A system and method to control a polymer-dispersed liquid crystals (PDLC) based automotive glazing

Abstract

Disclosed herein is a system (100) and a method (300) for controlling a state of a Polymer- Dispersed Liquid Crystals (PDLC) based automotive glazing in a vehicle Said system comprises an automotive glazing (200) like an active sunroof. The PDLC assembly (103) is operably configured to a control unit (104) to predict and control a modification on a state of the PDLC assembly (103) based on both obtained control factors and calculated control factors inclusive of at least vehicle's passenger(s) profile, activeness of passenger(s), environmental conditions of the vehicle, a vehicle status and a vehicle battery status. The control unit is a multi-sensing electronic unit operably configured to a global network. In the disclosed solution is provided an energy efficient solution to control the state of the PDLC based glazing (such as a sunroof). As opposed to known solutions, it offers control based on the choices or preferences of the user.

Description

[0001] A SYSTEM AND METHOD TO CONTROL A POLYMER-DISPERSED LIQUID CRYSTALS (PDLC) BASED AUTOMOTIVE GLAZING

[0002] TECHNICAL FIELD

[0003] The present disclosure relates to switchable automotive glazing in general. Particularly, it relates to a control mechanism for switchable automotive glazing. More particularly, it relates to a control mechanism for polymer-dispersed liquid crystals (PDLC) based automotive glazing.

[0004] BACKGROUND

[0005] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.

[0006] It is known to one skilled in the art that glazing refers to any and all the glass or similar material within a structure or the installation of any piece of glass or the similar material within a sash or frame. The glass windows of an automobile are referred to as glazing. For laminated glazing, two or more layers of glass or a similar material, are fused together with an interlayer in the middle. The fusion is completed with pressure and heat, and it prevents the sheets of glass or the similar material from breaking. While some pieces of glass or the similar material might end up breaking into larger pieces, those pieces will stay together with the help of the interlayer, making it shatterproof. Windshield or windscreen, backlite, sidelite, quaterlite, sunroof etc., are regarded as some instances of glazing in a vehicle. It is known that Polymer Dispersed Liquid Crystal (PDLC) based glazing are a type of smart glazing that includes a film which enables the transparency of the glazing in response to an electrical impulse or a voltage. When no voltage is supplied to the PDLC based glazing the liquid crystals are randomly arranged, and thus scattering the light as it permeates the screen. This results in an opaque appearance with haze and whenever, a voltage is applied, an electrical field is created between the connectors of the film causing the liquid crystals to align, thereby allowing light to pass through consequently turning the screen transparent.

[0007] Reference is made to CN114932856A that provides a vehicle skylight light control method, comprising the steps of acquiring the change of vehicle state information or vehicle environment information through a signal acquisition unit and generating a signal. The luminous control unit changes the luminous state of the luminous element according to the signal and the sensory experience of people in the vehicle during traveling is improved. Thus, this solution only considers the vehicle’s external parameters for controlling the skylight. However, external parameters are not the sole factor for determining the illumination or non-illumination of the switchable glazing and user preference may not always be in alignment with external factor.

[0008] Another reference is made to CN111717003A that discloses solutions for adjusting dimming associated with a vehicle sunroof. A disclosed sunroof dimming system for a vehicle includes a dimmable panel for a vehicle sunroof. The sunroof dimming system also includes a controller operably coupled to the dimmable panel. The controller is configured to obtain data during operation of the vehicle. The controller is further configured to determine, based on the data, that a vehicle occupant will be exposed to external light via the dimmable panels when the vehicle is in a first predicted position of the roadway. The controller is further configured to control the dimmable panel to reduce a brightness of exterior light that will be encountered by an occupant of the vehicle before the vehicle is in the first predicted position. This solution considers the lighting parameters alone to determine whether the PDLC assembly is in an appropriate desired state. Similar to the previous case, even this solution does not take into account the preference of the user or the user activeness, besides the environmental parameter.

[0009] Yet another reference is made to W02021 / 060020 that provides a controller arranged in a vehicle to continuously adjust the intensity of light-shielding by a dimming window arranged in the vehicle, it talks about a surveyor continuously calculates the position of the sun seen from the position of the vehicle on the earth on the basis of a satellite positioning system and the time. A compass continuously detects the orientation of the vehicle at the position of the vehicle on the earth. A goniometer uses the position of the sun and the orientation of the vehicle to continuously calculate the elevation angle and azimuthal angle of sunlight emitted directly onto the vehicle. A modulator continuously and electrically adjusts the intensity of light-shielding by the dimming window depending on the calculated change in the elevation angle and azimuthal angle of sunlight. The modulator increases light-shielding of the dimming window when sunlight crosses the dimming window. The modulator decreases light shielding of the dimming window when sunlight does not cross the dimming window. Just like the previous solutions, even this prior art solution does not take in account, either the user preference or the user’s activeness.

[0010] In view of the solutions known so far, it has been observed that such solutions are static in terms of the control of the lighting assembly in glazing. It considers merely the external parameters for determining the lighting condition of the switchable glazing. However, for enhanced user experience, it is desirable to consider the mood or preference of the user as well besides the intuitive factors such as external parameters. Besides, it is desirable to have a reliable automatic control of active glazing especially like segmented active sunroof since individual manual control is difficult to have. Additionally, it has been observed that the existing prior art solution exhibits control of the glazing like sunroof only at the beginning and the ending of the driving cycle and not throughout the process of driving.

[0011] In view of the fore-going and the drawbacks discussed in the existing solution, there exists a dire requirement of providing an improved solution for automatic control of the polymer-dispersed liquid crystals (PDLC) based automotive glazing in a vehicle with improved user experience.

[0012] SUMMARY OF THE DISCLOSURE

[0013] An object of the present invention is to provide a solution for overcoming the drawbacks of the prior art solutions.

[0014] Another object of the present invention is to provide a solution to automatically control the PDLC based automotive glazing considering a passenger’s preference, activeness, and mood.

[0015] Yet another object of the present invention is to provide a solution to automatically control the PDLC based automotive glazing considering the external environmental parameters of the glazing.

[0016] Still another object of the present invention is to provide a solution to predict the state change and exhibit control on the PDLC based automotive glazing.

[0017] These and other objects of the invention are achieved by the following aspects of the invention. The following disclosure presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This presents some concept of the invention in a simplified form to a more detailed description of the invention presented later. It is a comprehensive summary of the disclosure, and it is not an extensive overview of the present invention. The intend of this summary is to provide a fundamental understanding of some of the aspects of the present invention.

[0018] In an aspect of the present invention is provided a system for controlling a state of a Polymer-Dispersed Liquid Crystals (PDLC) based automotive glazing in a vehicle. Said system comprises an automotive glazing having a first substrate a of glass or polymer, a second substrate b of glass or polymer, one or more interlayers and an electrically switchable PDLC assembly disposed between said first and second substrates. The PDLC assembly is operably configured to a control unit to predict and control a modification on a state of the PDLC assembly based on both obtained control factors and calculated control factors inclusive of at least vehicle’s passengers’ profile, activeness of passengers, environmental conditions of the vehicle, a vehicle status, and a vehicle battery status. The control unit being a multi-sensing electronic unit, is configured to operate in either a manual mode or an automatic mode and the system is operably configured to a global network. The control unit is configured to determine the environmental conditions of the glazing by detecting lighting conditions outside the vehicle, the angle of solar isolation, humidity and temperature at the location of the vehicle. The system comprises a global positioning system, (GPS), module configured to provide data to determine the angle of solar isolation based on location of the vehicle, a temperature sensor configured to determine the temperature of the glazing, a light intensity sensor, and an ambient light sensor configured to determine the light intensity respectively outside and inside the vehicle. The control unit is configured to create and store an individual passenger profile for every passenger. It is further configured to continuously update the passengers profile based over usage using machine learning module, wherein said passengers profile includes at least an identifier of the passenger, a preferred state of the PDLC assembly and a comfort level factor. The system in said aspect includes one or more devices operably configured to detect and determine the activeness of the passenger inside the vehicle. These devices may include a multimedia electronic control unit to detect status of multimedia modules, a body control module to determine control factors associated with the vehicle conditions, a means to detect the speech inside the vehicle, a means for battery monitoring, and a means to detect body vitals of one or more passengers. Additionally, the system may comprise a realtime clock (RTC) configured to place the system in right time zone settings to obtain the precise location data of the vehicle. The system can comprise a user interface to obtain at least an input for selecting a mode of controlling the PDLC assembly, in which the modes are either automatic or manual. The control unit is further operably configured to change the state of the PDLC assembly based on the power supply to said assembly, in which said supply to control the PDLC assembly is provided through a connecting inverter circuit. The different state changes of the PDLC assembly include partial or full switching ‘ON’ or ‘OFF’ of the PDLC assembly and / or change of haze level of the assembly. Again, the control unit is operably configured to a vehicle electronic control unit for enabling the control of the change of state of the PDLC assembly. The control unit is operational configured to a computational unit to predict a desired state of the PDLC assembly, and the computational unit may be a cloud-based unit. The control unit is configured to communicate state change of the PDLC assembly based on said predicted desired state to the vehicle electronic control unit. The PDLC assembly could be segmented PDLC assembly, in which case, the control unit is operably configured to change the state of one or more segments of the PDLC assembly based on the predicted state. The computational unit of the system is configured to predict a desired state of the PDLC assembly based on priority levels of control factors and said computational unit includes a machine learning module. The system further comprises a cloud-based storage unit operably coupled with computational unit and control unit, in which said cloud-based storage unit is configured to store historical data of the predicted desired states for a given set of obtained control factors and calculated control factors and said cloud-based storage media is configured to store the passengers profile details. In the system, the control unit is configured to identify the vehicle’s passenger behaviour pattern at different scenarios by monitoring the different scenarios and by monitoring the different moods of vehicle passenger behaviour. The control unit is configured to predict the user’s preference prediction based on a priority sequence, said priority sequence being chosen as identification of user mood, identification of possible environment factors for user mood, ambience control based on the identified environment factors, passenger behaviour analysis at the time of ambience control and self-learning based on user emotion.

[0019] In another aspect of the present invention is disclosed a method for predicting and controlling a state of the PDLC assembly in an automotive glazing. The method comprises obtaining the present input parameters from the vehicle by one or more data sensing units, calculating the present state parameters of the passengers and the vehicle by a control unit, obtaining said present input parameters, present state parameters, and historical data record by a computational unit. Said present input parameters and present state parameters include both obtained control factors and calculated control factors. The method further includes the computational unit performing the step of analysing said present input parameters, present state parameters, and historical data record using a machine learning module to predict a desirable state of the PDLC assembly of the automotive glazing and the control unit performing the step of checking, if the predicted desired state of the PDLC assembly requires a change in state. The method includes the step of control unit instructing to change the state of the PDLC assembly based on the predicted desired state, by signalling the PDLC assembly for changing the state in an automatic mode and verifying in a closed loop mode, if the predicted state of the PDLC assembly in alignment with the passenger’s comfort. Further provided in the method is the step of storing, by the computational unit, the predicted desired state, for a given set of obtained control factors and calculated control factors in a cloud-based storage unit for further training the machine learning module. The method also includes the step of obtaining data from passengers to create and / or update a passenger profile, wherein said passenger profile data is being stored in the cloud-based storage unit. The one or more embodiments of the present invention is targeted at providing a control unit for PDLC based glazing (especially sunroof) that could read input from multiple sensors and process these data to predict the mood and preference of the user and control the PDLC layered glass installed in the sunroof. It provides the user convenience to control the PDLC sunroof in energy efficient way but turning ON or OFF only the selective segments of the sunroof (for segmented PDLC sunroof) based on the energy availability and the outside environment condition. The control unit incorporates with artificial intelligence or machine learning algorithm (AI / ML) module to build a user profile to predict the user preference. The disclosed system understand user needs and perform intelligently. This system simply the control of active Sunroofs. Instead of having multiple manual controls for different segments in sunroof, it provides more energy efficient solution and high system reliability.

[0020] The significant features of the present invention and the advantages of the same will be apparent to a person skilled in the art from the detailed description that follows in conjunction with the annexed drawings.

[0021] BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The following briefly describes the accompanying drawings, illustrating the technical solution of the embodiments of the present invention or the prior art, for assisting the understanding of a person skilled in the art to comprehend the invention. It would be apparent that the accompanying drawings in the following description merely show some embodiments of the present invention, and persons skilled in the art can derive other drawings from the accompanying drawings without deviating from the scope of the disclosure.

[0023] FIG. 1 A illustrates instances of segmented PDLC as known in the art. FIG. IB illustrates a laminated glazing having PDLC unit (segmented PDLC) according to an embodiment of the present invention.

[0024] FIG. 2A-2C illustrate different block diagrams showing the configuration of the control unit in the system according to an embodiment of the present invention.

[0025] FIG. 3 illustrates a block diagram of the control unit and its configuration with sensing devices according to an embodiment of the present invention.

[0026] FIG. 4 illustrates steps of a method of controlling the PDLC based automotive glazing according to an embodiment of the present invention.

[0027] Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the disclosure.

[0028] DETAILED DESCRIPTION

[0029] The present disclosure is now discussed in more detail referring to the drawings that accompany the present application. It would be appreciated by a skilled person that this description to assist the understanding of the invention, but these are to be regarded as merely exemplary.

[0030] The terms and words used in the following description are not limited to the bibliographical meanings and the same are used to enable a clear and consistent understanding of the invention. Accordingly, the terms / phrases are to be read in the context of the disclosure and not in isolation. Additionally, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0031] The one or more embodiments of the present invention is configured to be in alignment with the user preference and smartly control the haze / transparency of the PDLC based automotive glazing of a vehicle. In the present disclosure, the PDLC glazing may be regarded to be either in a state of being ‘ON’ or in a state of being ‘OFF’ state. When the PDLC based glazing is in an ‘ON’ state, it would be transparent (no haze, opaque) and when in an ‘OFF’ state it would be opaque (shows haze). The change of state of the PDLC based glazing may also include change in haze level.

[0032] The PDLC based glazing as disclosed in the one or more embodiments of the present invention may include a segmented PDLC assembly. A segmented PDLC assembly refers to the PDLC assembly that include one or more PDLC films arranged between the two substrates of a glazing. The segmented PDLC based glazing may include PDLC films being arranged to provide a gradient lighting, or a patterned lighting as depicted in FIG. 1 A. The one or more segments of the PDLC film (204a, 204b, 204c, ... ) or the single PDLC film may be arranged between the substrates (201a, 201b) of the glazing sandwiched between one or more interlayers (202, 203) for lamination thereafter to obtain a laminated glazing (200) as depicts FIG. IB.

[0033] Reference is made to FIG. 2A that discloses a system (100) for controlling a state of a PDLC based automotive glazing in a vehicle. Said glazing in a preferred embodiment is the sunroof of the vehicle, however, not limited to this. Said system comprises a control unit (104) operably coupled to control the state of the PDLC unit or film (103) of the glazing (200). The PDLC assembly (104) is operably configured to a control unit (104). The control unit (104) is configured to predict and control a modification on a state of the PDLC assembly (103) based on control factors including different obtained control factors and different calculated control factors. The automotive glazing (200) includes a first substrate (201a) of glass or polymer; a second substrate (201b) of glass or polymer; and one or more interlayers (202, 203). It further includes an electrically switchable PDLC assembly (103) disposed between said first and second substrates (201a, 201b). In a preferred embodiment, the glazing may include two interlayers. The system may be operably configured to a global network. Said global network may be a wide area network following suitable wireless communication protocols.

[0034] In an implementation as depicted in FIG. 2A, the control factors may include present state parameters (Pl), present input parameters (P2) and historical data (P3). Present input parameters may include data obtained as input such as passenger profile, preference, activeness. Present state parameters include the data like instantaneous values like, temperature data, light intensity data and like, which primarily the basically the sensed values.

[0035] Obtained control factors refer to the control parameters that may be directly obtained or measured by means of an external device such as a sensor. Calculated control factors, on the other hand, refers to those control parameters that are to be calculated or estimated indirectly from other measured data. For instance, the sun angle is a calculated control factors that which is obtained indirectly. Sun angle for an instance of time, given a location can be calculated from data obtained from global positioning device (like a Global Positioning Module, GSM).

[0036] In an embodiment of the present invention, the control factors to predict and control the PDLC assembly may include at least a vehicle’s passenger(s) profile, activeness level of passenger(s), environmental conditions of the vehicle, vehicle status and a vehicle battery status. In an implementation, said control unit is multi-sensing unit. It may be configured to operate in either in a manual mode or in an automatic mode. In an implementation of the present invention, in a manual mode, the control unit (104) may or may not predict the desired or favourable state of the PDLC. If it is configured to predict, then such a prediction may be provided to a user in a suitable output means such as a display or an audio output as suggestion. However, the control unit (104) does not signal or instruct the PDLC film to undergo a state change (if not in the predicted state) in a manual mode. On the other hand, in an automatic mode, the control unit (104) is configured to not only predict the desired or favourable state of the PDLC film but also signal the PDLC film to undergo a state change to achieve the predicted state change (if needed). If the PDLC assembly (103) is segmented PDLC assembly, the control unit (104) is configured to change the state of one or more segments of the PDLC assembly (103) based on the predicted modification.

[0037] Reference is made to FIG. 2B that discloses the system (100) according to an embodiment of the present invention. The system includes a multi-sensing control unit

[0038] (104) configured to the PDLC assembly. In an implementation, said multi-sensing control unit (104) may be directly configured to the PDLC assembly (103) or via a vehicle electronic control unit (1000) for controlling the state of the PDLC assembly. The vehicle electronic unit (106) is configured to control all the electrical systems or subsystems in a vehicle. The control unit (104) is configured to obtain both measured data and calculated data for predicting the best suitable state of the PDLC assembly (103). In said embodiment, the control unit (104) is operationally configured to a computational unit (105). Said computation unit (105), in a preferred implementation, is a cloud-based processing unit or a cloud computing engine. Said computational unit

[0039] (105) includes machine learning modules to analyse the different control factors and data to suggest a change or no change in the state of the PDLC assembly (103) of the automotive glazing (200).

[0040] In an embodiment of the present invention is provided the system level functions. The control unit (104) is configured to determine the environmental conditions of the glazing by detecting lighting conditions outside the vehicle based on the angle of solar isolation, solar light intensity, and temperature at the location of the vehicle. In an implementation, the multi-sensing control unit (105) is configured to store one or more user profile based on specific user preferences. The storage unit for this purpose may be a cloud-based storage unit or a local storage unit coupled to control unit (104). To obtain the environmental information, the control unit is configured to estimate sun angle from the information of the positioning device (for instance Global positioning system module, GPS). Other location specific information regarding solar isolation, scenic view with respect to the vehicle may be obtained from the GPS coordinates received. Similarly, the ambient temperature and humidity data may be received from data sensing modules / units / devices. These sensing modules / units / devices are suitably disposed at various locations in and on the vehicle. The control unit (104) is operably coupled with said sensing modules / units / devices to obtain the data of the control factors. Similarly, a light intensity information may be obtained from light intensity sensor. The control unit (104) is configured to estimate a battery range prediction for enabling energy efficiency parameter. The control unit (104) may be further configured to obtain the data to determine a vehicle condition inclusive of vehicle position and driving conditions. The obtained data and calculated data provide the present input parameters and the present state parameters.

[0041] In this implementation, the sensor data and a user settings data are transmitted to the computational unit (105). In the computational unit (105) the machine learning modules process said data to provide behaviour prediction, thereby suggest a desirable state of the PDLC assembly. The control unit (104) may include an inverter circuit (1041) configured to drive the PDLC based automotive glazing. In this implementation, the control unit (104) may be configured to the vehicle electronic control unit (106) as well. The control unit (104) is configured to communicate to said vehicle electronic control unit (106) about the activities of the multi-sensing control unit (104) and status of these activities inclusive of state of PDLC assembly. The control unit (104) is configured to create and store an individual passenger profile for every passenger. It is further configured to continuously update the passenger profile based over usage and historical data. Said passenger profile includes at least an identifier of the passenger, preferred state of the PDLC assembly (103) and a comfort level factor. However, said parameters are not limiting in nature, other suitable parameters may also be included as part of the passenger profile.

[0042] Reference is made to FIG. 2C that discloses an exemplary embodiment of the system construction for obtaining the present state parameters and present input parameters. The multi-sensing control unit (104) is configured to predict the sun angle, solar insolation, condition of the vehicle and the driver status. Said control unit (104) includes the means to control the PDC assembly. In an exemplary embodiment, the system (100) comprises a global positioning system, GPS, module (111) configured to provide location co-ordinates to determine the angle of solar isolation and location of the vehicle. It further includes a real-time clock (RTC) configured to place the system in accurate time zone settings. The control unit (104) is configured to determine a precise angle of solar isolation at an instance based on the location of the vehicle, depending on data obtained from said GPS module (111) and RTC module. The system (100) comprises a temperature sensor (112) configured to determine the temperature of the glazing, a light intensity sensor (113) configured to determine the lighting conditions outside the vehicle and an ambient light sensor (115) configured to determine the solar light intensity inside the vehicle. The system includes a user interface (114) to obtain at least an input for selecting a mode of controlling the PDLC assembly (103) and said modes being either automatic or manual. The interface may include touch-based input (for instance and not as limitation).

[0043] In this exemplary embodiment, the system (100) includes one or more devices operably configured to detect and determine the activeness of the passenger inside the vehicle. These devices include a multimedia electronic control unit (116) to detect status of multimedia modules, a body control module (117) to determine control factors associated with the vehicle conditions, a means to detect the speech (to determine if the passengers inside the vehicle are engaged in a conversation) inside the vehicle, a means (118) for battery monitoring, and a means to detect body vitals (such as and not limited to sensors to detect heartbeat, drowsiness of the passenger or the drive and the like) of one or more passengers. The system may also include means such as a touch interface or a button for the user to provide preferences (if any). The positioning device (111), temperature sensor (112), Light intensity sensor (113), user input interface (114), ambient light sensor (115) are some instances of the data source that provide direct obtained parameters. Multimedia electronic control unit (116), body control module

[0044] (117) of the vehicle (to understand the vehicle conditions) and batter monitoring means

[0045] (118) are some instances of data source that provide data for the calculated parameters.

[0046] In an implementation of the present invention is provided a computational unit (105) configured to predict the desired state of the PDLC assembly based on priority levels of the control factors. An example, such a priority level of control factors or parameters may be of the order a vehicle passenger(s) profile, activeness of passenger(s), environmental conditions of the vehicle and the vehicle battery status. The logic of the machine learning module in the computational unit (105) implements said priority level for analysing the input and predicting a desired state of the PDLC assembly. The passenger(s) profile includes individual user profile that would be created for every user. The user profile may be continuously updated based over the usage using machine learning concept. The activeness of passenger(s) may be determined via detection of the activeness of user inside the vehicle, for instance, it may include the data obtained from the checking of the status of multimedia system in the vehicle. Again, the environment conditions may be obtained by gathering data on the sun angle at the particular location and time of travel, the scenic view outside the vehicle based on the GPS data, the light intensity data and temperature outside the vehicle obtained from sensing modules / devices / units.

[0047] In an exemplary implementation of the present invention is provided how the control unit (104) facilitates sensing of different inputs for the dynamic control of the PDLC assembly-based glazing. The temperature, humidity and light intensity are measured using respective sensors. Sun angle is estimated using the data pf the location coordinates obtained from the GPS module. The weather conditions of the location where the vehicle is may be obtained via temperature and humidity, sun angle as well. The Light intensity sensor is configured to detect the illumination level of lighting available at the particular location where the vehicle is, at any given instance of time. This intensity shall be transmitted to processing unit of the control unit (104). The obtained parameter and the calculated parameters are served as the input for determining a user comfort level. FIG. 3 depicts a block diagram of hardware architecture of the control unit. It provides an exemplary embodiment of how the different modules may be disposed within the multi-sensing control unit. The connections of different sensors and data sources may be either wired or wireless. The PDLC control circuit has modules to convert the low voltage DC input to High voltage AC output that is needed to drive the PDLC, as per an implementation of the present invention. The control unit includes a three-tier architecture including means for peripheral devices, middleware and applications thereof. It would be appreciated by one skilled in the art that said architecture is purely for means of understanding and not by means of any limitation.

[0048] In an embodiment of the present invention is disclosed a method (300) for predicting and controlling a state of the PDLC assembly in an automotive glazing. A block diagram of said method is provided in FIG. 4. Said method comprises obtaining (S301) data from one or more data sensing units, for obtaining the present input parameters from the vehicle. From the obtained present input parameters, control unit performs calculation (S302) of the present state parameters of the passengers and the vehicle. Further to this, the computational unit obtains (S303) said present input parameters, present state parameters, and historical data record. The historical data record includes data that have been stored from the past events activities of the PDLC based glazing and circumstantial data associated with activity as well. For instance, it may include the last state of the PDLC based glazing and the data which lead to the choice of such a state. The method (300) further includes analysing (S304), by the computational unit, using a machine learning module to predict a desirable state of the PDLC assembly of the automotive glazing. Additionally, it includes the step of checking (S305), by the control unit, if the predicted desired state of the PDLC assembly requires a change in state. The method further comprises instructing (S306), by the control unit, to change the state of the PDLC assembly based on the predicted desired state, by signalling the PDLC assembly for changing the state in an automatic mode.

[0049] In this embodiment of the method for predicting and controlling a state of the PDLC assembly in an automotive glazing includes storing (S312), by the computational unit, the predicted desired state, for a given set of obtained control factors and calculated control factors in a cloud-based storage unit for further training the machine learning module. It also includes obtaining (S311), by the control unit, data from passenger(s) to create and update a passenger profile. This passenger profile data may be stored in the cloud-based storage unit. The method includes a step of verifying (S313) if the user is comfortable by the suggested change in state of the PDLC assembly by assessing the user comfort level. In an implementation, this verification of the passenger(s) comfort is performed in a closed loop mode.

[0050] The present invention provides an automatic control of PDLC based glazing by predicting the user comfort, based on passenger’s profile and mood of the passenger(s) inside the vehicle, by detection of sun angle, light intensity and temperature at the location of the vehicle. The disclosed system being connected to GPS is capable to detect in advance the scenic environment conditions. This advantageously provides for automatic selective turn ON or OFF of the PDLC based automotive glazing on scenic scenario, glare, and such external parameters, in addition to the historic record and the instant mood of the passenger(s) in the vehicle. The automatic sensing of passenger’s occupancy in each row and selective turn ON of PDLC based on it.

[0051] In the disclosed solution, the control of the PDLC based glazing may be in manual mode and in automatic mode depending on requirements of the prediction. There exists an energy efficient operation, in which the user may select which subnet or PDLC segment needs to be activated if the PDLC is a segmented PDLC. The control unit (104) is configured to estimate light intensity, angle of the Sun outside the vehicle, estimate temperature of the outside environment, detect and analyse sound (for instance by Speech recognition techniques) inside the vehicle cabin to determine if the passenger is awake and active. The system makes use of a self-training of user profile based on machine learning techniques, as per one implementation of the present invention.

[0052] It offers users comfort and the freedom of personalization based on their needs. The disclosed solution provides automatic control of the PDLC glazing by predicting a user comfort level. The control of the haze of the PDLC glazing may be based on user profile, mood of the user inside the vehicle, the environmental conditions associated with the glazing such as detection of sun angle, light intensity, and temperature at the location of the vehicle. The present invention exhibits automatic selective switching ON or OFF the PDLC based glazing depending on scenes, locations, glare, and the like outside the glazing. For the purpose, the disclosed system includes a positioning device to detect in advance the scenic environment conditions among other parameters, additionally, the system is configured for selective switch OFF or ON the PDLC based sunroof based on passenger(s) preference and predicted by a machine learning algorithm. In an embodiment, the system disclosed in the present invention targets at an automatic and more personalized control of the PDLC based automotive glazing. The customized, improved automatic control of the PDLC based automatic glazing is obtained by predicting a passenger comfort based on a passenger profile, mood of the passenger inside the vehicle. Additionally, it may be controlled by detection of sun angle, light intensity, and temperature at the location of the vehicle.

[0053] In an implementation of the present invention is provided a means for self training of user profile based on machine learning techniques. In this implementation, a priority level for prediction of customized for users is provided. This includes creation of impactful profiles of the passengers in the vehicle. The individual user profile creation includes creation of configurable profiles and possible addition or deletion of profiles. It further includes identification of user behaviour for different pattern at different scenarios. This in turn comprises monitoring scenarios and monitoring user moods. The profile may be developed based on preference based on light intensity, preference based on temperature condition, time and rainfall conditions. The control unit is responsible for creation and further dynamic updation of the vehicle’s passenger profile.

[0054] In an implementation of the disclosed system, the control unit (104) may be configured to predict the user’s preference prediction based on a priority sequence, said priority sequence being chosen as identification of user mood, identification of possible environment factors for user mood, ambience control based on the identified environment factors, passenger behaviour analysis at the time of ambience control and self-learning based on user emotion. The identification of user mood may include assessing if the user is annoyed, Happy, Angry, and the like. The identification of possible environment factors for user mood comprises factoring the impact of extreme sun light / high temperature if roof is transparent. In an instance, ambience control based on the identified environment factor may include maintaining the ambience if user is happy and accordingly changing the ambience if user is annoyed / unhappy. In an instance, the self-learning based on user emotion may include upon panic identification, self-learn to control ambience at slower rate and upon annoyed / take over manual control, update the profile based on the learning. In an exemplary implementation of the present invention the means to identify the is provided a means for Facial emotion detection

[0055] Example Embodiment: A control unit for a PDLC based sunroof is provided in a vehicle. The control unit is configured to obtain the profile of one or more users of the vehicle. It is configured to a cloud computing engine that implements a machine learning method, consider the present state, present input parameters and suggests for either switching the PDLC based sunroof ON or OFF. It may even suggest no change as well. The system is configured to perform this monitoring constantly and not just at the beginning of a driving cycle. The control unit thus has access to data from the different data sources and to the historical record as well. The data sources provide the present state of user comfort and weather input conditions as well. This data includes GPS location, sun angle, local environmental conditions (like ambient temperature, humidity, pressure and the like), current or instantaneous lighting conditions inside and outside vehicle, instantaneous weather conditions, the behaviour of different users and the time of the day, and safety parameters if any. These data which includes both the directly obtained data and the calculated data along with historical data are provided to the computational unit which in turn uses AI / ML (artificial intelligence / machine learning) module to predict a change or retain the state of the PDLC sunroof. The user may choose to enable the control of the PDLC sunroof either in automatic mode or in manual mode, in an automatic mode, the control unit is configured to either turn ON the PDLC based sunroof or turn it OFF. The control unit is then configured to verify if the state change is in line with the need of the user for a given ambience conditions. All the data and said verification results are then pushed to a storage media and used for training the machine learning module for improved results. In this example, the multi-sensing control unit may be configured to provide power supply to control PDLC through a converter preferably a H-bridge DC to AC converter. The control unit may include with a GPS sensing module that may sense the precise GPS location. The system may further include a RTC for placing the system in right time zone settings. Depending on the location co-ordinates and the RTC data, the control unit may detect the sun angle at the instance of time. The system may also include a temperature and relative humidity sensor that provides information about the outside temperature. The light intensity sensor provides the intensity of the outside light level to the control unit. The system includes interfaces for user to communicate. The user may have a provision to enable or disable the prediction. The interface provides the user a customization option to pick and choose the parameters based on which the prediction of the state of the PDLC sunroof may be made.

[0056] In an embodiment of the present invention, is disclosed the system for controlling a state of a PDLC based automotive glazing in a vehicle offering both automatic mode and manual mode. The control unit is configured to monitor scenarios. The monitoring further includes monitoring driver’s response control to the scenarios, updation of the algorithm based on the user’s control response and continuously monitoring the situations. The control unit is configured to check the user profile, determine whether the profile exists and extract default data from the profile. The control unit is further configured to take the standard default data. The control unit is configured to monitor scenarios and driver emotions. It is further configured to control ambience as per default data for given scenario and emotion. The control unit is further configured to monitor driver behaviours, analyse the behaviour and provide suitable actions. For instance, if the emotions are panic and annoyed, the control signal may be directed towards controlling slowly to different state. Similarly, if it is happy and neutral, it is expected to be continue monitoring and per se no change. Some advantages of the present invention are enlisted in the following:

[0057] • In the disclosed solution, the state of the PDLC based glazing (such as a sunroof) is decided based on the choices or preferences of the passenger(s). Additionally, it continuously checks for prediction of a state change of the PDLC and not just at the beginning or end of the driving cycle. This ensures better user experience.

[0058] • The system understands the need of the user, the ambience of both inside and outside of the vehicle to control an active glazing (such as sunroof) catering to the need of the user intelligently.

[0059] • The manual controls for different segments in an active glazing is now mitigated, thereby resulting in improved energy consumption. Additionally, battery status is one of the parameters for predicting the state of the PDLC.

[0060] • With the advantageous use of machine learning algorithm, the mechanism of automatic control is improved with historical data. This in turn improves the reliability of the system.

[0061] Many alterations and modifications of the present invention will no doubt become apparent to a person skilled in the art after having read the foregoing description. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. It is to be understood that the description above contains many specifications, these should not be construed as limiting the scope of the invention but as merely providing illustrations of some of the personally preferred embodiments of this invention. Thus, the scope of the invention should be determined by the appended claims and their legal equivalents rather than by the examples given. Features that are described and / or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and / or in combination with or instead of the features of the other embodiments.

[0062] List of reference numerals appearing in the accompanying drawings and the corresponding features:

[0063] 200: glazing

[0064] 201a: first substrate

[0065] 201b: second substrate

[0066] 202, 203: interlayer

[0067] 204a, 204b, 204c, 204d: segments of the PDLC unit

[0068] 100: system

[0069] 103: PDLC unit

[0070] 104: control unit

[0071] 105: computational unit

[0072] 106: Vehicle Electronic control unit

[0073] 107: cloud-based storage

[0074] 1041 : Connecting Circuit

[0075] 111: GSM module

[0076] 112: Temperature sensor

[0077] 113: Light intensity sensor

[0078] 114: User input interface

[0079] 115: Ambient light sensor

[0080] 116: Multimedia ECU

[0081] 117: Body Control Module

[0082] 118: Battery Monitoring

[0083] 300: method

[0084] S301-S313: method steps

Claims

CLAIMS1. A system (100) for controlling a state of a Polymer-Dispersed Liquid Crystals (PDLC) based automotive glazing in a vehicle, wherein said system comprises: an automotive glazing (200) having a first substrate (101a) of glass or polymer; a second substrate (101b) of glass or polymer; one or more interlayers (102) and an electrically switchable PDLC assembly (103) disposed between said first and second substrates (101a, 101b) forming a sandwiched unit; wherein the PDLC assembly (103) is operably configured to a control unit (104) to predict and control a modification on a state of the PDLC assembly (103) based on both obtained control factors and calculated control factors inclusive of at least vehicle’s passenger(s) profile, activeness of passenger(s), environmental conditions of the vehicle, a vehicle status and a vehicle battery status; the control unit being a multi-sensing electronic unit, is configured to operate in either a manual mode or an automatic mode; and the system is operably configured to a global network.

2. The system (100) for control as claimed in claim 1, wherein the control unit (104) is configured to determine the environmental conditions of the glazing by detecting lighting conditions outside the vehicle, the angle of solar isolation, humidity and temperature at the location of the vehicle.

3. The system (100) for control as claimed in claim 2 or claim 3, wherein the system comprises a global positioning system, GPS, module (111) configured to provide data to determine the angle of solar isolation based on location of the vehicle, a temperature sensor (112) configured to determine the temperature of the glazing, a light intensity sensor (112), and an ambient light sensor (113) configured to determine the light intensity respectively outside and inside the vehicle.

4. The system (100) for control as claimed in any one of the claims 1-3, wherein the control unit (104) is configured to create and store an individual passenger profile for every passenger; and said control unit (104) is further configured to continuously update the passenger(s) profile based over usage using machine learning module, wherein said passenger(s) profile includes at least an identifier of the passenger, a preferred state of the PDLC assembly (103) and a comfort level factor.

5. The system (100) for control as claimed in any one of the claims 1-4, further comprising one or more devices operably configured to detect and determine the activeness of the passenger inside the vehicle; wherein said devices include a multimedia electronic control unit (116) to detect status of multimedia modules, a body control module (117) to determine control factors associated with the vehicle conditions, a means to detect the speech inside the vehicle, a means (118) for battery monitoring, and a means to detect body vitals of one or more passengers.

6. The system (100) for control as claimed in any one of the claims 1-5, comprising a realtime clock (RTC) configured to place the system in right time zone settings to obtain the precise location data of the vehicle.

7. The system (100) for control as claimed in in any one of the claims 1-6, further comprises a user interface to obtain at least an input for selecting a mode of controlling the PDLC assembly (103); said modes being either automatic or manual.

8. The system (100) for control as claimed in any one of the claims 1-7, wherein the control unit (104) is operably configured to change the state of the PDLC assembly (103) based on the power supply to said assembly, wherein said supply to control the PDLC assembly (103) is provided through a connecting inverter circuit (1041).

9. The system (100) for control as claimed in any one of the claims 1-8, wherein the different state changes of the PDLC assembly (103) include partial or full switching ‘ON’ or ‘OFF’ of the PDLC assembly (103) and / or change of haze level of the assembly.

10. The system (100) for control as claimed in any one of the claims 1-9, wherein said control unit (104) is operably configured to a vehicle electronic control unit (106) for enabling the control of the change of state of the PDLC assembly (103); said control unit (104) being operational configured to a computational unit (105) to predict a desired state of the PDLC assembly, said computational unit being cloud-based; and said control unit (104) is configured to communicate state change of the PDLC assembly based on said predicted desired state to the vehicle electronic control unit.

11. The system (100) for control as claimed in any one of the claims 1-10, wherein said PDLC assembly (103) is segmented PDLC assembly; and said control unit (104) is operably configured to change the state of one or more segments of the PDLC assembly (103) based on the predicted state.

12. The system (100) for control as claimed in any one of the claims 1-11, wherein said computational unit (105) is configured to predict a desired state of the PDLC assembly (103) based on priority levels of control factors; and said computational unit (105) includes a machine learning module.

13. The system (100) for control as claimed in any one of the claims 1-12, wherein said priority level of control factors is having the order: a vehicle passenger(s) profile, activeness of passenger(s), environmental conditions of the vehicle and the vehicle battery status.

14. The system (100) as claimed in any one of the claims 1-13, further comprises a cloudbased storage unit (107) operably coupled with computational unit (105) and control unit (104), wherein said cloud-based storage unit (107) is configured to store historical data of the predicted desired states for a given set of obtained control factors and calculated control factors; and said cloud-based storage media (106) is configured to store the passenger(s) profile details.

15. The system (100) as claimed in any one of the claims 1-14, wherein control unit (104) is configured to create and build the vehicle’s passenger profile based on a combination of factors including preference on comfortable sun angle position, preference based on light intensity, preference based on temperature condition and time and rainfall condition.

16. The system (100) as claimed in any one of the claims 1-15, wherein the control unit (104) is configured to identify the vehicle’s passenger behaviour pattern at different scenarios by monitoring the different scenarios and by monitoring the different moods of vehicle passenger behaviour.

17. The system (100) as claimed in in any one of the claims 1-16, wherein the control unit (104) is configured to predict the user’s preference prediction based on a priority sequence, said priority sequence being chosen as identification of user mood, identification of possible environment factors for user mood, ambience control based on the identified environment factors, passenger behaviour analysis at the time of ambience control and self-learning based on user emotion.

18. A method (S300) for predicting and controlling a state of the PDLC assembly in an automotive glazing, wherein said method comprises: obtaining (S301), by one or more data sensing units, the present input parameters from the vehicle; calculating (S302), by a control unit, the present state parameters of the passengers and the vehicle; obtaining (S303), by a computational unit, said present input parameters, present state parameters, and historical data record, wherein said present input parameters and present state parameters include both obtained control factors and calculated control factors; analysing (S304), by the computational unit, said present input parameters, present state parameters, and historical data record using a machine learning module to predict a desirable state of the PDLC assembly of the automotive glazing; checking (S305), by the control unit, if the predicted desired state of the PDLC assembly requires a change in state; and instructing (S306), by the control unit, to change the state of the PDLC assembly based on the predicted desired state, by signalling the PDLC assembly for changing the state in an automatic mode.

19. The method (S300) as claimed in claim 18, comprising: verifying (S313), by the control unit in a closed loop mode, if the predicted state of the PDLC assembly in alignment with the passenger(s)’s comfort.

20. The method (S300) as claimed in claim 19, comprising storing (S312), by the computational unit, the predicted desired state, for a given set of obtained control factors and calculated control factors in a cloud-based storage unit for further training the machine learning module.

21. The method (S300) as claimed in claim 18, comprising: obtaining (S311), by the control unit, data from passenger(s) to create and / or update a passenger profile, wherein said passenger profile data is being stored in the cloud-based storage unit.

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