An electric vehicle cabin thermal management method considering air quality

Abstract

The present application relates to a kind of electric vehicle cabin thermal management method considering air quality, method includes: obtaining the state feedback data of current time electric vehicle, driving data and environmental information, obtains the estimated temperature trajectory of cabin inside quality and wall surface estimated temperature trajectory in prediction time domain based on Taylor first-order expansion;Set upper air conditioning system optimization problem, upper controller minimizes upper optimization goal, and the upper optimal temperature sequence and upper optimal fresh air rate sequence are obtained by solving;Establish lower air conditioning system optimization problem, lower controller minimizes lower optimization goal, and the lower optimal temperature sequence is obtained by solving;With the first value of upper optimal fresh air rate sequence and lower optimal refrigeration capacity sequence as output control electric vehicle cabin air conditioning heating system.Compared with prior art, the present application has the advantages of balancing air conditioning system heat load and reducing HVAC system energy consumption.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle cabin thermal management technology, and in particular to an electric vehicle cabin thermal management method that takes air quality into account. Background Technology

[0002] Electric vehicles, as the mainstay of new energy vehicles, have become an important component in addressing energy security and environmental sustainability challenges. However, the "range anxiety" caused by the low energy density of battery systems has challenged the promotion and selection of electric vehicles. Since electric vehicles lack the engine cooling and waste heat recovery systems found in traditional internal combustion engine vehicles, they rely on the battery system's electro-thermal conversion to meet temperature requirements. Electric vehicle energy consumption is primarily concentrated in driving and onboard accessories, such as the cabin heating, ventilation, and air conditioning (HVAC) system, especially in high-temperature environments, where this system accounts for approximately 30% or even more of energy consumption. Cabin thermal management aims to automatically adjust temperature and air quality based on occupant preferences and internal / external environmental conditions, while simultaneously reducing HVAC system energy consumption to improve driving range, thus extending battery life to some extent.

[0003] Current research on vehicle cabin thermal management methods that consider air quality is limited. Most studies on cabin temperature control strategies typically rely on rules to track the desired set temperatures for passengers and drivers, while cabin air quality is usually regulated by a fixed amount of fresh air. However, minimizing HVAC system energy consumption while aiming for the desired cabin temperature is insufficient to meet the air conditioning system's heat load under all operating conditions. Conversely, meeting the air conditioning system's heat load under all operating conditions results in high HVAC system energy consumption. Therefore, current vehicle cabin thermal management methods fail to balance the air conditioning system's heat load and HVAC system energy consumption, leading to poor temperature and fresh air regulation effects and high energy consumption. Summary of the Invention

[0004] The purpose of this invention is to provide a thermal management method for the electric vehicle cabin that takes into account air quality, in order to balance the heat load of the air conditioning system and the energy consumption of the HVAC system.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for thermal management of the cabin of an electric vehicle that takes air quality into account, the method comprising:

[0007] Acquire the current state feedback data, driving data, and environmental information of the electric vehicle;

[0008] Based on the cabin air temperature dynamics model and Taylor first-order expansion, the estimated temperature trajectory of the cabin's internal mass in the predicted time domain is obtained. and wall surface estimated temperature trajectory

[0009] Based on the vehicle cabin air quality dynamics model and the air conditioning system energy consumption model, an upper-level air conditioning system optimization problem is set up. This problem includes an upper-level optimization objective and upper-level constraints. Vehicle cabin air temperature and CO2 concentration are selected as state variables, while the cooling capacity and fresh air rate of the air conditioning system are selected as control variables. The temperature trajectory is estimated by combining state feedback data, driving data, environmental information, and vehicle cabin internal mass data. and wall surface estimated temperature trajectory The optimal temperature sequence and the optimal fresh air velocity sequence of the upper layer are obtained by solving the problem.

[0010] By combining the upper-level optimal temperature sequence and the upper-level optimal fresh air rate sequence, an optimization problem for the lower-level air conditioning system is established. The lower-level air conditioning system optimization problem includes the lower-level optimization objective and lower-level constraints. The cooling capacity of the air conditioning system is selected as the control variable to solve the lower-level optimal cooling capacity sequence.

[0011] The optimal fresh air rate sequence at the upper level and the optimal cooling capacity sequence at the lower level are integrated into a thermal management strategy for the electric vehicle cabin. The first value of the two sequences is used as the output of the optimal strategy to control the air conditioning system of the electric vehicle cabin. The above steps are repeated to update the two sequences.

[0012] Furthermore, the status feedback data includes the internal mass temperature T. m Wall temperature T w Cabin air temperature T a and CO2 concentration in the cabin air a Driving data includes vehicle speed v veh Environmental information includes ambient temperature T. e CO2 concentration in the outside air κ e The internal mass temperature represents the temperature of solids inside the cabin of an electric vehicle.

[0013] Furthermore, the specific steps for obtaining the first derivatives of the mass temperature and wall temperature based on the cabin air temperature dynamics model are as follows:

[0014] Let the current time be k, and let the mass temperature T at time k+i be... m and wall temperature T w A first-order Taylor expansion is performed, and combined with the cabin air temperature dynamics model, the first-order derivatives of the mass temperature and wall temperature at time k are obtained, where i represents the index number in the prediction time domain.

[0015] Furthermore, the first derivatives of the mass temperature and the wall temperature at time k are:

[0016]

[0017]

[0018]

[0019]

[0020] Among them, U a2m U a2w and U w2e A is the convective heat transfer coefficient between the cabin air, walls, and outside air. m and A w It refers to the internal mass of the vehicle compartment and the corresponding surface area of ​​the compartment walls, in meters. a m m and m w These are the cabin air, interior mass, and lumped wall mass, C. a C m and C w These are the cabin air, internal mass, and wall specific heat capacity, respectively; σ is the Stefan-Boltzmann constant; ε m It is the internal mass of the vehicle cabin and the emissivity between the walls, τ g It is the transmittance of the glass, I sun It is radiation per unit area, A g It is the surface area of ​​the cockpit glass, α m and α w It refers to the cabin interior mass and wall absorptivity, v veh (k) represents the vehicle speed at the current time k.

[0021] Furthermore, the temperature inside the vehicle cabin was estimated by mass. Trajectory and wall surface temperature estimation The trajectory is:

[0022]

[0023]

[0024] Where f and g represent the estimated temperature trajectories of the vehicle cabin's internal mass, respectively. and wall surface estimated temperature trajectory With the current internal mass temperature T m Wall temperature T w and cabin air temperature T a The mapping relationship, where i represents the index number in the prediction time domain.

[0025] Furthermore, the specific optimization objectives at the higher level are:

[0026]

[0027]

[0028] Among them, the control variables in the upper-level air conditioning system optimization problem Indicates the cooling capacity of the air conditioning system. N represents the fresh air rate of the air conditioning system. l It is the prediction time domain of the upper-level controller, ω 1,2 It is the weight parameter, P ac Let T be the power consumption of the air conditioning system in the energy consumption model, Δt be the sampling step size, γ be the correction factor, ε be the relaxation coefficient, and T be the power consumption of the air conditioning system in the model. set Indicates the cabin air temperature setting, T a,max T represents the maximum air temperature inside the vehicle cabin. a (k+i+1) represents the cabin air temperature at time k+i+1.

[0029] Furthermore, the upper-level constraints are:

[0030]

[0031] Among them, f Ta and f κa It is the discrete form of the cabin air temperature dynamics and mass dynamics model, T m (k+i) represents the internal mass temperature at time k+i in the prediction time domain, T w (k+i) represents the predicted wall temperature at time k+i in the time domain, T a,min κ represents the minimum air temperature in the vehicle cabin. a (k+i+1) represents the CO2 concentration in the cabin air at time k+i+1 in the prediction time domain, κ a,min κ represents the minimum CO2 concentration in the vehicle cabin air. a,max This indicates the maximum CO2 concentration in the vehicle cabin air. This indicates the minimum cooling capacity of the air conditioning system. This indicates the maximum cooling capacity of the air conditioning system. This represents the minimum fresh air rate of the air conditioning system. T represents the maximum fresh air velocity of the air conditioning system. a (k+i+1) represents the cabin air temperature at time k+i+1.

[0032] Furthermore, the lower-level optimization objective is:

[0033]

[0034] Among them, the control variables in the optimization problem of the lower-level air conditioning system N s This represents the prediction time domain of the optimization problem for the lower-level air conditioning system, satisfying N s ≤N l ω3 is the weight parameter. This represents the optimal cabin air temperature at time k+i+1 in the upper-level optimal temperature sequence.

[0035] Furthermore, the lower-level constraints are as follows:

[0036]

[0037] in, For the optimal fresh air velocity sequence in the upper layer, f Tm and f Tw This represents the discrete form of the dynamic model of the mass inside the vehicle compartment and the temperature of the walls.

[0038] Furthermore, the optimization problem of the upper-level air conditioning system is solved using a dynamic programming algorithm, while the optimization problem of the lower-level air conditioning system is solved using a particle swarm optimization algorithm.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention proposes a cascaded strategy for planning the trajectory of cabin air temperature and air quality, and for tracking and predictive control of the air temperature trajectory. An upper-level optimization problem is set up to plan the trajectory of cabin air temperature and air quality, while a lower-level optimization problem tracks and controls the air temperature trajectory output by the upper-level optimization problem. This satisfies the heat load of the air conditioning system under all operating conditions while minimizing energy consumption. Furthermore, this invention adopts a hierarchical control approach, where the upper-level controller plans the optimal state trajectory of the cabin system, and the lower-level controller solves for the optimal control quantity of the lower-level air conditioning system. Compared to centralized predictive control, this significantly reduces the burden of solving the optimization problem. Attached Figure Description

[0041] Figure 1 This is a flowchart of the present invention;

[0042] Figure 2 This is a schematic diagram of the heat exchange process in the vehicle cabin model.

[0043] Figure 3 This is a comparison chart of rule-based control and hierarchical predictive control strategies, in which... Figure 3 (a) is a diagram of the vehicle speed trajectory. Figure 3 (b) shows the cabin air temperature diagrams under regular control and hierarchical predictive control. Figure 3 (c) shows the CO2 concentration in the cabin air under regular control and stratified predictive control. Figure 3 (d) shows the cooling capacity diagrams of the air conditioning system under regular control and hierarchical predictive control. Figure 3 (e) is a diagram of the fresh air rate of the air conditioning system under regular control and hierarchical predictive control; Figure 3 (f) shows the energy consumption diagrams of the air conditioning system under regular control and hierarchical predictive control.

[0044] Figure 4 This is a comparison chart of centralized predictive control and hierarchical predictive control strategies. Figure 4 (a) shows the cabin air temperature diagrams under centralized predictive control and hierarchical predictive control. Figure 4 (b) shows the CO2 concentration diagrams in the cabin air under centralized predictive control and stratified predictive control. Figure 4 (c) shows the cooling capacity diagrams for air conditioning systems under centralized predictive control and hierarchical predictive control. Figure 4 (d) shows the fresh air rate diagrams for air conditioning systems under centralized predictive control and hierarchical predictive control.

[0045] Figure 5 A comparison of the solution time for centralized predictive control and hierarchical predictive control strategies is shown in the figure. Figure 5 (a) represents the single-step calculation time for centralized predictive control. Figure 5 (b) represents the single-step computation time for hierarchical predictive control. Detailed Implementation

[0046] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0047] To balance the heat load of the air conditioning system and the energy consumption of the HVAC system, this invention proposes a thermal management method for the electric vehicle cabin that takes into account air quality. The flowchart of this invention is as follows: Figure 1 As shown.

[0048] The method of the present invention includes the following steps:

[0049] Acquire the current state feedback data, driving data, and environmental information of the electric vehicle;

[0050] Based on the cabin air temperature dynamics model and Taylor first-order expansion, the estimated temperature trajectory of the cabin's internal mass in the predicted time domain is obtained. and wall surface estimated temperature trajectory

[0051] Based on the vehicle cabin air quality dynamics model and the air conditioning system energy consumption model, an upper-level air conditioning system optimization problem is set up. This problem includes an upper-level optimization objective and upper-level constraints. Vehicle cabin air temperature and CO2 concentration are selected as state variables, while the cooling capacity and fresh air rate of the air conditioning system are selected as control variables. The temperature trajectory is estimated by combining state feedback data, driving data, environmental information, and vehicle cabin internal mass data. and wall surface estimated temperature trajectory The optimal temperature sequence and the optimal fresh air velocity sequence of the upper layer are obtained by solving the problem.

[0052] By combining the upper-level optimal temperature sequence and the upper-level optimal fresh air rate sequence, an optimization problem for the lower-level air conditioning system is established. The lower-level air conditioning system optimization problem includes the lower-level optimization objective and lower-level constraints. The cooling capacity of the air conditioning system is selected as the control variable to solve the lower-level optimal cooling capacity sequence.

[0053] The optimal fresh air rate sequence at the upper level and the optimal cooling capacity sequence at the lower level are integrated into a thermal management strategy for the electric vehicle cabin. The first value of the two sequences is used as the output of the optimal strategy to control the air conditioning system of the electric vehicle cabin. The above steps are repeated to update the two sequences.

[0054] Before implementing the method of the present invention, the cabin dynamics model of the electric vehicle is first analyzed. The cabin dynamics model includes the cabin air temperature dynamics model, the cabin air mass dynamics model, and the air conditioning system energy consumption model.

[0055] 1. Car cabin air temperature dynamics model

[0056] The cabin temperature can be divided into three parts: cabin air temperature, mass temperature, and wall temperature (denoted as "a", "m", and "w"). The heat exchange flow diagram is shown below. Figure 2 As shown. Mass temperature represents the temperature of the solids inside the cabin.

[0057] Its dynamic expression can be represented as follows:

[0058]

[0059] Where m a m m and m w These are the cabin air, interior mass, and lumped mass of the walls. C a C m and C w These are the cabin air, internal mass, and wall specific heat capacity. This is the heat generated by auxiliary devices (such as additional electronic laptops), assumed to be constant; It is the heat generated by the metabolism of the driver and passengers; It is the heat generated by the fresh air ventilating the vehicle cabin; It refers to the cooling capacity of the air conditioning system; and It is the convective heat transfer between the cabin air and internal mass, the air and the walls, and the walls and the outside air; and Q is the heat generated by solar radiation on the mass of the cabin and the walls; m2w This refers to the radiative heat transfer between the internal mass and the wall surface. These heat load models will be presented below.

[0060] Heat generated by the metabolism of the cabin occupants It can be represented as follows:

[0061]

[0062] in and is the heat load of the driver and a single passenger, and n is the number of passengers.

[0063] Heat load generated by ventilation air As shown below:

[0064]

[0065] in It is the airflow velocity entering the cabin, T e For ambient temperature, It is the recirculation of cabin air. It represents all fresh air. (Represents all cabin air). Defines the fresh air flow rate.

[0066] Cooling capacity The heat load carried away by the evaporator is the direct control variable of the controller.

[0067] Convection heat transfer and It is expressed as follows:

[0068]

[0069] U a2m U a2w and U w2e A is the convective heat transfer coefficient A between the cabin air, walls, and outside air. m and A w This refers to the internal mass of the vehicle compartment and the corresponding surface area of ​​the compartment walls. For parameter U... w2e It consists of internal convection, conduction through the surface, and external convection, and is written as follows:

[0070]

[0071] Where δ w and λ w It refers to the thickness and thermal conductivity of the bulkhead; c1 and c2 are empirical parameters; v a and v e This refers to the airflow velocity inside or outside the vehicle cabin. Assume v a =0,v e =v veh .

[0072] Interior mass of the vehicle cabin and radiative heat transfer from the walls As shown below:

[0073]

[0074] Where σ is the Stefan Boltzmann constant, ε m It refers to the internal mass of the vehicle cabin and the emissivity between the walls, which depends on the color of the internal mass.

[0075] Solar radiation heat transfer and It is expressed as follows:

[0076]

[0077] Where, τ g It is the transmittance of the glass, I sun It is radiation per unit area, A g It is the surface area of ​​the cockpit glass, α m and α w It refers to the internal mass of the cabin or the absorption rate of the walls.

[0078] 2. Cabin aerodynamics model

[0079] The air quality in the train cabin deteriorates due to the breathing of the driver and passengers. Therefore, it is necessary to regulate air quality through ventilation equipment. The appropriate CO2 concentration (κ) for the train cabin air should be selected. a As an air quality assessment standard, its dynamic model is represented by the following formula:

[0080]

[0081] Among them, V a f is the amount of air in the vehicle cabin. hum V represents the driver's / passenger's breathing rate. hum ρ is the volume of a single person's breath in a single breath. a For the air density in the vehicle cabin, κ hum and κ e This refers to the CO2 concentration in the respiratory and external air.

[0082] 3. Energy consumption model of air conditioning system

[0083] The energy consumption model of an air conditioning system is usually established as an energy efficiency model, that is:

[0084]

[0085] Where, η cop ,η stoich These represent the HVAC system efficiency coefficients under certain air pressure driving and parking conditions, respectively, where γ is related to vehicle speed v. veh Related correction factors.

[0086] After the analysis is completed, the steps of this invention are executed, firstly to obtain the current state feedback data, driving data and environmental information of the electric vehicle.

[0087] Status feedback data includes internal mass temperature T m Wall temperature T w Cabin air temperature T a and CO2 concentration in the cabin air a Driving data includes vehicle speed v veh Environmental information includes ambient temperature T. e CO2 concentration in the outside air κ e The internal mass temperature represents the temperature of solids inside the cabin of an electric vehicle.

[0088] Then, the optimization problems of the upper-level air conditioning system and the lower-level air conditioning system are established. The two models adopt a hierarchical approach and are solved based on the upper-level controller and the lower-level controller, respectively.

[0089] The upper-level controller plans the temperature and air quality trajectories within the vehicle cabin, while the lower-level controller precisely controls the cabin temperature. During the cabin temperature planning process, the heat load generated by ventilation is a significant factor. Therefore, the upper-level controller plans the trajectories for air temperature and quality.

[0090] In this invention, the first derivatives of the mass temperature and wall temperature are obtained based on the vehicle cabin air temperature dynamics model, and the estimated mass temperature inside the vehicle cabin in the prediction time domain is obtained based on the first derivatives. Trajectory and wall surface temperature estimation The specific steps for tracing the trajectory are as follows:

[0091] Let the current time be k, and let the mass temperature T at time k+i be... m and wall temperature T w A first-order Taylor expansion is performed, the sampling time is set, and the first derivatives of the mass temperature and wall temperature at time k are obtained by combining the cabin air temperature dynamics model. Here, i represents the time index in the prediction time domain of the upper-level air conditioning system optimization problem. Based on the first derivatives, the estimated mass temperature inside the cabin in the prediction time domain is obtained. Trajectory and wall surface temperature estimation Trajectory.

[0092] Specifically, considering the complexity of solving high-order nonlinear optimization problems, and due to the indirect correlation between the vehicle cabin's internal mass and wall thermodynamics models and the control variables, as well as their large heat capacity, estimations are performed to simplify the vehicle cabin thermodynamic model. Based on a first-order Taylor expansion, the variable T at time k... m and T w Represented as:

[0093]

[0094] In this model, the sampling time Δt for the cabin thermal and air quality models is set to 1 second. According to formula (1), the variables at time k... and It can be represented as follows:

[0095]

[0096] in:

[0097]

[0098] Therefore, the temperature inside the vehicle cabin is estimated in the predicted time domain. and wall surface temperature estimation The trajectory is described as follows:

[0099]

[0100] Next, based on the cabin air quality dynamics model and the air conditioning system energy consumption model, we set up an upper-level air conditioning system optimization problem, which includes upper-level optimization objectives and upper-level constraints.

[0101] The upper-level optimization objective is defined as minimizing the energy consumption of the air conditioning system while meeting the thermal comfort and air quality requirements of passengers. This upper-level optimization objective, or cost function, consists of three parts: the first term J1 is the energy consumption of the HVAC system, the second term J2 is the cabin air set temperature T, and so on. set The tracking deviation, and the last term J3, is a relaxation term reflecting cabin temperature regulation related to speed sensitivity. These expressions are shown below:

[0102]

[0103] Where ε is a very small positive number (e.g., 10). -5 To avoid invalidating relaxation terms, the upper and lower boundaries of cabin air quality are used as constraints in trajectory planning. Therefore, the optimization problem of the upper-level air conditioning system at time k is as follows:

[0104]

[0105]

[0106] Among control variables N l It is the prediction time domain of the upper-level controller, ω 1,2 It is the weight parameter, f Ta and f κa It is a discrete form of cabin air temperature and mass dynamics, and the disturbance This involves estimating the cabin air, internal mass, and wall temperature based on sensor feedback at time k within the prediction time domain (ignoring measurement noise). In the optimization problem, T... a and κ a Selected as a state variable and The state variable was selected as the control variable. Due to the highly nonlinear nature of the state-space equations (the state and control variables are multiplicative), the temperature was estimated based on a dynamic programming algorithm, combined with state feedback data, driving data, environmental information, and the mass of the vehicle cabin. Trajectory and wall surface temperature estimation The trajectory is obtained by minimizing the upper-level optimization objective by the upper-level controller, and the upper-level optimal temperature sequence and upper-level optimal fresh air rate sequence are obtained by solving the problem.

[0107] Because the optimization problem of the upper-level air conditioning system neglects the higher-order Taylor expansion remainder terms of the cabin interior mass and wall temperature, the optimal control variable trajectory of the upper-level controller is... The model is relatively crude. Therefore, further optimization of the air temperature is crucial. Meanwhile, since the model is not yet simplified, further optimization of the cabin air quality dynamics is unnecessary. The optimization objective of the lower-level air conditioning system optimization problem is to minimize the energy consumption of the cabin air conditioning system while tracking the temperature trajectory output by the upper-level controller. The temperature dynamics model is of full order, unlike the reduced-order temperature model in the upper-level controller.

[0108] The optimal temperature sequence and optimal fresh air rate sequence of the upper-level air conditioning system optimization problem are output and input into the lower-level air conditioning system optimization problem.

[0109] The optimization problem of the lower-level air conditioning system at time k is:

[0110]

[0111]

[0112] Among them, the control variables in the optimization problem of the lower-level air conditioning system i represents the index of the time in the prediction time domain of the upper-level air conditioning system optimization problem, N s This represents the prediction time domain of the optimization problem for the lower-level air conditioning system, satisfying N s ≤N l ω3 is the weighting parameter, T a (k+i+1) represents the cabin air temperature at time k+i+1. This represents the optimal cabin air temperature at time k+i+1 in the upper-level optimal temperature sequence.

[0113] This represents the sequence of optimal fresh air rates in the predicted time domain given by the upper-level controller, which is also the sequence of fresh air rates output by the upper-level controller. The particle swarm optimization algorithm is used to solve the optimization problem of the lower-level air conditioning system. This lower-level air conditioning system optimization problem is a single-controller, multi-state optimization problem. By adjusting the iteration exit criteria, such as the maximum number of iterations and the current global optimum duration, a reasonable solution close to the global optimum can be obtained within a finite computation time. The resulting solution is the lower-level optimal temperature sequence.

[0114] Finally, the optimal fresh air rate sequence obtained from the upper-level air conditioning system optimization problem and the optimal temperature sequence obtained from the lower-level air conditioning system optimization problem are integrated into an electric vehicle cabin thermal management strategy, and the electric vehicle cabin air conditioning system is controlled based on the electric vehicle cabin thermal management strategy.

[0115] The simulation experiment will be conducted below:

[0116] To evaluate the energy-saving potential of the hierarchical control architecture proposed in this invention, it is compared with a rule-based control strategy, and its control law expression is as follows:

[0117]

[0118] To avoid loss of generality, the cabin temperature was set to 26℃, the comfort range to [25, 27]℃, and the upper limit of air quality to 700ppm. Initial cabin air quality, mass, and wall temperatures were set to 35℃, 40℃, and 35℃ respectively; air quality was set to 400ppm. With a prediction time domain of 9s, the simulation results are as follows... Figure 3 As shown, the results indicate that the hierarchical control strategy can effectively improve computing efficiency and has high energy-saving potential without sacrificing cabin comfort, and can effectively alleviate the energy consumption burden of HVAC systems in hot weather.

[0119] Furthermore, to verify the effectiveness of the hierarchical control architecture proposed in this invention, it is compared with the nonlinear MPC control strategy of the full-order dynamic model. The optimization problem is as follows:

[0120]

[0121]

[0122] Simulation results are as follows Figure 4-5 As shown, the results demonstrate that the designed hierarchical control strategy can effectively improve computational efficiency without sacrificing control performance.

[0123] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for thermal management of the cabin of an electric vehicle that takes air quality into account, characterized in that, The methods include: Acquire the current state feedback data, driving data, and environmental information of the electric vehicle; Based on the cabin air temperature dynamics model and Taylor first-order expansion, the estimated temperature trajectory of the cabin's internal mass in the predicted time domain is obtained. and wall surface estimated temperature trajectory Based on the vehicle cabin air quality dynamics model and the air conditioning system energy consumption model, an upper-level air conditioning system optimization problem is set up. This problem includes an upper-level optimization objective and upper-level constraints. Vehicle cabin air temperature and CO2 concentration are selected as state variables, while the cooling capacity and fresh air rate of the air conditioning system are selected as control variables. The temperature trajectory is estimated by combining state feedback data, driving data, environmental information, and vehicle cabin internal mass data. and wall surface estimated temperature trajectory The optimal temperature sequence and the optimal fresh air velocity sequence of the upper layer are obtained by solving the problem. By combining the upper-level optimal temperature sequence and the upper-level optimal fresh air rate sequence, an optimization problem for the lower-level air conditioning system is established. The lower-level air conditioning system optimization problem includes the lower-level optimization objective and lower-level constraints. The cooling capacity of the air conditioning system is selected as the control variable to solve the lower-level optimal cooling capacity sequence. The optimal fresh air rate sequence at the upper level and the optimal cooling capacity sequence at the lower level are integrated into a thermal management strategy for the electric vehicle cabin. The first value of the two sequences is used as the output of the optimal strategy to control the air conditioning system of the electric vehicle cabin. The above steps are repeated to update the two sequences.

2. The method for thermal management of an electric vehicle cabin considering air quality according to claim 1, characterized in that, Status feedback data includes internal mass temperature T m Wall temperature T w Cabin air temperature T a and CO2 concentration in the cabin air a Driving data includes vehicle speed v veh Environmental information includes ambient temperature T. e CO2 concentration in the outside air κ e The internal mass temperature represents the temperature of solids inside the cabin of an electric vehicle.

3. The method for thermal management of an electric vehicle cabin considering air quality according to claim 2, characterized in that, The specific steps for obtaining the first derivatives of mass temperature and wall temperature based on the cabin air temperature dynamics model are as follows: Let the current time be k, and let the mass temperature T at time k+i be... m and wall temperature T w A first-order Taylor expansion is performed, and combined with the cabin air temperature dynamics model, the first-order derivatives of the mass temperature and wall temperature at time k are obtained, where i represents the index number in the prediction time domain.

4. The method for thermal management of an electric vehicle cabin considering air quality according to claim 3, characterized in that, The first derivatives of the mass temperature and the wall temperature at time k are: Among them, U a2m U a2w and U w2e A is the convective heat transfer coefficient between the cabin air, walls, and outside air. m and A w It refers to the internal mass of the vehicle compartment and the corresponding surface area of ​​the compartment walls, in meters. a m m and m w These are the cabin air, interior mass, and lumped wall mass, C. a C m and C w These are the cabin air, internal mass, and wall specific heat capacity, respectively; σ is the Stefan-Boltzmann constant; ε m It is the internal mass of the vehicle cabin and the emissivity between the walls, τ g It is the transmittance of the glass, I sun It is radiation per unit area, A g It is the surface area of ​​the cockpit glass, α m and α w It refers to the cabin interior mass and wall absorptivity, v veh (k) represents the vehicle speed at the current time k.

5. A method for thermal management of an electric vehicle cabin considering air quality according to claim 4, characterized in that, Estimated temperature of the vehicle cabin interior Trajectory and wall surface temperature estimation The trajectory is: Where f and g represent the estimated temperature trajectories of the vehicle cabin's internal mass, respectively. and wall surface estimated temperature trajectory With the current internal mass temperature T m Wall temperature T w and cabin air temperature T a The mapping relationship, where i represents the index number in the prediction time domain.

6. A method for thermal management of an electric vehicle cabin considering air quality according to claim 1, characterized in that, The specific optimization objectives at the upper level are: Among them, the control variables in the upper-level air conditioning system optimization problem Indicates the cooling capacity of the air conditioning system. N represents the fresh air rate of the air conditioning system. l It is the prediction time domain of the upper-level controller, ω 1,2 It is the weight parameter, P ac Let T be the power consumption of the air conditioning system in the energy consumption model, Δt be the sampling step size, γ be the correction factor, ε be the relaxation coefficient, and T be the power consumption of the air conditioning system in the model. set Indicates the cabin air temperature setting, T a,max T represents the maximum air temperature inside the vehicle cabin. a (k+i+1) represents the cabin air temperature at time k+i+1.

7. A method for thermal management of an electric vehicle cabin considering air quality according to claim 6, characterized in that, The upper-level constraints are: Among them, f Ta and f κa It is the discrete form of the cabin air temperature dynamics and mass dynamics model, T m (k+i) represents the internal mass temperature at time k+i in the prediction time domain, T w (k+i) represents the predicted wall temperature at time k+i in the time domain, T a,min κ represents the minimum air temperature in the vehicle cabin. a (k+i+1) represents the CO2 concentration in the cabin air at time k+i+1 in the prediction time domain, κ a,min κ represents the minimum CO2 concentration in the vehicle cabin air. a,max This indicates the maximum CO2 concentration in the vehicle cabin air. This indicates the minimum cooling capacity of the air conditioning system. This indicates the maximum cooling capacity of the air conditioning system. This represents the minimum fresh air rate of the air conditioning system. T represents the maximum fresh air velocity of the air conditioning system. a (k+i+1) represents the cabin air temperature at time k+i+1.

8. A method for thermal management of an electric vehicle cabin considering air quality according to claim 7, characterized in that, The lower-level optimization objective is: Among them, the control variables in the optimization problem of the lower-level air conditioning system N s This represents the prediction time domain of the optimization problem for the lower-level air conditioning system, satisfying N s ≤N l ω3 is the weight parameter. This represents the optimal cabin air temperature at time k+i+1 in the upper-level optimal temperature sequence.

9. A method for thermal management of an electric vehicle cabin considering air quality according to claim 8, characterized in that, The lower-level constraints are as follows: in, For the optimal fresh air velocity sequence in the upper layer, f Tm and f Tw This represents the discrete form of the dynamic model of the mass inside the vehicle compartment and the temperature of the walls.

10. A method for thermal management of an electric vehicle cabin considering air quality according to claim 9, characterized in that, The dynamic programming algorithm is used to solve the optimization problem of the upper-level air conditioning system; the particle swarm optimization algorithm is used to solve the optimization problem of the lower-level air conditioning system.

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