An adaptive energy-saving control system for a vehicle corrosion environment chamber

Abstract

The application discloses a kind of self-adaptive energy-saving control systems of automobile corrosion environment bin, it is related to material corrosion test technical field, including heat parameter calculation module, thermal modeling module, time optimization module, instruction scheduling module, power distribution module and closed-loop self-adaptive module.The application solves the technical problem that the energy of each subsystem of existing environment bin is islanded, leading to low-grade waste heat across operating conditions is directly dissipated, realizes the directional recovery and recycling of dry section exhaust heat, refrigeration condensing heat and saturated barrel heat storage, solves the technical problem that existing fixed time program switching leads to energy hedging in switching process, realizes dynamic optimization of switching time and minimization of switching process energy consumption, solves the technical problem that existing actuator independent control leads to energy consumption within stage, realizes the optimal allocation of each actuator power within stage and minimization of operating energy consumption.

Description

Technical Field

[0001] This invention relates to the field of material corrosion testing technology, and specifically to an adaptive energy-saving control system for an automotive corrosion environment chamber. Background Technology

[0002] The automotive corrosion environment chamber is a key piece of equipment for verifying the corrosion resistance of whole vehicles and components, and is widely used in multi-condition cyclic corrosion tests such as salt spray, damp heat, drying, and static conditions. Mainstream equipment uses fixed-time program control for switching operating conditions, maintaining temperature, humidity, and salt spray parameters within the chamber through independent heaters, refrigeration compressors, spray solenoid valves, circulating fans, and dehumidification valves. With increasing energy-saving requirements, existing energy-saving measures mostly focus on hardware aspects such as variable frequency compressors, double-layer hollow insulation structures, or one-button energy-saving switches. The control system still relies on traditional feedback algorithms to adjust the output of each actuator, showing significant shortcomings in collaborative energy-saving control at the software level.

[0003] The existing energy-saving control of automotive corrosion environment chambers has the following technical problems: the control of operating condition switching is crude and does not take into account the thermal inertia of the chamber and the difference in sample load, resulting in serious energy waste during the switching process; multiple actuators operate independently and lack a collaborative optimization mechanism, which easily leads to energy consumption within a stage and low operating efficiency; the energy flow of each subsystem is consumed in one direction and lacks a cross-operating condition energy recycling mechanism, resulting in a large amount of low-grade waste heat being directly dissipated. Summary of the Invention

[0004] The purpose of this invention is to propose an adaptive energy-saving control system for automotive corrosive environment chambers, which solves the technical problem of energy islanding in existing environmental chamber subsystems leading to direct dissipation of low-grade waste heat across operating conditions.

[0005] This application provides an adaptive energy-saving control system for a corrosive environment chamber in an automobile, including:

[0006] The thermal parameter calculation module is used to obtain the total mass, material type and total surface area of ​​the sample, calculate the total heat capacity and latent heat load of the sample; obtain the heat capacity of the box structure, the equivalent thermal resistance of the environment and the air heat capacity, and establish a coupled thermodynamic model.

[0007] The thermal modeling module is used to collect temperature, humidity, salt spray parameters, wall temperature, saturated tank water temperature and water level, calculate the heat storage capacity of the saturated tank, compare the temperature, humidity, and salt spray parameters with the target tolerance band to obtain state constraints, predict the switching power consumption for different delay times based on the coupled thermodynamic model, and solve the optimal switching time with state compliance as the constraint and the minimum predicted power consumption as the optimization objective.

[0008] The timing optimization module is used to calculate the state transition theoretical energy and generate feedforward control quantity by calling the coupled thermodynamic model according to the optimal switching timing, and to pre-schedule the heat storage of the saturated tank and the wall temperature according to the heat demand of the next operating condition, and generate a gradual transition command.

[0009] The instruction scheduling module is used to construct the energy consumption coupling matrix of multiple actuators. It uses the feedforward control quantity as the initial value and the target temperature, humidity and salt spray parameters as constraints to solve the optimal power allocation of each actuator and adjust the output of each actuator according to the gradual connection instructions.

[0010] The power allocation module is used to determine the waste heat source and the heat sink and establish a corresponding relationship, recover the heat from the waste heat source and input the heat sink to obtain the recovered heat value, and correct the optimal power allocation of the actuator based on the recovered heat value.

[0011] The closed-loop adaptive module is used to identify the equivalent thermal resistance of the environment, the total heat capacity of the sample and the waste heat recovery efficiency coefficient based on the operating data, the optimal power allocation of the actuator and the recovered heat value, update the coupled thermodynamic model and the energy consumption coupling matrix, and feed back to the optimal switching timing sequence and feedforward control quantity to form a full-cycle adaptive energy-saving closed loop.

[0012] In one embodiment, obtaining the total mass, material type, and total surface area of ​​the sample, and calculating the total heat capacity and latent heat of vaporization of the sample, includes:

[0013] A weighing sensor is deployed at the bottom of the sample holder to obtain the total mass of the sample.

[0014] The sample material type is obtained through the material input interface, the material specific heat capacity database is called, and the total mass of the sample is multiplied by the corresponding specific heat capacity to obtain the total heat capacity of the sample.

[0015] The geometric contour of the sample is obtained by laser contour scanning or manual input, the total surface area is calculated, and the latent heat load of vaporization is calculated by combining the latent heat coefficient of the salt solution.

[0016] In one embodiment, the thermal capacity of the enclosure structure, the equivalent thermal resistance of the environment, and the thermal capacity of the air are obtained to establish a coupled thermodynamic model, including:

[0017] Obtain the thermal capacity of the enclosure structure, the equivalent thermal resistance of the environment, and the thermal capacity of the air;

[0018] The total heat capacity of the sample is superimposed with the heat capacity of the chamber structure and the air heat capacity to obtain the total heat capacity parameter;

[0019] The latent heat load of vaporization is used as an additional heat source term for the spray section, and a coupled thermodynamic model is constructed together with the equivalent thermal resistance of the environment to output the total heat capacity parameters, equivalent thermal resistance, latent heat load of vaporization, and steady-state heat loss power.

[0020] In one embodiment, temperature, humidity, salt spray parameters, wall temperature, saturation tank water temperature and water level are collected, and the heat storage capacity of the saturation tank is calculated, including:

[0021] Temperature sensors were placed at the geometric center and four corners of the environmental chamber to collect temperature, humidity and salt spray parameters.

[0022] A wall temperature sensor is embedded in the wall of the environmental chamber to collect the wall temperature.

[0023] A water temperature sensor and a water level sensor are installed inside the saturation tank to collect the water temperature and water level in the saturation tank.

[0024] Calculate the heat storage capacity of the saturation tank based on the water temperature and level in the saturation tank.

[0025] In one embodiment, state constraints are obtained by comparing temperature, humidity, and salt spray parameters with the target tolerance band. Based on a coupled thermodynamic model, the switching power consumption for different delay durations is predicted. With state compliance as the constraint and minimizing predicted power consumption as the optimization objective, the optimal switching timing is solved, including:

[0026] Compare the temperature and humidity salt spray parameters with the target tolerance band;

[0027] When the temperature, humidity and salt spray parameters are within the target tolerance zone, the state is deemed to be up to standard, and the state is used as a state constraint.

[0028] The coupled thermodynamic model is invoked, and the temperature, humidity and salt spray parameters, wall temperature, saturated tank heat storage and state constraints are input. The power consumption for maintaining the current operating condition and the power consumption for state transition are predicted. The power consumption for maintaining the current operating condition is calculated based on the coupled thermodynamic model and the temperature, humidity and salt spray parameters, and the power consumption for state transition is calculated based on the coupled thermodynamic model and the wall temperature.

[0029] The current operating condition power consumption and the state transition power consumption are combined to form the switching power consumption.

[0030] With state compliance as a constraint and minimum switching power consumption as the optimization objective, the switching opportunities are iterated to find the optimal switching time.

[0031] In one embodiment, based on the optimal switching timing, the coupled thermodynamic model is invoked to calculate the state transition theoretical energy and generate feedforward control quantities. The heat storage capacity of the saturated tank and the wall temperature are pre-scheduled according to the heat demand of the next operating condition, generating a gradual transition command, including:

[0032] Based on the optimal switching timing, the coupled thermodynamic model is invoked to calculate the theoretical energy of the state transition from the current state to the target state;

[0033] Based on the state transition theory, energy is generated as a feedforward control quantity;

[0034] Based on the heat demand of the next operating condition, the heat storage capacity of the saturated tank and the wall temperature are pre-scheduled respectively;

[0035] Generate the output decay curve of the actuator in the previous operating condition and the output increase curve of the actuator in the next operating condition;

[0036] At the switching boundary, the actuator output of the previous operating condition decreases according to the output decay curve, and the actuator output of the next operating condition increases according to the output increment curve, with the output gradually transitioning to the command.

[0037] In one embodiment, constructing a multi-actuator energy consumption coupling matrix includes:

[0038] Establish a linear relationship between heater power consumption and output power;

[0039] Establish a discrete mapping relationship between the coefficient of performance of a refrigeration compressor and the compressor frequency;

[0040] Establish the cubic relationship between the power of the circulating fan and the air volume;

[0041] Establish a positive proportional relationship between the consumption of the spray solenoid valve and the amount of salt spray deposition;

[0042] Establish a linear relationship between the power of the dehumidification valve and the dehumidification capacity;

[0043] By combining the power consumption functions of the above actuators, a multi-actuator energy consumption coupling matrix is ​​obtained.

[0044] In one embodiment, using the feedforward control variable as the initial value and the target temperature, humidity, and salt spray parameters as constraints, the optimal power allocation for each actuator is solved, and the output of each actuator is adjusted according to the gradual transition command, including:

[0045] Using the feedforward control variable as the initial value, the target temperature, humidity and salt spray parameters as constraints, and the minimum total power consumption of multiple actuators as the optimization objective, the optimal allocation of heater power, compressor frequency, fan speed, spray solenoid valve opening and dehumidifier power is solved by rolling time domain optimization.

[0046] Adjust the heater power, compressor frequency, fan speed, spray solenoid valve opening and dehumidifier power gradually according to the gradual transition command until the new operating condition is fully entered into a steady state.

[0047] The outputs of each actuator are adjusted.

[0048] In one embodiment, determining the waste heat source and the heat sink and establishing a corresponding relationship includes:

[0049] The saturated tank heat storage capacity, exhaust waste heat, and condensation heat are defined as waste heat sources.

[0050] The salt solution storage tank, humidification water tank, and humidification link in the drying section are defined as heat sinks;

[0051] Establish the correspondence between waste heat sources and heat sinks.

[0052] In one embodiment, recovering heat from the waste heat source and inputting it into the heat sink to obtain the recovered heat value, and using the recovered heat value as feedback to correct the optimal power allocation of the actuator, includes:

[0053] Waste heat is recovered based on the correspondence between waste heat sources and heat sinks;

[0054] The recovered heat is input into the heat sink to obtain the recovered heat value;

[0055] Adjust the output power requirements of the heater and the refrigeration compressor based on the recovered heat value;

[0056] Output the corrected actuator optimal power allocation.

[0057] In one embodiment, based on operating data, optimal power allocation of the actuator, and recovered heat value, the equivalent thermal resistance of the environment, the total heat capacity of the sample, and the waste heat recovery efficiency coefficient are identified, including:

[0058] Collect temperature, humidity, salt spray parameters, wall temperature, saturated tank water temperature, and water level as operational data;

[0059] Establish a power consumption correlation by correlating the operating data with the optimal power allocation and heat recovery values ​​of the actuator;

[0060] Based on the correlation of power consumption, the equivalent thermal resistance of the environment, the total heat capacity of the sample, and the waste heat recovery efficiency coefficient are identified.

[0061] In one embodiment, the coupled thermodynamic model and energy consumption coupling matrix are updated and fed back to the optimal switching timing sequence and feedforward control quantity to form a full-cycle adaptive energy-saving closed loop, including:

[0062] The environmental equivalent thermal resistance, total sample heat capacity, and waste heat recovery efficiency coefficient were updated to the coupled thermodynamic model.

[0063] The updated coupled thermodynamic model is fed back to the optimal switching timing sequence and feedforward control quantity to form a full-cycle adaptive energy-saving closed loop.

[0064] The beneficial effects of this invention are:

[0065] (1) This invention determines the waste heat source and the heat sink and establishes a corresponding relationship, recovers the heat from the waste heat source and inputs it into the heat sink to obtain the recovered heat value, corrects the optimal power allocation of the actuator based on the recovered heat value, and identifies the equivalent thermal resistance of the environment, the total heat capacity of the sample and the waste heat recovery efficiency coefficient to update the model to form a full-cycle adaptive energy-saving closed loop. This solves the technical problem of energy islanding in the existing environmental chamber subsystems leading to the direct dissipation of low-grade waste heat across working conditions, and realizes the directional recovery and recycling of exhaust waste heat in the drying section, refrigeration condensation heat and saturated tank heat storage.

[0066] (2) This invention obtains state constraints by comparing temperature, humidity and salt spray parameters with the target tolerance zone and predicts the switching power consumption based on the coupled thermodynamic model to solve the optimal switching timing, generates feedforward control quantity, and pre-schedules the saturated tank heat storage and wall temperature according to the heat demand of the next working condition. This solves the technical problem of energy offset during the switching process caused by the existing fixed-time program switching, and realizes dynamic optimization of switching timing and minimization of energy consumption during the switching process.

[0067] (3) By constructing a multi-actuator energy consumption coupling matrix and solving the optimal power allocation of each actuator, this invention solves the technical problem of energy consumption within a stage caused by independent control of existing actuators, and realizes the optimal allocation of power of each actuator within a stage and the minimization of operating energy consumption. Attached Figure Description

[0068] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0069] Figure 1 This is a flowchart of the system architecture proposed in this invention;

[0070] Figure 2 This is a flowchart of the thermal parameter calculation module proposed in this invention;

[0071] Figure 3 The flowchart of the thermal modeling module proposed in this invention is as follows.

[0072] Figure 4 The flowchart of the timing optimization module proposed in this invention.

[0073] Figure 5 The flowchart of the instruction scheduling module proposed in this invention is as follows.

[0074] Figure 6 This is a flowchart of the power distribution module proposed in this invention;

[0075] Figure 7 This is a flowchart of the closed-loop adaptive module proposed in this invention. Detailed Implementation

[0076] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0077] In related technologies, the following problems typically exist when implementing energy-saving control in automotive corrosive environment chambers:

[0078] The timing of switching is arbitrary. Existing equipment switches between salt spray, humid heat, drying, and static conditions according to a preset fixed time program without considering the thermal inertia of the chamber and the differences in sample load. This results in energy offsetting and wasted energy consumption during the switching process. The stage control is crude. The actuators for heating, cooling, spraying, air supply, and dehumidification are adjusted independently without establishing an energy consumption coupling model. In the steady state, heating and cooling may operate simultaneously, and humidification and dehumidification may consume energy internally, resulting in low energy efficiency within each stage. Energy is dissipated in one direction. Waste heat from the exhaust of the drying section, condensation heat from the cooling system, and heat stored in the saturated tank are directly discharged into the environment. The energy flow of each subsystem is consumed in one direction without establishing a cross-condition energy cycle mapping, resulting in a large amount of low-grade heat energy wasted.

[0079] Therefore, this application provides an adaptive energy-saving control system for an automotive corrosive environment chamber, which can provide a theoretical basis for energy-saving control in corrosive environment chambers. Please refer to... Figure 1 , Figure 1 This is a schematic diagram of the architecture of the adaptive energy-saving control system for an automotive corrosion environment chamber provided in this application embodiment. The adaptive energy-saving control system for an automotive corrosion environment chamber provided in this application embodiment is applied in an edge computing controller, and the system is executed through embedded application software installed in the edge computing controller. The edge computing controller communicates with temperature sensors, humidity sensors, salt spray deposition data collectors, wall temperature sensors, saturated tank water temperature sensors, water level sensors, weighing sensors, heaters, refrigeration compressors, spray solenoid valves, circulating fans, dehumidifying valves, gas-liquid heat exchangers, and solenoid valve assemblies via an RS485 bus.

[0080] It should be noted that the application scenarios described in the following embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0081] The adaptive energy-saving control system for the automotive corrosive environment chamber provided in this application will be described in detail below.

[0082] like Figure 1 As shown, the system includes the following modules: thermal parameter calculation module, thermal modeling module, timing optimization module, instruction scheduling module, power allocation module, and closed-loop adaptive module.

[0083] The thermal parameter calculation module is used to obtain the total mass, material type and total surface area of ​​the sample, calculate the total heat capacity and latent heat load of the sample; obtain the heat capacity of the box structure, the equivalent thermal resistance of the environment and the heat capacity of the air, and establish a coupled thermodynamic model.

[0084] Specifically, the thermal parameter calculation module obtains the total mass of the sample through a weighing sensor deployed at the bottom of the sample holder, obtains the sample material type through the material input interface, calls the material specific heat capacity database, and multiplies the total sample mass by the corresponding specific heat capacity to obtain the total sample heat capacity; it obtains the sample geometric contour through laser contour scanning or manual input, calculates the total surface area, and calculates the latent heat load of vaporization by combining the latent heat coefficient of the salt solution; it simultaneously obtains the heat capacity of the box structure, the equivalent thermal resistance of the environment, and the heat capacity of the air, and superimposes the total sample heat capacity with the heat capacity of the box structure and the heat capacity of the air to obtain the total heat capacity parameter, and uses the latent heat load of vaporization as an additional heat source term to construct a coupled thermodynamic model together with the equivalent thermal resistance of the environment.

[0085] In one embodiment, such as Figure 2 As shown, the thermal parameter calculation module includes the following sub-steps: obtaining the total mass, material type, and total surface area of ​​the sample; calculating the total heat capacity and latent heat load of the sample; obtaining the heat capacity of the box structure, the equivalent thermal resistance of the environment, and the air heat capacity; and establishing a coupled thermodynamic model.

[0086] Specifically, a weighing sensor with a range of 0 to 500 kg, an accuracy of 0.1%FS, and a sampling frequency of 1 Hz is deployed at the bottom of the sample holder. It is connected to an edge computing controller via an RS485 bus to acquire the total sample mass in real time. A material input interface is set up on the controller's human-machine interface. The operator enters the sample material type, and the system calls the built-in material specific heat capacity database, multiplying the total sample mass by the corresponding specific heat capacity to obtain the total sample heat capacity.

[0087] Total heat capacity of sample The calculation formula is:

[0088] ;

[0089] In the formula, The total heat capacity of the sample is expressed in kJ / K. Total sample mass, in units of ; This represents the specific heat capacity of the material, expressed in kJ / (kg·K).

[0090] Vaporization latent heat load The calculation formula is:

[0091] ;

[0092] In the formula, This is the latent heat load of vaporization, expressed in kJ. The mass of atomized salt solution per unit time in the spray section is expressed in kg / h. The latent heat of vaporization of the salt solution is expressed in kJ / kg. In one embodiment, The value of 2260 kJ / kg was determined based on the experimental measurement of the latent heat of phase change of the salt solution at the test temperature.

[0093] Box structure heat capacity The calculation formula is:

[0094] ;

[0095] In the formula, The heat capacity of the box structure is expressed in kJ / K. The mass of the stainless steel wall of the box is in kg. This refers to the specific heat capacity of stainless steel, expressed in kJ / (kg·K).

[0096] air heat capacity The calculation formula is:

[0097] ;

[0098] In the formula, The heat capacity of air is expressed in kJ / K. This refers to the air density inside the warehouse, expressed in kg / m³. 3 ; The volume of air inside the chamber is expressed in cubic meters (m³). 3 ; This is the specific heat capacity of air at constant pressure, expressed in kJ / (kg·K).

[0099] Total heat capacity parameters The calculation formula is:

[0100] ;

[0101] In the formula, This is the total heat capacity parameter, in kJ / K.

[0102] Environmental equivalent thermal resistance The calculation formula is:

[0103] ;

[0104] In the formula, Environmental equivalent thermal resistance, expressed in K / kW; This refers to the thickness of the insulation layer, in meters (m). The thermal conductivity of the insulation layer is expressed in kW / (m·K). The external surface area of ​​the box is expressed in m². 2 ; The external convective heat transfer coefficient is expressed in kW / (m²). 2 ·K).

[0105] The coupled thermodynamic model consists of a parallel thermal capacity network and a thermal resistance network. The total thermal capacity parameter is the arithmetic sum of the total heat capacity of the sample, the thermal capacity of the chamber structure, and the air thermal capacity. The latent heat of vaporization is used as an additional nodal heat source for the spray section. The equivalent environmental thermal resistance is the series equivalent of the thermal resistance of the chamber insulation layer and the convective thermal resistance of the outer surface. The coupled thermodynamic model outputs steady-state heat loss power. The calculation formula is:

[0106] ;

[0107] In the formula, The power loss is in steady state, expressed in kW. Set the temperature inside the chamber, in Kelvin (K). The ambient temperature is expressed in Kelvin (K).

[0108] For example, the dimensions of the corrosive environment chamber are length × width × height = 2000mm × 1500mm × 1800mm, and the stainless steel wall mass of the chamber is... 450kg, specific heat capacity of stainless steel The value is 0.50 kJ / (kg·K), determined based on the standard measured value of the specific heat capacity of 304 stainless steel; the volume of air inside the silo. It is 5.4m 3 air density Take 1.20 kg / m 3 Specific heat capacity of air at constant pressure Take 1.005 kJ / (kg·K); insulation layer thickness 100mm, thermal conductivity The value is 0.04 kW / (m·K), which is determined based on the standard thermal conductivity of polyurethane foam insulation materials; the external convective heat transfer coefficient is... Take 0.02kW / (m 2 The value of K is determined based on the empirical correlation of natural convection heat transfer under an air velocity of 0.5 m / s. The heat capacity of the box structure is then... Substituting the values, we get 225 kJ / K; air heat capacity. Substituting the values, we get 6.51 kJ / K. If the total sample mass is 200 kg and the material is low-carbon steel, the specific heat capacity is... If the total heat capacity of the sample is 0.46 kJ / (kg·K), then the total heat capacity of the sample is... Substituting the values, we get 92 kJ / K; total heat capacity parameter. The result is obtained by summing the total heat capacity of the sample, the heat capacity of the chamber structure, and the air heat capacity. After substituting the values, we get 323.51 kJ / K.

[0109] The thermal modeling module is used to collect temperature, humidity, salt spray parameters, wall temperature, saturated tank water temperature and water level, calculate the heat storage capacity of the saturated tank, compare the temperature, humidity, and salt spray parameters with the target tolerance band to obtain state constraints, predict the switching power consumption for different delay times based on the coupled thermodynamic model, and solve for the optimal switching time with state compliance as the constraint and the minimum predicted power consumption as the optimization objective.

[0110] Specifically, temperature sensors are placed at the geometric center and four corners of the environmental chamber to collect temperature, humidity, and salt spray parameters; wall temperature sensors are embedded in the walls of the environmental chamber to collect wall temperature; and water temperature and level sensors are placed in the saturation tank to collect water temperature and level. The heat storage capacity of the saturation tank is calculated based on the water temperature and level. The temperature, humidity, and salt spray parameters are compared with the target tolerance band. When the temperature, humidity, and salt spray parameters are within the target tolerance band, the state is considered to be in compliance, and this compliance is used as a state constraint. A coupled thermodynamic model is invoked, inputting the temperature, humidity, and salt spray parameters, wall temperature, saturation tank heat storage capacity, and state constraints, to predict the switching power consumption under different delay durations. With state compliance as a hard constraint and minimizing switching power consumption as the optimization objective, candidate delay durations are traversed to solve for the optimal switching timing.

[0111] In one embodiment, such as Figure 3 As shown, the thermal modeling module includes the following sub-steps: collecting temperature, humidity, and salt spray parameters, wall temperature, saturated tank water temperature and water level; calculating the heat storage capacity of the saturated tank; comparing the temperature, humidity, and salt spray parameters with the target tolerance band; predicting the switching power consumption; and solving for the optimal switching timing.

[0112] Saturated tank heat storage The calculation formula is:

[0113] ;

[0114] In the formula, The heat storage capacity of the saturated tank is expressed in kJ. is the specific heat capacity of water, expressed in kJ / (kg·K); The mass of water in the saturated tank is calculated from the water level sensor data and is expressed in kg. The saturated tank water temperature is expressed in Kelvin (K). In one embodiment, The value is 4.18 kJ / (kg·K), which is determined based on the standard physical constant of the specific heat capacity of pure water at room temperature.

[0115] The target tolerance band includes temperature tolerance and humidity tolerance. The temperature tolerance is set at ±0.5. The threshold is set according to the requirements for temperature fluctuation in the GB / T2423.17 salt spray test standard to ensure the uniformity of salt spray deposition; the humidity tolerance is set to ±2%RH, which is set according to the humidity control accuracy requirements of the cyclic corrosion test chamber to avoid the corrosion rate deviation caused by humidity fluctuation.

[0116] The switching power consumption prediction model uses a coupled thermodynamic model as its core and adopts a two-branch parallel structure. One branch predicts the power consumption to maintain the current operating condition, and the other predicts the power consumption during the transition between operating conditions. The calculation formula is:

[0117] ;

[0118] In the formula, The switching power consumption is expressed in kJ. The delay duration is expressed in seconds (s). The power consumption is measured in kJ to maintain the current operating conditions. This represents the power consumption during state transition, expressed in kJ.

[0119] Maintain current power consumption The calculation formula is:

[0120] ;

[0121] In the formula, This represents the steady-state heat loss power, output by the coupled thermodynamic model, in kW.

[0122] State transition power consumption The calculation formula is:

[0123] ;

[0124] In the formula, The temperature difference between the target state and the current state, in K. The overall energy efficiency coefficient is dimensionless. In one embodiment, The value ranges from 0.6 to 0.85. This range is determined by a weighted average of the heater's electrothermal conversion efficiency (0.95) and the refrigeration compressor's coefficient of performance (COP). The COP is based on a compressor frequency discrete mapping table ranging from 30Hz to 90Hz. It can be obtained by looking up a table within the specified range.

[0125] The timing optimization module is used to calculate the state transition theoretical energy and generate feedforward control quantities by calling the coupled thermodynamic model according to the optimal switching timing. It also pre-schedules the heat storage of the saturated tank and the wall temperature according to the heat demand of the next operating condition, and generates a gradual transition command.

[0126] Specifically, based on the optimal switching timing, a coupled thermodynamic model is invoked to calculate the theoretical energy for the transition from the current state to the target state. Feedforward control quantities are generated based on this theoretical energy. According to the heat demand of the next operating condition, the heat storage capacity of the saturated tank and the wall temperature are pre-scheduled. The output decay curve of the actuator in the previous operating condition and the output increase curve of the actuator in the next operating condition are generated. At the switching boundary, the output of the actuator in the previous operating condition decreases according to the output decay curve, and the output of the actuator in the next operating condition increases according to the output increase curve, resulting in a gradual transition command.

[0127] In one embodiment, such as Figure 4 As shown, the timing optimization module includes the following sub-steps: calling the coupled thermodynamic model to calculate the state transition theoretical energy; generating feedforward control quantities; pre-scheduling the saturated bucket heat storage; pre-scheduling the wall temperature; and generating gradual transition instructions.

[0128] Feedforward control quantity The calculation formula is:

[0129] ;

[0130] In the formula, This is the feedforward control quantity, in kJ. The target temperature is expressed in Kelvin (K). The current temperature, in Kelvin (K). This represents the latent heat of vaporization, expressed in kJ. When the next operating condition is the spray section, Take the product of the mass of atomized salt solution per unit time in the spray section and the latent heat of vaporization coefficient of the salt solution; when the next operating condition is the non-spray section, Take 0.

[0131] The pre-scheduling strategy is determined based on the heat demand of the next operating condition. If the next operating condition is the drying section and requires a high-temperature and low-humidity environment, the heating power of the saturation tank is reduced in advance, and the excess heat is transferred to the tank wall for storage through the circulating air duct to increase the wall temperature. If the next operating condition is the salt spray section and requires humidification, the heating power of the saturation tank is reduced in advance, and the waste heat is retained in the hot water of the saturation tank to reduce the energy consumption of reheating the saturation tank in the salt spray section.

[0132] The gradual transition command is generated by combining the output decay curve and the output increment curve. The output decay curve uses an exponential decay function:

[0133] ;

[0134] In the formula, The output value of the decay curve is dimensionless. This is the initial output value of the actuator under the previous operating condition, and is dimensionless. This is the attenuation slope coefficient, in seconds. -1 ; The time unit is seconds (s).

[0135] The output increasing curve uses an S-shaped increasing function:

[0136] ;

[0137] In the formula, The output value is a dimensionless, increasing curve. The target output value of the actuator for the next operating condition is dimensionless. The increasing slope coefficient, in seconds. -1 ; The time at the midpoint of the curve is given in seconds. In one embodiment, and All values ​​are taken as 1.0s. -1 This value is set based on the actuator response time constant to ensure that the switching process is completed within 20 seconds. Completed internally to avoid energy clashes.

[0138] The instruction scheduling module is used to construct the energy consumption coupling matrix of multiple actuators. It uses the feedforward control quantity as the initial value and the target temperature, humidity and salt spray parameters as constraints to solve the optimal power allocation of each actuator and adjust the output of each actuator according to the gradual connection instructions.

[0139] Specifically, a multi-actuator energy consumption coupling matrix is ​​constructed, establishing a linear relationship between heater power consumption and output power, a discrete mapping relationship between the coefficient of performance (COP) of the refrigeration compressor and compressor frequency, a cubic relationship between the power of the circulating fan and the air volume, a proportional relationship between the power consumption of the spray solenoid valve and the salt spray deposition, and a linear relationship between the power of the dehumidifier valve and the dehumidification capacity. The power consumption functions of each actuator are combined to obtain the multi-actuator energy consumption coupling matrix. Using the feedforward control variable as the initial value, the target temperature, humidity, and salt spray parameters as constraints, and minimizing the total power consumption of the multi-actuator as the optimization objective, the optimal power allocation for each actuator is solved through rolling time-domain optimization. The output of each actuator is gradually adjusted according to a gradual transition command until the new steady-state condition is fully entered.

[0140] In one embodiment, such as Figure 5 As shown, the instruction scheduling module includes the following sub-steps: constructing a multi-executor energy consumption coupling matrix; solving for the optimal power allocation of each actuator; and adjusting the output of each actuator according to the gradual connection instructions.

[0141] Heater power consumption The calculation formula is:

[0142] ;

[0143] In the formula, The power consumption of the heater is expressed in kW. This refers to the heater's output power, measured in kW. The electrothermal conversion efficiency is dimensionless. In one embodiment, The value is set to 0.95, which was determined based on the calibration experiment of the energy conversion efficiency of the resistance heater.

[0144] Refrigeration compressor power consumption The calculation formula is:

[0145] ;

[0146] In the formula, The power consumption of the refrigeration compressor is expressed in kW. Cooling capacity, unit: kW; For compressor frequency The corresponding performance coefficients are dimensionless and are determined through a discrete mapping table.

[0147] Circulating fan power consumption The calculation formula is:

[0148] ;

[0149] In the formula, The power consumption of the circulating fan is expressed in kW. This is the fan proportionality coefficient, in kW / min. 3 ·m 3 ; The fan speed is expressed in r / min.

[0150] Spray solenoid valve consumption The calculation formula is:

[0151] ;

[0152] In the formula, The power consumption of the spray solenoid valve is expressed in kW. This is the nozzle flow rate proportionality coefficient, in units of... ; Salt spray deposition, in L / (m²) 2 ·h).

[0153] Dehumidifier power consumption The calculation formula is:

[0154] ;

[0155] In the formula, The power consumption of the dehumidifier valve is expressed in kW. This is the dehumidification ratio coefficient, with units of kW / h·kg; This is the dehumidification capacity, expressed in kg / h.

[0156] Total power consumption of multiple actuators The calculation formula is:

[0157] ;

[0158] In the formula, Total power consumption of multiple actuators, in kW.

[0159] The objective function for rolling temporal optimization is The constraint condition is the equation for achieving the temperature, humidity, and salt spray parameters. The optimized solver employs a quadratic programming algorithm, with a prediction time domain of 60. This value is determined based on the typical transition time of the operating condition switch; the control time domain is set to 10. This value is determined based on the actuator response delay characteristics; the sampling period is 1. This value is determined based on the sensor's sampling frequency.

[0160] The power allocation module is used to determine the waste heat source and the heat sink and establish a corresponding relationship, recover the heat from the waste heat source and input the heat sink to obtain the recovered heat value, and correct the optimal power allocation of the actuator based on the recovered heat value.

[0161] Specifically, the saturated tank's heat storage, exhaust waste heat, and condensation heat are defined as waste heat sources, while the brine solution storage tank, humidification water tank, and humidification stage in the drying section are defined as heat sinks. A correspondence between waste heat sources and heat sinks is established, forming a matching matrix. Based on the matching matrix, heat from the waste heat sources is recovered and input into the heat sinks to obtain the recovered heat value. The recovered heat value is fed back in real-time to the optimal power allocation for each actuator. Based on the recovered heat value, the output power requirements of the heater and the refrigeration compressor are adjusted, and the adjusted optimal power allocation for the actuators is output.

[0162] In one embodiment, such as Figure 6 As shown, the power distribution module includes the following sub-steps: determining the waste heat source; determining the heat sink; establishing the corresponding relationship; recovering heat from the waste heat source; inputting the heat sink; and providing feedback to correct the power distribution.

[0163] Matching matrix It is a 3x3 matrix with elements A value of 0 or 1 indicates a waste heat source. The mapping relationship with the heat sink j. Waste heat sources include the heat storage capacity of the saturated tank, exhaust waste heat, and condensation heat; heat sinks include the brine solution storage tank, the humidification water tank, and the humidification stage of the drying section. The matching matrix is ​​dynamically activated based on the current operating condition and the next operating condition. If the current operating condition is the drying stage and the next operating condition is the salt mist stage, the mapping from exhaust waste heat to the brine solution storage tank is activated; if the current operating condition is the humid heat stage and refrigeration and dehumidification are required, the mapping from condensation heat to the saturated tank is activated.

[0164] Heat recovery value The calculation formula is:

[0165] ;

[0166] In the formula, The unit for the recovered heat value is 1. ; For the first The heat power of each waste heat source is expressed in kW.

[0167] Revised heater power requirements The calculation formula is:

[0168] ;

[0169] In the formula, The corrected heater power requirement is expressed in kW. The optimized heater power is expressed in kW.

[0170] Revised power requirements for refrigeration compressors The calculation formula is:

[0171] ;

[0172] In the formula, The revised power requirement for the refrigeration compressor is expressed in kW. The optimized refrigeration compressor power is expressed in kW.

[0173] The closed-loop adaptive module is used to identify the equivalent thermal resistance of the environment, the total heat capacity of the sample and the waste heat recovery efficiency coefficient based on the operating data, the optimal power allocation of the actuator and the recovered heat value, update the coupled thermodynamic model and the energy consumption coupling matrix, and feed back to the optimal switching timing sequence and feedforward control quantity to form a full-cycle adaptive energy-saving closed loop.

[0174] Specifically, temperature, humidity, salt spray parameters, wall temperature, saturated tank water temperature, and water level are collected as operational data. This operational data is then correlated with the optimal power allocation of the actuator and the recovered heat value to establish a power consumption correlation. Based on this correlation, the equivalent thermal resistance of the environment, the total heat capacity of the sample, and the waste heat recovery efficiency coefficient are identified using the recursive least squares method. The equivalent thermal resistance of the environment and the total heat capacity of the sample are updated in the coupled thermodynamic model, and the waste heat recovery efficiency coefficient is updated in the energy consumption coupling matrix. The updated coupled thermodynamic model is fed back to the optimal switching timing sequence, and the updated energy consumption coupling matrix is ​​fed back to the feedforward control quantity, forming a full-cycle adaptive energy-saving closed loop.

[0175] In one embodiment, such as Figure 7As shown, the closed-loop adaptive module includes the following sub-steps: collecting operating data; establishing power consumption correlation; identifying model parameters; updating the coupled thermodynamic model; updating the energy consumption coupling matrix; and feedback to form a closed loop.

[0176] The parameter update formula for recursive least squares is:

[0177] ;

[0178] In the formula, For parameter vectors, The units are [K / kW, kJ / K, dimensionless]; The iteration time is dimensionless; This is the gain matrix; These are actual energy consumption observations, in units of... ; This is the regression vector.

[0179] Gain matrix The calculation formula is:

[0180] ;

[0181] In the formula, The covariance matrix is ​​dimensionless. The forgetting factor is dimensionless.

[0182] covariance matrix The update formula is:

[0183] ;

[0184] In the formula, It is an identity matrix, dimensionless.

[0185] Regression vector The construction formula is:

[0186] ;

[0187] In the formula, This represents the temperature and humidity deviation, expressed in Kelvin (K). This represents the total power of all actuators, expressed in kW. The value of recovered heat is expressed in kW.

[0188] Forgetting factor The value is set to 0.98, which is based on the time-varying characteristics of the model parameters and ranges from 0.95 to 0.995. A value closer to 1 indicates a higher weight for historical data. Initial covariance matrix. A diagonal matrix is ​​used, with all diagonal elements set to 100. This initial value is determined based on the prior uncertainty of the parameters, ensuring sufficient correction capability for the algorithm in the initial stage. The initial values ​​of the parameter vector are set based on the offline calculation results of the thermal parameter calculation module. The initial value is determined by calculation based on the insulation layer thickness and thermal conductivity. The initial values ​​are determined by calculation based on the total mass of the sample and the specific heat capacity of the material. The initial value is 0.60, which is determined based on the measured value of the calibrated efficiency of the gas-liquid heat exchanger under rated operating conditions.

[0189] Training process every 10 Each execution collects current temperature, humidity, salt spray parameters, wall temperature, saturated tank water temperature, water level, power of each actuator, and recovered heat value, and constructs a regression vector. Calculate the actual energy consumption observation value Update the gain matrix Covariance Matrix This leads to an update of the parameter vector. The convergence criterion is that the rate of change of parameters is less than 1% in 50 consecutive iterations. This threshold is set according to the control accuracy requirements to ensure the reliability of switching decisions and feedforward compensation after the model parameters stabilize.

[0190] Updated environmental equivalent thermal resistance Total heat capacity of the sample Write the parameters into the coupled thermodynamic model, replacing the original parameters; the updated waste heat recovery efficiency coefficient is obtained. The energy consumption coupling matrix is ​​written to correct the calculation relationship of recovered heat. The updated coupled thermodynamic model is fed back to the optimal switching timing sequence to correct the switching power consumption prediction; the updated energy consumption coupling matrix is ​​fed back to the feedforward control quantity to correct the calculation of the feedforward compensation quantity.

[0191] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An adaptive energy-saving control system for an automotive corrosive environment chamber, characterized in that, include: The thermal parameter calculation module is used to obtain the total mass, material type and total surface area of ​​the sample, calculate the total heat capacity and latent heat load of the sample; obtain the heat capacity of the box structure, the equivalent thermal resistance of the environment and the air heat capacity, and establish a coupled thermodynamic model. The thermal modeling module is used to collect temperature, humidity, salt spray parameters, wall temperature, saturated tank water temperature and water level, calculate the heat storage capacity of the saturated tank, compare the temperature, humidity, and salt spray parameters with the target tolerance band to obtain state constraints, predict the switching power consumption for different delay times based on the coupled thermodynamic model, and solve the optimal switching time with state compliance as the constraint and the minimum predicted power consumption as the optimization objective. The timing optimization module is used to calculate the state transition theoretical energy and generate feedforward control quantity by calling the coupled thermodynamic model according to the optimal switching timing, and to pre-schedule the heat storage of the saturated tank and the wall temperature according to the heat demand of the next operating condition, and generate a gradual transition command. The instruction scheduling module is used to construct the energy consumption coupling matrix of multiple actuators. It uses the feedforward control quantity as the initial value and the target temperature, humidity and salt spray parameters as constraints to solve the optimal power allocation of each actuator and adjust the output of each actuator according to the gradual connection instructions. The power allocation module is used to determine the waste heat source and the heat sink and establish a corresponding relationship, recover the heat from the waste heat source and input the heat sink to obtain the recovered heat value, and correct the optimal power allocation of the actuator based on the recovered heat value. The closed-loop adaptive module is used to identify the equivalent thermal resistance of the environment, the total heat capacity of the sample and the waste heat recovery efficiency coefficient based on the operating data, the optimal power allocation of the actuator and the recovered heat value, update the coupled thermodynamic model and the energy consumption coupling matrix, and feed back to the optimal switching timing sequence and feedforward control quantity to form a full-cycle adaptive energy-saving closed loop.

2. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 1, characterized in that, By comparing temperature, humidity, and salt spray parameters with the target tolerance band to obtain state constraints, and predicting switching power consumption for different delay durations based on a coupled thermodynamic model, the optimal switching timing is solved with state attainment as the constraint and minimum predicted power consumption as the optimization objective. This includes: Compare the temperature and humidity salt spray parameters with the target tolerance band; When the temperature, humidity and salt spray parameters are within the target tolerance zone, the state is deemed to be up to standard, and the state is used as a state constraint. The coupled thermodynamic model is invoked, and the temperature, humidity and salt spray parameters, wall temperature, saturated tank heat storage and state constraints are input. The power consumption for maintaining the current operating condition and the power consumption for state transition are predicted. The power consumption for maintaining the current operating condition is calculated based on the coupled thermodynamic model and the temperature, humidity and salt spray parameters, and the power consumption for state transition is calculated based on the coupled thermodynamic model and the wall temperature. The current operating condition power consumption and the state transition power consumption are combined to form the switching power consumption. With state compliance as a constraint and minimum switching power consumption as the optimization objective, the switching opportunities are iterated to find the optimal switching time.

3. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 1, characterized in that, Based on the optimal switching timing, the coupled thermodynamic model is invoked to calculate the state transition theoretical energy and generate feedforward control quantities. The heat storage capacity of the saturated tank and the wall temperature are pre-scheduled according to the heat demand of the next operating condition, generating gradual transition commands, including: Based on the optimal switching timing, the coupled thermodynamic model is invoked to calculate the theoretical energy of the state transition from the current state to the target state; Based on the state transition theory, energy is generated as a feedforward control quantity; Based on the heat demand of the next operating condition, the heat storage capacity of the saturated tank and the wall temperature are pre-scheduled respectively; Generate the output decay curve of the actuator in the previous operating condition and the output increase curve of the actuator in the next operating condition; At the switching boundary, the actuator output of the previous operating condition decreases according to the output decay curve, and the actuator output of the next operating condition increases according to the output increment curve, with the output gradually transitioning to the command.

4. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 1, characterized in that, Constructing the energy coupling matrix for multiple actuators includes: Establish a linear relationship between heater power consumption and output power; Establish a discrete mapping relationship between the coefficient of performance of a refrigeration compressor and the compressor frequency; Establish the cubic relationship between the power of the circulating fan and the air volume; Establish a positive proportional relationship between the consumption of the spray solenoid valve and the amount of salt spray deposition; Establish a linear relationship between the power of the dehumidification valve and the dehumidification capacity; By combining the power consumption functions of the above actuators, a multi-actuator energy consumption coupling matrix is ​​obtained.

5. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 4, characterized in that, Using the feedforward control variable as the initial value and the target temperature, humidity, and salt spray parameters as constraints, the optimal power allocation for each actuator is solved, and the output of each actuator is adjusted according to the gradual transition command, including: Using the feedforward control variable as the initial value, the target temperature, humidity and salt spray parameters as constraints, and the minimum total power consumption of multiple actuators as the optimization objective, the optimal allocation of heater power, compressor frequency, fan speed, spray solenoid valve opening and dehumidifier power is solved by rolling time domain optimization. Adjust the heater power, compressor frequency, fan speed, spray solenoid valve opening and dehumidifier power gradually according to the gradual transition command until the new operating condition is fully entered into a steady state. The outputs of each actuator are adjusted.

6. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 1, characterized in that, Identify waste heat sources and heat sinks and establish their corresponding relationships, including: The saturated tank heat storage capacity, exhaust waste heat, and condensation heat are defined as waste heat sources. The salt solution storage tank, humidification water tank, and humidification link in the drying section are defined as heat sinks; Establish the correspondence between waste heat sources and heat sinks.

7. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 1, characterized in that, The recovered heat is fed into the heat sink to obtain the recovered heat value. Based on the recovered heat value, the optimal power allocation of the actuator is corrected, including: Waste heat is recovered based on the correspondence between waste heat sources and heat sinks; The recovered heat is input into the heat sink to obtain the recovered heat value; Adjust the output power requirements of the heater and the refrigeration compressor based on the recovered heat value; Output the corrected actuator optimal power allocation.

8. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 1, characterized in that, Based on operating data, optimal power distribution of the actuator, and recovered heat value, identify the equivalent thermal resistance of the environment, the total heat capacity of the sample, and the waste heat recovery efficiency coefficient, including: Collect temperature, humidity, salt spray parameters, wall temperature, saturated tank water temperature, and water level as operational data; Establish a power consumption correlation by correlating the operating data with the optimal power allocation and heat recovery values ​​of the actuator; Based on the correlation of power consumption, the equivalent thermal resistance of the environment, the total heat capacity of the sample, and the waste heat recovery efficiency coefficient are identified.

9. The adaptive energy-saving control system for an automotive corrosive environment chamber according to claim 1, characterized in that, The coupled thermodynamic model and energy consumption coupling matrix are updated and fed back to the optimal switching timing sequence and feedforward control quantity, forming a full-cycle adaptive energy-saving closed loop, including: The environmental equivalent thermal resistance, total sample heat capacity, and waste heat recovery efficiency coefficient were updated to the coupled thermodynamic model. The updated coupled thermodynamic model is fed back to the optimal switching timing sequence and feedforward control quantity to form a full-cycle adaptive energy-saving closed loop.

Images