An air conditioner energy-saving control method based on future vehicle speed information prediction

Abstract

The present application belongs to the technical field of air conditioning energy-saving control, and particularly relates to an air conditioning energy-saving control method based on future vehicle speed information prediction; a prediction model receives state information and vehicle speed information from a vehicle to select a suitable cabin set temperature T zc,r . Subsequently, the information is transmitted to the prediction model to obtain state variables at a future time through calculation. The optimal control amount is calculated within the prediction step through a cost function to minimize the sum of temperature tracking error and energy consumption, and the optimal control amount is output to realize rolling optimization; the method dynamically adjusts the operation mode of the air conditioning system through networked vehicle speed information to improve energy efficiency and comfort.

Description

Technical Field

[0001] This invention belongs to the technical field, specifically relating to an air conditioning energy-saving control method based on prediction of future vehicle speed information. Background Technology

[0002] Due to the cooling requirements of the cabin, the driving range of electric vehicles is significantly reduced. Current air conditioning control strategies are often based on simple rules, which not only fail to maintain cabin thermal comfort when the external environment changes, but also exhibit poor adaptability and may even cause overcooling, leading to energy waste. Model predictive control, a model-based control method, can effectively handle the multi-input multi-output problem in air conditioning systems by accurately modeling and optimizing the control of multivariable systems. Simultaneously, connected vehicle information makes it possible to obtain future vehicle speed signals in real time, providing 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, so as to achieve dynamic optimization control of the air conditioning system under different operating conditions. Summary of the Invention

[0004] To overcome the above problems, this invention provides an air conditioning energy-saving control method based on future vehicle speed information prediction. This method dynamically adjusts the operating mode of the air conditioning system through connected vehicle speed information to improve energy efficiency and comfort.

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

[0006] I. Establishing the Predictive Model:

[0007] A control-oriented predictive model for the air conditioning system is established, which uses a polynomial fitting method to describe the cabin temperature T. zc and evaporator wall temperature T e The dynamic behavior of the blower; the model uses the blower mass flow rate M g Evaporator wall temperature setpoint T es Cabin set temperature T zc,r For input data, use evaporator wall temperature T e Cabin intake temperature T jq Cabin temperature T zc cockpit shell temperature T kt Cabin interior temperature T cn Compressor power P ys Blower power P gf The output data is shown in the following formula:

[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 T represents the mass flow rate of the blower. es The evaporator wall temperature setpoint is given by k, where k is the current time.

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

[0012]

[0013]

[0014] Where σ1-σ5 and γ3 represent the parameters to be identified, P ys P is the compressor power. gf This refers to the power of the blower.

[0015] Third, establish the following optimization problem, with the optimization objective being to minimize the sum of cabin temperature tracking error and air conditioning system energy consumption in the prediction time domain, as shown in the following formula:

[0016]

[0017] The cabin temperature T needs to be met. zc and evaporator wall temperature T e The dynamic equations and state constraints for the evaporator temperature are as follows:

[0018]

[0019] In the formula, i represents the index of the prediction time, ranging from 0 to N. p -1, where N p T is the length of the prediction time domain, k is the index of the current time, representing the current time point, (i|k) represents the value of the corresponding variable at the i-th prediction time when prediction is performed at the current time k, and T zc,r To set the cabin temperature, P gf The value represents the blower power, and Δt represents the time interval.

[0020] Fourth, obtain the cabin temperature T in the predicted time domain. zc Cabin set temperature T zc,r Vehicle speed, compressor power P ysBlower power P gf Solve the objective function, i.e., formula (5), to obtain the optimal control quantity: evaporator wall temperature setpoint T. es Blower mass flow rate M g ;

[0021] Fifth, the obtained evaporator wall temperature setpoint T es Blower mass flow rate M g The input is sent to the vehicle's infotainment controller, which then adjusts the compressor speed and blower voltage based on the optimal control parameters.

[0022] The cabin set temperature T zc,r Adjust according to vehicle speed.

[0023] When the vehicle speed is above 60km / h, the cabin set temperature T zc,r The cabin temperature is set at 23℃. When the vehicle speed is below 30km / h, the cabin temperature setting is T. zc,r The cabin temperature is set at 26℃ at other vehicle speeds. zc,r The temperature is 25℃.

[0024] In step four, 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): Represents the cabin temperature T at the (i+1)th prediction time when prediction is made at the current time k. zc It is equal to the cabin temperature at the current time k, i.e., the cabin temperature at the i-th prediction time. This represents the evaporator wall temperature T at the (i+1)th prediction time when prediction is made at the current time k. e It is equal to the evaporator wall temperature at the current time k, i.e., the evaporator wall temperature at the i-th prediction time.

[0026] The T zc，min ≤T zc (i|k)≤T zc，max : Indicates cabin temperature T zc The cabin temperature must be limited to between its maximum and minimum values.

[0027] T e，min ≤T e (i|k)≤T e，max : Indicates the evaporator wall temperature T e The evaporator wall temperature T should be limited. 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 The blower's mass flow rate must be limited to between its maximum and minimum values.

[0029] T es，min ≤T es (i|k)≤T es，max : Indicates the evaporator wall temperature setpoint T es The temperature should be limited to between the maximum and minimum values ​​of the evaporator wall temperature setpoint.

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

[0031] The beneficial effects of this invention are:

[0032] 1. This invention proposes a vehicle speed sensitivity for vehicle air conditioning systems, verifies the difference in air conditioning energy efficiency at different vehicle speeds, and realizes the efficient utilization of vehicle air conditioning systems.

[0033] 2. This invention develops an ecological cooling strategy based on model predictive control, which dynamically changes the working mode of the air conditioning system by utilizing vehicle speed preview information provided in the connected environment, thereby achieving low energy consumption of the air conditioning system.

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

[0035] 4. This invention proposes an optimization problem that simultaneously considers 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, ensuring cabin thermal comfort while optimizing energy efficiency.

[0036] In summary, this invention solves the problems of high energy consumption and insufficient comfort in existing electric vehicle air conditioning systems. This invention utilizes the efficiency sensitivity of the air conditioning system to vehicle speed and the heat storage capacity of the cabin to dynamically adjust the cabin temperature within a comfortable range. This goal is achieved by actively transferring the heat load of the air conditioning system to a more efficient area under higher vehicle speed conditions. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the content of the embodiments of the present invention and these drawings without creative effort.

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

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

[0040] Figure 3 The results are for the validation of the control-oriented prediction model.

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

[0042] Figure 5 This diagram illustrates how vehicle speed information is used by connected vehicle systems to predict changes in vehicle speed over a future period. Detailed Implementation

[0043] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0044] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0045] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0046] In the description of this embodiment, the terms "upper," "lower," "left," and "right," etc., refer to the orientation or positional relationship shown in the accompanying drawings. They are used only for ease of description and simplification of operation, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first" and "second" are used only for distinction in description and have no special meaning.

[0047] Example 1

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

[0049] The automotive air conditioning system used in this method includes a refrigerant circuit and a cabin air circuit. The system components include a compressor, a condenser, a thermal expansion valve, an evaporator, a blower, and a cabin. One end of the condenser is connected to one end of the thermal expansion valve, and the other end of the thermal expansion valve is connected to the air inlet of the evaporator. The air outlet of the evaporator is connected to one end of the compressor, and the other end of the compressor is connected to the other end of the condenser. The return air vent of the cabin is also connected to the air inlet of the evaporator, and the air outlet of the evaporator is also connected to the air inlet of the blower. The air outlet of the blower is also connected to the air inlet of the cabin.

[0050] I. Establishing the Predictive Model:

[0051] A control-oriented predictive model for the air conditioning system is established, which uses a polynomial fitting method to describe the cabin temperature T. zc and evaporator wall temperature T e The dynamic behavior of the blower; the model uses the blower mass flow rate M g Evaporator wall temperature setpoint T es Cabin set temperature T zc,r For input data, use evaporator wall temperature T e Cabin intake temperature T jq Cabin temperature T zc cockpit shell temperature T kt Cabin interior temperature T cnCompressor power P ys Blower power P gf The output data is shown in the following formula:

[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 T represents the mass flow rate of the blower. es The evaporator wall temperature setpoint is given by k, where k is the current time.

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

[0056]

[0057] Where σ1-σ5 and γ3 represent the parameters to be identified, P ys P is the compressor power. gf This refers to the power of the blower.

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

[0059]

[0060] The cabin temperature T needs to be met. zc and evaporator wall temperature T e The dynamic equations and state constraints for the evaporator temperature are as follows:

[0061]

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

[0063] Fourth, obtain the cabin temperature T in the predicted 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 setpoint T. es Blower mass flow rate M g ;

[0064] Fifth, the obtained evaporator wall temperature setpoint T es Blower mass flow rate M g The input is sent to the vehicle's infotainment controller, which then adjusts the compressor speed and blower voltage based on the optimal control parameters.

[0065] The cabin set temperature T zc,r Adjust according to vehicle speed.

[0066] When the vehicle speed is above 60km / h, the cabin set temperature T zc,r The cabin temperature is set at 23℃. When the vehicle speed is below 30km / h, the cabin temperature setting is T. zc,r The cabin temperature is set at 26℃ at other vehicle speeds. zc,r The temperature is 25℃.

[0067] Vehicle speed is used to dynamically adjust the cabin temperature setting. The normal cabin temperature setting is 25°C. When the vehicle speed is high, the air conditioning efficiency is high, so to allow the air conditioning to do more work, the cabin temperature setting is adjusted to 23°C to allow the cabin to store coolness. When the vehicle speed is low, the air conditioning efficiency is low, so to make full use of the coolness stored in the cabin at high speeds, the air conditioning does less work, so the cabin temperature setting is adjusted to 26°C.

[0068] In step four, 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): Represents the cabin temperature T at the (i+1)th prediction time when prediction is made at the current time k. zc It is equal to the cabin temperature at the current time k, i.e., the cabin temperature at the i-th prediction time.

[0070] This represents the evaporator wall temperature T at the (i+1)th prediction time when prediction is made at the current time k. eIt is equal to the evaporator wall temperature at the current time k, i.e., the evaporator wall temperature at the i-th prediction time.

[0071] The T zc，min ≤T zc (i|k)≤T zc，max : Indicates cabin temperature T zc The cabin temperature must be limited to between its maximum and minimum values.

[0072] T e，min ≤T e (i|k)≤T e，max : Indicates the evaporator wall temperature T e The evaporator wall temperature T should be limited. 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 The blower's mass flow rate must be limited to between its maximum and minimum values.

[0074] T es，min ≤T es (i|k)≤T es，max : Indicates the evaporator wall temperature setpoint T es The temperature should be limited to between the maximum and minimum values ​​of the evaporator wall temperature setpoint.

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

[0076] Example 2

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

[0078] Overall technical block diagram as follows Figure 4 As shown, the prediction model receives vehicle status and speed information to select an appropriate cabin set temperature T. zc,r Subsequently, this information is fed into the prediction model to calculate the state variables for future time steps. The optimal control quantity is then calculated within the prediction step using a cost function, minimizing the sum of temperature tracking error and energy consumption, and outputting the optimal control quantity to achieve rolling optimization.

[0079] The specific technical solution includes the following:

[0080] (1) High-fidelity model

[0081] The schematic diagram of the air conditioner model used in this invention is attached. Figure 1 As shown in the diagram, the vehicle 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. In hot weather conditions, the actuators in the refrigerant loop (including the compressor, condenser fan, and thermal expansion valve) coordinate to adjust the evaporator wall temperature to meet the cooling power requirements of the cabin air loop. Circulating air from the cabin is cooled by the evaporator and then blown into the cabin by the blower. The model's inputs and outputs are shown in Appendix Table 2.

[0082] (2) Vehicle speed sensitivity detection:

[0083] During real-vehicle testing, the efficiency of the air conditioning system varied significantly at different vehicle speeds. To verify the sensitivity of the air conditioning system to changes in vehicle speed, this method employed a high-fidelity model for testing. (Appendix) Figure 2 A high-fidelity model of the response using a simple rule-based temperature tracking controller at different vehicle speeds is shown. Simulations were performed for 600 seconds at different constant vehicle speeds (vh = 0, 20, 40, 60, 80, 100, and 120 km / h) and the same cabin air temperature setpoint Tcasp. Simulation results show that the efficiency of the air conditioning system increases with increasing vehicle speed. When the vehicle speed increases from 0 km / h to 120 km / h, the efficiency of the air conditioning system increases by approximately 40%. This speed sensitivity can be utilized in eco-friendly cooling strategies.

[0084] (2) Establishment of the prediction model:

[0085] A control-oriented predictive model for the air conditioning system is established, which uses a polynomial fitting method to describe the cabin temperature T. zc and evaporator wall temperature T e The model exhibits dynamic behavior; it 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] Where θ1-θ8 and γ1, γ2 represent the parameters to be identified, and the input and output of the prediction model are shown in Appendix Table 2.

[0089] This invention also establishes an energy consumption prediction model for estimating the power of the main energy-consuming components in an air conditioning system—the compressor and the blower. The mathematical form of the model is as follows:

[0090]

[0091] Where σ1-σ5 and γ3 represent the parameters to be identified.

[0092] Appendix Figure 3 The validation results of the control-oriented predictive model are shown. This model predicts the system's behavior over the next 600 seconds given only an initial time step, and predicts cabin temperature, evaporator temperature, compressor power, and blower power over the next 600 seconds. Figure 3 The horizontal axis represents time, E_ac represents air conditioning energy consumption, and v_veh represents vehicle speed. The root mean square error (RMSE) between the predicted values ​​of the established prediction model and the simulation results of the high-fidelity model (obtained through 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 reasonable and accurate results, which can meet the requirements of controller design.

[0093] (3) Dynamic mode switching strategy

[0094] Based on predictive and energy consumption models, this invention designs an ecological mode switching strategy that dynamically adjusts the operating mode of the air conditioning system according to future vehicle speed predictions. The overall architecture of the ecological cooling strategy consists of a mode selection module and an air conditioning control module. Vehicle speed preview information can be obtained based on the connected environment, and cabin air temperature information can be obtained based on the vehicle's CAN signal. The mode selection module first determines the current system mode (including high-efficiency cooling, heat preservation, and energy-saving modes) based on the vehicle speed information. Then, the air conditioning control module calculates the adjusted cabin temperature setpoint Tzc,r based on the currently selected mode.

[0095] (4) Nonlinear Model Predictive Controller

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

[0097]

[0098] The dynamic equations and state constraints for cabin temperature and evaporator temperature must be satisfied:

[0099]

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

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

[0102] This represents a function that, when making a prediction at the current time k, the evaporator temperature at the (i+1)th prediction time is equal to the evaporator temperature at the current time K and the evaporator temperature at the ith prediction time.

[0103] T zc，min ≤T zc (i|k)≤T zc，max This indicates that the cabin temperature should be limited to between the maximum and minimum cabin temperature values.

[0104] T e，min ≤T e (i|k)≤T e，max This indicates that the evaporator temperature should be limited to between the maximum and minimum values ​​of the evaporator temperature.

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

[0106] T es，min ≤T es (i|k)≤T es，max This indicates that the evaporator temperature setpoint should be limited to between the maximum and minimum values ​​of the evaporator temperature setpoint.

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

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

[0109] As shown in Appendix Table 1, by selecting an appropriate mode under actual operating conditions, the eco-cooling strategy achieved an average energy saving of 3.89% compared to the baseline control strategy. Furthermore, the average cabin temperature regulated by the eco-cooling strategy differed from the average temperature regulated by the baseline control strategy by 1.09%.

[0110] Example 3

[0111] This embodiment takes the driving conditions of Chinese light-duty vehicles as an example. The execution process of the air conditioning energy-saving control method based on future vehicle speed information prediction under 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 then establishes a prediction model using a polynomial fitting method. The dynamic equations for the cabin temperature and evaporator wall temperature are shown in equations (1) and (2).

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

[0115] Vehicle connectivity systems provide future vehicle speed information, and based on this information, the system predicts changes in vehicle speed over a future period of time, such as... Figure 5 As shown.

[0116] Step 3: Select the appropriate operating mode

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

[0118] Step 4: Adjust the air conditioning system setpoint

[0119] The system dynamically adjusts the setpoints based on the selected operating mode. The specific adjustment strategy is as follows:

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

[0121] Heat preservation mode: Maintain the current cabin temperature, with the evaporator wall temperature setpoint at 7°C.

[0122] Energy Saving Mode: Increase the cabin temperature setpoint to 25°C and the evaporator wall temperature setpoint to 10°C.

[0123] Step 5: Execute nonlinear model predictive control

[0124] The system uses a nonlinear model to predict the controller and calculate the optimized control input. The objective function of the optimization problem is given by equation (5).

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

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

[0127] In summary, through the above steps, this invention achieves dynamic optimization control of the electric vehicle air conditioning system under different operating conditions, improves system energy efficiency, extends vehicle range, and enhances passenger comfort.

[0128] Table 1

[0129]

[0130]

[0131] Table 2

[0132]

[0133] Table 3

[0134] parameter Notes numerical values unit <![CDATA[N p ]]> Prediction Time Domain 20 1 <![CDATA[T s ]]> Sampling time 1 s <![CDATA[λ c ]]> Weighting factors <![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 mass flow rate of blower 0 kg / s <![CDATA[M g,max ]]> Maximum mass flow rate of blower 0.1 kg / s <![CDATA[T es,min ]]> Minimum set temperature of evaporator wall 2 ℃ <![CDATA[T es,max ]]> Maximum set temperature of evaporator wall 16 ℃

[0135] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the scope of protection of the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, any person skilled in the art can make equivalent substitutions or changes based on the technical solution and inventive concept of the present invention within the scope of the technology disclosed in the present invention. These simple modifications are all within the scope of protection 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 describe the various possible combinations separately.

[0137] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. An air conditioning energy-saving control method based on future vehicle speed information prediction, characterized in that, Includes the following: I. Establishing the Predictive Model: A control-oriented predictive model for the air conditioning system is established, which uses a polynomial fitting method to describe the cabin temperature T. zc and evaporator wall temperature T e The dynamic behavior of the blower; the model uses the blower mass flow rate M g Evaporator wall temperature setpoint T es Cabin set temperature T zc，r For input data, use evaporator wall temperature T e Cabin intake temperature T jq Cabin temperature T zc cockpit shell temperature T kt Cabin interior temperature T cn Compressor power P ys Blower power P gf For 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 T represents the mass flow rate of the blower. es The evaporator wall temperature setpoint is given by k, where k is the current time. Second, establish an energy consumption prediction model to estimate the power of the main energy-consuming components in the air conditioning system—the compressor and the blower. The mathematical form of the model is as follows: Where σ1-σ5 and γ3 represent the parameters to be identified, P ys P is the compressor power. gf This refers to the power of the blower. Third, establish the following optimization problem, with the optimization objective being to minimize the sum of cabin temperature tracking error and air conditioning system energy consumption in the prediction time domain, as shown in the following formula: The cabin temperature T needs to be met. zc and evaporator wall temperature T e The dynamic equations and state constraints for the evaporator temperature are as follows: In the formula, i represents the index of the prediction time, ranging from 0 to N. p -1, where N p T is the length of the prediction time domain, k is the index of the current time, representing the current time point, (i|k) represents the value of the corresponding variable at the i-th prediction time when prediction is performed at the current time k, and T zc,r To set the cabin temperature, P gf The value represents the blower power, and Δt represents the time interval. Fourth, obtain the cabin temperature T in the predicted 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 setpoint T. es Blower mass flow rate M g ; Fifth, the obtained evaporator wall temperature setpoint T es Blower mass flow rate M g The input is sent to the vehicle's infotainment controller, which then adjusts the compressor speed and blower voltage based on the optimal control parameters.

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

3. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 2, characterized in that, When the vehicle speed is above 60km / h, the cabin set temperature T zc,r The cabin temperature is set at 23℃. When the vehicle speed is below 30km / h, the cabin temperature setting is T. zc,r The cabin temperature is set at 26℃ at other vehicle speeds. zc,r The temperature is 25℃.

4. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1, characterized in that, In step four, 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, characterized in that, The T zc (i+1|k)=f Tzc (i|k): Represents the cabin temperature T at the (i+1)th prediction time when prediction is made at the current time k. zc It is equal to the cabin temperature at the current time k, i.e., the cabin temperature at the i-th prediction time. This represents the evaporator wall temperature T at the (i+1)th prediction time when prediction is made at the current time k. e It is equal to the evaporator wall temperature at the current time k, i.e., the evaporator wall temperature at the i-th prediction time.

6. The air conditioning energy-saving control method based on future vehicle speed information prediction according to claim 1, characterized in that, The T zc，min ≤T zc (i|k)≤T zc，max : Indicates cabin temperature T zc The cabin temperature must be limited to between its maximum and minimum values. T e，min ≤T e (i|k)≤T e，max : Indicates the evaporator wall temperature T e The evaporator wall temperature T should be limited. 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, characterized in that, The M g，min ≤M g (i|k)≤M g，max : Indicates the blower mass flow rate M g The blower's mass flow rate must be limited to between its maximum and minimum values. T es，min ≤T es (i|k)≤T es，max : Indicates the evaporator wall temperature setpoint T es The temperature should be limited to between the maximum and minimum values ​​of the evaporator wall temperature setpoint.

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

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