1, where N p is the length of the prediction time domain, k is the current time index, indicating the current time point, (i|k) indicates the corresponding variable value of the i-th prediction time when the prediction is made at the current time k, min in the subscript represents the minimum value, max represents the maximum value, T zc,rSet the cabin temperature, P gf is the blower power, Δt represents the time interval, i.e., the time difference between the current time step and the next time step;

[0063] 4. Obtain the cabin temperature T within the prediction time domain zc , Cabin set temperature T zc,r , vehicle speed, compressor power P ys , Blower power P gf , solve the objective function, i.e., formula (5), to obtain the optimal control quantity: evaporator wall temperature setting value T es Blower mass flow rate M g ;

[0064] 5. Obtain the evaporator wall temperature setting value T es Blower mass flow rate M g The input is sent to the vehicle controller, which adjusts the speed of the compressor and the voltage of the blower according to the optimal control quantity.

[0065] The cabin setting temperature T zc,r Adjusts according to vehicle speed.

[0066] When the vehicle speed is higher than 60km / h, the cabin temperature is set to T zc,r When the vehicle speed is lower than 30 km / h, the cabin temperature is set to T zc,r The cabin temperature is set to 26°C at other vehicle speeds. zc,r is 25℃.

[0067] The vehicle speed is used to dynamically adjust the cabin temperature setting value. The normal cabin temperature setting value is 25°C. When the vehicle speed is high, the air conditioning efficiency is high. In order to make the air conditioning do more work, the cabin temperature setting value is adjusted to 23°C to allow the cabin to store cold. When the vehicle speed is low, the air conditioning efficiency is low. In order to make full use of the cold storage of the cabin at high speeds, the air conditioning does less work and the cabin temperature setting value is adjusted to 26°C.

[0068] In the step 4, the objective function, i.e., formula (5), is solved using the fmincon toolbox in Matlab to obtain the optimal control quantity.

[0069] The T zc (i+1|k)=f Tzc (i|k): indicates the cabin temperature T at the i+1th prediction time when the prediction is made at the current time k zc is equal to the function of the cabin temperature at the current time k, i.e. the i-th prediction time;

[0070] Indicates the evaporator wall temperature T at the i+1th prediction time when the prediction is made at the current time k eIt is equal to the function of the evaporator wall temperature at the current time k, that is, the i-th prediction time.

[0071] The T zc，min ≤T zc (i|k)≤T zc，max : Indicates the cabin temperature T zc To be limited within the maximum and minimum cabin temperature;

[0072] T e，min ≤T e (i|k)≤T e，max : Indicates the evaporator wall temperature T e To limit the evaporator wall temperature T e between the maximum and minimum values.

[0073] The M g，min ≤M g (i|k)≤M g，max : Indicates the blower mass flow rate M g To limit the blower mass flow rate between the maximum and minimum values;

[0074] T es，min ≤T es (i|k)≤T es，max : Indicates the evaporator wall temperature setting value T es To be limited between the maximum and minimum evaporator wall temperature set points.

[0075] The T zc (0|k) = T zc (k), T e (0|k) = T e (k): indicates the cabin temperature T at the first predicted moment when the prediction is made at the current moment k zc and evaporator wall temperature T e They are equal to the cabin temperature and evaporator temperature at the current moment respectively.

[0076] Example 2

[0077] An air conditioning energy-saving control method based on prediction of future vehicle speed information includes the following contents:

[0078] The overall technical block diagram is as follows: Figure 4 As shown, the prediction model receives the vehicle status information and vehicle speed information to select the appropriate cabin setting temperature T zc,r . Then, this information is passed to the prediction model, and the state variables at the future moment are calculated. The optimal control amount is calculated within the prediction step through the cost function, so that the sum of temperature tracking error and energy consumption is minimized, and the optimal control amount is output to achieve rolling optimization.

[0079] The specific technical solutions include the following:

[0080] (1) High-fidelity model

[0081] The schematic diagram of the air conditioning model used in the present invention is shown in the attached Figure 1 As shown. The on-board battery powers the main electrical equipment of the air conditioning system (i.e., the compressor and blower). The model contains two main loops, the green line represents the refrigerant loop, and the blue line represents the cabin air loop. Under hot weather conditions, the actuators in the refrigerant loop (including the compressor, condenser fan, and thermal expansion valve, etc.) coordinate to adjust the evaporator wall temperature to meet the cooling power requirements of the cabin air loop. The circulating air from the cabin is cooled by the evaporator, and then the blower blows the cold air into the cabin. The input and output of the model are shown in Appendix 2.

[0082] (2) Vehicle speed sensitivity discovery:

[0083] When conducting real vehicle tests, the efficiency of the air conditioning system varies greatly at different vehicle speeds. In order to verify the sensitivity of the air conditioning system to vehicle speed changes, this method conducts a high-fidelity model test. Figure 2 An example of a high-fidelity model response at different vehicle speeds using a simple rule-based temperature tracking controller is shown. A 600-second simulation was performed at different constant vehicle speeds (vh = 0, 20, 40, 60, 80, 100, and 120 km / h) and the same cabin air temperature set point Tcasp. The simulation results show that the efficiency of the air conditioning system increases with increasing vehicle speed. The efficiency of the air conditioning system increases by about 40% when the vehicle speed increases from 0 km / h to 120 km / h. This speed sensitivity can be exploited in an eco-cooling strategy.

[0084] (2) Establishment of prediction model:

[0085] A control-oriented prediction model for the air conditioning system is established. The model uses a polynomial fitting method to describe the cabin temperature T zc and evaporator wall temperature T e The dynamic behavior of ; the model includes two system states and two control inputs:

[0086]

[0087] T e (k+1)=γ2+T e (k)+θ5T e (k)+θ6T es (k)+θ7M g (k)+θ8T e (k)M g (k)#(2)

[0088] Among them, θ1-θ8 and γ1, γ2 represent the parameters to be identified, and the input and output of the prediction model are shown in Appendix 2.

[0089] The present invention also establishes an energy consumption prediction model to estimate the power of the main energy-consuming components in the air-conditioning system, namely the compressor and the blower. The mathematical form of the model is as follows:

[0090]

[0091] Among them, σ1-σ5 and γ3 represent the parameters to be identified.

[0092] Attached Figure 3 The validation results of the control-oriented prediction model are shown. The model predicts the behavior of the system for the next 600 seconds given only the initial time step, and predicts the cabin temperature, evaporator temperature, compressor power, and blower power for the next 600 seconds. Figure 3 The horizontal axis is time, E_ac represents air conditioning energy consumption, and v_veh represents vehicle speed. The root mean square error RMSE (indicating the degree of deviation between the predicted value and the true value) between the predicted value of the established prediction model and the simulation result of the high-fidelity model (obtained by simulation verification using matlab) is 0.00131℃, 0.123℃, 0.0000173W, and 2.44W, respectively. It can be seen that the identified models (1)-(4) provide reasonably accurate results that can meet the requirements of controller design.

[0093] (3) Dynamic mode switching strategy

[0094] Based on the prediction model and energy consumption model, the present invention designs an eco-mode switching strategy to dynamically adjust the working mode of the air-conditioning system according to the future vehicle speed prediction information. The overall architecture of the eco-refrigeration strategy consists of a mode selection module and an air-conditioning control module. Based on the network environment, the vehicle speed preview information can be obtained, and based on the vehicle CAN signal, the cabin air temperature information can be obtained. The mode selection module first determines which mode the current system should be in (including three modes: efficient refrigeration, heat preservation, and energy saving) based on the vehicle speed information, and then the air-conditioning control module calculates the adjusted cabin temperature set point Tzc,r according to the currently selected mode.

[0095] (4) Nonlinear Model Predictive Controller

[0096] The present invention develops a nonlinear model predictive controller to minimize the cabin temperature tracking error and energy consumption by solving an optimization problem. The optimization goal is to minimize the sum of the cabin temperature tracking error and the air conditioning system energy consumption in the prediction time domain:

[0097]

[0098] The cabin temperature and evaporator temperature dynamic equations and state constraints need to be satisfied:

[0099]

[0100] Where i represents the index of the prediction time, ranging from 0 to N p -1, where N p is the length of the prediction time domain. k: the current time index, indicating the current time point, (i|k) indicates the variable value at the i-th prediction time when the prediction is made at the current time k. In the subscript, min represents the minimum value and max represents the maximum value.

[0101] T zc (i+1|k)=f Tzc (i|k): indicates that when prediction is made at the current time k, the cabin temperature at the i+1th prediction time is equal to the current time K, a function of the cabin temperature at the i-th prediction time.

[0102] It means that when prediction is made at the current time k, the evaporator temperature at the i+1th prediction time is equal to the function of the evaporator temperature at the current time K and the i-th prediction time.

[0103] T zc，min ≤T zc (i|k)≤T zc，max : Indicates that the cabin temperature is limited between the maximum and minimum cabin temperatures.

[0104] T e，min ≤T e (i|k)≤T e，max : Indicates that the evaporator temperature is limited between the maximum and minimum values ​​of the evaporator temperature.

[0105] M g，min ≤M g (i|k)≤M g，max : Indicates that the blower mass flow rate must be limited between the maximum and minimum values ​​of the blower mass flow rate.

[0106] T es，min ≤T es (i|k)≤T es，max : Indicates that the evaporator temperature setting value is limited between the maximum and minimum values ​​of the evaporator temperature setting value.

[0107] T zc (0|k) = T zc (k), Te(0|k)=T e (k): indicates that when prediction is performed at the current time k, the cabin temperature and evaporator temperature at the first prediction time are equal to the cabin temperature and evaporator temperature at the current time, respectively.

[0108] The specific meanings of the parameters in the formula are shown in Appendix 3.

[0109] As shown in the results of Appendix 1, by selecting the appropriate mode under actual working conditions, the eco-cooling strategy saves an average of 3.89% energy compared to the baseline control strategy. In addition, the difference between the average cabin temperature adjusted by the eco-cooling strategy and the average temperature of the baseline control strategy is 1.09%.

[0110] Example 3

[0111] This embodiment takes the Chinese light vehicle driving condition as an example, and the execution process of the air conditioning energy-saving control method based on the prediction of future vehicle speed information in this condition is divided into the following six steps:

[0112] Step 1: Initialize system parameters and prediction model

[0113] The system first receives the vehicle's current state information and establishes a prediction model based on the polynomial fitting method. The dynamic equations of cabin temperature and evaporator wall temperature are as follows:

[0114] Step 2: Receive future vehicle speed prediction information

[0115] The vehicle networking system provides future vehicle speed information, and the system predicts vehicle speed changes in the future based on this information, such as Figure 5 shown.

[0116] Step 3: Select the appropriate operating mode

[0117] The operating mode is dynamically selected based on the future vehicle speed prediction information and the current system status. The specific judgment conditions for mode selection are shown in Appendix 4. The high-efficiency cooling mode is suitable for high vehicle speed conditions, and the energy-saving mode is suitable for low vehicle speed conditions.

[0118] Step 4: Adjust the air conditioning system set points

[0119] According to the selected operation mode, the system dynamically adjusts the set point. The specific adjustment strategy is as follows:

[0120] High-efficiency cooling mode: lowers the cabin temperature set point to 22°C and the evaporator wall temperature set point to 5°C.

[0121] Heat preservation mode: Maintain the current cabin temperature, the evaporator wall temperature set point is 7℃.

[0122] Energy saving mode: Increase the cabin temperature set point to 25°C and the evaporator wall temperature set point to 10°C.

[0123] Step 5: Perform nonlinear model predictive control

[0124] The system calculates the optimized control input through the nonlinear model predictive controller. The objective function of the optimization problem is as shown in formula (5).

[0125] Step 6: Real-time monitoring and feedback adjustment

[0126] The system monitors the operating status of the air-conditioning system in real time, compares the actual data obtained by sensors with the prediction model, and dynamically adjusts the control parameters.

[0127] In summary, through the above steps, the present invention realizes dynamic optimization control of the electric vehicle air-conditioning system under different working conditions, improves system energy efficiency, extends vehicle cruising range, and improves passenger comfort.

[0128] Table 1

[0129]

[0130]

[0131] Table 2

[0132]

[0133] Table 3

[0134] parameter Notes Numeric unit <![CDATA[N p ]]> Prediction time domain 20 1 <![CDATA[T s ]]> Sampling time 1 s <![CDATA[λ c ]]> Weight Factor <![CDATA[1×10 -5 ]]> 1 <![CDATA[T zc,min ]]> Minimum cabin temperature 10 ℃ <![CDATA[T zc,max ]]> Maximum cabin temperature 40 ℃ <![CDATA[T e,min ]]> Minimum evaporator wall temperature 2 ℃ <![CDATA[T e,max ]]> Maximum evaporator wall temperature 16 ℃ <![CDATA[M g,min ]]> Minimum blower mass flow 0 kg / s <![CDATA[M g,max ]]> Maximum mass flow of blower 0.1 kg / s <![CDATA[T es,min ]]> Minimum setting temperature of evaporator wall 2 ℃ <![CDATA[T es,max ]]> Maximum set temperature of evaporator wall 16 ℃

[0135] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the protection scope of the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention. These simple variations all belong to the protection scope of the present invention.

[0136] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0137] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. An air conditioning energy-saving control method based on future vehicle speed information prediction, characterized in that: It includes the following:

1. Establishment of prediction model: A control-oriented prediction model for the air conditioning system is established. The model uses a polynomial fitting method to describe the cabin temperature T zc and evaporator wall temperature T e Dynamic behavior of the blower; the model is based on the blower mass flow rate M g , evaporator wall temperature setting value T es , Cabin set temperature T zc，r As input data, the evaporator wall temperature T e , Cabin air intake temperature T jq , Cabin temperature T zc , cabin shell temperature T kt 、Cabin internal temperature T cn , compressor power P ys , Blower power P gf To output data; The specific formula is as follows: T e (k+1)=γ2+T e (k)+θ5T e (k)+θ6T es (k)+θ7M g (k)+θ8T e (k)M g (k)#(2) where θ1-θ8 and γ1, γ2 represent the parameters to be identified, M g is the blower mass flow rate, T es is the set value of the evaporator wall temperature, k is the current moment; Second, an energy consumption prediction model is established to estimate the power of the compressor and blower, the main energy-consuming components in the air-conditioning system. The mathematical form of the model is as follows: Among them, σ1-σ5 and γ3 represent the parameters to be identified, P ys is the compressor power, P gf is the blower power; Third, establish the following optimization problem. The optimization goal is to minimize the sum of the cabin temperature tracking error and the air conditioning system energy consumption in the prediction time domain. The formula is as follows: The cabin temperature T zc and evaporator wall temperature T e The dynamic equations and state constraints of the evaporator temperature are as follows: Where i represents the index of the prediction time, ranging from 0 to N p -1, where N p is the length of the prediction time domain, k is the current time index, indicating the current time point, (i|k) indicates the corresponding variable value at the i-th prediction time when the prediction is made at the current time k, T zc,r Set the cabin temperature, P gf is the blower power, Δt represents the time interval; 4. Obtain the cabin temperature T within the prediction time domain zc , Cabin set temperature T zc,r , vehicle speed, compressor power P ys , Blower power P gf , solve the objective function, i.e., formula (5), to obtain the optimal control quantity: evaporator wall temperature setting value T es Blower mass flow rate M g ; 5. Obtain the evaporator wall temperature setting value T es Blower mass flow rate M g The input is sent to the vehicle controller, which adjusts the speed of the compressor and the voltage of the blower according to the optimal control quantity.

2. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1 is characterized in that: The cabin setting temperature T zc,r Adjusts according to vehicle speed.

3. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 2 is characterized in that: When the vehicle speed is higher than 60km / h, the cabin temperature is set to T zc,r When the vehicle speed is lower than 30 km / h, the cabin temperature is set to T zc,r The cabin temperature is set to 26°C at other vehicle speeds. zc,r is 25℃.

4. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1 is characterized in that: In the step 4, the objective function, i.e., formula (5), is solved using the fmincon toolbox in Matlab to obtain the optimal control quantity.

5. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1 is characterized in that: The T zc (i+1|k)=f Tzc (i|k): indicates the cabin temperature T at the i+1th prediction time when the prediction is made at the current time k zc is equal to the function of the cabin temperature at the current time k, i.e. the i-th prediction time; Indicates the evaporator wall temperature T at the i+1th prediction time when the prediction is made at the current time k e It is equal to the function of the evaporator wall temperature at the current time k, that is, the i-th prediction time.

6. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1 is characterized in that: The T zc，min ≤T zc (i|k)≤T zc，max : Indicates the cabin temperature T zc To be limited within the maximum and minimum cabin temperature; T e，min ≤T e (i|k)≤T e，max : Indicates the evaporator wall temperature T e To limit the evaporator wall temperature T e between the maximum and minimum values.

7. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1 is characterized in that: The M g，min ≤M g (i|k)≤M g，max : Indicates the blower mass flow rate M g To limit the blower mass flow rate between the maximum and minimum values; T es，min ≤T es (i|k)≤T es，max : Indicates the evaporator wall temperature setting value T es To be limited between the maximum and minimum evaporator wall temperature set points.

8. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1 is characterized in that: The T zc (0|k) = T zc (k), T e (0|k) = T e (k): indicates the cabin temperature T at the first predicted moment when the prediction is made at the current moment k zc and evaporator wall temperature T e They are equal to the cabin temperature and evaporator temperature at the current moment respectively.

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