WO1999036280A1 - Dispositif de conditionnement d'air pour vehicules et procede de commande du dispositif - Google Patents

Dispositif de conditionnement d'air pour vehicules et procede de commande du dispositif Download PDF

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Publication number
WO1999036280A1
WO1999036280A1 PCT/JP1998/003528 JP9803528W WO9936280A1 WO 1999036280 A1 WO1999036280 A1 WO 1999036280A1 JP 9803528 W JP9803528 W JP 9803528W WO 9936280 A1 WO9936280 A1 WO 9936280A1
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WO
WIPO (PCT)
Prior art keywords
temperature
vehicle
air conditioner
air
vehicle interior
Prior art date
Application number
PCT/JP1998/003528
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English (en)
Japanese (ja)
Inventor
Yoshihiro Adachi
Shigenobu Itoh
Mamoru Masauji
Original Assignee
Zexel Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from JP16900998A external-priority patent/JP2000001114A/ja
Priority claimed from JP10170372A external-priority patent/JP3046798B2/ja
Application filed by Zexel Corporation filed Critical Zexel Corporation
Publication of WO1999036280A1 publication Critical patent/WO1999036280A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00735—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models

Definitions

  • the present invention relates to an air conditioner, and more particularly to an air conditioner for a vehicle for appropriately controlling the temperature in a passenger compartment to a target temperature and a control method thereof.
  • the first 1 Figure is a diagram showing a control flow of a conventional vehicle weight air conditioner, a conventional air conditioning apparatus for a vehicle, and the target temperature T z set by the temperature setting unit 4, the installed car cabin
  • the control logic 1 p is used to control the measured temperature T i measured by the inside air temperature sensor 6 as a room temperature detection means as a parameter, and the control logic 1 p controls the temperature of the air-conditioned air blown into the vehicle compartment. Control the air volume etc. to control the vehicle interior temperature T. To keep it properly.
  • the inside air temperature sensor 6 is usually installed below the front panel, the measured temperature ⁇ ⁇ is the temperature near the occupant's seating position due to the effects of outside air temperature and solar radiation. T. Is not necessarily the same.
  • the vehicle in order to bring the measured temperature Ti of the inside air temperature sensor 6 closer to the actual vehicle interior temperature T0, the vehicle is provided with an outside air temperature sensor and a solar radiation sensor, and the measured temperature ⁇ ⁇ is determined based on the output of each sensor.
  • the measurement temperature T i is corrected or corrected according to the air-conditioning air blowing mode.
  • Japanese Patent Application Laid-Open No. 6-1955323 also discloses that, as shown in FIG. 12, a target temperature T 7 , a measured temperature of the internal air temperature sensor 6 ⁇ , an outside air temperature T A , and a solar radiation amount T s. And the input signal and There is disclosed a technology for controlling the amount of air-conditioned air to be blown using the neural network type additional learning device described above.
  • the neural network the final airflow is controlled by learning and calculating each arithmetic expression such as the target air outlet temperature, air outlet mode status, and blower air volume.
  • the learning is difficult to converge and that the learning time becomes longer because of the large number of teacher signals used in the training.
  • the present invention has been made in view of the conventional problems, and accurately adjusts a control logic by accurately estimating a vehicle interior temperature which is a main element of a control logic of an air conditioner. It is an object of the present invention to provide a vehicle air conditioner capable of controlling the temperature with high accuracy and a control method thereof. Disclosure of the invention
  • the control method for a vehicle air conditioner includes: setting a temperature model for estimating a vehicle interior temperature from an output value of a vehicle interior temperature detecting means for detecting a vehicle interior temperature; The temperature and air volume of the conditioned air blown into the cabin are adjusted based on the estimated value to control the temperature in the cabin. That is, instead of the output value of the vehicle interior temperature detecting means itself, an estimated value of the vehicle interior temperature that is very close to the actual temperature of the vehicle interior is obtained, and the temperature in the vehicle interior is controlled using the above estimated value. The temperature in the passenger compartment is quickly controlled to the target temperature.
  • control method of the vehicle air conditioner of the present invention is characterized in that the temperature model is formed by a dual network in which an environmental factor of the vehicle and a state of the air conditioner are input signals and an estimated value of the temperature in the vehicle compartment is an output value.
  • the system estimates the temperature of the cabin temperature with high accuracy.
  • the environmental factors may be the outside air temperature and the amount of solar radiation, and any one of the information of the state of the air conditioner, the blow mode, the blow temperature, the blow air volume, and the position of the suction port. Estimate the vehicle interior temperature with a small number of input signals.
  • the vehicle air conditioner of the present invention includes a temperature estimator constituted by a neural network, and adjusts the temperature and the amount of air-conditioned air blown into the vehicle interior based on the estimated value of the vehicle interior temperature from the temperature estimator.
  • the temperature in the vehicle compartment is controlled.
  • the vehicle air conditioner of the present invention controls the temperature in the vehicle cabin by adjusting the temperature and air volume of the air-conditioned air blown into the vehicle cabin using the above-mentioned estimated value as a feedback value, thereby achieving accurate and quick temperature control. Is what you do.
  • the environmental factors are the outside air temperature and the amount of solar radiation
  • the state of the air conditioner is any combination of the following information: a blow mode, a blow temperature, a blow air volume, and a suction port position.
  • the entire system is designed to reliably estimate the temperature in the cabin.
  • the temperature data of the outlet other than the outlet set in the outlet mode is fixed to a predetermined value in the outlet temperature data. is there. As a result, unnecessary temperature fluctuations were suppressed, and the estimated value of the temperature in the vehicle interior was obtained more accurately.
  • the vehicle air conditioner of the present invention includes a temperature estimator configured as a neural network that outputs an estimated value of the temperature at the front part and the rear part of the vehicle interior as an output value. It controls the temperature in the cabin by adjusting the temperature, air volume, etc. of the air-conditioned air sent to the rear and rear sections. As a result, even when the set temperature and air flow are different between the front and rear seats, control is performed in consideration of the mutual influences, and the temperatures at the front and rear in the passenger compartment are quickly adjusted to each. Set the target temperature.
  • the vehicle air conditioner of the present invention controls the temperature of the vehicle interior by adjusting the temperature and air volume of the conditioned air blown to the front and rear portions of the vehicle interior using the above two estimated values as feedback values, The temperature at the front and rear of the vehicle compartment is accurately and quickly set to the target temperature.
  • the environmental factors are the outside air temperature and the amount of solar radiation
  • the state of the air conditioner is the front and rear blow modes, the blow temperature, the blow air volume, and the suction port position. Any combination or all of the information is used to ensure that control is performed in consideration of the settings of the front and rear seats.
  • the teacher signal used at the time of learning of the neural network is set as an average temperature of a position corresponding to a head and a foot of a driver's seat and a passenger's seat, and a vehicle interior temperature is obtained. The difference between the estimated value and the actual cabin temperature is made extremely small.
  • the vehicle air conditioner of the present invention is configured such that the input state of the teacher signal used during learning of the neural network is changed from 0.02 to 0.98, and the neural network learning is performed. Learning efficiency and safety are improved.
  • the absolute value of the error between the output value in each input range of the linear function and the output value of the sigmoid function is set to 3%. The approximation is made by a function in which the input range and the coefficient of the linear function are set so as to be within the range. As a result, the number of required memories can be reduced, and the calculation time can be significantly reduced, and the temperature in the vehicle interior can be more quickly set to the target temperature.
  • FIG. 1 is a diagram illustrating a configuration of a vehicle air conditioner according to a first embodiment of the present invention
  • FIG. 2 is a diagram illustrating a configuration of a neural network of a temperature estimator according to the first embodiment. It is.
  • FIG. 3 is a diagram showing a sigmoid function used in the neural network.
  • FIG. 4 is a diagram showing a relationship between an estimated value and a measured value of the vehicle interior temperature by the temperature estimator of the first embodiment
  • FIG. 5 is a control flow of the vehicle air conditioner according to the first embodiment.
  • FIG. 6 is a diagram showing the configuration of a neural network of a temperature estimator according to the second embodiment of the present invention.
  • FIG. 7 is a diagram showing the estimation of the vehicle interior temperature by the temperature estimator according to the second embodiment. It is a figure showing the relation between a value and an actual measurement value.
  • FIG. 8 is a diagram showing an approximation method of a sigmoid function according to the third embodiment of the present invention
  • FIG. 9 is a diagram showing an error between the sigmoid function and a linear function
  • FIG. 10 is a diagram showing a control flow of the vehicle air conditioner according to Embodiment 3 of the present invention.
  • FIG. 11 is a diagram showing a control flow of a conventional vehicle air conditioner
  • FIG. FIG. 2 is a diagram showing a configuration of a conventional neural network of a vehicle air conditioner.
  • FIG. 1 is a diagram showing a configuration of a vehicle air conditioner according to a first embodiment of the present invention, wherein 1 is a control device, 2 is an air duct, 3 is a setting panel for setting the temperature and air volume of the conditioned air, 4 temperature settings for outputting to the control unit 1 sets the target temperature T z of the vehicle interior which is input from the setting panel 3, 5 or perform opening degree adjustment of the air air mix door 2 e in the air duct 2, the inside and outside air
  • a driving device for switching the switching door 2a and the outlet switching door, and for driving the blower 2b, 6 is an inside air temperature sensor as room temperature detecting means usually installed below the front panel, and 7 is The outside air temperature sensor is installed near the bumper of the vehicle, and the solar radiation sensor 8 is installed above the front panel.
  • the air duct 2 includes an inside / outside air switching door 2a that adjusts a ratio of inside air and outside air introduced into the air duct, a blower 2b that blows air taken in from the inside / outside air switching door 2a, and cools the blown air.
  • the temperature of the blown air is controlled by controlling the amount of air that passes through the heater 2d of the blown air cooled by the evaporator 2p, based on the evaporator 2p, the heater 2d that warms the blast air, and the opening / closing degree of the heater.
  • the upper outlet 2 f is provided with an upper outlet temperature sensor 2 m for detecting the temperature of the air blown from the upper outlet 2 f
  • the lower outlet 2 g is provided with a lower outlet 2 g from the lower outlet 2 g.
  • a lower outlet temperature sensor 2n that detects the temperature of the blast air is installed.
  • the driving device 5 includes a control device of the actuator 1c that drives the inside / outside air switching door 2a, a control device of the control device 2c for controlling the rotation speed of the blower 2b, and a laser door 2e.
  • the control device 1 transmits a control signal for controlling the opening degree of the air mix door 2 e according to the environmental factors such as the outside air temperature and the condition (control factors) of the air conditioning equipment such as the air-conditioning air blowing mode.
  • Control logic 1a to be output to 5 and measured temperature 1 from inside air temperature sensor 6 and information on environmental factors and air conditioner status from control logic 1a as input signals and output to control logic 1a above
  • a temperature estimator 1b configured by a neural network that outputs an estimated value 1 of the temperature in the vehicle compartment as an output value.
  • the above control factors include, for example, the output from the outside air temperature sensor 7 and the solar radiation sensor 8, the switching mode of the inside / outside air switching door 2a, the opening degree of the air mixing door 2e, and the switching mode of the outlet switching door 2h.
  • the switching mode of the inside / outside air switching door 2a has two modes, an inside air introduction mode and an outside air introduction mode.
  • the switching mode of the outlet switching door 2h includes a vent mode, a foot blowing mode, There are four modes, bi-level mode and defrost mode.
  • Fig. 2 is a diagram showing the configuration (temperature model) of the neural network in the temperature estimator 1b.
  • the neural network is a three-layer hierarchical network consisting of an input layer, a hidden layer, and an output layer.
  • the input signals are the outside air temperature T A (° C) from the outside air temperature sensor 7, the measured temperature 1 ⁇ (° C) from the inside air temperature sensor (Inc.s) 6, and the opening P of the air mix door 2 e. (%), Blower duty ratio D (%) corresponding to the drive voltage that controls the speed of blower 2b, foot outlet temperature TF (° C) from lower outlet temperature sensor 2n, upper outlet temperature sensor 2m Venting temperature T B (° C) from the outlet, a blowing mode switching signal M x indicating the switching mode of the outlet 2 h set on the setting panel 3, indicating the inside / outside air switching mode of the inside / outside air switching door 2 a made from Intel Ichiku signal M y, and insolation T s (Kc al / m 2 hour) from solar radiation sensor 8, the output value is an estimate of the temperature of the passenger compartment T N (° C).
  • y j l / (1 + exp (-
  • ) > (1)
  • yj is The output signal from layer i (the input signal to layer :) '). Note that this sigmoid function outputs 0 to 1 when the input variable is (1 ⁇ to + ⁇ ) as shown in FIG.
  • the temperature estimator lb uses the four-point average temperatures of the driver's seat and the passenger's seat at positions corresponding to the head and feet as teacher signals when learning the neural network, and estimates the temperature in the above-mentioned cabin.
  • the value of weight w and bias bj for each input signal in the formula for calculating the value TN is determined by learning, and at the time of control, the estimated value TN of the temperature in the vehicle compartment for each of the input signals is controlled by control logic. Output to 1a.
  • each input signal is normalized from the minimum value to the maximum value of the measurement data from 0 to 1 so that the weight Wij for each of the above input signal types can be evaluated equally.
  • the teacher signal takes the maximum and minimum values of the measurement data from 0.02 to 0.98 in consideration of the fact that 0 and 1 are saturation output values as output characteristics of the sigmoid function. Is becoming In other words, in learning a neural network using the above sigmoid function as an input function of a neuron, considering the learning efficiency and safety, a range of 0 which is slightly narrower than that of normalizing the teacher signal from 0 to 1 is considered. The convergence speed is faster and more effective when the normalization is performed from .02 to 0.98.
  • Graph Figure 4 is have your temperature estimator 1 b made of a neural network as described above, was thoroughly learned by the learning under the following conditions, estimating the vehicle interior temperature T 0 by entering Isseki outside air temperature de It is.
  • the above vehicle interior temperature T. Is the average value of the measured temperatures measured at the positions corresponding to the head and feet of the driver's seat and the passenger's seat. Learning conditions
  • the maximum error is 4.9 ° C when the outside air temperature changes suddenly, but the average of the absolute value of the error is 0.
  • the temperature inside the vehicle, T which is as small as 83 ° C and indicated by ⁇ . Almost follow the change in c.
  • the temperature T in the vehicle compartment is much higher. It can be seen that the value is close to.
  • the maximum error is 2.8 ° C, and the average absolute error is 0.64 ° C
  • the maximum error is 2.4 ° C and the average absolute error is 0.90 ° C
  • the maximum error is 4.0 ° C, and the average absolute error is 0.69 ° C
  • the maximum error is 2.8 and the average absolute error is 1.04 ° C
  • the estimated value T N close to can be obtained.
  • the foot air temperature T F (° C)
  • the vent blowout temperature T B (° C)
  • the number of input signal Even if the number is set to 5 the maximum error is 1.5 ° C and the average of the absolute error values is 0.5 ° C, which is enough to meet the specifications.
  • the control logic la is a control factor that becomes an input signal of the temperature estimator 1b from each control factor consisting of an environmental factor such as the input outside air temperature T A and an air conditioner state such as an air conditioning air blowing mode. Is extracted and output to the temperature estimator 1b.
  • the temperature estimator lb receives the control factor extracted above and the measured temperature 1 ⁇ from Inc. s (internal temperature sensor) 6 as input signals, and estimates the interior temperature T N of the vehicle interior temperature using a neural network. And outputs it to the control logic 1a.
  • Control logic 1 a said from the control factors and the estimated value T N, as vehicle interior temperature T 0 is set target temperature T z at a temperature setter 4, the opening degree of the air mixing door 2 e [rho
  • the feedback control of the temperature in the passenger compartment of the control element of the air duct 2 such as () is performed according to the preset control logic 1a. That is, the temperature in the cabin is measured by the inside air temperature sensor 6 and then input to the temperature estimator 1b, and is sent to the control logic 1a as the estimated value TN. It is eavesdropped. Therefore, the control logic la is not the measured temperature ⁇ ⁇ from the internal air temperature sensor 6 but the vehicle interior temperature T. Since the estimated value TN of the cabin temperature from the temperature estimator 1b, which is very close to the above, is used as the feedback value, it is possible to accurately and quickly bring the cabin temperature to the target temperature Tz. it can.
  • the measured temperature T i from the inside air temperature sensor 6, the outside air temperature T A , the opening degree P (%) of the air mixing door 2 e, and the blow duty ratio D (%) As input signals, and a temperature estimator lb composed of a neural network that uses the estimated value TN of the cabin temperature as an output value as input signals, and a control logic 1a
  • control is performed using the above-mentioned estimated value T N as a feedback value, so that the temperature in the vehicle compartment can be accurately and quickly set to the target temperature T z .
  • the learning of the neural network is easy because learning of the temperature estimator lb is easy because the teacher signal is only the four-point average temperature at the positions corresponding to the head and foot of the driver's seat and the passenger's seat.
  • the learning of the estimated value T N can be performed in a short time.
  • the estimated value T N of the vehicle interior temperature used in the control logic 1 a is the vehicle interior temperature T. Since the value is very close to, not only does the matching accuracy of the control logic 1a greatly improve, but also the time required for the matching process and the number of wind tunnel experiments in the matching process can be significantly reduced.
  • control temperature of the internal temperature sensor 6 as a control factor is simply changed to the estimated value 1 of the vehicle interior temperature at the control port jack 1a. Since there is no need to adjust the temperature, there is no need to change the control logic 1a for each vehicle type, and only the temperature model for each vehicle type needs to be adjusted, thus significantly improving development efficiency.
  • the temperature estimator 1b is obtained from the environmental factors such as the outside air temperature input to the control logic 1a and the control factors such as the state of the air conditioner such as the air-conditioning air blowing mode.
  • the control factors may be independently input to the control logic 1a and the temperature estimator 1b.
  • the control factors input to the control logic 1a do not need to be all the parameters used in the control logic 1a, and the parameters used in the control logic 1a are not necessarily the temperature estimator 1b. Need not include all of the input signals Needless to say.
  • FIG. 6 is a diagram showing a configuration (temperature model) of a neural network according to the second embodiment of the present invention.
  • the neural network has an input layer, a hidden layer, This is a three-layer hierarchical network consisting of an output layer.
  • the input signals are the measured temperature ⁇ ⁇ (° C) from the inside air temperature sensor (Inc. s) 6, the outside air temperature T A (° C) from the outside air temperature sensor 7, and the amount of solar radiation T s ( Kc al / m 2 hour), blower duty ratio D (% corresponding to a drive voltage of the blower 2 b), the set blowing mode switching signal M x at setting panel 3, foot from the lower air outlet temperature sensor 2 n It consists of the outlet temperature T F (° C) and the vent outlet temperature T B (° C) from the upper outlet temperature sensor 2 m, and the output value is the estimated value T N (° C) of the cabin temperature. .
  • the foot outlet temperature TF (° C) and the vent outlet temperature T B (° C) are defined as a predetermined value (for example, the temperature data of outlets other than the outlets set in the outlet mode). , 25 ° C).
  • each input signal is normalized from the minimum value of the measurement data to the maximum value from 0 to 1 so that the Eight for each type of the input signal can be evaluated in the same manner. I have to.
  • the teacher signal is normalized from the minimum value to the maximum value of the measured data from 0.02 to 0.98. are doing.
  • Figure 7 is have your temperature estimator 1 b made of a neural network as described above, was thoroughly learned by the learning conditions below, is a graph estimating the vehicle interior temperature T 0 by entering the outside air temperature data .
  • the above vehicle interior temperature T. are the average values of the measured temperatures measured at the positions corresponding to the head and feet of the driver's seat and front passenger seat, respectively. Learning conditions
  • the maximum error is 1.9. C, the average of the absolute value of the error is 0.5 ° C, Temperature is the input signal (maximum error; 4.9 ° C, average of absolute value of error; 0.83 ° C)), and the vehicle interior temperature T indicated by 2 in the figure. Good follow-up to changes in This is because not only the factor used as the input signal is properly selected, but also the temperature data of the outlets other than the outlets set in the blowout mode is fixed to a predetermined value (for example, 25 ° C). As a result, unnecessary temperature fluctuations have been eliminated, and the estimated value T N of the vehicle interior temperature can be obtained more accurately. Note that the broken line in the figure is the detection temperature of Inc. s (internal temperature sensor) 6.
  • the sigmoid function shown in equation (1) was used for the neural network.
  • approximation of this sigmoid function with a plurality of straight lines required the temperature estimator 1b. Since the number of memories can be reduced and the calculation time can be significantly reduced, the temperature in the vehicle compartment can be more quickly brought to the target temperature Tz .
  • Fig. 9 shows the error between the sigmoid function of equation (1) and the function obtained by approximating the sigmoid function with the linear function. If there are 17 straight lines, the magnitude of the error is The range can be within ⁇ 0.005.
  • the sigmoid function is set so that the error is within ⁇ 0.005. Although 17 straight lines were approximated, there is no practical problem if the error is within ⁇ 0.03 ( ⁇ 3%).
  • the function approximated above does not necessarily have to be a broken line, and in some cases, it may be better to make the function discontinuous in order to reduce the number of straight lines (the number of divisions of the input range) and increase the calculation speed.
  • Equation (1) the logarithmic sigmoid function of Equation (1) is used has been described.
  • Equation (2) a tanh sigmoid function as shown in Equation (2) below is used.
  • other types of sigmoid functions may be used.
  • the set temperature T .zeta.1, T ?? 2 and air volume W z! , W Z2 can also be set in a one-box twin type vehicle air conditioner, such as the environmental factors such as the outside air temperature, the state of the air conditioning equipment such as the air-conditioning air blowing mode, and the inside air temperature sensor of the front seat (front seat temperature) sensor) 6 a and an inside air temperature sensor (rear temperature sensor in the rear seat) as a measurement temperature i ⁇ have the input signal and T i2 from 6 b, the front seat estimated temperature T N i and rear estimated temperature T N2 by controlling the vehicle dual air conditioner based on providing the temperature estimator 1 B configured in a neural network to an output value in the above front seat estimated temperature T N i and rear estimated temperature T N 2 the passenger compartment
  • the temperature of the front and rear seats can be accurately and quickly set to the respective target temperatures ⁇ ⁇ 1
  • FIG. 10 is a control flow chart showing a method of controlling the temperature in the passenger compartment of the vehicle air conditioner according to the fourth embodiment of the present invention.
  • the control factors input to the front seat control logic 11 and the rear seat control logic 12 have been omitted.
  • the temperature estimator 1B calculates the outside air temperature ⁇ ⁇ , the amount of solar radiation T s , the amount of air blown by the front seat 9 and the rear seat 10, the air mix door opening of each of the front and rear air ducts (not shown), the front and rear
  • Each of the blowout modes and the measured temperatures T and Ti2 from the front seat temperature sensor 6a and the rear seat temperature sensor 6b installed near the front and rear seats, respectively, are used as input signals, and a neural network is used.
  • the estimated values T N1 and T N2 of the temperatures at the front and rear of the vehicle cabin are obtained and output to the control outlet jacks 11 and 12, respectively. .
  • the estimated value T N1 of the temperature at the front part is the amount of air blown to the rear seat 10, the opening degree of the air mix door at the rear part, the blowing mode at the rear part, and the measured temperature from the rear seat temperature sensor 6b.
  • the control logic 11 that can obtain values very close to the actual temperatures T i and T 2 at the front of the passenger compartment is From the above control factors and the above estimated value T N1 , the temperature T! So it becomes the target temperature T z i is set at a temperature setter 4 a, to control the front of Eadaku bets.
  • the control logic 12 the from the regulator and the estimated value 1 2, urchin by the temperature T 2 of the cabin front reaches a target temperature T z 2 is set at a temperature setter 4 b, the rear of the air duct Control. Therefore, by performing control using the estimated values T N1 and T N2 of the front and rear temperatures in the passenger compartment as feedback values, the temperatures in the front and rear of the passenger compartment can be accurately and promptly set to the respective target temperatures. T z ⁇ ⁇ 2 .
  • the measured temperatures T ii and T i 2 of the temperature sensor 6b were used as input signals, but are not limited thereto. For example, it was used as the input signal in the above best mode 1, may be added to the input signal a foot blow-out temperature T F and the vent outlet air temperature T B, and the like. Further, one or more of the input signals may be appropriately deleted.
  • the present invention is excellent as a vehicle air conditioner control method and a vehicle air conditioner for appropriately controlling the temperature in a vehicle cabin to a target temperature.
  • the control method is effective in improving the development efficiency of vehicle air conditioners.

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  • Thermal Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Air-Conditioning For Vehicles (AREA)

Abstract

L'invention concerne une logique de commande (1a) d'un dispositif de conditionnement d'air pour véhicules, la logique de commande comprenant une unité d'estimation de température (1b) permettant d'estimer la température ambiante du véhicule, ce qui représente un facteur essentiel de la logique de commande. L'unité d'estimation de température (1b) est constituée d'un réseau neuronal qui reçoit des signaux d'entrée représentant des facteurs environnementaux du véhicule tels que la température Ti mesurée avec un détecteur de température intérieure (6), la température extérieure TA, l'ouverture P(%) d'un volet de mélange d'air (2e), le taux d'utilisation du ventilateur D(%), etc., ainsi que l'état du dispositif de conditionnement d'air; le réseau neuronal calcule ensuite une valeur estimée TN de la température ambiante. On commande le dispositif de conditionnement d'air en utilisant la valeur estimée TN comme valeur de rétroaction, la température ambiante étant rapidement réglée à une température cible TZ.
PCT/JP1998/003528 1998-01-19 1998-08-05 Dispositif de conditionnement d'air pour vehicules et procede de commande du dispositif WO1999036280A1 (fr)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
JP10/7930 1998-01-19
JP793098 1998-01-19
JP10/169009 1998-06-16
JP16900998A JP2000001114A (ja) 1998-06-16 1998-06-16 車両用空調装置
JP10/170372 1998-06-17
JP10170372A JP3046798B2 (ja) 1998-01-19 1998-06-17 車両用空調装置とその制御方法

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Cited By (4)

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US20140100716A1 (en) * 2011-05-18 2014-04-10 Toyota Jidosha Kabushiki Kaisha Air-condition remote control system for vehicle, server, mobile terminal, and vehicle
CN107199845A (zh) * 2017-06-12 2017-09-26 吉林大学 一种驾驶室内环境主动控制系统及其控制方法
DE102020109299A1 (de) 2020-04-03 2021-10-07 Bayerische Motoren Werke Aktiengesellschaft Verfahren zum Steuern einer Klimatisierungseinrichtung für ein Kraftfahrzeug, Klimatisierungseinrichtung und Kraftfahrzeug
CN114670599A (zh) * 2022-01-12 2022-06-28 北京新能源汽车股份有限公司 一种汽车空调的控制方法及系统

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JPH09286222A (ja) * 1996-04-23 1997-11-04 Zexel Corp 車両用空気調和装置
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US20140100716A1 (en) * 2011-05-18 2014-04-10 Toyota Jidosha Kabushiki Kaisha Air-condition remote control system for vehicle, server, mobile terminal, and vehicle
US9085215B2 (en) * 2011-05-18 2015-07-21 Toyota Jidosha Kabushiki Kaisha Air-conditioner remote control system for vehicle, server, mobile terminal, and vehicle
CN107199845A (zh) * 2017-06-12 2017-09-26 吉林大学 一种驾驶室内环境主动控制系统及其控制方法
CN107199845B (zh) * 2017-06-12 2018-07-06 吉林大学 一种驾驶室内环境主动控制系统及其控制方法
DE102020109299A1 (de) 2020-04-03 2021-10-07 Bayerische Motoren Werke Aktiengesellschaft Verfahren zum Steuern einer Klimatisierungseinrichtung für ein Kraftfahrzeug, Klimatisierungseinrichtung und Kraftfahrzeug
CN113492641A (zh) * 2020-04-03 2021-10-12 宝马股份公司 用于控制机动车的空调装置的方法、空调装置和机动车
DE102020109299B4 (de) 2020-04-03 2022-08-25 Bayerische Motoren Werke Aktiengesellschaft Verfahren zum Steuern einer Klimatisierungseinrichtung für ein Kraftfahrzeug und Klimatisierungseinrichtung damit
CN114670599A (zh) * 2022-01-12 2022-06-28 北京新能源汽车股份有限公司 一种汽车空调的控制方法及系统

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