Energy-saving control method, controller and vehicle for network-connected electric vehicle heat pump air conditioning system

Abstract

This invention discloses an energy-saving control method, controller, and vehicle for a heat pump air conditioning system in a connected electric vehicle. The control method establishes a mathematical model of the heat pump air conditioning system for energy-saving control, solves it using a dynamic programming algorithm, and ultimately designs an online rule-based control strategy for the heat pump air conditioning system. The model calculates the cabin temperature, evaporator pressure, and heat pump air conditioning system power based on compressor speed, blower air intake, and vehicle speed information read at a specific frequency. In the online rule-based control strategy, the operating mode is divided according to the deviation between the target cabin temperature and the actual cabin temperature. The compressor speed and blower air intake are controlled within a speed range defined by the heat pump air conditioning system's sensitivity to vehicle speed. This invention, combined with intelligent network information, ensures that the cabin temperature is maintained at the set temperature while considering the impact of future vehicle speed changes on the efficiency of the heat pump air conditioning system, thus improving the energy economy under heating conditions.

Description

Technical Field

[0001] This invention belongs to the field of automotive air conditioning control, and is particularly applicable to heat pump air conditioning systems for connected electric vehicles. Specifically, it relates to energy-saving control methods, controllers, and vehicles for heat pump air conditioning systems in connected electric vehicles. Background Technology

[0002] Electric vehicles have become an inevitable choice for the automotive industry due to their significant advantages of high energy efficiency and low pollution. However, when facing the need for passenger cabin heating in cold climates, electric vehicles lack the waste heat provided by the engine, and some of the energy used to provide power is used to meet heating needs, resulting in a significant reduction in their actual driving range.

[0003] Because heat pump air conditioning systems have a high energy efficiency ratio, optimizing and controlling them can have a positive effect on simultaneously meeting the heating and power needs of electric vehicles. Therefore, their optimization and control has gradually attracted attention in the industry.

[0004] In cold climates where passenger compartment heating is required, there is a strong coupling between the powertrain (battery, drive motor, etc.) that provides energy for vehicle operation and the thermal chain (battery, heat pump air conditioning system, etc.) that provides heat to ensure passenger thermal comfort in electric vehicles. In cold climates, the energy consumption of electric vehicles is not only affected by the driver's power demands during transient driving but also significantly impacted by the passenger compartment heating needs. The intervention of the air conditioning system drastically reduces the driving range of electric vehicles.

[0005] Therefore, based on the understanding of heat pump air conditioning systems in electric vehicles, developing energy-saving control methods and systems for heat pump air conditioning systems in electric vehicles is of great significance for reducing vehicle energy consumption and increasing vehicle range in order to solve the "range anxiety" of vehicles in cold climates.

[0006] First, establishing a high-precision and low-computational-cost mathematical model for the optimized control of heat pump air conditioning systems in connected electric vehicles is the foundation for designing energy-saving control systems for electric vehicles in cold climates, and a necessary condition for fully tapping the energy-saving potential of electric vehicles.

[0007] Currently available models of electric vehicle heat pump air conditioning systems typically focus on the heat pump air conditioning system itself, considering only the design and selection of system components and structural optimization. They do not take into account the dynamics of heat transfer involved in the entire thermal chain of the vehicle—"battery—heat pump air conditioning system—passenger compartment" and its relationship with battery energy consumption. This makes it difficult to guarantee the completeness of the model in meeting heating needs and the consistency of modeling accuracy.

[0008] For example, patent CN 113128110 B discloses a lumped parameter model for a heat pump air conditioning system. This model only models the heat pump air conditioning system based on the working fluid state at the compressor, condenser, and evaporator in the heat pump air conditioning system cycle, without considering the thermodynamic state of the passenger compartment outside the heat pump air conditioning system in the thermal chain loop.

[0009] The heat transfer process in a heat pump air conditioning system involves complex thermodynamic mechanisms. Due to the high-order characteristics of the system, most of the established models are distributed, and their complexity is not suitable for the design and development of control algorithms. Currently, there is no research in the existing technology on constructing a low-computational-cost, high-accuracy mathematical model for a control-oriented heat pump air conditioning system.

[0010] Secondly, most of the publicly disclosed control strategies in existing technologies are rule-based and do not utilize the mechanistic characteristics within the mathematical model. However, establishing a model-based optimization controller can not only ensure that the controller meets the thermodynamic properties of the heat pump air conditioning system, but also further improve the energy-saving effect of traditional rule-based controllers by using global optimization principles as guidance.

[0011] For example, patent CN 109080406 A discloses a heat pump vehicle air conditioning system and its control method that incorporates thermal management. This control method includes seven operating modes: heating mode, defrosting mode, outdoor unit defrosting mode, cooling mode, dehumidification and defogging mode, defrosting and defogging mode, and battery cooling mode. The air conditioning system achieves different functions by switching between different operating modes, thus improving the energy efficiency ratio of the air conditioning system. Another example is patent CN 106004329 A, which discloses an ultra-low temperature heat pump air conditioning system and its control method for new energy vehicles. This invention can achieve cooling, heating, dehumidification, and defrosting modes by adjusting the circuit connection method, ensuring the thermal comfort of passengers in ultra-low temperature environments.

[0012] Although the existing control strategy achieves comprehensive control of the air conditioning mode, it fails to fully tap the energy-saving potential of the heat pump air conditioning system because it does not consider the thermodynamic properties of the model, resulting in limited improvement in heating performance and poor control effect.

[0013] Finally, with the development of intelligent connected technologies, combining the energy-saving control methods of heat pump air conditioning systems with system and network information by utilizing network-based pre-planning information can further improve the energy-saving effect of the system by planning the control strategy of the air conditioning system in advance.

[0014] For example, patent CN 113128110 B quantifies the impact of vehicle speed changes on battery heat generation and heat pump air conditioning heat exchange. It utilizes model predictive control strategies to optimize the coupled model of the electric vehicle battery pack, reducing system energy consumption while shortening heating time. Although this invention considers the impact of vehicle speed on vehicle thermal management, it only establishes the relationship between vehicle speed, required power, and the battery. It primarily focuses on battery thermal management, with the main control target being battery pack temperature. It does not address the control of cabin temperature and its modeling of the heat pump air conditioning system power is not comprehensive enough.

[0015] Currently, only a portion of the published patents address the impact of connected information on vehicle cabin thermal management. Therefore, designing an optimized controller for the heat pump air conditioning system of connected electric vehicles in extremely cold environments, enabling it to meet cabin temperature requirements while reducing energy consumption, is a pressing issue for researchers in this field. Summary of the Invention

[0016] To address the shortcomings of the existing technologies, this invention provides an energy-saving control method, controller, and vehicle for a heat pump air conditioning system in a connected electric vehicle. By combining intelligent network information, this invention ensures that the cabin temperature is maintained at the set temperature while considering the impact of future vehicle speed changes on the efficiency of the heat pump air conditioning system, thereby improving the energy economy of the heat pump air conditioning system under heating conditions.

[0017] Referring to the accompanying drawings, the technical solution of this invention is as follows:

[0018] In a first aspect, the present invention discloses an energy-saving control method for a heat pump air conditioning system. The control method is to establish a mathematical model of the heat pump air conditioning system for energy-saving control, take the minimum energy consumption of the heat pump air conditioning system and the minimum deviation between the target temperature and the actual temperature of the vehicle cabin as the optimization objective, and take the limitations of compressor speed, blower air intake, cabin temperature and evaporator pressure as constraints. The optimal compressor speed and blower air intake are solved by dynamic programming algorithm as control variables. The designed online rule-based control strategy for the heat pump air conditioning system is as follows:

[0019] The mathematical model of the heat pump air conditioning system is based on the compressor speed, blower air intake, and vehicle speed information read from intelligent connected data at a certain frequency. It calculates the cabin temperature, evaporator pressure, and heat pump air conditioning system power, and updates synchronously with the reading frequency of the vehicle speed information of the intelligent connected electric vehicle.

[0020] In the online rule-based control strategy of the heat pump air conditioning system, the working mode is divided according to the deviation between the target temperature and the actual temperature of the vehicle cabin. Based on the sensitivity of the heat pump air conditioning system to its own vehicle speed, the compressor speed and blower intake volume are controlled according to the divided vehicle speed range in the corresponding working mode.

[0021] Furthermore, in the online rule-based control strategy of the heat pump air conditioning system, the operating modes are divided according to the deviation between the target temperature and the actual temperature of the vehicle cabin, including:

[0022] Heating mode: When the cabin temperature is lower than the target cabin temperature and the difference between the two is ≥ t1, the cabin is in an extremely cold state and needs to be heated quickly to raise the temperature to the preset range. The function of this mode is to provide rapid heating. At this time, the compressor and blower are running at the highest power.

[0023] Continuous heating mode: When the cabin temperature is lower than the target cabin temperature, and t2≤difference<t1, this mode is for continuous heating. At this time, the compressor runs at medium power as a transition to low power operation, while the blower continues to run at maximum power.

[0024] Continue heating mode: When the cabin temperature is lower than the target cabin temperature, and t3 ≤ the difference between the two < t2, this mode continues to heat up. At this time, the compressor switches to the lowest power operation, while the blower still operates at the highest power to ensure that the cabin temperature enters the next mode.

[0025] Adjustment mode: When the cabin temperature is lower than the target cabin temperature, t4≤difference<t3, this mode transitions to constant temperature mode. At this time, the compressor speed and blower intake volume are adjusted according to the vehicle speed range divided based on the sensitivity of the heat pump air conditioning system to its own vehicle speed.

[0026] Constant temperature mode: When the target temperature of the cabin - t5 ≤ the cabin temperature is less than the target temperature of the cabin + t5, the cabin temperature is within the comfortable range. The function of this mode is to maintain a constant temperature in the cabin. At this time, the compressor speed and blower air intake are adjusted according to the vehicle speed range divided based on the sensitivity of the heat pump air conditioning system to its own vehicle speed.

[0027] Overheat mode: When the cabin temperature is greater than the target cabin temperature + 0.5℃, this mode is designed to prevent the cabin temperature from overheating. In this mode, the compressor and blower operate at the lowest power consumption.

[0028] Furthermore, in the adjustment mode and constant temperature mode, based on the sensitivity of the heat pump air conditioning system to its own vehicle speed, the vehicle speed range is divided according to the vehicle speed, and the compressor speed and blower air intake are controlled to be inversely proportional to the vehicle speed.

[0029] Furthermore, the process of establishing a mathematical model for a heat pump air conditioning system oriented towards energy-saving control includes:

[0030] Build a high-precision physical simulation model of a heat pump air conditioning system;

[0031] A mathematical model of a heat pump air conditioning system is established, consisting of a cabin temperature model, an evaporator pressure model, and a heat pump air conditioning energy consumption model.

[0032] Calibrate the parameters of the mathematical model of the heat pump air conditioning system;

[0033] Verify the effectiveness of the mathematical model for the heat pump air conditioning system.

[0034] Furthermore, a high-precision physical simulation model of the heat pump air conditioning system was built on the Dymola platform, as detailed below:

[0035] The compressor was modeled using the EffCompressor module, the gas cooler and evaporator were modeled using the MPET.MoistAirVLEFluid.DetailedCrossFlowHX module, the regenerator was modeled using the TubeInTube.VLEFluidVLEFluid.ParallelFlowHX module, the expansion valve was modeled using the OrificeValve module, and the blower was modeled using the SimpleFan module.

[0036] Furthermore, the cabin temperature model is as follows:

[0037]

[0038] Where: α1, α2, α3, and τ1 are model parameters, and T c For the cabin temperature, T a，c，i To regulate the temperature of the air supplied to the vehicle cabin, T represents the air intake volume of the blower. c，s For the temperature of the vehicle cabin exterior, T c，i Temperature of the equipment inside the vehicle cabin;

[0039] The evaporator pressure model is as follows:

[0040]

[0041] Where: p e Evaporator pressure, To match its own speed v veh Positive correlation with evaporator intake air volume, T amb For ambient temperature, T a，e，o T represents the evaporator outlet air temperature. a，c，i For the cabin air temperature, T c For the temperature inside the vehicle, ω represents the air intake volume of the blower. c α4 represents the compressor speed, and α5, α6, α7, and τ2 are model parameters;

[0042] The energy consumption model for heat pump air conditioners includes a compressor power consumption model and a blower power model, wherein:

[0043] The compressor power consumption model is as follows:

[0044]

[0045] Where: P c For compressor power, ω c p is the compressor speed. e τ represents the evaporator pressure, and β1, β2, and τ4 are model parameters.

[0046] The blower power model is as follows:

[0047]

[0048] Where: P b For blower power, β3 represents the blower's air intake volume, and β4 represents the model parameters.

[0049] Furthermore, the specific parameters for calibrating the mathematical model of the heat pump air conditioning system are as follows:

[0050]

[0051]

[0052]

[0053] P c (k)=f(ω c (k), p e (k))

[0054]

[0055] Where k represents the k-th sampling time, T c For the cabin temperature, T a，c，i To regulate the temperature of the air supplied to the vehicle cabin, T represents the air intake volume of the blower. c，s For the temperature of the vehicle cabin exterior, T c，i p represents the temperature of the equipment inside the vehicle cabin. e v is the evaporator pressure. veh For its own speed, T a，e，o ω represents the evaporator outlet air temperature. c P is the compressor speed. c P is the compressor power. b This refers to the power of the blower.

[0056] Furthermore, taking the minimum energy consumption of the heat pump air conditioner and the minimum deviation between the target temperature and the actual temperature of the vehicle cabin as the optimization objective, and using the limitations of compressor speed, blower intake air volume, cabin temperature, and evaporator pressure as constraints, the process of using dynamic programming algorithm to solve for the optimal compressor speed and blower intake air volume as control variables is as follows:

[0057] The temperature regulation of the vehicle cabin in a heat pump air conditioning system is mainly achieved through soft constraints added to the terminal constraints and transfer costs. The energy consumption of the heat pump air conditioning system mainly consists of compressor energy consumption and blower energy consumption. Therefore, the cost function is constructed as follows:

[0058]

[0059] in:

[0060] G N (x N The terminal constraint takes the following form:

[0061] G N (x N )=γ(T c -T c，set ) 2

[0062] T c，set γ represents the target temperature of the vehicle cabin, and γ is a weighting factor.

[0063] L(x k u k The cost function is as follows:

[0064]

[0065] g(v veh (k) is a normalized numerical function related to its own vehicle speed, used to reflect the system's speed sensitivity, and its specific form is shown in the following formula:

[0066]

[0067] s c (k) is a soft constraint related to the cabin temperature, a numerical function relating the cabin temperature to the target cabin temperature, where T is the cabin temperature. c Distance from target temperature T in the vehicle cabin c，set The farther away, s c The higher the value, the more severe the penalty; conversely, the lower the value, the more severe the penalty. c The smaller the number, the lighter the penalty;

[0068] Calculate g(v) using the aforementioned formula. veh (k) When optimizing the objective, the following constraints must be met:

[0069] (1) The compressor speed is within its hardware constraints;

[0070] (2) The air intake volume of the blower is within its hardware constraints;

[0071] (3) The cabin temperature is within the preset range;

[0072] (4) The evaporator pressure is within the preset range;

[0073] The mathematical description of the constraints is as follows:

[0074]

[0075]

[0076] ω c，min ≤ω c (k)≤ω c，max

[0077]

[0078] T c，min ≤T c (k)≤T c，max

[0079] p e，min ≤p e (k)≤p e，max

[0080] In the above set of mathematical descriptions, f c The state transition equation has the following specific form:

[0081]

[0082] Wherein: T c For the cabin temperature, T a，c，i To regulate the temperature of the air supplied to the vehicle cabin, T represents the air intake volume of the blower. c，s For the temperature of the vehicle cabin exterior, T c，i Let α1, α2, α3, and τ1 represent the temperature of the equipment inside the vehicle cabin, and α1, α2, α3, and τ1 represent model parameters.

[0083] f e The state transition equation has the following specific form:

[0084]

[0085] Where: p e Evaporator pressure, To match its own speed v veh Positive correlation with evaporator intake air volume, T amb For ambient temperature, T a，e，o T represents the evaporator outlet air temperature. a，c，i For the cabin air temperature, T c For the temperature inside the vehicle, ω represents the air intake volume of the blower. c α4 represents the compressor speed, and α5, α6, α7, and τ2 are model parameters;

[0086] ω c，min ω represents the minimum compressor speed. c，max This represents the maximum compressor speed. This is the minimum air intake volume for the blower. p is the maximum air intake volume of the blower. c，min T represents the minimum cabin temperature. c，max p represents the maximum temperature inside the vehicle cabin. e，min p is the minimum pressure of the evaporator. e，max This represents the maximum pressure of the evaporator.

[0087] Secondly, the present invention discloses an energy-saving controller for a heat pump air conditioning system, wherein the controller receives vehicle speed information from intelligent connected vehicles at a certain frequency to realize the energy-saving control method for the heat pump air conditioning system as described above.

[0088] Thirdly, the present invention discloses an electric vehicle, wherein the heat pump air conditioning system of the electric vehicle contains an energy-saving controller for the heat pump air conditioning system as described above.

[0089] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0090] 1. This invention establishes a model of an electric vehicle heat pump air conditioning system for energy-saving control. By constructing regression equations for key system state variables, including cabin temperature and evaporator pressure, this model not only describes the complex dynamic heat transfer process of the heat pump air conditioning system but also facilitates real-time calculation. Furthermore, by comparing the model with Dymola simulation data, the accuracy of the electric vehicle heat pump air conditioning system model in extremely cold environments has been verified, providing a foundation for the development and design of a real-time optimization control system for electric vehicles.

[0091] 2. This invention reveals the energy-saving mechanism of a heat pump air conditioning system model and establishes the connection between the energy consumption of the heat pump air conditioning system and intelligent connected information. First, it fully considers the relationship between external information, including vehicle speed, and internal thermodynamics in the modeling process. Second, it integrates intelligent connected vehicle speed information into the temperature controller of the electric vehicle heat pump air conditioning system in extremely cold environments, making it possible to consider the impact of future vehicle speed changes on the heating performance and system energy consumption of the heat pump air conditioning system during the optimization solution process.

[0092] 3. This invention addresses optimization problems with known global road condition information. It constructs a dynamic programming algorithm with temperature tracking and system energy consumption as optimization objectives, so that the system minimizes energy consumption under the condition of satisfying temperature tracking, and quantitatively evaluates the maximum energy-saving potential of the heat pump air conditioning system.

[0093] 4. This invention addresses the need for real-time computing with low computational cost in vehicles. Based on the principles of dynamic programming and offline global optimal strategies, it designs a rule-based online temperature controller with low computational cost. Attached Figure Description

[0094] Figure 1 The flowchart below illustrates the design and implementation process of the energy-saving control method for a heat pump air conditioning system in Example 1.

[0095] Figure 2 This is a flowchart illustrating the process of establishing a control-oriented mathematical model for a heat pump air conditioning system in Example 1.

[0096] Figure 3 Schematic diagram of the heat chain-power chain loop of a heat pump air conditioning system;

[0097] Figure 4 This is a schematic diagram of the heat load in the vehicle compartment in Example 1;

[0098] Figure 5 This is a schematic diagram of the heat exchange principle of the evaporator in Example 1;

[0099] Figure 6 This is a schematic diagram illustrating the relationship between vehicle speed and evaporator intake volume in Example 1.

[0100] Figure 7 This is a schematic diagram of the variable relationships in the mathematical model of the heat pump air conditioning system in Example 1;

[0101] Figure 8 This is a schematic diagram illustrating the verification results of the predicted values ​​of the mathematical model of the heat pump air conditioning system in Example 1.

[0102] Figure 9 In Example 1, the soft constraint related to the cabin temperature is represented by a numerical function s, which characterizes the difference between the cabin temperature and the target cabin temperature. c (k) line diagram;

[0103] Figure 10 This is a schematic diagram comparing the cabin temperature curve and power consumption curve of an electric vehicle under NEDC conditions in a low-temperature environment using dynamic programming algorithm and traditional PID control method, as shown in Example 1.

[0104] Figure 11 This is a schematic diagram comparing the cabin temperature curve and power consumption curve of an electric vehicle operating under SC03 conditions in a low-temperature environment, using dynamic programming algorithm and traditional PID control method, as shown in Example 1.

[0105] Figure 12 This is a schematic diagram comparing the cabin temperature curves and power consumption curves of an electric vehicle operating under UDDS conditions in a low-temperature environment using dynamic programming algorithm and traditional PID control method, as shown in Example 1.

[0106] Figure 13 The above is a flowchart of the online rule-based control strategy for the heat pump air conditioning system in Example 1.

[0107] Figure 14 The above describes the cabin temperature and power consumption curves of an electric vehicle operating under NEDC conditions in a low-temperature environment, using an online, rule-based control strategy for a heat pump air conditioning system.

[0108] Figure 15 The above describes the cabin temperature and power consumption curves of an electric vehicle operating under SC03 conditions in a low-temperature environment, using an online regularized control strategy for a heat pump air conditioning system.

[0109] Figure 16 The figures for Example 1 are the cabin temperature and power consumption curves of an electric vehicle operating under UDDS (Under Low Temperature Regulations) with an online, regularized control strategy for a heat pump air conditioning system. Detailed Implementation

[0110] To clearly and completely describe the technical solution and its specific working process of the present invention, the specific embodiments of the present invention are as follows, in conjunction with the accompanying drawings:

[0111] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0112] Example 1:

[0113] This embodiment addresses electric vehicles operating in extremely cold environments, and discloses an energy-saving control method for a heat pump air conditioning system, incorporating intelligent connected vehicle speed information. Figure 1 As shown, the design and implementation process of the control method is as follows:

[0114] S1: Collects vehicle speed information from intelligent connected vehicle data.

[0115] In step S1, the intelligent connected electric vehicle reads its own speed information from the intelligent connected data once per second and transmits the read speed information to the on-board controller of the heat pump air conditioning system, so as to provide data support for control quantities including compressor speed and blower intake volume.

[0116] S2: Establish a control-oriented mathematical model for the heat pump air conditioning system.

[0117] The mathematical model constructed in step S2 is as follows: based on the two control quantities of compressor speed and blower intake volume, as well as the vehicle speed information from the intelligent connected vehicle data, the cabin temperature, evaporator pressure and heat pump air conditioning system power of the electric vehicle are calculated and updated synchronously with the frequency of reading the vehicle speed information of the intelligent connected electric vehicle (1 second in this embodiment).

[0118] like Figure 2 As shown, the mathematical model establishment process for the control-oriented heat pump air conditioning system is as follows:

[0119] S201. Build a high-precision physical simulation model of the heat pump air conditioning system.

[0120] In step S201, the heat pump air conditioning system's thermal chain loop is designed, the selection and parameter setting of key components are completed, and a high-precision physical model is built on the Dymola platform. The specific process is as follows:

[0121] This first embodiment establishes a heat pump air conditioner's thermal chain loop and describes its heat exchange process with the external environment and the vehicle cabin. Details are as follows:

[0122] like Figure 3 As shown, the heat chain of a heat pump air conditioning system includes a heat circulation path within the vehicle cabin and a heat transfer path to the environment. Key components in the heat chain loop of a heat pump air conditioning system include: a compressor, a gas cooler, a regenerator, an expansion valve, and an evaporator. The heat exchange process of the working fluid in the heat chain loop of a heat pump air conditioning system is as follows: First, the CO2 working fluid is isentropically compressed by the compressor and enters the gas cooler, releasing heat to the cabin air through the ventilation system. Then, it enters the regenerator, where it exchanges heat with the working fluid from the separator, further reducing its temperature, and then enters the expansion valve for isenthalpic expansion. Finally, it enters the evaporator to evaporate and absorb heat, exchanging heat with the external environment.

[0123] Based on the aforementioned heat pump air conditioning thermal chain loop, a high-precision physical simulation model of the heat pump air conditioning system was built on the Dymola platform. Details are as follows:

[0124] The compressor was modeled using the EffCompressor module for easy calculation of instantaneous power consumption. The gas cooler and evaporator were modeled using the MPET.MoistAirVLEFluid.DetailedCrossFlowHX module, the regenerator using the TubeInTube.VLEFluidVLEFluid.ParallelFlowHX module, the expansion valve using the OrificeValve module, and the blower using the SimpleFan module. The key components of the above simulation model and their corresponding selections and parameters are shown in Table 1 below:

[0125] Table 1

[0126]

[0127] S202. Establish a cabin temperature model.

[0128] In step S202, the heat load of the vehicle compartment is analyzed based on the heat balance method. Considering the heat load and key variables that have a significant impact on the temperature of the vehicle compartment, a simplified regression model for the temperature of the vehicle compartment is obtained, which is the vehicle compartment temperature model. The specific process is as follows:

[0129] The research object of this invention is a small electric vehicle, such as... Figure 4 As shown, the heat load in the vehicle cabin includes: metabolic load, radiation load, environmental load, ventilation load, and heat pump air conditioning load.

[0130] Analysis using the heat balance method shows that the heat energy gained by the cabin per unit time is equal to the sum of all heat loads in the cabin. The calculation formula is as follows:

[0131]

[0132] In the above formula (1), This indicates the net total heat load of the vehicle compartment. This represents the total heat load in the vehicle compartment. All the above values ​​represent the heat energy obtained per unit time. ∑ is the summation symbol, representing the sum of various types of heat loads.

[0133] The heat load in the vehicle cabin generally includes the following types:

[0134] 1) Metabolic load

[0135] Passengers' internal metabolic activities continuously generate heat and transfer it into the cabin air. This heat is called metabolic load, and its calculation formula is as follows:

[0136]

[0137] In the above formula (1), R represents metabolic load, n represents the number of crew members, and R represents the number of crew members. me This represents the occupant's metabolic heat production rate, and A represents the occupant's body surface area.

[0138] 2) Radiation load

[0139] Radiative load is caused by solar radiation, mainly by direct solar radiation load. Diffuse radiation load and reflected radiation load The composition, and its calculation formula is:

[0140]

[0141] Direct radiation refers to the portion of solar radiation that directly strikes the surface of a vehicle body; direct radiation load. The calculation formula is:

[0142]

[0143] In the above formula (4), S is the surface area of ​​the vehicle cabin, and τ is the transmittance of the surface unit. θ represents the thermal gain per unit area from direct radiation, and θ is the angle between the ground normal and the sun.

[0144] Diffuse radiation refers to solar radiation that reaches the vehicle body through non-direct rays, such as through diffuse reflection from clouds. The calculation formula is:

[0145]

[0146] In the above formula (5), S is the surface area of ​​the vehicle cabin, and τ is the transmittance of the surface unit. This represents the thermal gain of diffuse radiation per unit area.

[0147] Reflected radiation refers to the portion of solar radiation reflected from the ground onto the vehicle's surface; reflected radiation load. The calculation formula is:

[0148]

[0149] In the above formula (6), S is the surface area of ​​the vehicle cabin, and τ is the transmittance of the surface unit. This represents the thermal gain of reflected radiation per unit area.

[0150] 3) Environmental load

[0151] The heat load caused by the temperature difference between ambient air and cabin air is called the environmental load. Heat exchange between the ambient air and cabin air is mainly accomplished through the metal outer shell of the cabin. The calculation formula is:

[0152]

[0153] In the above formula (7), S is the surface area of ​​the vehicle compartment, U is the overall heat transfer coefficient of the surface unit, and T is the total heat transfer coefficient of the surface unit. c，s For the temperature of the vehicle cabin exterior, T c This refers to the temperature inside the vehicle cabin.

[0154] 4) Ventilation load

[0155] During vehicle operation, the breathing of occupants causes an increase in carbon dioxide concentration inside the cabin. To maintain air quality, a suitable amount of fresh air is introduced into the cabin. The heat load resulting from the introduction of fresh air is called the ventilation load. Assuming the temperature and relative humidity of the introduced fresh air are the same as the ambient air, and the inflow and outflow mass flow rates remain constant (i.e., ensuring stable air pressure inside the cabin), then the ventilation load is... The calculation formula is:

[0156]

[0157] In the above formula (8), H is the mass flow rate of fresh air introduced into the cabin. o H represents the enthalpy of the environment. i This indicates the enthalpy of the vehicle compartment.

[0158] 5) Heat pump air conditioning load

[0159] The heat pump air conditioning load comes from the heating effect of the heat pump air conditioner. When the heat pump air conditioning load is balanced with other heat loads, the cabin temperature remains constant.

[0160] For the purpose of simplifying calculations, and based on a comprehensive analysis of the heat loads of the various types of vehicle compartments mentioned above, the following assumptions are considered when establishing the control-oriented mathematical model:

[0161] ① Assume there are no occupants in the cabin, and ignore metabolic and ventilation loads;

[0162] ②The vehicle's air return ratio is 0, meaning that all the air intake in the vehicle cabin comes from the vehicle cabin itself;

[0163] ③ The radiation load only considers the direct radiation load, ignoring diffuse radiation and reflected radiation;

[0164] ④ The temperature of the vehicle cabin exterior and the temperature of the vehicle cabin interior are slow variables compared to the temperature of the vehicle cabin, and are considered as externally measurable and known variables.

[0165] At this point, the heat load affecting the cabin temperature includes direct radiation load, environmental load, and heat pump air conditioning load. Therefore, the following cabin temperature model can be established: The cabin temperature is determined by the cabin supply air temperature T.a，c，i Temperature of the vehicle cabin exterior T c，s Temperature T of interior equipment in the vehicle cabin (such as steering wheel, dashboard, interior trim, seats, etc.) c，i and cabin temperature T c It consists of four parts, of which:

[0166] Car cabin air supply temperature T a，c，i The temperature of the air delivered into the cabin by the blower is used to reflect the heat pump air conditioning load.

[0167] Car cabin exterior temperature T c，s Used to reflect direct radiation load and environmental load;

[0168] Temperature T inside the vehicle cabin c，i Used to reflect direct radiation load.

[0169] The load of a heat pump air conditioner is also related to the mass flow rate of the blown hot air, i.e., the air intake volume of the blower. From this, we can derive the vehicle cabin temperature dynamics equation, i.e., the vehicle cabin temperature model is:

[0170]

[0171] In the above formula (9), α1, α2, α3 and τ1 are model parameters, which are obtained by calibration using the Dymola high-precision physical model.

[0172] S203. Establish an evaporator pressure model.

[0173] In step S203, based on the thermodynamic energy balance equations of the working fluid side and the air side of the evaporator, and considering the key variables affecting the evaporator pressure, the evaporator pressure regression model is obtained, which is the evaporator pressure model. The specific process is as follows:

[0174] In the evaporator pressure model of this embodiment, the evaporator adopts a parallel flow heat exchanger, and the evaporator pressure model is based on the following assumptions:

[0175] ① Assume that the heating cycle is an ideal cycle, that is, ignore the pressure drop of the working fluid in the evaporator, and the working fluid exists only in two phases, and do not consider the superheated region;

[0176] ②Assume that the mass flow rate of the working fluid entering and leaving the evaporator remains constant;

[0177] ③ Assume that the thermodynamic properties of the working fluid are uniformly distributed in the evaporator, and take the average value of the thermodynamic properties of the entire heat exchange process;

[0178] ④ Assume that the heat transfer from the working fluid to the evaporator wall is instantaneous.

[0179] like Figure 5As shown, during the operation of the evaporator, the working fluid and air undergo sufficient heat exchange at the evaporator, and the temperature of the working fluid in the evaporator is T. r，e and evaporator wall temperature T w，e Based on the properties of the working fluid on the working fluid side (working fluid mass flow rate) The specific enthalpy of the working fluid at the working fluid inlet (h4) and the specific enthalpy of the working fluid at the working fluid outlet (h1) and the properties of the air on the air side (mass flow rate at the air inlet). Temperature T at the air inlet a，e，i Therefore, the thermodynamic energy balance equation for the working fluid can be obtained as follows:

[0180]

[0181] In the above formula (10), V e Let ρ be the volume of refrigerant inside the evaporator, ρ be the average density of the working fluid, and u be the specific internal energy of the working fluid. This refers to the heat transferred from the evaporator wall to the working fluid per unit time. h1 is the mass flow rate of the working fluid on the working fluid side, h4 is the specific enthalpy of the working fluid at the inlet on the working fluid side, and h1 is the specific enthalpy of the working fluid at the outlet on the working fluid side.

[0182] According to the definition of specific enthalpy, the average specific enthalpy h of the working fluid in the evaporator is equal to the specific internal energy u of the working fluid and the pressure p of the working fluid. e The sum of the density ratios ρ is calculated using the following formula:

[0183]

[0184]

[0185] In the above formula (11), h is the average specific enthalpy of the working fluid in the evaporator, u is the specific internal energy of the working fluid, and p e Let ρ be the pressure of the working fluid, and ρ be the density of the working fluid. ρ is the average porosity of the working fluid. l h is the density of the working fluid in its saturated liquid state. l ρ is the specific enthalpy of the working fluid in its saturated liquid state. g h is the density of the working fluid in its saturated vapor state. g Let p be the specific enthalpy of the working fluid in its saturated vapor state, and let p be the density, average porosity, density, specific enthalpy, and vapor density of the working fluid, all of which depend only on the pressure p of the working fluid. e .

[0186] Substituting the above formula (11) into the aforementioned formula (10), we obtain the thermodynamic energy balance equation of the working fluid as follows:

[0187]

[0188] Similarly, the energy balance equation for the evaporator wall can be obtained:

[0189]

[0190] In the above formula (13), M w，e Let T be the mass of the evaporator wall metal, c be the specific heat of the evaporator wall metal, and T be the mass of the evaporator wall metal. w，e This refers to the evaporator wall temperature. This represents the amount of heat transferred from the outside air to the evaporator wall per unit time. This represents the amount of heat transferred from the evaporator wall to the working fluid per unit time.

[0191] Since it is assumed that the heat transfer process from the working fluid to the evaporator wall is instantaneous, the temperatures of the two can be approximated as equal, and the specific formula is as follows:

[0192]

[0193] In the above formula (14), T r，e This refers to the temperature of the working fluid in the evaporator.

[0194] The heat exchange efficiency of the working fluid in the evaporator is related to the heat exchange between the evaporator wall and the ambient air. The specific calculation formula is as follows:

[0195]

[0196] In the above formula (15), This refers to the heat transferred from the evaporator wall to the working fluid per unit time. c is the mass flow rate of the air at the evaporator inlet. p，a T is the specific heat capacity of air. a，e，i T represents the air temperature at the evaporator inlet. a，e，o This refers to the air temperature at the evaporator outlet.

[0197] Combining the above equations, we can obtain the thermodynamic mechanism equation as follows:

[0198]

[0199] Analyze the above thermodynamic mechanism equation (16): where the volume of refrigerant in the evaporator is V e Specific heat capacity of air c p，a and the air temperature T at the evaporator inlet a，e，i (equal to ambient temperature T) amb All are constants. The left side of the above thermodynamic mechanism equation (16) is a constant that depends only on the evaporator pressure p. e The relevant partial derivative can be obtained using the evaporator pressure p. ePolynomial fitting; the right side of the above thermodynamic mechanism equation (16) shows the evaporator inlet flow rate. It is related to the vehicle speed v veh The relevant variable is the evaporator outlet air temperature T. a，e，o It is a measurable variable, the working fluid mass flow rate. With compressor speed ω c The enthalpy change value can be fitted using the heat output of the vehicle cabin.

[0200] The kinetic expression of the evaporator, i.e., the evaporator pressure model, can be represented by the following formula:

[0201]

[0202] In the above formula (17), the evaporator intake air volume It is related to the vehicle speed v veh The positive correlation function, in the form of: Figure 6 As shown; ω represents the air intake volume of the blower. c The compressor speed; p e The pressure is the evaporator pressure; model parameters α4, α5, α6, α7, and τ2 were obtained through high-precision physical model calibration using Dymola; T a，c，i Temperature of the air supplied to the vehicle cabin; T c This refers to the temperature inside the vehicle cabin.

[0203] S204. Establish a heat pump air conditioning energy consumption model.

[0204] In step S204, based on the energy consumption mechanism of heat pump air conditioning, the energy consumption model of the heat pump air conditioning system is obtained. The dwell and stop process is as follows:

[0205] In a heat pump air conditioning system, the two main energy-consuming devices are the compressor and the blower. The energy consumption of the condenser, evaporator, etc., which are heat exchange components, is negligible. Therefore, the power P of the heat pump air conditioning system is... hp The calculation formula is as follows:

[0206] P hp =P c +P b ······················(18)

[0207] In the above formula (18), P c For compressor power, P b This refers to the power of the blower.

[0208] The process of the compressor doing work on the working fluid involves the working fluid moving from the compressor's suction side to the compressor's discharge side; that is, the working fluid moves from the evaporator at pressure p. e to condenser pressure p cAssuming the process is isentropic, the formula for the compressor's isentropic work, i.e., its theoretical work, is:

[0209]

[0210] In the above formula (19), W i p represents the theoretical work done by the compressor. e p is the evaporator pressure. c Where γ is the condenser pressure, γ is the heat capacity ratio, and m r R is the molecular weight of the working fluid. r T is the specific gas constant of the working fluid. e This refers to the temperature of the working fluid in the evaporator (i.e., the temperature of the working fluid on the suction side of the compressor).

[0211] Since the actual compression process of the working fluid by a compressor is not an isentropic process, an isentropic efficiency η is defined. i The ratio of theoretical work to actual work is expressed by the following formula:

[0212]

[0213] Under steady-state conditions, the actual power of the compressor is:

[0214]

[0215] In the above formula (21), P c，a This indicates the actual power of the compressor (unit: W). This indicates the mass flow rate of the working fluid (unit: kg / s).

[0216] In the above formula (21), the isentropic efficiency η i The heat capacity ratio of the working fluid γ and the gas constant of the working fluid R r All are constants, while the condenser pressure p in this heat pump air conditioning energy consumption model is... c If it remains unchanged, it can also be regarded as a constant term. Therefore, the only variable in formula (21) is the mass flow rate of the working fluid. Evaporator working fluid temperature T e and evaporator pressure p e Among them, the mass flow rate of the working fluid Mainly related to compressor speed ω c Related to the evaporator working fluid temperature T e With evaporator pressure p e Since there is a coupling relationship, the following compressor power consumption model can be established:

[0217]

[0218] In the above formula (22), the model parameters β1, β2 and τ4 are obtained by calibration using a high-precision physical model.

[0219] The power of the blower is mainly related to the blower's air intake volume. The following blower power model can be established:

[0220]

[0221] In the above formula (23), the model parameters β3 and β4 are obtained by calibration using a high-precision physical model.

[0222] According to the aforementioned formula (18), the compressor power consumption model represented by formula (22) and the blower power model represented by formula (23) together constitute the energy consumption model of the heat pump air conditioning system.

[0223] The aforementioned "cabin temperature model", "evaporator pressure model", and "heat pump air conditioning energy consumption model" together form the "mathematical model of heat pump air conditioning system oriented towards energy-saving control".

[0224] S205. Parameter calibration and validity verification of mathematical models for heat pump air conditioning systems oriented towards energy-saving control.

[0225] In step S205, the parameters of the mathematical model for the heat pump air conditioning system of electric vehicles for energy-saving control are calibrated and the validity is verified through hybrid modeling of "data-mechanism".

[0226] Using the high-precision physical simulation model of the heat pump air conditioning system of an electric vehicle built on the Dymola platform in step S201, the output results of important variables of the proposed mathematical model of the heat pump air conditioning system and the high-precision physical simulation model of the heat pump air conditioning system are compared at an ambient temperature of -10℃. This verifies the accuracy of cabin temperature, evaporator pressure, cabin air supply temperature, compressor power and blower power under extremely cold climate conditions.

[0227] The mathematical model of the heat pump air conditioning system for energy-saving control proposed in this embodiment is a discrete-time model with two state variables x = [T]. c p e ] T Two control inputs A system outputs y = [T] a，c，i ], four measurement inputs v = [T c，i T c，s v veh T a，e，o ] T The meanings of each variable in the mathematical model of the heat pump air conditioning system are detailed in Table 2 below:

[0228] Table 2

[0229]

[0230] In this first embodiment, the relationships between the variables in the mathematical model of the heat pump air conditioning system for energy-saving control are as follows: Figure 7 As shown.

[0231] The mathematical model of a heat pump air conditioning system for energy-saving control is as follows:

[0232]

[0233]

[0234]

[0235]

[0236]

[0237] In the above formulas (24)-(28), k represents the evaporator intake air volume at the kth sampling time. Subject to vehicle speed v veh (k) has an effect, so formulas (24)-(28) can be written as:

[0238]

[0239]

[0240]

[0241] P c (k)=f(ω c (k), p e (k)).................(32)

[0242]

[0243] The accuracy of the mathematical model of the heat pump air conditioning system for energy-saving control was verified using the high-precision physical simulation model of the electric vehicle heat pump air conditioning system built on the Dymola platform in step S201. The specific verification process is as follows:

[0244] At an ambient temperature of -10℃, a Dymola model was excited using random input signals as compressor speed and blower air intake signals, with the vehicle speed signal being a random sequence from 0 to 120 km / h. After obtaining the simulation results, each variable was sampled at a frequency of 1 Hz to obtain data used to identify unknown parameters in the formulas.

[0245] Parameter identification using the least squares method in MATLAB: The goal of parameter identification is to select a set of parameters α. i (i = 1, 2, ..., 9), βj (j = 1, 2, 3, 4) and τ k (k = 1, 2, 3, 4) is used to make the estimated system variable values ​​closest to the Dymola simulation results. Substituting the identified parameters into formulas (24)-(28) yields the specific mathematical model. Figure 8 As shown, the mathematical model of the heat pump air conditioning system for energy-saving control predicts the system's output for the next 600 seconds based solely on the initial values ​​given at the initial moment, including: blower power P. b Compressor power P c Car cabin air supply temperature T a，c，i Evaporator pressure p e and cabin temperature T c The values ​​of the variables, including those mentioned above, show a high degree of consistency between the predicted and simulated values ​​in terms of line overlap.

[0246] The root mean square error between the predicted values ​​of the mathematical model of the heat pump air conditioning system for energy-saving control and the simulated values ​​of the high-precision physical simulation model of the heat pump air conditioning system for electric vehicles was calculated. The results are shown in Table 3 below:

[0247] Table 3

[0248] Model variables Root mean square error unit <![CDATA[ΔT c ]]> 0.0497 ℃ <![CDATA[Δp e ]]> 0.334 bar <![CDATA[T a，c，i ]]> 1.14 ℃ Pc 39.5 W Pb 6.32 W

[0249] As can be seen from the root mean square error results in Table 3 above, the mathematical model of the heat pump air conditioning system for energy-saving control established in step S2 of this embodiment can cope with different vehicle speeds. For key variables such as cabin temperature, compressor power and blower power, it can maintain extremely high accuracy under extremely cold climate conditions, providing a model basis for energy-saving optimization control technology for electric vehicles.

[0250] This first embodiment introduces vehicle speed as an external variable by modeling the intake air volume of the external heat exchanger, serving as a window for integration with intelligent network information. Considering the complexity of the heat transfer mechanism of the heat pump air conditioning system, this first embodiment employs the lumped parameter method for mathematical modeling. The established mathematical model of the electric vehicle heat pump air conditioning system features low order, ordinary differential equation form, limited calibration parameters, and ease of real-time calculation, providing a mathematical model foundation for the design of real-time control systems.

[0251] S3. The optimization objective is to minimize the energy consumption of the heat pump air conditioner and the deviation between the target temperature and the actual temperature of the vehicle cabin. The constraints are the limitations of compressor speed, blower intake air volume, vehicle cabin temperature and evaporator pressure. The optimal compressor speed and blower intake air volume are solved using dynamic programming algorithm as control variables to adjust the heating process.

[0252] In step S3, dynamic programming, a multi-step optimization algorithm, is used. Based on the Bellman optimality principle, dynamic programming is particularly suitable for solving multi-stage decision-making problems. It can obtain the global optimal solution to complex problems, which can then be used as a reference for designing online strategies. Therefore, in this embodiment, dynamic programming is chosen as the basis for designing the energy-saving control strategy of the heat pump air conditioning system.

[0253] Formulas (24) and (25) are chosen as the state transition equations. The optimization objectives are "cabin temperature comfort" (i.e., minimizing the deviation between the target cabin temperature and the actual cabin temperature) and "system energy saving" (i.e., minimizing the energy consumption of the heat pump air conditioning system). The cabin temperature regulation of the heat pump air conditioning system is mainly achieved by the soft constraints added to the terminal constraints and the transfer costs. The energy consumption of the heat pump air conditioning system mainly consists of the energy consumption of the compressor and the energy consumption of the blower. Therefore, the cost function is constructed as follows:

[0254]

[0255] In the above formula (34), G N (x N The terminal constraint takes the following form:

[0256] G N (x N )=γ(T c -T c，set )2·················(35)

[0257] In the above formula (35), T c，set γ represents the target temperature of the vehicle cabin, and γ is the weighting factor.

[0258] In the above formula (34), L(x) k u k The cost function is as follows:

[0259]

[0260] In the above formula (36), g(v) veh (k) is a normalized numerical function related to vehicle speed, used to reflect the system's speed sensitivity, and its specific form is shown in the following formula:

[0261]

[0262] In the above formula (36), s c (k) is a soft constraint related to the cabin temperature, and is a numerical function relating to the difference between the cabin temperature and the target cabin temperature, such as... Figure 9 As shown, the cabin temperature Tc Distance from target temperature T in the vehicle cabin c，set The farther away, s c The higher the value, the more severe the penalty; conversely, the lower the value, the more severe the penalty. c The smaller the value, the lighter the penalty. c (k) is a manually adjusted function, mainly aimed at achieving better temperature tracking performance.

[0263] According to the aforementioned formula (37), g(v) is obtained. veh (k) When optimizing the objective, certain constraints need to be met, mainly including:

[0264] (1) The compressor speed is within its hardware constraints;

[0265] (2) The air intake volume of the blower is within its hardware constraints;

[0266] (3) The cabin temperature is within a reasonable range;

[0267] (4) The evaporator pressure is within a reasonable range.

[0268] The mathematical description of the constraints is as follows:

[0269]

[0270]

[0271] ω c，min ≤ω c (k)≤ω c，max

[0272]

[0273] T c，min ≤T c (k)≤T c，max

[0274] p e，min ≤p e (k)≤p e，max ...(38)

[0275] In the above mathematical descriptive formula group (38), f c The state transition equation is in the form of the aforementioned formula (24); f e The state transition equation is in the form of the aforementioned formula (25); ω c，min The minimum compressor speed is 10Hz; ω c，max The maximum compressor speed is 100Hz; The minimum air intake volume for the blower is 0.05 kg / s; The maximum air intake volume of the blower is 0.11 kg / s, T c，min The minimum cabin temperature is -10℃; T c，max The maximum cabin temperature is 35℃; p e，min The minimum evaporator pressure is 17 bar; p e，max The maximum pressure for the evaporator is 35 bar.

[0276] like Figure 10 , Figure 11 and Figure 12 As shown in the first embodiment, the ambient temperature is set to -7℃ and the target temperature to 23.5℃. Using the aforementioned dynamic programming algorithm (DP), simulations are performed under three known operating conditions: NEDC (New European Driving Cycle), SC03, and UDDS (Urban Dynamometer Driving Schedule). The results are compared with those obtained using the traditional PID control method. The results show that, based on global operating condition information, the dynamic programming algorithm DP can achieve a maximum energy saving of 15.17% to 33.39% compared to the traditional PID benchmark controller. The global optimization results show that at the beginning of the operating condition, the optimization strategy tends to allow the compressor to output maximum power to quickly meet the heating demand. Subsequently, the control quantity is adjusted according to the operating condition while ensuring energy saving.

[0277] S4. Design an online rule-based control strategy for the heat pump air conditioning system.

[0278] In step S4, considering limited onboard computing resources, and combining vehicle speed information and cabin temperature setting information, an online rule-based control strategy for the heat pump air conditioning system of electric vehicles in extremely cold environments is formulated.

[0279] As mentioned earlier, dynamic programming can be used to find the global optimum; however, it has a high computational load, making it difficult to apply to online control applications. Therefore, this first embodiment will design a rule-based energy management strategy for online control applications based on the optimization results of dynamic programming.

[0280] The rule-based energy management strategy designed for online control applications should, as far as possible, meet the following objectives:

[0281] (1) Meet the temperature comfort requirements, that is: make the cabin temperature reach the target cabin temperature as soon as possible and keep it near the target cabin temperature value;

[0282] (2) The total energy depletion is likely small;

[0283] (3) Do not switch modes too frequently.

[0284] Based on the conclusions of the aforementioned dynamic programming, such as Figure 13 As shown, the control strategy can be divided into the following six operating modes based on the difference between the cabin temperature and the set target cabin temperature:

[0285] 1. Heating Mode: When the cabin temperature is lower than the target temperature, and the difference is greater than 20°C, the cabin is in an extremely cold state and requires rapid heating to raise the temperature to a reasonable range. Therefore, this mode functions as rapid heating, with both the compressor and blower operating at maximum power.

[0286] 2. Continuous heating mode: When the cabin temperature is lower than the target temperature and the difference between the two is between 10℃ and 20℃, this mode is for continuous heating. At this time, the compressor runs at medium power as a transition to low power operation, while the blower still maintains the highest power operation.

[0287] 3. Continued Heating Mode: When the cabin temperature is lower than the target temperature, and the difference is between 5℃ and 10℃, this mode continues to heat up. At this time, the compressor switches to the lowest power operation, while the blower continues to operate at the highest power to ensure that the cabin temperature enters the next mode.

[0288] IV. Adjustment Mode: When the cabin temperature is lower than the target temperature, and the difference between the two is between 0.5 and 5°C, this mode is used to transition to constant temperature mode. At this time, the compressor speed and blower air intake are adjusted according to the vehicle speed range divided based on the sensitivity of the heat pump air conditioning system to its own vehicle speed.

[0289] V. Constant Temperature Mode: When the cabin temperature is within ±0.5℃ of the target temperature, the cabin temperature is within a comfortable range. Therefore, the function of this mode is to maintain a constant cabin temperature. In this mode, the compressor speed and blower air intake are adjusted according to the vehicle speed range defined by the heat pump air conditioning system's sensitivity to vehicle speed.

[0290] 6. Overheating mode: When the cabin temperature is 0.5°C higher than the target temperature, this mode is designed to prevent the cabin temperature from overheating. In this mode, the compressor and blower operate at the lowest power consumption.

[0291] Furthermore, based on the conclusions of the vehicle speed sensitivity analysis, the control strategy can be divided into the following three intervals according to vehicle speed:

[0292] I. Low-speed range: vehicle speed from 0 to 30 km / h;

[0293] II. Medium speed range: vehicle speed is 30-80 km / h;

[0294] III. High-speed section: speed range of 80-120 km / h.

[0295] The efficiency of the heat pump differs across the three vehicle speed ranges mentioned above, and therefore the control parameters used also differ.

[0296] In both the adjustment mode and constant temperature mode, based on the sensitivity of the heat pump air conditioning system to the vehicle speed, the compressor speed and the air intake of the blower are controlled according to the vehicle speed range, and are inversely proportional to the vehicle speed.

[0297] In this first embodiment, the specific energy management strategy of the rule-based heat pump air conditioning system is shown in Table 4 below.

[0298] Table 4

[0299]

[0300] A controller based on the online rule-based control strategy for the heat pump air conditioning system of an electric vehicle in extremely cold environments was built on the Dymola platform. The controller was verified under three test conditions: NEDC, SC03, and UDDS. The energy consumption of the heat pump air conditioning system under the three test conditions, based on the traditional PID control method, dynamic programming algorithm, and rule-based energy management strategy, is compared in Table 5 below.

[0301] Table 5

[0302]

[0303]

[0304] like Figure 14 , Figure 15 and Figure 16 As shown in the figure, the verification results of the controller based on the online rule-based control strategy of the heat pump air conditioning system show that the designed controller based on the online rule-based control strategy of the heat pump air conditioning system saves 32.36%, 16.27% and 11.54% more energy than the controller based on the traditional PID control method under the three test conditions of NEDC, SC03 and UDDS, respectively. While ensuring temperature tracking, it achieves the energy-saving effect of a near-global offline strategy with low computational cost.

[0305] Example 2:

[0306] This second embodiment discloses a controller used for controlling the heat pump air conditioning system of a connected electric vehicle. The controller receives vehicle speed information from the intelligent connected vehicle at a specified frequency and implements energy-saving control of the heat pump air conditioning system according to the energy-saving control method for the heat pump air conditioning system described in Embodiment 1.

[0307] Example 3:

[0308] This second embodiment discloses an electric vehicle, in which the heat pump air conditioning system includes the controller described in this embodiment, which can realize the energy-saving control method of the heat pump air conditioning system described in this embodiment.

[0309] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

[0310] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. An energy-saving control method for a heat pump air conditioning system of a connected electric vehicle, characterized in that: The control method is to establish a mathematical model of a heat pump air conditioning system for energy-saving control, take minimizing the energy consumption of the heat pump air conditioning system and minimizing the deviation between the target temperature and the actual temperature of the vehicle cabin as the optimization objective, and take the limits of compressor speed, blower air intake, cabin temperature and evaporator pressure as constraints. The optimal compressor speed and blower air intake are solved by dynamic programming algorithm as control variables, and the designed online rule-based control strategy of the heat pump air conditioning system is thus achieved. The mathematical model of the heat pump air conditioning system is based on the compressor speed, blower air intake, and vehicle speed information read from intelligent connected data at a certain frequency. It calculates the cabin temperature, evaporator pressure, and heat pump air conditioning system power, and updates synchronously with the reading frequency of the vehicle speed information of the intelligent connected electric vehicle. In the online rule-based control strategy of the heat pump air conditioning system, the working mode is divided according to the deviation between the target temperature and the actual temperature of the vehicle cabin. Based on the sensitivity of the heat pump air conditioning system to its own vehicle speed, the compressor speed and blower intake volume are controlled according to the divided vehicle speed range in the corresponding working mode. The process of establishing a mathematical model for a heat pump air conditioning system aimed at energy-saving control includes: Build a high-precision physical simulation model of a heat pump air conditioning system; A mathematical model of a heat pump air conditioning system is established, consisting of a cabin temperature model, an evaporator pressure model, and a heat pump air conditioning energy consumption model. Calibrate the parameters of the mathematical model of the heat pump air conditioning system; Verify the validity of the mathematical model of the heat pump air conditioning system; The cabin temperature model is as follows: ； in: , , and For model parameters, For the temperature inside the vehicle, To regulate the temperature of the air supplied to the vehicle cabin, This refers to the air intake volume of the blower. Temperature of the vehicle cabin exterior. Temperature of the equipment inside the vehicle cabin; The evaporator pressure model is as follows: ； in: Evaporator pressure, To match its own speed Positive correlation with evaporator intake air volume For ambient temperature, The evaporator outlet air temperature. To regulate the temperature of the air supplied to the vehicle cabin, For the temperature inside the vehicle, This refers to the air intake volume of the blower. This refers to the compressor speed. , , , and These are model parameters; The energy consumption model for heat pump air conditioners includes a compressor power consumption model and a blower power model, wherein: The compressor power consumption model is as follows: ； Where: P c For compressor power, This refers to the compressor speed. Evaporator pressure, , and These are model parameters; The blower power model is as follows: ； Where: P b For blower power, This refers to the air intake volume of the blower. and These are model parameters; The specific parameters for calibrating the mathematical model of the heat pump air conditioning system are as follows: ； ； ； ； ； in, Indicates the first Each sampling time, For the temperature inside the vehicle, To regulate the temperature of the air supplied to the vehicle cabin, This refers to the air intake volume of the blower. Temperature of the vehicle cabin exterior. Temperature of the equipment inside the vehicle cabin. Evaporator pressure, For its own speed, The evaporator outlet air temperature. P is the compressor speed. c P is the compressor power. b This refers to the power of the blower.

2. The energy-saving control method for a heat pump air conditioning system of a connected electric vehicle as described in claim 1, characterized in that: In the online rule-based control strategy of the heat pump air conditioning system, the operating modes are divided according to the deviation between the target temperature and the actual temperature of the vehicle cabin, including: Heating mode: When the cabin temperature is lower than the target cabin temperature and the difference between the two is ≥ t1, the cabin is in an extremely cold state and needs to be heated quickly to raise the temperature to the preset range. The function of this mode is to provide rapid heating. At this time, the compressor and blower are running at the highest power. Continuous heating mode: When the cabin temperature is lower than the target cabin temperature, and t2≤difference<t1, this mode is for continuous heating. At this time, the compressor runs at medium power as a transition to low power operation, while the blower continues to run at maximum power. Continue heating mode: When the cabin temperature is lower than the target cabin temperature, t3≤difference<t2, this mode continues to heat up. At this time, the compressor switches to the lowest power operation, while the blower still operates at the highest power to ensure that the cabin temperature enters the next mode. Adjustment mode: When the cabin temperature is lower than the target cabin temperature, t4≤difference<t3, this mode transitions to constant temperature mode. At this time, the compressor speed and blower intake volume are adjusted according to the vehicle speed range divided based on the sensitivity of the heat pump air conditioning system to its own vehicle speed. Constant temperature mode: When the target temperature of the cabin - t5 ≤ the cabin temperature is less than the target temperature of the cabin + t5, the cabin temperature is within the comfortable range. The function of this mode is to maintain a constant temperature in the cabin. At this time, the compressor speed and blower air intake are adjusted according to the vehicle speed range divided based on the sensitivity of the heat pump air conditioning system to its own vehicle speed. Overheating mode: When the cabin temperature is greater than the target cabin temperature + 0.5°C, this mode is designed to prevent the cabin temperature from overheating. In this mode, both the compressor and blower operate at the lowest power consumption.

3. The energy-saving control method for a heat pump air conditioning system of a connected electric vehicle as described in claim 2, characterized in that: In both the adjustment mode and constant temperature mode, based on the sensitivity of the heat pump air conditioning system to its own vehicle speed, the vehicle speed range is divided according to the vehicle speed, and the compressor speed and blower air intake are controlled to be inversely proportional to the vehicle speed.

4. The energy-saving control method for a heat pump air conditioning system of a connected electric vehicle as described in claim 1, characterized in that: The optimization objective is to minimize the energy consumption of the heat pump air conditioning system and the deviation between the target temperature and the actual temperature in the vehicle cabin. Constraints are set on compressor speed, blower intake air volume, cabin temperature, and evaporator pressure. The process of using dynamic programming to solve for the optimal compressor speed and blower intake air volume as control variables is as follows: The temperature regulation of the vehicle cabin in a heat pump air conditioning system is mainly achieved through soft constraints added to the terminal constraints and transfer costs. The energy consumption of the heat pump air conditioning system mainly consists of compressor energy consumption and blower energy consumption. Therefore, the cost function is constructed as follows: ； in: For terminal constraints, the specific form is as follows: ； The target temperature for the vehicle cabin, As a weighting factor; The cost function takes the following form: ； It is a normalized numerical function related to its own vehicle speed, used to reflect the system's speed sensitivity, and its specific form is shown in the following formula: ； It is a soft constraint related to the cabin temperature, a numerical function relating the cabin temperature to the target cabin temperature. Distance from target temperature in the vehicle cabin The farther away, s c The higher the value, the more severe the penalty; conversely, the lower the value, the more severe the penalty. c The smaller the number, the lighter the penalty; Calculate using the aforementioned formula. When optimizing the objective, the following constraints must be met: (1) The compressor speed is within its hardware constraints; (2) The air intake volume of the blower is within its hardware constraints; (3) The cabin temperature is within the preset range; (4) The evaporator pressure is within the preset range; The mathematical description of the constraints is as follows: ； In the above set of mathematical descriptions, The state transition equation has the following specific form: ； in: For the temperature inside the vehicle, To regulate the temperature of the air supplied to the vehicle cabin, This refers to the air intake volume of the blower. Temperature of the vehicle cabin exterior. Temperature of the equipment inside the vehicle cabin. , , and For model parameters, The state transition equation has the following specific form: ； in: Evaporator pressure, To match its own speed Positive correlation with evaporator intake air volume For ambient temperature, The evaporator outlet air temperature. To regulate the temperature of the air supplied to the vehicle cabin, For the temperature inside the vehicle, This refers to the air intake volume of the blower. This refers to the compressor speed. , , , and These are model parameters; This is the minimum compressor speed. This represents the maximum compressor speed. This is the minimum air intake volume for the blower. This represents the maximum air intake volume of the blower. This represents the minimum temperature inside the vehicle cabin. This represents the maximum temperature inside the vehicle cabin. This represents the minimum pressure of the evaporator. This represents the maximum pressure of the evaporator.

5. An energy-saving controller for a heat pump air conditioning system of a connected electric vehicle, characterized in that: The controller receives vehicle speed information from intelligent connected vehicles at a certain frequency, thereby implementing the energy-saving control method for the heat pump air conditioning system as described in any one of claims 1-4.

6. A vehicle, characterized in that: The vehicle is a connected electric vehicle, and its heat pump air conditioning system contains an energy-saving controller for a connected electric vehicle heat pump air conditioning system as described in claim 5.

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