DE102005006312A1 - Control module assembly for regulation of an automotive air conditioning system operates in conjunction with sensors and processor algorithm - Google Patents

Abstract

An automotive air conditioning system (F) has three sensors (S1-3) and a control module with a processor (P). Air is abstracted from the passenger compartment and passes through a duct in which the sensors are located. The first sensor (S1) is highly selective for carbon monoxide and hydrogen; a second sensor is highly selective for nitrogen oxides, and the third sensor (S3 ) is highly selective for hydrocarbons. The processor operates with an algorithm using the sensor-derived information and regulates the air conditioning assembly in conjunction with predetermined target values. The sensors focus on tobacco smoke, organic-effluents, food odors, agricultural odors, emissions arising from materials within the vehicle, microorganisms, and substances emerging from e.g. gas containers under pressure. The air is treated by e.g. active carbon filter, an ozone generator, an ultraviolet catalyst with semiconductor, an electrostatic filter, or an air ionization unit, or a combination of these.

Description

The The invention relates to a control module for controlling a ventilation unit or air conditioning unit for the treatment of indoor air in a closed room of a Vehicle, in particular a motor vehicle, with one or more Sensors for the detection of different chemical components an air flow to be examined and with a processor unit, the due to signals from the sensors, a control signal for driving the ventilation unit generated.

Such a system is known, for example from the EP 0 757 632 B1 ,

such Devices have long been used for pollutant-controlled ventilation of I. W. Vehicle cabins in motor vehicles with switching to recirculation mode used. For this purpose, the current pollutant content using sensors or smell of the outside air measured and dependent switched from the sensor signals to either recirculation mode, if the outside air is too dirty, or in "good" fresh air fresh air fed.

In addition, in the EP 1 256 470 B1 proposed to make the responsiveness of the Umluftinrichtung for recirculation except the measured pollutant levels of the outside air and the number of vehicle occupants, the interior temperature, the humidity in the interior, the speed of the vehicle, the fan speed of the air conditioner, their air filter function or the compressor function. This should be avoided even with a consideration of the outdoor air quality fogging of the discs.

there However, it is also just the current quality of the outdoor air considered, while the quality the indoor air completely except Consideration remains.

task the present invention, it is in contrast, with the simplest possible technical Means a control module of the type described above to the effect continue to develop that improved ventilation and climate management the interior atmosphere of the vehicle allows becomes.

According to the invention this Task on as surprising simple and effective way solved by that the air flow to be examined from the interior air of the closed Room is tapped, that at least 3 sensors are provided, of which a first sensor with higher selectivity to carbon monoxide and / or hydrogen, a second sensor with higher selectivity to nitrogen oxides and a third sensor with higher Selectivity Hydrocarbons responds, and that the processor unit a Contains algorithm, the from the signals of all Sensors a characteristic for calculates a specific condition that is characteristic of the current one Composition of the examined indoor air is and with in the Processor unit stored, predetermined characteristics for a certain condition for different scenarios of a possible air composition is compared.

By the processing of sensor signals for indoor air instead the outdoor air will be a "focus" specifically on the purely on the interior related air quality data allows. The way this way enormously increased location reference of the measured values to the actual air quality in the passenger compartment of the vehicle opens up completely new possibilities significantly improved and up-to-date circumstances precisely tailored control of the ventilation and air conditioning units in closed vehicles.

there the mentioned "sensors" are not necessarily to understand as a variety of physical individual sensors, but you can in the sense of "sensor functions", for example also by different modes of operation of a single physical sensor element be realized, which contains several independent information delivers with the measured data of several different sensor elements are comparable (keyword: "virtual sensors") compact arrangements also several differently reacting sensor elements spatial be arranged adjacent to a common board.

Also, the "airflow" does not necessarily presuppose a physical flow of air, but rather the detection of the chemical composition of the indoor air can also be done in a pure diffusion mode.

A "characteristic for one certain state "finally can in demanding and technically sophisticated realizations of the Invention require a complex pattern recognition, but simpler Variants is enough For example, a comparison of the slope of curves of the measured Signals.

Especially preferred is an embodiment the control module according to the invention, which is characterized by the fact that the algorithm is a qualitative one Part has, with which from the signals of the sensors, the characteristic for one determined condition and with the given characteristics for certain conditions is compared, and that the algorithm also has a quantitative Part with which the intensities and / or variances of the Signals are determined, with the calculated characteristic for the certain state along with the intensities and / or variances over one Lookup table an assignment to a stored, predetermined Air quality value made and therefrom the control signal for controlling the ventilation unit is derived. This can be done on a (arbitrarily large and predefinable) Series of standard situations with very targeted measures to improve air quality in the vehicle interior.

to Reduction of the amount of data to be processed may be advantageous if with the qualitative part of algorithm the calculated characteristic for one certain state is assigned a weighting factor that is on the intensities determined by the qualitative part of the algorithm and / or Variances, thereby obtaining a weighted parameter by means of which the assignment to the air quality value from the look-up table will be produced.

A Further data reduction is achieved by establishing a discrete number achieved by Ansteuerstufen, in each case a specific, predetermined Range of the determined overall parameter a certain, predetermined Assigned control signal of a finite number n of control signals becomes.

Especially the number n of the given control signals can be selected in a range 3 ≦ n ≦ 20, preferably n = 5.

The quality the with the control module according to the invention To be influenced indoor air can in a development of above embodiments the special needs of human beings in general or of specially selected groups of people by it be adjusted that the weighting factor from a statistical determined hedonic sensation of a group of human Test persons is won and one of the stored, predetermined Characteristics for a certain state is assigned.

A Further improvement can be achieved by using the quantitative Part of the algorithm the intensities with the variances of the measured Signals multiplied and from this a quantitative sum parameter is formed.

In In practice, it proves to be particularly advantageous if the quantitative sum parameters by multiplying the largest intensity value all signals with the largest variance value all signals is formed.

• a) Tabakrauch und/oder
• b) Bioeffluenten und/oder
• c) Lebensmittelgerüche und/oder
• d) landwirtschaftliche Gerüche und/oder
• e) Emissionen der Innenausstattung des Fahrzeugs und/oder
• f) Emissionen von Mikroorganismen und/oder
• g) gasförmige Substanzen, typischerweise aus Druckgasbehältern.

Specifically, the predetermined characteristic stored in the processor unit for a given state may comprise the following different scenarios of a possible air composition:

• a) tobacco smoke and / or
• b) Bioeffluents and / or
• c) food odors and / or
• d) agricultural odors and / or
• e) emissions from the interior of the vehicle and / or
• f) emissions of microorganisms and / or
• g) gaseous substances, typically from pressurized gas containers.

In order to i.a. captured most of the important standard situations.

Around the ventilation and air conditioning management of indoor air not only on the Decision "Fresh air supply yes / no "reduce to have to, may in particularly preferred embodiments of the invention the controlled ventilation unit one or more devices for active cleaning of indoor air comprise and the control signal of the control module in dependence from which the signals of all Sensors calculated characteristic for a given state activate at least one of these facilities.

Especially can in embodiments of these embodiments, the control signal an activated carbon filter and / or an ozonization device and / or a device for UV catalysis on semiconductor oxide surfaces and / or an electrostatic filter and / or a device for ionization of air.

Further Features and advantages of the invention will become apparent from the following detailed description of embodiments of the invention with reference to the figures of the drawing, the essential to the invention details shows, as well as from the claims. The individual features can each individually for one or more in any combination at variants be realized the invention.

In the schematic drawing are embodiments of the invention illustrated, which are explained in more detail in the following description.

It demonstrate:

1 a schematic representation of a vehicle with inventive control module for controlling a ventilation and air conditioning unit; and

2 a history diagram for calculating a Luftgütestufe from the sensor data.

In 1 you recognize a vehicle 1 , whose interior air in the enclosed interior 2 by means of a ventilation and / or air conditioning unit, of which in the drawing, only a ventilation 3 are schematically indicated, is conditioned. The control of the ventilation and / or air conditioning unit via control signals, which generates a control module according to the invention, the processor unit P and sensors S1, S2, S3 for examining the indoor air in the closed space 2 of the vehicle 1 includes. The sensors S1, S2, S3 are in turn shown only very schematically. In the exemplary embodiment shown, the sensor S1 should be particularly sensitive to carbon monoxide and / or hydrogen, the sensor S2 particularly sensitive to nitrogen oxides, and the sensor S3 particularly sensitive to hydrocarbons. The processor unit P in this embodiment operates with an algorithm comprising an electronic filter F, for example a median filter for removing artifacts and smoothing the measurement curve and for preparing a baseline correction.

Of the Algorithm calculated from the signals of the sensors S1, S2, S3 for the indoor air a characteristic for a particular condition that is characteristic of the current composition the examined indoor air is and with in the processor unit P stored, predetermined characteristics for one certain condition for different scenarios of a possible air composition is compared.

The internal air quality in the vehicle 1 can only be improved by measures such as air exchange and active purification. An efficient implementation of appropriate measures requires that the status of indoor air quality 2 of the vehicle 1 exactly known.

• 1. Tabakrauch
• 2. Bioeffluenten
• 3. Lebensmittel
• 4. Landwirtschaft
• 5. Innenausstattungsemissionen
• 6. Schimmel

The multi-gas sensor module of the present invention is intended to provide an internal air quality factor within the passenger compartment with respect to the presence and concentration of odorants such as cigarette smoke, foul odors, manure, and occupant-related emissions. The significance of the scenarios is structured taking into account the feasibility as follows:

• 1. Tobacco smoke
• 2. Biofluents
• 3. Food
• 4. Agriculture
• 5. Interior equipment emissions
• 6. mold

In dependence from the air quality factor the parameters of the climate control system should be adapted. the goal is it thereby to improve the internal air quality.

• 1.: Hohe Luftgüte innen -hohe Luftgüte außen – keine Maßnahme
• 2.: Hohe Luftgüte innen -schlechte Luftgüte außen (z.B. Tunnelfahrt oder Innenstadtverkehr) – Umluftbetrieb, aktive Reinigung von Außenluft und Zumischung bei lang anhaltenden Ereignissen wg. steigender CO₂-Konzentration
• 3.: Schlechte Luftgüte innen- hohe Luftgüte außen (z.B. Rauchen auf einer Landstraße) – Luftwechselrate erhöhen
• 4.: Schlechte Luftgüte innen -schlechte Luftgüte außen (z.B. Rauchen im Tunnel) – Umluftbetrieb mit aktiver Reinigung, bei lang anhaltenden Ereignissen Zumischung von Außenluft

The following cases can be simplified:

• 1 .: High air quality inside -High air quality outside - no action
• 2 .: high air quality inside - bad air quality outside (eg tunnel travel or inner city traffic) - recirculation mode, active cleaning of outside air and admixture in case of long lasting events wg. increasing CO ₂ concentration
• 3 .: poor air quality indoor high air quality outside (eg smoking on a country road) - increase air exchange rate
• 4 .: poor air quality inside - poor air quality outside (eg smoking in the tunnel) - recirculation mode with active cleaning, in the case of long-lasting events admixing of outside air

Multi gas sensor approach

• • Externe Emissionsquellen – Gülle (Phenole, Ketone, Aldehyde, Fettsäuren, Amine etc., komplexe Mischung mit individuellen Konzentrationen im ppb-Bereich)
• • Interne Emissionsquellen – Tabakrauch – Bioeffluenten – Lebensmittel – Innenausstattungsemissionen – Schimmel

From the experience with "electronic noses" collected in the past, it follows that a classification into good and bad smells is hardly possible. This follows from the fact that no clear correlation can be established between the chemical structure of a substance and its hedonic effect. Nevertheless, there is a potential possibility of detecting unpleasant odor events with respect to the following points:

• • External sources of emissions - manure (phenols, ketones, aldehydes, fatty acids, amines, etc., complex mixture with individual concentrations in the ppb range)
• • Internal sources of emissions - Tobacco smoke - Bioeffluents - Food - Interior equipment emissions - Mildew

For this will be the respective signal pattern with the for the specific scenarios typical, stored patterns compared and based on its similarity assigned.

sensors

MOS sensors generally consist of a substrate (eg ceramic or Si) which is coated with the sensitive materials, often tin dioxide. In order to achieve different selectivities, the tin dioxide is doped with noble metals (Pd, Pt, Au etc.) in different concentrations. The term "doping" has become established in the literature, but the effect of the noble metals is mainly catalytic.) In addition, the substrate is heated to temperatures between 200 and 450 ° C. The oxygen species present on the surface of the MOS sensor react with components from the gas phase and lead to a change in the concentration of free charge carriers in the n-type metal oxide reducing gases (eg CO) lead to a decrease and oxidizing gases (eg NO ₂ ) to an increase in resistance Very often, nonlinear behavior can be observed , which can be compensated in the subsequent data treatment ("forced linerization").

In the following, the different substrate technologies are listed and the advantages and disadvantages of each method are shown: Micromechanical substrate: Material frame: silicon Material membrane: - Electrode material: mostly platinum Heater material: mostly platinum Substrate temperature: Si frame approx. 50 ° C (at membrane temperature of 350 ° C) power consumption: about 20-50mW (at operating temperature of 350 ° C) Packaging: COB (chip-on-board) or on socket (eg TO 39) coating: drop methods Advantages: - low substrate frame temperature with optimal membrane temperature - low power consumption - flexible packaging options through the use of different materials or technologies and thus significant cost savings - long-term stability - state of the art (Si technology) Disadvantage: - relatively new technology (about 5 years)

algorithm

Of the Evaluation algorithm is the core of the multi-gas sensor, since he imposes a certain "intelligence" on the sensor signals. That of the presence of gaseous substances dependent Signal from the sensors must be in a ventilation level (AQL), which a Relation to human perception / sensation has transformed become. Due to the hedonic relation of the air quality level the approach goes beyond the purely linear transformation of physical quantities. By different Odor perception (odor threshold) and association are the same Concentrations of different chemical substances vary greatly rated. Detecting defined scenarios based on their signal pattern is therefore necessary to give the output the desired hedonic To give weight.

• Transformation der Widerstandswerte in die Leitfähigkeit
• Sensoranpassung durch einen Kalibrierfaktor c, damit alle Module das gleiche Verhalten zeigen. x_{transfer Sensor i} = x_{Sensor i} × c_{Sensor i}
• Median-Filter zum Entfernen von Artefakten und Glättung der Messkurve
• Basislinienkorrektur, um die zeitliche Fluktuation der Basislinie auszugleichen.
• Standardisierung der Variablen damit die Daten der unterschiedlichen Sensoren relativ zu ihrer Detektionsgrenze (LOD) gleich gewichtet werden.
  
  cSensor i = 5 × σ
• Intensitätsberechnung durch Projektion der dreidimensionalen Daten auf eine Gerade. Hierzu wird jeweils zu jedem Messzeitpunkt i die größte standardisierte Variable der drei Sensoren als Intensität ausgegeben. Intensität(i) = Max(xstd Sensor 1(i), xstd Sensor 2(i), xstd Sensor 3(i))
• Varianzabschätzung für jeden Sensor e(i) = xSensor i(i) – m(i – 1) m(i) = m(i – 1) + μm·e(i) V(i) = V(i – 1) + μV·e(i) Vm(i) = Vm(i – 1) + μVm(V(i – 1) – Vm(i – 1))wobei: e(i) das Fehlersignal m(i) der geschätzter Mittelwert V(i) die geschätzte Varianz Vm(i) die geschätzte mittlere Varianz Normalisierte Varianz
  
• Max. Varianz wird wie die Intensitätsberechnung durch Projektion der dreidimensionalen Daten auf eine Gerade bestimmt. Max.Varianz(i) = Max(Vnorm Sensor 1(i), Vnorm Sensor 2(i), Vnorm Sensor 3(i))
• Signalverbesserung zur Mustererkennung bei schwach- und undotierten Sensoren durch eine neue zusammengesetzte Größe. Hierzu wird der Anteil der standardisierten Leitfähigkeit der kleiner Null ist, durch den invertierten Widerstand ersetzt. Durch diese Maßnahme werden die Vorteile bei der Basislinienkorrektur aus beiden Größen kombiniert.
• Mustererkennung beruht auf den Prinzip, dass die Daten über die Vektorlänge (VL) normiert werden und anschließend aus der Differenz zu vordefinierten Vektoren, welche den Szenarien entsprechen, eine Ähnlichkeit (sim) in Prozent
  
  errechnet wird. Um die Fälle einer Fehlklassifizierung einzuschränken, wurden zwei weitere Kriterien eingeführt. Für eine Angabe des Szenarios muss die Ähnlichkeit im Fall „Fast Food" und „Gülle" über 85% liegen, in den anderen Fällen über 65%. Weiter wird für jedes Szenario ein Bereich für die Vektorlänge definiert, um kleine oder ungewöhnlich große Ereignisse auszufiltern.
• Gewichtungsfaktor und Gain werden abhängig vom erkannten Szenario definiert. Der Gewichtungsfaktor (w(i)) gibt an, um welchen Faktor das Ausgangssignal verstärkt werden soll. Mit dieser Maßnahme soll die Korrelation des Ausgangssignals mit der hedonischen Wirkung eines Szenarios verbessert werden. Über den Gain wird das Abklingverhalten des AQL Signals entsprechend dem erkannten Szenario eingestellt.
• Neue Intensität des Ausgangssignals ist das Produkt aus Intensität, Varianz und Gewichtungsfaktor. I new(i) = V(i)·I(i)·w(i) Durch die Verwendung von zwei unterschiedlichen Anteilen zur Generierung des Ausgangssignals wird die Dynamik des Signals erhöht, Drifteffekte verkleinert und in Folge die Anzahl „falsch" detektierter Ereignisse vermindert.
• Nachschlagetabelle für die den Intensitäten zugeordneten Luftgüteklassen. Tabelle 1: Beispiel für eine Nachschlagetabelle
  
• Signalabklingen des AQL-Signals berechnen. Aufgrund der physikalischen Gegebenheiten kann von einer exponentiellen Abnahme der Konzentration gasförmiger Stoffe im Innenraum ausgegangen werden. Daher wurde das Abklingen des AQL-Signals dieser Gesetzmäßigkeit angepasst. Allerdings wurde im Hinblick auf eine bessere Implementierbarkeit auf dem Mikrokontroller nur eine Näherung der Exponentialfunktion verwendet. AQLAbklingen(i) = (1 – gain)·AQLAbklingenl(i – 1) Mit dem Parameter Gain lässt sich die Abnahmegeschwindigkeit des Signals einstellen. Der neu berechnete AQL wird natürlich vor der Ausgabe auf diskrete Werte gerundet. Sollte der aktuelle AQL-Wert größer sein als AQL. Abklingen wird dieser als neuer AQL_Abklingen gesetzt.

A possible, preferred method sequence for calculating an air quality level from the sensor data is in 2 shown. The individual steps are as follows:

• Transformation of the resistance values into the conductivity
• Sensor adjustment by a calibration factor c, so that all modules show the same behavior. x _{transfer sensor i} = x _{sensor i} × c _{sensor i}
• Median filter for removing artifacts and smoothing the trace
• Baseline correction to compensate for the temporal fluctuation of the baseline.
• Standardization of the variables so that the data of the different sensors are weighted equally relative to their detection limit (LOD).
  
  c Sensor i = 5 × σ
• Intensity calculation by projection of the three-dimensional data on a straight line. For this purpose, the largest standardized variable of the three sensors is output as intensity at each measurement time i. Intensity (i) = Max (x std sensor 1 (i), x std sensor 2 (i), x std sensor 3 (I))
• Variance estimation for each sensor e (i) = x Sensor i (i) - m (i-1) m (i) = m (i-1) + μ m ·egg) V (i) = V (i-1) + μ V ·egg) Vm (i) = Vm (i-1) + μ vm (V (i-1) -Vm (i-1)) where: e (i) the error signal m (i) the estimated mean V (i) the estimated variance Vm (i) the estimated mean variance normalized variance
  
• Max. Variance, like the intensity calculation, is determined by projecting the three-dimensional data onto a straight line. Max. Variance (i) = Max (V standard sensor 1 (i), V standard sensor 2 (i), V standard sensor 3 (I))
• Signal enhancement for pattern recognition in weak and undoped sensors with a new composite size. For this purpose, the proportion of the standardized conductivity is less than zero, replaced by the inverted resistance. This measure combines the benefits of baseline correction from both sizes.
• Pattern recognition is based on the principle that the data is normalized by the vector length (VL) and then from the difference to predefined vectors, which correspond to the scenarios, a similarity (sim) in percent
  
  is calculated. To limit the cases of misclassification, two further criteria were introduced. For an indication of the scenario, the similarity in the case of 'fast food' and 'manure' must be above 85%, in other cases above 65%. Further, for each scenario, an area is defined for the vector length to filter out small or unusually large events.
• Weighting factor and gain are defined depending on the detected scenario. The weighting factor (w (i)) indicates by what factor the output signal should be amplified. This measure is intended to improve the correlation of the output signal with the hedonic effect of a scenario. The gain adjusts the decay behavior of the AQL signal according to the detected scenario.
• New intensity of the output signal is the product of intensity, variance and weighting factor. I new (i) = V (i) * I (i) * w (i) By using two different proportions to generate the output signal, the dynamics of the signal is increased, drift effects are reduced and consequently the number of "false" detected events is reduced.
• Look-up table for the air quality classes assigned to the intensities. Table 1: Example of a look-up table
  
• Calculate signal decay of the AQL signal. Due to the physical conditions, an exponential decrease in the concentration of gaseous substances in the interior can be assumed. Therefore, the decay of the AQL signal has been adjusted to this law. However, only an approximation of the exponential function was used in view of better implementability on the microcontroller. AQL subside (i) = (1 - gain) · AQL Abklingenl (i - 1) The parameter Gain can be used to set the decay rate of the signal. Of course, the newly calculated AQL is rounded to discrete values before output. Should the current AQL value be greater than AQL. Decaying this is set as a new AQL _subsidence .

Technical possibilities for active air purification

• – Aktivkohlefilter
• – Ozonisierung
• – UV-Katalyse an Halbleiteroxidoberflächen
• – Elektrostatische Filter
• – Ionisierung

Possible methods of air purification are:

• - Activated carbon filter
• - ozonation
• - UV catalysis on semiconductor oxide surfaces
• - Electrostatic filters
• - Ionization

Use of active filtering

There the cleaning performance of all currently known active filter methods is limited by the technically integrable volume of construction, Only a partial air flow can always be passed through the filter. Therefore, as for Case 2 and 4, the influx of poor quality outdoor air the recirculation mode can be reduced. In both cases, however, an influx must be sustained to the accumulation of carbon dioxide in the Reduce vehicle.

Of the Increased location reference of the measured data in the evaluation of indoor air, the with the multi-gas sensor module according to the invention reached, opened completely new possibilities for an improved ventilation and climate management.

Claims (11)

Control module for controlling a ventilation and / or air conditioning unit ( 3 ) for the treatment of indoor air in a closed room ( 2 ) of a vehicle ( 1 ), in particular of a motor vehicle, with one or more sensors for the detection of different chemical components of an air flow to be examined and with a processor unit (P), which on the basis of signals from the sensors, a control signal for controlling the ventilation and / or air conditioning unit ( 3 ), characterized in that the air flow to be examined from the indoor air of the closed space ( 2 ) is tapped that at least 3 sensors (S1, S2, S3) are provided, of which a first sensor (S1) with higher selectivity to carbon monoxide and / or hydrogen, a second sensor (S2) with higher selectivity to nitrogen oxides and a third Sensor (S3) with higher selectivity to hydrocarbons responds, and that the processor unit (P) contains an algorithm consisting of the signals of all sensors (S1, S2, S3) calculates a characteristic for a particular state that is characteristic of the current composition of the indoor air being examined and is compared with predetermined characteristics for a given state stored in the processor unit (P) for different scenarios of a possible air composition. Control module according to claim 1, characterized in that the algorithm comprises a qualitative part, with which from the signals of the sensors (S1, S2, S3) the characteristic is calculated for a given state and compared with the predetermined characteristics for a particular state, and in that the algorithm additionally comprises a quantitative part with which the intensities and / or variances of the signals are determined, wherein from the calculated characteristic for a given state together with the intensities and / or variances via a look-up table an assignment to a stored, predetermined air quality value produced and from the control signal for controlling the ventilation and / or air conditioning unit ( 3 ) is derived. Control module according to claim 2, characterized in that that with the qualitative part of algorithm the calculated characteristic for one certain state is assigned a weighting factor that is on the intensities determined by the qualitative part of the algorithm and / or Variances, thereby obtaining a weighted parameter by means of which the assignment to the air quality value from the look-up table will be produced. Control module according to claim 3, characterized in that that the weighting factor is derived from a statistically determined hedonic Feeling a group of test subjects is won and one the stored, predetermined characteristic for one certain state is assigned. Control module according to one of claims 3 or 4, characterized that in each case a specific, predetermined range of the determined Gesamtparameters a specific, predetermined control signal from a finite number n of control signals is assigned. Control module according to claim 5, characterized in that that for the number n of the predetermined control signals is: 3 ≤ n ≤ 20, preferably n = 5. Control module according to one of Claims 2 to 6, characterized that in the quantitative part of the algorithm the intensities with multiplied by the variances of the measured signals and then inferred quantitative sum parameter is formed. Control module according to claim 7, characterized that the quantitative sum parameter by multiplying the largest intensity value all signals with the largest variance value all signals is formed. Control module according to one of the preceding claims, characterized in that the predetermined characteristics stored in the processor unit (P) for a given state comprise the following different scenarios of a possible air composition: a) tobacco smoke and / or b) bioeffluents and / or c) food odors and / or (d) agricultural odors and / or (e) emissions from the interior of the vehicle ( 1 ) and / or f) emissions of microorganisms and / or g) gaseous substances, typically from compressed gas containers. Control module according to one of the preceding claims, characterized in that the controlled ventilation and / or air conditioning unit ( 3 ) comprises one or more devices for active cleaning of the indoor air, and that the control signal of the control module, depending on the characteristic calculated from the signals of all the sensors (S1, S2, S3), activates, if necessary, at least one of these devices for a given state. Control module according to claim 10, characterized that the control signal is an activated carbon filter and / or an ozonization device and / or a device for UV catalysis on semiconductor oxide surfaces and / or an electrostatic filter and / or a device for ionization of air can activate.