A cabin comfort control method and apparatus

Abstract

The application provides a cabin comfort control method and device, and the method specifically comprises the following steps: (1) receiving a reservation instruction, wherein the reservation instruction carries a reservation time and a given comfort index; (2) obtaining a cabin optimal control strategy for reaching the comfort index at the reservation time through a grey-box heat-humidity coupling state equation. The application constructs a grey-box heat-humidity coupling state equation, and obtains the cabin optimal control strategy for reaching the comfort index at the reservation time through the grey-box heat-humidity coupling state equation, so that the hardware life is guaranteed as much as possible, the energy consumption and noise are reduced as much as possible, and the cabin comfort is ensured.

Description

Technical Field

[0001] This invention belongs to the field of vehicle control technology, and more specifically, this invention relates to a method and device for controlling cabin comfort. Background Technology

[0002] With the increasing popularity of smart electric vehicles, users are more and more likely to make reservations for vehicles in advance through mobile apps, and the demand for a comfortable cabin environment in the vehicle at the reserved time is also gradually increasing.

[0003] Existing vehicle air conditioning and thermal management systems mostly adopt rule-based threshold control strategies, such as turning on the air conditioning in advance according to a fixed time, or switching between internal and external circulation modes based on the temperature difference threshold between the inside and outside of the vehicle. Although the above methods are simple to implement, they have obvious shortcomings in terms of energy saving effect and precise control of comfort. Summary of the Invention

[0004] In view of this, this application provides a cabin comfort control method to improve at least one of the above-mentioned problems.

[0005] Specifically, the following technical solutions are included:

[0006] On the one hand, embodiments of this application provide a cabin comfort control method, the method being as follows:

[0007] (1) Receive reservation instructions, which include the reservation time and the given comfort index;

[0008] (2) Obtain the optimal cabin control strategy that achieves the comfort index at the scheduled time by using the gray box thermal-humid coupling state equation.

[0009] In some embodiments of the present invention, the thermal-humid coupling equation of state of the gray box includes: a difference equation for the cabin temperature changing with time, a difference equation for the equivalent temperature of the interior changing with time, a difference equation for the absolute humidity of the cabin changing with time, a difference equation for the concentration of pollutants changing with time, and a difference equation for the temperature of the inner surface of the glass changing with time.

[0010] In some embodiments of the present invention, the difference equation for the change of cabin temperature over time is as follows:

[0011] ;

[0012] Where T_cab(k+1) and T_cab(k) represent the cabin temperature at step (k+1) and step k, respectively; C_air represents the equivalent heat capacity of the cabin air; Δt represents the sampling period; UA_out represents the overall heat transfer coefficient between the cabin and the outside; T_cab(k) and T_out(k) represent the cabin temperature and the outside temperature at the current step k; UA_trim represents the coupling heat transfer coefficient between the interior and the cabin; T_trim(k) represents the equivalent temperature of the interior at the current step k; Q_cool(k) represents the air conditioning cooling at the current step k; Q_solar(k) represents the solar heating power of the cabin air at the current step k; and Q_vent(k) represents the heat power of the ventilation at the current step k.

[0013] In some embodiments of the present invention, the difference equation for the change of the equivalent temperature of the interior interior over time is as follows:

[0014] ;

[0015] Where T_trim(k+1) and T_trim(k) represent the equivalent interior temperature at step k+1 and step k, respectively; C_trim represents the equivalent heat capacity of the interior; UA_trim represents the coupling heat transfer coefficient between the interior and the cabin; K_sol_trim represents the coefficient of solar radiation absorption by the interior; G_sol(k) represents the solar irradiance at the current step k; and T_cab(k) represents the cabin temperature at the current step k.

[0016] In some embodiments of the present invention, the difference equation for the change of cabin absolute humidity over time is as follows:

[0017] ;

[0018] Where H_cab(k+1) and H_cab(k) represent the absolute humidity of the cabin at step (k+1) and step k, respectively; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k, and V_cab represents the cabin volume; W_out(k) represents the absolute humidity of the outside air at step k; ṁ_dehum(k) represents the dehumidification rate of the air conditioner at step k; and ρ_air represents the air density.

[0019] In some embodiments of the present invention, the difference equations for the change of pollutant concentration over time are as follows:

[0020] ;

[0021] Where C_poll(k+1) and C_poll(k) represent the cabin pollutant concentrations at steps k+1 and k, respectively; C_out(k) represents the external pollutant concentration at step k; η_filter(k) represents the filter efficiency at step k; k_sink represents the natural sinking rate of pollutants; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k; and V_cab represents the cabin volume.

[0022] In some embodiments of the present invention, the difference equation for the change of the inner surface temperature of the glass over time is expressed as follows:

[0023] ;

[0024] Where T_glass(k+1) and T_glass(k) represent the inner surface temperature of the glass at step k+1 and step k, respectively; C_glass represents the heat capacity of the glass; h_in represents the convective heat transfer coefficient of the air inside the vehicle to the glass; h_out represents the convective heat transfer coefficient of the air outside the vehicle to the glass; K_sol_glass represents the coefficient of solar radiation absorption by the glass; G_sol(k) represents the solar irradiance at step k; T_dew represents the dew point temperature; T_cab(k) and T_out(k) represent the cabin temperature and the outside temperature at step k, respectively.

[0025] In some embodiments of the present invention, the timestamp t of the reservation instruction receiving time and the reservation time T_res are obtained, and the time period from time t to reservation time T_res is divided into N steps. Then the optimization objective J is expressed as follows:

[0026] ;

[0027] Where w_e, w_s, w_n, w_l, w_aq, and w_terminal are all weight values; P_comp(k) and P_bl(k) represent the compressor power and fan power at step k, respectively; S_err(k) represents the comfort deviation at step k; Δf_comp(k) and Δn_bl(k) represent the compressor frequency change rate and fan speed change rate at step k, respectively; Δβ(k) represents the change rate of the external circulation ratio at step k; σ_com and f_comp(k) represent the weight coefficient of compressor life loss and compressor frequency at step k, respectively; σ_bl and n_bl(k) represent the weight coefficient of fan life loss and fan speed at step k, respectively; C_poll(k) and C_lim represent the current pollutant concentration and pollutant concentration threshold in the vehicle at step k, respectively; and S_terminal(N) represents the difference between the current comfort index in the vehicle and the given comfort index when the reservation time is reached.

[0028] On the other hand, embodiments of this application provide a cabin comfort control device, the device comprising:

[0029] The instruction receiving unit is used to receive reservation instructions, which carry the reservation time and given comfort indicators.

[0030] The data acquisition unit is used to measure state quantities in real time.

[0031] The thermal-humidity coupled state equation update unit periodically updates the parameters based on the measured state quantities so that the state quantities predicted by the thermal-humidity coupled state equation of the gray box are close to the measured state quantities.

[0032] The prediction unit obtains the optimal cabin control strategy to achieve the comfort index at the scheduled time based on the updated thermal-humidity coupled state equation.

[0033] In some embodiments of the present invention, the timestamp t of the reservation instruction receiving time and the reservation time T_res are obtained, and the time period from time t to reservation time T_res is divided into N steps. Then the optimization objective J is expressed as follows:

[0034] ;

[0035] Where w_e, w_s, w_n, w_l, w_aq, and w_terminal are all weight values; P_comp(k) and P_bl(k) represent the compressor power and fan power at step k, respectively; S_err(k) represents the comfort deviation at step k; Δf_comp(k) and Δn_bl(k) represent the compressor frequency change rate and fan speed change rate at step k, respectively; Δβ(k) represents the change rate of the external circulation ratio at step k; σ_com and f_comp(k) represent the weight coefficient of compressor life loss and compressor frequency at step k, respectively; σ_bl and n_bl(k) represent the weight coefficient of fan life loss and fan speed at step k, respectively; C_poll(k) and C_lim represent the current pollutant concentration and pollutant concentration threshold in the vehicle at step k, respectively; and S_terminal(N) represents the difference between the current comfort index in the vehicle and the given comfort index when the reservation time is reached.

[0036] This invention constructs a thermal-humidity coupled state equation for a gray box, and uses this equation to obtain the optimal cabin control strategy that achieves the comfort index at the scheduled time. While ensuring cabin comfort, it minimizes energy consumption and noise, and maximizes hardware lifespan. Attached Figure Description

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

[0038] Figure 1 This is a flowchart of the cabin comfort control method provided in the embodiments of the present invention;

[0039] Figure 2 This is a schematic diagram of the cabin comfort control device provided in this embodiment;

[0040] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0041] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] Unless otherwise defined, all technical terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art.

[0043] This invention aims to provide an optimal cabin control strategy that minimizes energy consumption and noise while ensuring cabin comfort during scheduled times. Figure 1 This is a flowchart of a cabin comfort control method provided in an embodiment of the present invention. The method is as follows:

[0044] (1) Receive the reservation instruction sent by the customer, which carries the reservation time T_res and the given comfort index;

[0045] In this embodiment of the invention, the suitability conditions include: the user's desired target temperature T_set, the maximum absolute humidity of the cabin RH_max, and / or include: the upper limit of energy consumption E_budget, and the minimum remaining power SoC_min.

[0046] (2) Obtain the optimal cabin control strategy that achieves the comfort index at the scheduled time by using the gray box thermal-humid coupling state equation.

[0047] In this embodiment of the invention, the cockpit control process based on the gray box thermal-humid coupling state equation is as follows:

[0048] (21) Read the current state x(k) measured by the sensor;

[0049] (22) Divide the time period from the current time to the scheduled time T_res into N prediction steps, and obtain the perturbation vectors d(1), d(2), ..., d(N) for the next N steps;

[0050] (23) Predict the state x(1)~x(N) of each control sequence for the next N steps using the gray box thermal-humid coupling state equation, and calculate the total cost J corresponding to each control sequence; find the optimal control sequence with the minimum total cost J, the control sequence is the control vector of N steps, execute the first control vector in the optimal control sequence, return to step (21), until the control of N steps is completed, and the control vector of N steps constitutes the optimal control strategy of the cockpit.

[0051] When the energy consumption limit E_budget or the minimum remaining battery power SoC_min limit prevents the target temperature T_set from being reached at the scheduled time T_res, the system will provide the user with feedback on the "optimal achievable comfort value" and the "energy consumption-comfort trade-off slider", and offer a one-click switching strategy; the Pareto optimal solution is adopted by default.

[0052] In this embodiment of the invention, the current state vector x(k) at step k includes: the current cabin temperature T_cab(k) at step k, the current absolute humidity content of the cabin H_cab(k) at step k; the current trim equivalent temperature T_trim(k) at step k, the current glass inner surface temperature T_glass(k) at step k, and the current pollutant concentration C_poll(k) in the cabin at step k.

[0053] The control vector u(k) at the current k-th step includes: the compressor frequency or torque f_comp(k) at the current k-th step, with a value of [0,1]; the blower speed n_bl(k) at the current k-th step, with a value of [0,1]; the external fresh air ratio β(k) at the current k-th step, with a value of [0,1]; the throttle valve opening ξ(k) at the current k-th step, with a value of [0,1]; and the demisting duct allocation ratio α_def(k) at the current k-th step, with a value of [0,1].

[0054] The perturbation vector d(k) at the current k-th step includes: the current ambient temperature T_out(k), the current ambient relative humidity RH_out(k), the current ambient absolute humidity W_out(k), the current ambient solar irradiance G_sol(k), the current ambient wind speed v_wind(k), and the current ambient air quality index AQ_out(k).

[0055] The parameter vector θ to be identified includes: equivalent heat capacity of cabin air C_air, equivalent heat capacity of interior trim C_trim, total heat transfer coefficient between cabin and outside UA_out, coupled heat transfer coefficient between interior trim and cabin UA_trim, irradiation gain coefficient K_sol, evaporator cooling capacity model η_cool(·) (in W); dehumidification capacity model η_dehum(·) (in kg / s); compressor power model P_comp(·) (in W); blower power model P_bl(·) (in W); filtration efficiency function η_filter(·); permeation ventilation rate n_inf; and cabin volume V_cabn. The parameter vector θ is identified online and periodically based on the measured real state quantities. By adjusting the parameters in the gray box thermal-humidity coupling state equation, the state vector predicted by the gray box thermal-humidity coupling state equation is made close to the real state vector measured by the sensor.

[0056] In this embodiment of the invention, the thermal-humidity coupling state equation of the gray box includes: a difference equation for the cabin temperature changing with time, a difference equation for the equivalent temperature of the interior trim changing with time, a difference equation for the absolute humidity of the cabin changing with time, a difference equation for the pollutant concentration changing with time, and a difference equation for the temperature of the inner surface of the glass changing with time. The above five difference equations are explained in detail below:

[0057] In this embodiment of the invention, a difference equation for the change of cabin temperature over time is formed based on cabin thermal balance, as follows:

[0058] ;

[0059] Where T_cab(k+1) and T_cab(k) represent the cabin temperature at step (k+1) and step (k), respectively; C_air represents the equivalent heat capacity of the cabin air; and Δt represents the sampling period. This represents the vehicle body heat dissipation at the current k-th step, UA_out represents the total heat transfer coefficient between the cabin and the outside, and T_cab(k) and T_out(k) represent the cabin temperature and the outside temperature at the current k-th step. Let Q_cool(k) represent the interior heat exchange at step k, where UA_trim represents the coupling heat transfer coefficient between the interior and the cabin, and T_cab(k) and T_trim(k) represent the cabin temperature and interior equivalent temperature at step k, respectively. Q_cool(k) represents the air conditioning cooling at step k, which depends on the compressor speed f_comp, fan speed n_bl, throttle valve opening ξ, current temperature T_cab, and evaporator temperature T_evap at step k. Q_cool ≈ η_cool(·). Q_solar(k) represents the solar heating power to the cabin air at step k, Q_solar(k) = K_sol·G_sol(k), where K_sol represents the comprehensive gain coefficient for converting solar irradiance into actual heating power, and G_sol(k) represents the solar irradiance at step k. Q_vent(k) represents the heat power from ventilation at step k, which depends on the external circulation ratio β and the outside temperature T_out. Where ρ_air represents air density, c_p represents specific heat capacity of air, V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at the k-th step, and T_out(k) and T_cab(k) represent the outside air temperature and cabin temperature at the k-th step, respectively.

[0060] In this embodiment of the invention, a difference equation is formed based on the thermal balance of the interior interior to determine the equivalent temperature change over time, as follows:

[0061] ;

[0062] Where T_trim(k+1) and T_trim(k) represent the equivalent interior temperature at step (k+1) and step (k), respectively; C_trim represents the equivalent heat capacity of the interior; UA_trim represents the coupling heat transfer coefficient between the interior and the cabin; K_sol_trim represents the coefficient of solar radiation absorption by the interior; G_sol(k) represents the solar irradiance at the current step (k); and T_cab(k) represents the cabin temperature at the current step (k).

[0063] In this embodiment of the invention, a difference equation for the change of cabin absolute humidity over time is formed based on the cabin humidity balance, as follows:

[0064] ;

[0065] Where H_cab(k+1) and H_cab(k) represent the absolute humidity of the cabin at step (k+1) and step k, respectively; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k, and V_cab represents the cabin volume; W_out(k) represents the absolute humidity of the outside air at step k; ṁ_dehum(k) represents the dehumidification rate of the air conditioner at step k, ṁ_dehum ≈ η_dehum(·); and ρ_air represents the air density.

[0066] In this embodiment of the invention, the difference equation for the change of pollutant concentration over time is as follows:

[0067] ;

[0068] Where C_poll(k+1) and C_poll(k) represent the cabin pollutant concentrations at steps k+1 and k, respectively; C_out(k) represents the external pollutant concentration at step k; η_filter(k) represents the filter efficiency at step k, with a value of 0-1; k_sink represents the natural settling rate of pollutants; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k; and V_cab represents the cabin volume.

[0069] In this embodiment of the invention, the difference equation for the change of the inner surface temperature of the glass over time is expressed as follows:

[0070] ;

[0071] Where T_glass(k+1) and T_glass(k) represent the inner surface temperature of the glass at step k+1 and step k, respectively; C_glass represents the heat capacity of the glass; h_in represents the convective heat transfer coefficient of the air inside the vehicle to the glass; h_out represents the convective heat transfer coefficient of the air outside the vehicle to the glass; K_sol_glass represents the coefficient of solar radiation absorption by the glass; G_sol(k) represents the solar irradiance at step k; T_dew represents the dew point temperature; T_cab(k), T_out(k), and T_glass(k) represent the cabin temperature, the outside temperature, and the glass surface temperature at step k, respectively.

[0072] In this embodiment of the invention, the timestamp t of the reservation instruction reception time and the reservation time T_res are obtained. The time t to the reservation time T_res are divided into N steps. The optimization objective J in the time domain [t, T_res] is expressed as follows:

[0073] ;

[0074] wherein w_e, w_s, w_n, w_l, w_aq and w_terminal are all weight values; P_comp (k) and P_bl (k) represent the compressor power and blower power at the k-th step respectively, wherein k takes a value from 1 to N; S_err(k) represents the comfort deviation at the k-th step, which is the temperature deviation and relative humidity deviation of the cockpit, wherein the comfort deviation S_err is defined as the weighting of the temperature deviation and relative humidity deviation of the cockpit; Δf_comp(k) and Δn_bl(k) represent the change rate of compressor frequency and the change rate of fan speed at the k-th step respectively, and Δβ(k) represents the change rate of external circulation ratio at the k-th step; σ_com and f_comp(k) represent the weight coefficient of compressor life loss and the compressor frequency at the k-th step respectively; σ_bl and n_bl(k) represent the weight coefficient of fan life loss and the fan speed at the k-th step respectively; C_poll(k) and C_lim represent the current pollutant concentration inside the vehicle at the k-th step and the concentration threshold of pollutants respectively; S_terminal(N) represents the difference between the current comfort index inside the vehicle and the given comfort index when the reservation time is reached, and w_terminal represents a large penalty coefficient, which imposes a large penalty for failing to meet the comfort target at the end time.

[0075] In the embodiment of the present invention, the optimization process needs to satisfy constraint conditions, including comfort constraint, anti-condensation constraint, air quality constraint, energy constraint and actuator constraint. The actuators include a compressor, a fan and an air door. The above constraints are expressed as follows:

[0076] Comfort constraint: the cockpit temperature T_cab(N) when the reservation time T_res is reached satisfies T_cab(N)∈[T_set±ΔT], and the relative humidity in the cockpit RH_in (N)≤ RH_max;

[0077] Anti-condensation constraint: T_glass(k) ≥ T_dew+ΔT_safe, ΔT_safe ≥ 1°C, and the dew point temperature T_dew = f_dew(T_cab, RH_cab), wherein based on the glass temperature T_glass (estimated by coupling irradiation, external temperature, vehicle speed and internal convection) and the dew point T_dew of air in the cockpit, when T_glass<T_dew+ΔT_safe, part of the air volume is automatically distributed to the defogging channel, and dehumidification or heating is performed for a short time to ensure visual field safety. ΔT_safe represents the allowable safety redundancy value of dew point, and ΔT_safe ≥ 1°C

[0078] Air quality constraint: the concentration of PM2.5 / ozone / chemical volatile organic compounds (VOC) ≤ threshold. If the concentration exceeds the standard, the filtration efficiency is increased or the external fresh air ratio β is decreased;

[0079] Energy constraint: SoC(k) ≥ SoC_min ;

[0080] Actuator constraints: Physical upper and lower limits and slope restrictions for compressors, fans, and dampers, as detailed below: Actuator range:

[0081] Compressor constraints: 0 ≤ f_comp(k) ≤ 1, and | f_comp(k) - f_comp(k+1)| ≤ r_comp, where r_comp represents the maximum allowable compressor frequency change per step;

[0082] Fan constraints: 0 ≤ n_bl(k)≤ 1, and | n_bl(k)-n_bl(k+1)| ≤ r_bl, where r_bl represents the maximum allowable change in fan speed at each step;

[0083] Damper constraints: 0 ≤ β(k) ≤ 1, and |β(k) - β(k+1)| ≤ r_β, where r_β represents the maximum allowable damper change in each step.

[0084] In this embodiment of the invention, the net enthalpy difference benefit ;

[0085] Where h_in(k) and h_out(k) represent the enthalpy of the air before entering the air handling unit and the enthalpy of the air after leaving the heat exchange / processing unit in step k, respectively; V̇_in(k) represents the fresh air volume in step k; and P_unit represents the electrical power required to deliver a unit volume of air; quality penalty Condensation risk When ΔR_h(k) > 0, ΔR_aq(k) < 0 and ΔR_dew(k) < 0, increase the proportion of fresh air β(k); otherwise, decrease the proportion of fresh air β(k).

[0086] This invention constructs a thermal-humidity coupled state equation for a gray box, and uses this equation to obtain the optimal cabin control strategy that achieves the comprehensive comfort index at the scheduled time. While ensuring cabin comfort, it minimizes energy consumption and noise, and maximizes hardware lifespan.

[0087] Scenario Setting: High temperature and humidity in summer, vehicles parked outdoors in direct sunlight for extended periods. Users reserve a car 30 minutes later via a mobile app (T_res = 30 min), setting the target cabin temperature to 24°C ± 1.5°C and the maximum relative humidity to 60%. Battery power is sufficient, and no energy consumption limit is set. Initialization and Data Acquisition: Environmental Prediction: Outside temperature 35°C → 33°C, RH_out 70%, solar radiation 900 W / m² → decreasing, wind speed 2 m / s, AQI good. Initial State: T_cab(0) = 55°C, RH_cab(0) = 40%, C_poll(0) = 25 μg / m³. Online Model Parameter Identification: The system calls a pre-stored gray box model and uses real-time data from the first 2 minutes to recursively correct parameters such as C_trim and n_inf, improving prediction accuracy. MPC Rolling Optimization Process: Ventilation Priority Phase (0-12 minutes): Outside temperature 35°C is much lower than the vehicle interior temperature of 55°C, and air quality is good. MPC output β≈0.9 (high external circulation), n_bl=0.7, f_comp=0. Predicted T_cab drops to 42°C. Mixing transition and compressor intervention (13-25 minutes): insufficient ventilation cooling rate, β gradually changes from 0.9 to 0.4, f_comp smoothly increases from 0 to 0.5, n_bl drops to 0.4. Fine maintenance phase (26 minutes to T_res): β=0.1, f_comp=0.2, n_bl=0.3, low frequency maintains comfort. No condensation risk throughout, air quality meets standards, control results: T_cab = 24.8°C, RH_cab = 55%, meeting comfort constraints. Total energy consumption is 0.45 kWh, saving approximately 37.5% energy compared to a fixed threshold strategy (0.72 kWh).

[0088] Figure 2 This is a schematic diagram of the cabin comfort control device provided in this embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. The device includes:

[0089] The instruction receiving unit is used to receive reservation instructions, which carry the reservation time and given comfort indicators.

[0090] The data acquisition unit is used to measure state quantities in real time.

[0091] The thermal-humidity coupled state equation update unit periodically updates the parameters based on the measured state quantities so that the state quantities predicted by the thermal-humidity coupled state equation of the gray box are close to the measured state quantities.

[0092] The prediction unit obtains the optimal cabin control strategy to achieve the comfort index at the scheduled time based on the updated thermal-humidity coupled state equation.

[0093] In this embodiment of the invention, the prediction unit obtains the optimal cabin control strategy that achieves the comfort index at the reservation time based on the updated gray box thermal-humid coupling state equation. The control process of the optimal cabin control strategy is as follows: (21) Read the current state quantity x(k) measured by the sensor; (22) Divide the time period from the reservation instruction receiving time to the reservation time T_res into N prediction steps, and obtain the disturbance vectors d(1), d(2), ..., d(N) for the next N steps; (23) Predict the state x(1)~x(N) for the next N steps under each control sequence using the gray box thermal-humid coupling state equation, and calculate the total cost J corresponding to each control sequence; find the optimal control sequence with the smallest total cost J, the control sequence is the control vector of N steps, execute the first control vector in the optimal control sequence, return to step (21), until the control of N steps is completed, and the control vector of N steps constitutes the optimal control strategy of the cabin.

[0094] When the energy consumption limit E_budget or the minimum remaining battery power SoC_min limit prevents the target temperature T_set from being reached at the scheduled time T_res, the system will provide the user with feedback on the "optimal achievable comfort value" and the "energy consumption-comfort trade-off slider", and offer a one-click switching strategy; the Pareto optimal solution is adopted by default.

[0095] In this embodiment of the invention, the current state vector x(k) at step k includes: the cabin temperature T_cab(k) at step k, the cabin absolute humidity H_cab(k) at step k; the trim equivalent temperature T_trim(k) at step k, the glass inner surface temperature T_glass(k) at step k, and the contaminant concentration C_poll(k) in the cabin at step k; the control vector u(k) at step k includes: the compressor frequency at step k. Or torque f_comp(k), with a value of [0,1]; blower speed n_bl(k) in the current k-th step, with a value of [0,1]; external fresh air ratio β(k) in the current k-th step, with a value of [0,1]; throttle valve opening ξ(k) in the current k-th step, with a value of [0,1]; demisting duct allocation ratio α_def(k) in the current k-th step, with a value of [0,1]; disturbance vector d(k) in the current k-th step includes: external temperature T_out(k) in the current k-th step. The parameters to be identified include: the relative humidity RH_out(k) of the outside air at the current k-th step, the absolute humidity content of the outside air at the current k-th step, the solar irradiance G_sol(k) of the current k-th step, the wind speed outside the vehicle at the current k-th step, and the outside air quality index AQ_out(k) of the current k-th step; the parameter vector θ to be identified includes: the equivalent heat capacity of the cabin air C_air, the equivalent heat capacity of the interior trim C_trim, the total heat transfer coefficient between the cabin and the outside air UA_out, the coupled heat transfer coefficient between the interior trim and the cabin UA_trim, the irradiance gain coefficient K_sol, the evaporator cooling capacity model η_cool(·) in W; the dehumidification capacity model η_dehum(·) in kg / s; the compressor power model P_comp(·) in W, the blower power model P_bl(·) in W; the filtration efficiency function η_filter(·), the infiltration ventilation rate n_inf, and the cabin volume V_cabn. Among them, the parameter vector θ is identified online and periodically based on the measured true state quantities. By adjusting the parameters in the gray box thermal-humidity coupling state equation, the state quantities predicted by the gray box thermal-humidity coupling state equation are made close to the true state quantities measured by the sensor.

[0096] In this embodiment of the invention, the thermal-humidity coupling state equation of the gray box includes: a difference equation for the cabin temperature changing with time, a difference equation for the equivalent temperature of the interior trim changing with time, a difference equation for the absolute humidity of the cabin changing with time, a difference equation for the pollutant concentration changing with time, and a difference equation for the temperature of the inner surface of the glass changing with time. The above five difference equations are explained in detail below:

[0097] In this embodiment of the invention, a difference equation for the change of cabin temperature over time is formed based on cabin thermal balance, as follows:

[0098] ;

[0099] Where T_cab(k+1) and T_cab(k) represent the cabin temperature at step (k+1) and step (k), respectively; C_air represents the equivalent heat capacity of the cabin air; and Δt represents the sampling period. This represents the vehicle body heat dissipation at the current k-th step, where UA_out represents the total heat transfer coefficient between the cabin and the outside, and T_cab(k) and T_out(k) represent the cabin temperature and the outside temperature at the current k-th step, respectively. Let UA_trim represent the interior heat exchange at step k, and UA_trim represent the coupling heat transfer coefficient between the interior and the cabin. Here, T_cab(k) and T_trim(k) represent the cabin temperature and interior equivalent temperature at step k, respectively. Q_cool(k) represents the air conditioning cooling at step k, which depends on the compressor speed f_comp, fan speed n_bl, throttle valve opening ξ, current temperature T_cab, and evaporator temperature T_evap at step k. Q_cool ≈ η_cool(·). Q_solar(k) represents the solar heating power to the cabin air at step k, Q_solar(k) = K_sol·G_sol(k), where K_sol represents the comprehensive gain coefficient converting solar irradiance into actual heating power, and G_sol(k) represents the solar irradiance at step k. Q_vent(k) represents the heat power from ventilation at step k. Where ρ_air represents air density, c_p represents specific heat capacity of air, V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at the k-th step, and T_out(k) and T_cab(k) represent the outside air temperature and cabin temperature at the k-th step, respectively.

[0100] In this embodiment of the invention, a difference equation is formed based on the thermal balance of the interior interior to determine the equivalent temperature change over time, as follows:

[0101] ;

[0102] Where T_trim(k+1) and T_trim(k) represent the equivalent interior temperature at step (k+1) and step (k), respectively; C_trim represents the equivalent heat capacity of the interior; UA_trim represents the coupling heat transfer coefficient between the interior and the cabin; K_sol_trim represents the coefficient of solar radiation absorption by the interior; G_sol(k) represents the solar irradiance at the current step (k); and T_cab(k) represents the cabin temperature at the current step (k).

[0103] In this embodiment of the invention, a difference equation for the change of cabin absolute humidity over time is formed based on the cabin humidity balance, as follows:

[0104]

[0105] Where H_cab(k+1) and H_cab(k) represent the absolute humidity of the cabin at step (k+1) and step k, respectively; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k, and V_cab represents the cabin volume; W_out(k) represents the absolute humidity of the outside air at step k; ṁ_dehum(k) represents the dehumidification rate of the air conditioner at step k, ṁ_dehum ≈ η_dehum(·); and ρ_air represents the air density.

[0106] In this embodiment of the invention, the difference equation for the change of pollutant concentration over time is as follows:

[0107]

[0108] Where C_poll(k+1) and C_poll(k) represent the cabin pollutant concentrations at steps k+1 and k, respectively; C_out(k) represents the external pollutant concentration at step k; η_filter(k) represents the filter efficiency at step k, with a value of 0-1; k_sink represents the natural settling rate of pollutants; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k; and V_cab represents the cabin volume.

[0109] In this embodiment of the invention, the difference equation for the change of the inner surface temperature of the glass over time is expressed as follows:

[0110] ;

[0111] Where T_glass(k+1) and T_glass(k) represent the inner surface temperature of the glass at step k+1 and step k, respectively; C_glass represents the heat capacity of the glass; h_in represents the convective heat transfer coefficient of the air inside the vehicle to the glass; h_out represents the convective heat transfer coefficient of the air outside the vehicle to the glass; K_sol_glass represents the coefficient of solar radiation absorption by the glass; G_sol(k) represents the solar irradiance at step k; T_dew represents the dew point temperature; T_cab(k), T_out(k), and T_glass(k) represent the cabin temperature, the outside temperature, and the glass surface temperature at step k, respectively.

[0112] In this embodiment of the invention, the timestamp t of the reservation instruction reception time and the reservation time T_res are obtained. The time t to the reservation time T_res are divided into N steps. The optimization objective J in the time domain [t, T_res] is expressed as follows:

[0113] ;

[0114] wherein w_e, w_s, w_n, w_l, w_aq and w_terminal are all weight values; P_comp(k) and P_bl(k) respectively represent the compressor power and blower power at the k-th step, and k takes a value from 1 to N; S_err(k) represents the comfort deviation at the k-th step, which is the temperature deviation and relative humidity deviation of the cabin, wherein the comfort deviation S_err is defined as the weighting of the temperature deviation and relative humidity deviation of the cabin; Δf_comp(k) and Δn_bl(k) represent the change rate of compressor frequency and the change rate of fan speed at the k-th step, and Δβ(k) represents the change rate of external circulation proportion at the k-th step; σ_com and f_comp(k) respectively represent the weight coefficient of compressor life loss and the compressor frequency at the k-th step; σ_bl and n_bl(k) respectively represent the weight coefficient of fan life loss and the fan speed at the k-th step; C_poll(k) and C_lim respectively represent the current pollutant concentration inside the vehicle at the k-th step and the concentration threshold of pollutants; S_terminal(N) represents the difference between the current vehicle comfort index and the given comfort index when the appointment time is reached, and w_terminal represents a huge penalty coefficient, which imposes a huge penalty when the comfort target is not reached at the end time.

[0115] In the embodiment of the present invention, the optimization process needs to satisfy constraint conditions, including comfort constraint, anti-condensation constraint, air quality constraint, energy constraint and actuator constraint. The actuators include a compressor, a fan and an air damper, and the above constraints are expressed as follows:

[0116] Comfort constraint: when the appointment time T_res is reached, the cabin temperature T_cab(N)∈[T_set±ΔT], and the relative humidity in the cabin RH_in (N)≤ RH_max;

[0117] Anti-condensation constraint: T_glass(k) ≥ T_dew+ΔT_safe, ΔT_safe ≥ 1°C, and the dew point temperature T_dew = f_dew(T_cab, RH_cab), wherein based on the glass temperature T_glass (estimated by coupling irradiation, external temperature, vehicle speed and internal convection) and the dew point T_dew of the air inside the cabin, when T_glass<T_dew+ΔT_safe, part of the air volume is automatically distributed to the defogging channel, and dehumidification or heating is performed for a short time to ensure visual field safety, ΔT_safe represents the allowable safety redundancy value for dew point, and ΔT_safe ≥ 1°C

[0118] Air quality constraint: the concentration of PM2.5 / ozone / chemical volatile organic compounds (VOC) ≤ threshold, if the concentration exceeds the standard, the filtration efficiency is increased or the proportion of external fresh air β is reduced;

[0119] Energy constraint: SoC(k) ≥ SoC_min, ;

[0120] Actuator constraints: Physical upper and lower limits and slope restrictions for compressors, fans, and dampers, as detailed below: Actuator range:

[0121] Compressor constraints: 0 ≤ f_comp(k) ≤ 1, and | f_comp(k) - f_comp(k+1)| ≤ r_comp, where r_comp represents the maximum allowable compressor frequency change per step;

[0122] Fan constraints: 0 ≤ n_bl(k)≤ 1, and | n_bl(k)-n_bl(k+1)| ≤ r_bl, where r_bl represents the maximum allowable change in fan speed at each step;

[0123] Damper constraints: 0 ≤ β(k) ≤ 1, and |β(k) - β(k+1)| ≤ r_β, where r_β represents the maximum allowable damper change in each step.

[0124] In this embodiment of the invention, the net enthalpy difference benefit h_in(k) and h_out(k) represent the enthalpy of the air before entering the air handling unit and the enthalpy of the air after leaving the heat exchange / treatment unit in step k, respectively; V̇_in(k) represents the fresh air volume in step k; P_unit represents the electrical power required to deliver a unit volume of air; and mass penalty. Condensation risk When ΔR_h(k) > 0, ΔR_aq(k) < 0 and ΔR_dew(k) < 0, increase the proportion of fresh air β(k); otherwise, decrease the proportion of fresh air β(k).

[0125] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0126] This device constructs a thermal-humidity coupled state equation for a gray box, and uses this equation to obtain the optimal cabin control strategy that achieves the comfort index at the scheduled time. While ensuring cabin comfort, it minimizes energy consumption and noise, and maximizes hardware lifespan.

[0127] One embodiment of this application provides a terminal device including a processor and a memory. The processor may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory is used to store a computer program configured to be executed by one or more processors to implement the above-described cabin comfort control method.

[0128] In some embodiments, the terminal device may also optionally include: a peripheral device interface and at least one peripheral device. The processor, memory, and peripheral device interface can be connected via a bus or signal lines.

[0129] In an exemplary embodiment, a computer-readable storage medium is also provided, wherein a computer program is stored therein, which, when executed by a processor, implements the above-described cabin comfort control method.

[0130] In an exemplary embodiment, a computer program product is also provided, the computer program product including a computer program stored in a computer-readable storage medium. A processor of a terminal device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, causing the terminal device to perform the aforementioned cabin comfort control method.

[0131] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only.

[0132] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for controlling cabin comfort, characterized in that, The method is as follows: (1) Receive reservation instructions, which include the reservation time and the given comfort index; (2) Obtain the optimal cabin control strategy that achieves the comfort index at the scheduled time by using the gray box thermal-humid coupling state equation.

2. The cabin comfort control method as described in claim 1, characterized in that, The thermal-humid coupling equations of state for the gray box include: the difference equation for cabin temperature changing with time, the difference equation for interior equivalent temperature changing with time, the difference equation for cabin absolute humidity changing with time, the difference equation for pollutant concentration changing with time, and the difference equation for glass inner surface temperature changing with time.

3. The cabin comfort control method as described in claim 2, characterized in that, The difference equation for cabin temperature change over time is as follows: ； Where T_cab(k+1) and T_cab(k) represent the cabin temperature at step (k+1) and step k, respectively; C_air represents the equivalent heat capacity of the cabin air; Δt represents the sampling period; UA_out represents the overall heat transfer coefficient between the cabin and the outside; T_cab(k) and T_out(k) represent the cabin temperature and the outside temperature at the current step k; UA_trim represents the coupling heat transfer coefficient between the interior and the cabin; T_trim(k) represents the equivalent temperature of the interior at the current step k; Q_cool(k) represents the air conditioning cooling at the current step k; Q_solar(k) represents the solar heating power of the cabin air at the current step k; and Q_vent(k) represents the heat power of the ventilation at the current step k.

4. The cabin comfort control method as described in claim 2, characterized in that, The difference equation for the change of the equivalent temperature of the interior interior over time is as follows: ； Where T_trim(k+1) and T_trim(k) represent the equivalent interior temperature at step k+1 and step k, respectively; C_trim represents the equivalent heat capacity of the interior; UA_trim represents the coupling heat transfer coefficient between the interior and the cabin; K_sol_trim represents the coefficient of solar radiation absorption by the interior; G_sol(k) represents the solar irradiance at the current step k; and T_cab(k) represents the cabin temperature at the current step k.

5. The cabin comfort control method as described in claim 2, characterized in that, The difference equation for the change of cabin absolute humidity over time is as follows: ； Where H_cab(k+1) and H_cab(k) represent the absolute humidity of the cabin at step (k+1) and step k, respectively; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k, and V_cab represents the cabin volume; W_out(k) represents the absolute humidity of the outside air at step k; ṁ_dehumid(k) represents the dehumidification rate of the air conditioner at step k; and ρ_air represents the air density.

6. The cabin comfort control method as described in claim 2, characterized in that, The difference equation for the change of pollutant concentration over time is as follows: ； Where C_poll(k+1) and C_poll(k) represent the cabin pollutant concentrations at steps k+1 and k, respectively; C_out(k) represents the external pollutant concentration at step k; η_filter(k) represents the filter efficiency at step k; k_sink represents the natural sinking rate of pollutants; V̇_in(k) represents the volumetric flow rate of fresh air entering the vehicle at step k; and V_cab represents the cabin volume.

7. The cabin comfort control method as described in claim 2, characterized in that, The difference equation for the change of the inner surface temperature of the glass over time is expressed as follows: ； Where T_glass(k+1) and T_glass(k) represent the inner surface temperature of the glass at step k+1 and step k, respectively; C_glass represents the heat capacity of the glass; h_in represents the convective heat transfer coefficient of the air inside the vehicle to the glass; h_out represents the convective heat transfer coefficient of the air outside the vehicle to the glass; K_sol_glass represents the coefficient of solar radiation absorption by the glass; G_sol(k) represents the solar irradiance at step k; T_cab(k) and T_out(k) represent the cabin temperature and the outside temperature at step k, respectively.

8. The cabin comfort control method as described in claim 1, characterized in that, Obtain the timestamp t of the reservation instruction receipt time and the reservation time T_res. Divide the time period from time t to reservation time T_res into N steps. Then, the optimization objective J is expressed as follows: ； Where w_e, w_s, w_n, w_l, w_aq, and w_terminal are all weight values; P_comp(k) and P_bl(k) represent the compressor power and fan power at step k, respectively; S_err(k) represents the comfort deviation at step k; Δf_comp(k) and Δn_bl(k) represent the compressor frequency change rate and fan speed change rate at step k, respectively; Δβ(k) represents the change rate of the external circulation ratio at step k; σ_com and f_comp(k) represent the weight coefficient of compressor life loss and compressor frequency at step k, respectively; σ_bl and n_bl(k) represent the weight coefficient of fan life loss and fan speed at step k, respectively; C_poll(k) and C_lim represent the current pollutant concentration and pollutant concentration threshold in the vehicle at step k, respectively; and S_terminal(N) represents the difference between the current comfort index in the vehicle and the given comfort index when the reservation time is reached.

9. A cabin comfort control device, characterized in that, The device includes: The instruction receiving unit is used to receive reservation instructions, which carry the reservation time and given comfort indicators. The data acquisition unit is used to measure state quantities in real time. The thermal-humidity coupled state equation update unit periodically updates the parameters based on the measured state quantities so that the state quantities predicted by the thermal-humidity coupled state equation of the gray box are close to the measured state quantities. The prediction unit obtains the optimal cabin control strategy to achieve the comfort index at the scheduled time based on the updated thermal-humidity coupled state equation.

10. The cabin comfort control device as described in claim 8, characterized in that, Obtain the timestamp t of the reservation instruction receipt time and the reservation time T_res. Divide the time period from time t to reservation time T_res into N steps. Then, the optimization objective J is expressed as follows: ； Where w_e, w_s, w_n, w_l, w_aq, and w_terminal are all weight values; P_comp(k) and P_bl(k) represent the compressor power and fan power at step k, respectively; S_err(k) represents the comfort deviation at step k; Δf_comp(k) and Δn_bl(k) represent the compressor frequency change rate and fan speed change rate at step k, respectively; Δβ(k) represents the change rate of the external circulation ratio at step k; σ_com and f_comp(k) represent the weight coefficient of compressor life loss and compressor frequency at step k, respectively; σ_bl and n_bl(k) represent the weight coefficient of fan life loss and fan speed at step k, respectively; C_poll(k) and C_lim represent the current pollutant concentration and pollutant concentration threshold in the vehicle at step k, respectively; and S_terminal(N) represents the difference between the current comfort index in the vehicle and the given comfort index when the reservation time is reached.

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