Intelligent heating method and system for rear air window and rearview mirror, controller and storage medium

Abstract

The invention discloses an intelligent heating method and system for a rear air window and a rearview mirror, a controller and a storage medium. The method comprises the following steps: acquiring real-time environment data at least comprising environment temperature and environment humidity; determining a basic heating duration based on the environment temperature through a first control logic; compensating and correcting the basic heating duration based on the environment humidity through a second control logic so as to generate a target heating duration; and finally, controlling the heating device to operate according to the target heating duration. The method further comprises a cooperative control scheme of switching to a degradation control mode based on preset configuration parameters when sensor data are invalid and adjusting heating duration based on data acquired from the ADAS in severe weather. Through multi-parameter fusion and hierarchical cooperative control, accurate matching of heating requirements is achieved, and the defrosting efficiency, the energy saving performance and the system reliability are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of automotive thermal management technology, and more specifically, to an intelligent heating control method, system, vehicle controller, and computer-readable storage medium for the rear window and rearview mirror of new energy vehicles. Background Technology

[0002] With the development of electric and intelligent vehicles, rear window and rearview mirror heating functions have become standard equipment in vehicles. Their main function is to quickly remove frost, fog or water vapor from the glass surface in cold, humid or rainy and snowy weather, ensuring that the driver has a clear rear view and ensuring driving safety.

[0003] The existing rear window and rearview mirror heating control strategies mainly suffer from the following technical problems and defects:

[0004] First, the control precision and efficiency are low. The most common control method is fixed-duration heating, where the heating system operates at a constant power for a preset time (e.g., 15 minutes) after the user presses the switch, then automatically shuts off. This "one-size-fits-all" strategy cannot adapt to varying external environments. In extremely cold conditions, the preset time may not be sufficient for complete defrosting, resulting in poor performance; while in relatively mild temperatures, the time is too long, causing unnecessary energy waste. For electric vehicles, where driving range is particularly critical, this energy waste directly impacts driving economy. Furthermore, prolonged heating in unnecessary situations (such as accidental activation in hot weather) can cause components in the heating circuit, such as fuses, wiring harnesses, and connectors, to overheat, posing a safety hazard of thermal failure.

[0005] Secondly, it suffers from poor environmental adaptability. Some improved solutions introduce tiered control based on ambient temperature, setting different heating times according to several rough temperature ranges. However, this single-dimensional control model, relying solely on temperature, cannot effectively cope with complex real-world weather conditions. For example, it cannot fundamentally distinguish between a dry, sunny day at -10°C and a wet, cold, rainy / snowy day at -10°C. The latter, with its higher humidity and precipitation, significantly increases the difficulty of defrosting and defogging, requiring longer heating times—a problem that cannot be solved by temperature alone.

[0006] Secondly, the system lacks reliability and redundancy. Existing strategies heavily rely on a single or a few sensors (primarily temperature sensors). If a critical sensor fails or its signal transmission is interrupted, the control system loses its decision-making basis. To ensure safety, the system typically disables the heating function directly. This simple fail-safe mechanism is unacceptable in severe weather because it deprives the driver of clear visibility when it is most needed, posing a serious safety risk. The system lacks intelligent and effective fail-safe operating strategies.

[0007] Finally, there is a lack of system synergy. Modern cars are generally equipped with advanced driver assistance systems (ADAS), which integrate multiple high-precision sensors such as forward-facing cameras and radar to accurately identify the vehicle's surrounding environment, especially weather conditions (such as heavy rain or snow). However, the heating control system typically operates as an independent subsystem, resulting in significant data silos between it and the ADAS system. The heating system fails to utilize the valuable environmental perception data already acquired by the ADAS system to assist or optimize its own control decisions, thus limiting its intelligence and causing sluggish responses.

[0008] Therefore, there is an urgent need in this field for a new type of intelligent heating solution for rear windows and rearview mirrors. This solution should be able to accurately and efficiently cope with different climatic environments, take into account defrosting effect, energy economy and electrical safety, and have high reliability and system synergy capabilities. Summary of the Invention

[0009] The main objective of this invention is to overcome the aforementioned deficiencies in the prior art and provide an intelligent heating method, system, controller, and storage medium for the rear window and rearview mirror. This solution can achieve precise, energy-saving, safe, and reliable heating control.

[0010] To achieve this objective, a first aspect of the present invention provides a method for intelligent heating of the rear window and rearview mirror, the method comprising the following steps:

[0011] Acquire real-time environmental data including at least a first environmental parameter and a second environmental parameter, wherein the first environmental parameter is the ambient temperature and the second environmental parameter is the ambient humidity;

[0012] Based on the first environmental parameter, a basic heating duration is determined by the first control logic.

[0013] Based on the second environmental parameter, the second control logic compensates and corrects the basic heating time to generate a target heating time.

[0014] The heating devices for the rear window and rearview mirrors are controlled to operate according to the target heating duration.

[0015] This solution integrates two key environmental parameters, temperature and humidity, to construct a multi-dimensional control model. This breaks through the limitations of the single-dimensional control of existing technologies, enabling the determination of heating time to more accurately reflect the actual defrosting and defogging needs. Thus, while ensuring the effect, energy waste is avoided, and the accuracy and efficiency of control are improved.

[0016] In a preferred technical solution, the step of determining the basic heating duration based on a first environmental parameter specifically includes: dividing the ambient temperature into multiple preset temperature ranges, each temperature range corresponding to a different basic heating duration; and determining the basic heating duration based on the temperature range into which the acquired ambient temperature value falls. By finely classifying the temperature, the basic heating duration can be more accurately matched with the basic heat demand under different degrees of cold.

[0017] In a preferred technical solution, the step of compensating and correcting the basic heating time based on the second environmental parameter specifically includes: when the ambient humidity value exceeds a preset humidity threshold, increasing the basic heating time according to the degree to which the ambient humidity value exceeds the humidity threshold. This solution can effectively distinguish between "dry cold" and "wet cold" operating conditions, dynamically increase heat output (i.e., extend heating time) in high humidity environments, and significantly improve environmental adaptability.

[0018] In a preferred technical solution, the real-time environmental data also includes rainfall level as a third environmental parameter; the compensation and correction for the basic heating duration are also based on this third environmental parameter. By introducing rainfall information, the impact of precipitation on defrosting and defogging is further quantified, making the decision-making of the multi-dimensional model more accurate.

[0019] In a preferred embodiment, the method further includes monitoring the validity of first and second environmental parameters, and automatically switching to a degraded control mode when invalid data is detected for at least one environmental parameter. This solution provides a redundancy strategy in case of critical sensor failure, ensuring the basic availability of the heating function under abnormal conditions and greatly improving the reliability and safety of the system.

[0020] In a preferred embodiment, under degraded control mode, the method includes: reading a preset configuration parameter from a non-volatile memory; and controlling the heating device based on a default heating duration determined by the preset configuration parameter. By using preset parameters bound to the climate characteristics of the vehicle's sales area, a relatively reasonable heating service that ensures basic safety can be provided even in the event of a complete loss of real-time sensing data.

[0021] In a preferred embodiment, the method further includes acquiring independently perceived environmental data from the vehicle's Advanced Driver Assistance System (ADAS), and adjusting the target heating duration based on the ADAS environmental data when the ADAS environmental data indicates a preset severe weather type. This approach breaks down the data barriers between the heating system and ADAS, leveraging the advanced perception capabilities of ADAS to collaboratively optimize heating control, achieving a higher level of intelligence and faster response.

[0022] In a preferred technical solution, the target heating duration is adjusted based on ADAS environmental data. Specifically, this includes temporarily overriding the target heating duration generated by the first and second environmental parameters with the heating duration determined by the ADAS environmental data. This high-priority overriding mechanism ensures that the system can make the fastest and most accurate response when ADAS identifies critical weather events.

[0023] A second aspect of the present invention provides an intelligent heating system for the rear window and rearview mirror, the system comprising: a multi-parameter sensing module, a controller, and a heating device. The controller is configured to perform the method described in the first aspect of the present invention.

[0024] A third aspect of the present invention provides a vehicle controller including a processor and a memory, wherein the memory stores a computer program, and the processor executes the program to implement the method described in the first aspect of the present invention.

[0025] A fourth aspect of the present invention provides a computer-readable storage medium on which a computer program stored is executed by a processor to implement the method described in the first aspect of the present invention.

[0026] Compared with the prior art, the beneficial effects of the present invention are reflected in:

[0027] 1. Precise and efficient: By integrating and controlling multiple parameters such as temperature, humidity, and rainfall, the heating demand is precisely matched, and the defogging compliance rate is increased from 68% to 92%, significantly improving driving visibility in adverse weather conditions.

[0028] 2. Energy saving and safety: Effectively avoids ineffective and excessive heating. The energy consumption for high-temperature accidental contact is reduced from 450Wh to 210Wh, a reduction of 53%, saving valuable energy for electric vehicles. At the same time, the fuse temperature rise is reduced from 78℃ to 52℃, a reduction of 33%, eliminating electrical heat risks and reducing reliance on high-specification components, thereby saving costs.

[0029] 3. High reliability: The innovative degradation control mode ensures that the heating function is not interrupted when the sensor fails, providing necessary safety redundancy.

[0030] 4. Intelligent Collaboration: It breaks down the data silos with the ADAS system and uses its precise perception capabilities to coordinate control. The system response time has been improved from 2.1s to 1.3s, an improvement of 38%, making control more intelligent and agile. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the embodiments disclosed in this invention, the accompanying drawings of the embodiments will be briefly described below. These drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0032] Figure 1 This is a logic flowchart of an embodiment of the intelligent heating method of the present invention.

[0033] Figure 2 This is a structural block diagram of an intelligent heating system according to an embodiment of the present invention.

[0034] Figure 3 This is a physical connection block diagram of an intelligent heating system provided in an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram of a three-level control architecture for an intelligent heating system according to an embodiment of the present invention. Detailed Implementation

[0036] The technical solutions (including preferred technical solutions) of the present invention will be further described in detail below with reference to the accompanying drawings and by way of listing some optional embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0037] Example 1

[0038] This embodiment describes an intelligent heating method for the rear windshield and rearview mirror. Existing control strategies typically employ fixed durations or only consider temperature, resulting in poor defogging performance in complex, damp, and cold weather conditions, while causing unnecessary energy waste in relatively mild or dry environments, affecting the driving range of electric vehicles and posing electrical safety hazards. This embodiment introduces multi-dimensional environmental parameters to construct a dynamic control model, aiming to minimize energy consumption while ensuring optimal defrosting and defogging effects.

[0039] Reference Figure 1 The logical flow shown includes the following steps in a preferred implementation of this method:

[0040] First, when the vehicle system detects a user's request to turn on the heater, the first step of this method is to acquire real-time environmental data. This data forms the basis for subsequent intelligent decisions. In a basic implementation, the acquired data includes at least two different physical quantities: one is the ambient temperature as a first environmental parameter, and the other is the ambient humidity as a second environmental parameter.

[0041] Next, the method enters a core decision-making phase, which determines the heating time through a multi-level control logic.

[0042] The first layer of control logic determines a basic heating time based on the acquired first environmental parameter (ambient temperature). Temperature is the most direct factor affecting whether frosting or fogging occurs, so it is reasonable and necessary to determine a basic, rough heating time based on temperature.

[0043] The second layer of control logic refines the first layer. Based on the acquired second environmental parameter (ambient humidity), this logic compensates for and corrects the baseline heating time obtained in the previous step, thus generating a more precise target heating time. This is because humidity is a key factor determining whether water vapor can condense on the car window surface. Introducing the humidity parameter allows this method to effectively distinguish between "dry cold" and "wet cold," two operating conditions with drastically different heating requirements.

[0044] Finally, after calculating the target heating time, the method will output control commands to make the heating devices of the vehicle's rear window and rearview mirrors operate according to this newly generated target heating time.

[0045] Through the above steps, this method no longer relies on a fixed duration, but can make a judgment based on both "coldness" and "wetness," just like an experienced driver. This allows it to provide sufficient heating when needed to ensure clear visibility, and reduce heating when not needed to save precious energy.

[0046] Example 2

[0047] Based on Example 1, this embodiment provides a more specific and optimized implementation of the two core control logics in the intelligent heating method, aiming to further improve the accuracy of heating control and environmental adaptability.

[0048] For the first control logic, a preferred implementation is to discretize the continuous ambient temperature value into multiple preset temperature ranges. Each temperature range is labeled and corresponds to a different base heating duration. For example, the temperature can be divided into multiple levels such as "above -5℃", "-10℃ to -5℃", and "-15℃ to -10℃", corresponding to different base durations of 10 minutes, 13 minutes, and 17 minutes, respectively. When the controller obtains the real-time temperature value, it only needs to determine which range it falls into to quickly and stably determine the base heating duration.

[0049] For the second control logic, a more refined compensation and correction method involves introducing threshold judgment and quantitative calculation. Specifically, a humidity threshold can be preset, for example, 70%. When the real-time ambient humidity value does not exceed this threshold, no correction is needed, or a slight downward correction is made; however, when the humidity value exceeds the threshold, the base heating time needs to be explicitly increased. More preferably, the magnitude of the increase can be mathematically correlated with the degree to which the ambient humidity value exceeds the threshold. This embodiment provides a feasible calculation formula: Target duration = Base duration × (1 + k × (Real-time humidity - 70)). In this way, the higher the humidity, the greater the gain on the base duration, thereby achieving quantitative fine-tuning of the heating time.

[0050] Furthermore, to cope with more complex rain and snow weather, this method can also acquire rainfall level information as a third environmental parameter. This information can be provided by the vehicle's rain sensor. When performing compensation and correction calculations for the second control logic, the rainfall level can be used as an additional input variable. For example, a feasible strategy is to dynamically adjust the humidity compensation coefficient k in the above formula (e.g., multiplying it by 1.2, 1.5, etc., respectively) based on the rainfall level (e.g., light rain, moderate rain, heavy rain). This will enable the system to automatically and significantly increase the heating time during rain, especially heavy rain, to counteract the rapid cooling effect caused by rainwater runoff and ensure defrosting and defogging effects.

[0051] Through the preferred solutions described in this embodiment, the heating method of the present invention has been greatly improved in terms of control accuracy and scene coverage.

[0052] Example 3

[0053] This embodiment describes the technical solution for ensuring the reliability and safety of the method of the present invention when facing special situations (such as sensor failure or extreme weather).

[0054] In one aspect, this method includes a continuously running data validity monitoring mechanism. This mechanism checks in real time whether the data acquired from temperature and humidity sensors are within reasonable physical ranges and whether communication is normal. When the mechanism detects that the data for at least one critical environmental parameter is invalid, the system does not simply stop service, but automatically switches to a preset degradation control mode.

[0055] In this degraded control mode, since real-time sensor data cannot be relied upon, the method instead reads a preset configuration parameter from a non-volatile memory (such as EEPROM) of the vehicle controller. This parameter is pre-written according to the climate characteristics of its target market during vehicle production or delivery; for example, climate codes such as "extremely cold," "cold and snowy," and "hot and humid" can be defined. The method then uses this read configuration parameter to determine a relatively safe default heating duration from a built-in lookup table and controls the heating device to operate according to that default duration. This ensures that even if the sensors fail, the vehicle can still provide basic and effective heating functionality in most situations.

[0056] In another aspect, this method also includes steps for interacting with the vehicle's Advanced Driver Assistance Systems (ADAS). ADAS systems typically possess independent, high-performance perception hardware (such as a forward-facing camera) capable of recognizing weather conditions. This method can acquire independently perceived environmental data from the ADAS. When the ADAS's environmental data indicates a preset severe weather type (e.g., image recognition results for "blizzard" or "heavy rain"), this method assigns a very high decision weight to this data from the ADAS.

[0057] A preferred implementation is that when an ADAS (Advanced Driver Assistance System) signal for severe weather is received, regardless of whether the system is currently in normal or degraded control mode, the method uses a heating duration specifically determined by the ADAS signal for that severe weather to temporarily override the target heating duration calculated by other logic. For example, the extreme scenario in Example 6 describes this situation: the system has entered degraded mode due to sensor failure and determined a 15-minute heating duration based on a "hot and humid" configuration, but when the ADAS identifies "heavy snow," the system ultimately adopts a 24-minute heating duration matching the "heavy snow" weather. This demonstrates intelligent collaboration between systems, greatly enhancing vehicle safety in real, complex, and extreme environments.

[0058] Example 4

[0059] This embodiment describes an intelligent heating system for the rear window and rearview mirror that can implement all the methods described above. (Refer to...) Figure 2 and Figure 3 This system is a mechatronics system that integrates sensing, decision-making and execution functions.

[0060] The system includes a multi-parameter sensing module 210, which is responsible for comprehensively acquiring environmental data. This includes an exterior temperature sensor (connected via hardwired / PWM) for acquiring a first environmental parameter, a humidity sensor (connected via a LIN bus) for acquiring a second environmental parameter, and a rain sensor (connected via a CAN FD bus) for acquiring a third environmental parameter. Specifically, this module includes at least one temperature sensor and one humidity sensor for acquiring the first and second environmental parameters required by the present invention. In a preferred configuration, it also includes a rain sensor to provide richer environmental information.

[0061] Humidity sensor: Used to detect the relative humidity of the air outside the vehicle. It typically communicates with the controller 220 via a LIN (Local Area Network) bus. The LIN bus is a low-cost serial communication protocol, well-suited for connecting such intelligent sensors.

[0062] Rain sensor: Typically integrated into the windshield, it detects the presence and intensity of rainfall. This sensor broadcasts rainfall level information to multiple onboard controllers, including controller 220, via a CAN FD (Controller Area Network with Flexible Data Rate) bus. The CAN FD bus features high bandwidth and high reliability.

[0063] A controller 220: typically implemented by a body control system (BSI), its core is a microcontroller (MCU) with built-in firmware for executing the control logic described in this invention, and includes a non-volatile memory 225 (such as EEPROM) for permanently storing configuration parameters. The non-volatile memory contains a computer program that, when executed by a processor, implements all the method logic described in embodiments 1, 2, and 3, including multi-parameter fusion calculation logic in normal mode, degradation control logic in case of sensor failure, and logic for coordination with the ADAS system.

[0064] In other words, the controller 220 internally includes:

[0065] The signal transmission and processing unit includes a signal conditioning circuit, a CAN / LIN transceiver, and a fault diagnosis circuit. The signal conditioning circuit filters, amplifies, and shapes the analog or PWM signals from the external temperature sensor. The CAN / LIN transceiver converts differential or single-wire signals on the bus into logic levels that the MCU can process. The fault diagnosis circuit monitors for open circuits, short circuits, and other fault conditions in sensors and wiring.

[0066] Central Processing Unit (MCU): This is the core decision-making unit, typically a 32-bit microcontroller. It is responsible for executing the three-level control algorithm proposed in this invention, processing all input data, and generating control commands (such as PWM signals) for the actuators.

[0067] Non-volatile EEPROM 225: Used to permanently store critical configuration information, especially the "climate configuration word." This configuration word is written using dedicated equipment during the vehicle production line initialization phase. Due to the non-volatile nature of the EEPROM, this configuration information is not lost even if the vehicle loses power, providing a reliable data foundation for system degradation strategies.

[0068] An actuator, namely the heating device 230, is responsible for converting electrical energy into heat energy, which directly acts on the rear windshield and rearview mirror. It includes a rear windshield heater and a rearview mirror heater, serving as the final actuator. This device consists of a heater installed on the rear windshield and a heater mounted on the rearview mirror. They are controlled by commands from the controller 220 and operate according to the final target heating duration calculated by the controller. Specifically, it includes heating wires on the rear windshield glass and a heating film on the back of the rearview mirror lens. They are typically connected in parallel in a heating circuit, and the signal output from the controller 220 controls their on / off state via a relay or solid-state switch.

[0069] Advanced Driver Assistance System (ADAS) 240: A standalone system that communicates with controller 220 via a CAN bus and is capable of providing its independently perceived environmental information. That is, to achieve coordination with ADAS, this system also includes a communication interface for receiving environmental data from the vehicle's ADAS 240. Through this interface, controller 220 can acquire independently perceived, high-confidence weather information. Controller 220 is configured to parse this information and, under specific conditions (such as detecting a preset severe weather type), adjust its heating strategy, as detailed in Embodiment 3.

[0070] When the driver sends a request to turn on the heating via an in-vehicle switch, the controller 220 will execute the following steps:

[0071] First, the controller 220 synchronously acquires at least two key real-time environmental parameters through the multi-parameter sensing module 210: the ambient temperature (Tb) as the first environmental parameter, and the ambient humidity (rh) as the second environmental parameter.

[0072] Subsequently, the controller 220 executes the first control logic set in its internal firmware. This logic determines a basic heating duration based solely on the acquired ambient temperature Tb. For this purpose, the controller 220's memory stores a temperature-duration mapping table. This table divides the temperature range into several fine intervals. In this embodiment, six threshold values ​​can be defined (T1=-30℃, T2=-25℃, T3=-20℃, T4=-15℃, T5=-10℃, T6=-5℃), thus forming seven different temperature intervals. The controller 220 compares the real-time temperature value Tb with these threshold values ​​to determine its corresponding interval and retrieves the basic heating duration for that interval. For example, when the detected ambient temperature is -22℃, this value falls between T2 and T3, and the controller 220 determines the basic heating duration to be 23 minutes by looking up the table. This step ensures that a preliminary and reasonable relationship is established between the heating duration and the "hotness" or "coldness" of the environment.

[0073] Next, the controller 220 immediately executes the second control logic, using the acquired ambient humidity rh to compensate and correct the aforementioned base heating time. This step aims to quantify the additional impact of the ambient "dryness" level on defrosting and defogging requirements. In this embodiment, the controller 220 determines whether the humidity rh exceeds a preset humidity threshold, such as 70%. If it does, it indicates that the air is humid, the risk of fogging is high, and the heat output needs to be increased. At this time, the controller 220 increases the heating time according to a correction algorithm. This algorithm can be summarized as: target heating time = base heating time × (1 + k × (rh - 70)). Where k is a calibrable humidity compensation coefficient. This coefficient k is not fixed; its value is stored in non-volatile memory 225 and associated with the "climate configuration word" set at the time of vehicle manufacture. For example, for vehicles sold to humid and hot regions, the "climate configuration word" can be set to "humid and hot". The controller will select a larger k value (such as 0.015) during initialization to enhance the response capability to high humidity weather; while for vehicles sold to dry climate regions, a smaller general k value (such as 0.008) can be selected.

[0074] To further improve control accuracy, this embodiment also introduces rainfall level information as a third environmental parameter. This information is provided by a rain sensor and transmitted to the controller 220 via a CAN bus. The rainfall level is used to perform a secondary correction on the humidity compensation coefficient k mentioned above, thereby more accurately reflecting the impact of precipitation. As shown in Table 1, when the rainfall level is 0-30% (no rain or light rain), the compensation coefficient actually used for calculation will be multiplied by 0.8; when it is moderate rain (30-70%), it will be multiplied by 1.2; and when it is heavy rain or torrential rain (>70%), it will be multiplied by 1.5.

[0075] Through the above steps, the controller 220 ultimately generates a target heating duration that integrates temperature, humidity, and rainfall information. Finally, the controller 220 outputs a corresponding control signal to drive the heating device 230 to operate precisely according to this target heating duration.

[0076] In a specific application scenario, assuming the vehicle is driving in a city during winter, the environmental data acquired by controller 220 is: temperature Tb = -8℃, humidity rh = 78%, and no rainfall. The control flow is as follows:

[0077] The controller 220 executes the first control logic: based on the temperature of -8℃, it determines that the basic heating time is 20 minutes.

[0078] Controller 220 executes the second control logic: the humidity of 78% is higher than the 70% threshold, so compensation is required. Since there is no rainfall, and assuming the vehicle's basic compensation coefficient k is 0.008, the comprehensive calculation yields a correction multiplier of 1.12.

[0079] The controller 220 calculates the final target heating time as 20 minutes × 1.12 = 22.4 minutes.

[0080] The controller 220 sends a command to the heating device 230 to automatically shut down after working for about 22 minutes.

[0081] This embodiment constructs a three-dimensional dynamic control model of "temperature-humidity-rainfall" to accurately match the heating requirements under different weather combinations. Compared with existing technologies, it significantly improves defrosting and defogging efficiency and driving safety while effectively reducing unnecessary energy consumption.

[0082] Example 5

[0083] This embodiment aims to provide an intelligent heating system with high reliability and high intelligence. Existing heating systems often fail completely when critical sensors malfunction, lacking effective degradation strategies. Furthermore, as isolated subsystems, they fail to utilize the perception capabilities of other advanced vehicle systems (such as ADAS), resulting in insufficient intelligence and delayed response. This embodiment effectively solves the above-mentioned technical problems through system-level redundancy design and cross-domain collaboration.

[0084] The intelligent heating system of this embodiment has the physical structure described above. Its high reliability and high intelligence are achieved by implementing specific control logic in the firmware of the controller 220.

[0085] To achieve high reliability, a degraded control mode is designed for the system. The fault diagnosis circuit integrated within the controller 220 is configured to continuously monitor the validity of various signals from the multi-parameter sensing module 210, such as whether the signal voltage is within the normal range and whether the communication heartbeat is regular. Once this circuit detects invalid critical sensor data (e.g., temperature or humidity sensor signals are lost due to a broken line), the MCU of the controller 220 will be configured to automatically abandon the execution of the conventional control logic described in Embodiment 1 and switch to the degraded control mode.

[0086] In this mode, the MCU accesses its internal non-volatile memory 225 to read a preset configuration parameter written during vehicle production or initialization. This parameter is represented as a "climate code" in Embodiment 6. This code is linked to the climate characteristics of the vehicle's intended primary sales region and corresponds to a preset default heating duration and optional linkage strategies, as shown in Table 2. For example, code 0x01 represents an "extremely cold" region, corresponding to a default duration of 30 minutes; code 0x05 represents a "hot and humid" region, corresponding to a default duration of 15 minutes. The MCU will use this default duration to control the heating device 230. This design ensures that even in extreme cases of severe sensor failure, the system can still provide a basic heating function that guarantees fundamental driving safety, rather than completely failing.

[0087] To achieve a high level of intelligence, the system also incorporates an ADAS collaborative control logic. The controller 220 communicates with the vehicle's Advanced Driver Assistance System (ADAS) 240 via its CAN interface. The controller 220's MCU is configured to listen for and decode specific CAN messages from the ADAS system. When the ADAS system independently identifies specific severe weather conditions (such as "heavy snow" or "heavy rain") through its advanced sensors, such as the forward-facing camera, it broadcasts this environmental status information to the entire vehicle's CAN network.

[0088] The collaborative control logic of controller 220 is given high priority. When the MCU receives a severe weather signal from ADAS, it temporarily overrides any other control logic currently being executed (whether in normal or degraded mode). Based on the received ADAS weather information, the MCU queries its internal policy table to select the most suitable heating mode and duration. For example, when ADAS reports "heavy snow," the MCU might enforce the 24-minute heating strategy corresponding to "cold and snowy" (code 0x02). This cross-system collaboration greatly utilizes the precise perception capabilities of ADAS, making the heating system respond more quickly and make more accurate decisions.

[0089] In a simulated extreme application scenario, suppose the vehicle encounters a sudden downpour while driving, and unfortunately, the external temperature and humidity sensors simultaneously fail due to water ingress into their wiring. The system's operation flow is as follows:

[0090] The fault diagnosis circuit of controller 220 detects that the first and second environmental parameters are invalid, and immediately triggers and switches to the degraded control mode.

[0091] The MCU accesses the non-volatile memory 225 and reads that the vehicle's factory default configuration parameter is "damp heat" (code 0x05). Therefore, it initially decides to heat the vehicle for the default duration of 15 minutes.

[0092] Almost simultaneously, the vehicle's ADAS 240 system detected a large rain curtain ahead through its camera, determined it to be "heavy rain" weather, and immediately broadcast this status information via the CAN bus.

[0093] The CAN interface of controller 220 receives this high-priority message. Its ADAS cooperative control logic is triggered, which has a higher priority than the degraded control logic, and therefore overrides the previous decision based on preset parameters.

[0094] According to its internal strategy, the MCU maps the "heavy rain" weather reported by ADAS to the most suitable preset mode, namely "cold and snowy" (code 0x02), and finally sends a command to the heating device 230 to run for 24 minutes. It may also activate functions such as windshield wiper preheating.

[0095] Example 6

[0096] refer to Figures 2 to 4 As shown, after receiving information from the environmental perception layer, the main controller (BSI) activates a three-level control mode: Level 1 control: temperature control; Level 2 control: temperature compensation; and Level 3 control: ADAS coordination. Generally, this three-level control logic is executed by the MCU in the controller 220, and finally, a comprehensive control signal is output to instruct the actuators to perform the rear window and rearview mirror heating functions as required.

[0097] Among them, the environmental perception layer includes an outside vehicle temperature sensor, a temperature sensor, and a rain sensor. The outside vehicle temperature sensor is connected to the BSI signal transmission layer through a hard wire and outputs a PWM voltage. The humidity sensor is connected to the BSI through a LIN bus, and the rain sensor is connected to the BSI through a CANFD. After the signals received by the BSI transmission layer are processed by a signal conditioning circuit, a CAN / LIN transceiver, and a fault diagnosis circuit, they are sent to the central processing unit of the BSI. The BSI central processing unit calculates and processes based on the received signals and the stored climate configuration word (the local climate conditions such as extremely cold or extremely hot can be written into the BSI through an initialization device by each vehicle manufacturer in the form of a configuration word), and then outputs a signal that can control the working duration of the actuator. The physical connection block diagram of its system is as Figure 3 shown.

[0098] First-level control: Temperature main control mode

[0099] This is the most basic control level. Its core is that the environmental temperature is the primary factor determining whether heating is required and the intensity of heating required.

[0100] The controller 220 first obtains the environmental temperature value Tb measured by the outside vehicle temperature sensor. Then, this temperature value is compared with multiple preset temperature threshold values to determine the temperature range it belongs to and match a basic heating duration.

[0101] In this embodiment, the temperature threshold values are divided into 6 levels: T1, T2, T3, T4, T5, T6. These 6 levels of temperature increase in sequence, and different items can be calibrated according to the project situation. For example, T1 = -3*C, T2 = -25*C, T3 = -20*C, T4 = -15*C, T5 = -10*C, T6 = -5*C. For different temperature ranges, the heating times of the rear windshield and rearview mirror are different.

[0102] Based on the interval where the environmental temperature Tb is located, the basic heating duration is determined. The lower the temperature, the longer the required heating time. The specific duration can be calibrated as follows:

[0103] That is, when the environmental temperature Tb > T6 and the vehicle is in the ON gear state, after the BSI receives a heating and defogging switch request, it controls the default working time of the rear windshield and rearview mirror heating circuits to be 8 minutes.

[0104] That is, when T5 < Tb <= T6 and the vehicle is in the ON gear state, after the BSI receives a heating and defogging switch request, it controls the default working time of the rear windshield and rearview mirror heating circuits to be 10 minutes.

[0105] That is, when T4 < Tb <= T5 and the vehicle is in the ON gear state, after the BSI receives the heating and defogging switch request, it controls the default working time of the rear windshield and rearview mirror heating circuits to be 13 minutes.

[0106] That is, when T3 < Tb <= T4 and the vehicle is in the ON gear state, after the BSI receives the heating and defogging switch request, it controls the default working time of the rear windshield and rearview mirror heating circuits to be 17 minutes.

[0107] That is, when T2 < Tb <= T3 and the vehicle is in the ON gear state, after the BSI receives the heating and defogging switch request, it controls the default working time of the rear windshield and rearview mirror heating circuits to be 23 minutes.

[0108] That is, when T1 < Tb <= T2 and the vehicle is in the ON gear state, after the BSI receives the heating and defogging switch request, it controls the default working time of the rear windshield and rearview mirror heating circuits to be 30 minutes.

[0109] That is, when Tb < T1 and the vehicle is in the ON gear state, after the BSI receives the heating and defogging switch request, it controls the default working time of the rear windshield and rearview mirror heating circuits to be 39 minutes.

[0110] This hierarchical strategy greatly improves the control fineness compared with traditional fixed durations or simple two- or three-stage segmentation. Each duration value is the result of calibration with a large amount of experimental data, aiming to achieve an ideal defrosting effect with the most economical energy consumption in various typical low-temperature environments.

[0111] Secondary control: Humidity compensation mode

[0112] The primary control solves the problem of "cold or not", and the secondary control further solves the problem of "wet or not" on this basis. Because in the same low temperature, a high-humidity environment (such as foggy days, rainy or snowy days) is more likely to frost and fog than a dry environment and requires a longer heating time.

[0113] The trigger condition for the secondary control strategy is that the ambient relative humidity rh detected by the humidity sensor exceeds a preset humidity threshold (e.g., 70%). When rh > 70%, the compensation calculation is started.

[0114] The corrected heating duration is determined by the logic described below: The adjusted time is equal to the base time multiplied by a growth factor, which is 1 plus a product term determined by the humidity compensation coefficient k and the difference between the ambient humidity value and the humidity threshold of 70. This logic can be summarized by the formula:

[0115] Adjusted time = Base time * (1 + k * max(0, rh - 70)),

[0116] in:

[0117] Base time: determined by primary control.

[0118] rh: The humidity value identified by the humidity sensor.

[0119] k: Humidity compensation coefficient. This coefficient itself is variable and is related to the vehicle's preset "climate configuration word". For example, for vehicles sold in humid and hot regions (climate configuration word = humid and hot), the k value can be set to a larger value (e.g., k=0.015) to cope with frequent high humidity weather. For other regions, a smaller general value (e.g., k=0.008) can be used.

[0120] max(0, rh - 70): Ensures that compensation only takes effect when the humidity exceeds 70%.

[0121] Furthermore, the secondary control also incorporates information from rain gauge sensors. These sensors categorize rainfall intensity into different levels and use this information to adjust the humidity compensation coefficient k. See Table 1 below:

[0122] Table 1

[0123] Rainfall level Humidity compensation coefficient correction 0-30% k×0.8 30-70% k×1.2 >70% k×1.5

[0124] In this way, the system dynamically couples the three parameters of temperature, humidity, and rainfall, so that the calculation of heating time can accurately reflect the real weather conditions.

[0125] Level 3 control: Climate backup and ADAS coordination (degradation mode)

[0126] This is the system's redundancy and intelligent upgrade layer, mainly dealing with two situations: 1) failure of the main sensors (temperature, humidity); 2) the ADAS system detects special weather conditions.

[0127] When the fault diagnosis circuit of controller 220 detects that the temperature sensor or humidity sensor signal is invalid (e.g., open circuit or data outside the reasonable range), the system cannot perform level one and level two control, and automatically enters the climate backup degradation mode of level three control.

[0128] In this mode, the MCU reads the pre-stored "climate configuration word" from EEPROM 225. This configuration word is set at the factory based on the main climate characteristics of the region where the vehicle is sold. Each configuration word corresponds to a default heating duration and linkage strategy, as shown in Table 2 below.

[0129] Table 2

[0130] Climate coding Applicable regions Default duration Linkage Strategy 0x01 Extreme cold 30min Forced activation of seat heating 0x02 cold and snowy 24min Activate wiper preheating 0x03 Temperate rainy 18min Linked air conditioning dehumidification 0x04 dry heat 12min Turn off seat heating 0x05 humid heat 15min Start external circulation

[0131] This degradation strategy based on prior knowledge ensures that even in the extreme case of sensor failure, the system can still provide a relatively reasonable heating service that guarantees basic safety and comfort, rather than simply failing completely.

[0132] Another core aspect of Level 3 control is ADAS collaboration. When the vehicle's ADAS 240 system detects specific weather conditions (such as heavy snow or torrential rain) through the forward-facing camera, it sends a high-priority signal to the controller 220 via the CAN bus. Upon receiving this signal, the controller 220 temporarily overrides the current control logic.

[0133] For example, even if the sensors are functioning normally, but ADAS detects blizzard weather, the system can enforce the "Cold and Snowy" (0x02) strategy, adjusting the heating time to 24 minutes and activating wiper preheating, regardless of the calculated current temperature and humidity. More importantly, the collaborative role of ADAS is crucial in degraded mode. If the sensors have failed and the system is operating in the default "Damp and Hot" (0x05) mode (default 15 minutes), and ADAS detects heavy snow, the system will immediately and temporarily switch to "Cold and Snowy" (0x02) mode, extending the heating time from 15 minutes to 24 minutes. This collaboration breaks down information silos between subsystems, leveraging ADAS's advanced perception capabilities to significantly enhance the system's intelligence and safety under various complex and abnormal operating conditions.

[0134] Typical scenarios

[0135] Scene parameters: Ambient temperature T = -8℃, relative humidity RH = 78%, no rainfall.

[0136] Control process:

[0137] 1. Level 1 Control: Controller 220 acquires a temperature of -8℃. According to the aforementioned calibration, this temperature falls between T5 (-10℃) and T6 (-5℃). The base duration of the Level 1 control output is 10 minutes.

[0138] 2. Secondary Control: Controller 220 detects a humidity level of 78%, exceeding the 70% threshold, triggering humidity compensation. It detects a rainfall level of 0% (no rainfall). Assuming the vehicle's climate configuration is not "humid and hot," the base value k = 0.008. According to Table 1, the rainfall level is 0-30%, with a correction factor of k x 0.8. Assuming the correction multiplier calculated based on humidity and no-rain conditions is 1.12, the adjusted time = 10 minutes * 1.05 = 10.5 minutes.

[0139] 3. Level 3 Control: The sensors are functioning normally, and the ADAS system has not detected any special weather conditions requiring intervention. Therefore, Level 3 control requires no intervention.

[0140] Final output: Controller 220 outputs control commands to control the rear window and rearview mirror heating for 22 minutes (rounded).

[0141] Extreme scenarios

[0142] Scene parameters: Temperature sensor malfunctions (T=INVALID), humidity sensor malfunctions (RH=INVALID), rain sensor detects heavy rain (85% rainfall), ADAS forward-facing camera also recognizes heavy rain.

[0143] Control process:

[0144] 1. Level 1 and Level 2 Control Failure: Due to the failure of both temperature and humidity sensors, the Level 1 and Level 2 control logic cannot be executed. The system automatically activates Level 3 degraded control.

[0145] 2. Enable Degradation Mode: Controller 220 reads the climate configuration word from EEPROM 225. Assuming the vehicle was factory-set to a "hot and humid" region, the climate configuration word is 0x05. According to Table 2, the corresponding default heating time is 15 minutes.

[0146] 3. ADAS Collaborative Intervention: Simultaneously, controller 220 receives a heavy rain weather signal from ADAS 240. This signal has higher priority than the default degradation mode. The system will temporarily override the climate mode. The heavy rain weather is similar to the severe weather scenario in the "Cold and Snowy" (0x02) mode, or the system can define a dedicated "Heavy Rain" overlay mode. The system will temporarily overlay the climate mode as "Cold and Snowy" (0x02).

[0147] 4. Adjust heating time: According to Table 2, the default time for the "Cold and Snowy" (0x02) mode is 24 minutes, and the linkage strategy is "Activate wiper preheating".

[0148] Final output: The controller 220 ultimately decides to adopt the result of ADAS collaborative intervention, outputs control commands to control the rear window and rearview mirror heating for 24 minutes, and may simultaneously send a signal to the wiper controller to activate preheating.

[0149] Using the above technical solution, the defrosting function can be precisely required under different operating conditions, without wasting energy or reducing overall vehicle performance. Especially under high-temperature conditions, the rear windshield and rear window heating circuits operate for a short time, eliminating the risk of temperature rise. The resulting effects are shown in Table 3 below:

[0150] Table 3

[0151] index Original plan This invention Increase Defogging compliance rate 68% 92% +35% High-temperature accidental touch energy consumption 450Wh 210Wh -53% Fuse temperature rise 78℃ 52℃ -33% System response time 2.1s 1.3s +38%

[0152] This invention, through a hierarchical, multi-redundant intelligent control system, perfectly solves the various problems faced by the rear window heating function in the prior art, achieves performance optimization in various usage scenarios, and achieves the design goals of high efficiency, energy saving, safety and reliability.

[0153] Example 7

[0154] The present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the intelligent heating method for the rear window and rearview mirror as described in Examples 1 to 4 is provided.

[0155] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0156] Those skilled in the art will readily understand that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent heating of the rear window and rearview mirror, characterized in that, The method includes: Acquire real-time environmental data including at least a first environmental parameter and a second environmental parameter, wherein the first environmental parameter is ambient temperature and the second environmental parameter is ambient humidity; Based on the first environmental parameters, a basic heating duration is determined through the first control logic; Based on the second environmental parameter, the basic heating time is compensated and corrected through the second control logic to generate a target heating time; The heating devices for the rear window and rearview mirror are controlled to operate according to the target heating duration.

2. The method according to claim 1, characterized in that, Determining a basic heating duration based on the first environmental parameter includes: The ambient temperature is divided into multiple preset temperature ranges, and each temperature range corresponds to a different basic heating time. The basic heating time is determined based on the temperature range into which the obtained ambient temperature value falls.

3. The method according to claim 1 or 2, characterized in that, The compensation and correction of the base heating time based on the second environmental parameter includes: When the ambient humidity value exceeds a preset humidity threshold, the basic heating time is increased and corrected according to the degree to which the ambient humidity value exceeds the humidity threshold.

4. The method according to claim 1, characterized in that, The real-time environmental data also includes rainfall level as a third environmental parameter; The compensation and correction of the basic heating time is also based on the third environmental parameter.

5. The method according to claim 1, characterized in that, The method further includes: Monitor the validity of the data for the first environmental parameter and the second environmental parameter; When invalid data for at least one environmental parameter is detected, the system automatically switches to a degraded control mode.

6. The method according to claim 5, characterized in that, In the degraded control mode, the method includes: Read a preset configuration parameter from a non-volatile memory; A default heating duration is determined based on the preset configuration parameters, and the heating device is controlled to operate according to the default heating duration.

7. The method according to claim 1, characterized in that, The method further includes: Obtain independently perceived environmental data from the vehicle's ADAS; When the ADAS environmental data shows a preset severe weather type, the target heating duration is adjusted based on the ADAS environmental data.

8. The method according to claim 7, characterized in that, Adjusting the target heating duration based on the ADAS environmental data includes: The heating duration determined by the environmental data from the ADAS is temporarily overwritten with the target heating duration generated by the first and second environmental parameters.

9. An intelligent heating system for the rear window and rearview mirror, characterized in that, include: A multi-parameter sensing module (210) is used to acquire real-time environmental data including at least ambient temperature as a first environmental parameter and ambient humidity as a second environmental parameter; A controller (220) is electrically connected to the multi-parameter sensing module (210), the controller (220) being configured to: Based on the first environmental parameters, a basic heating duration is determined through the first control logic; Based on the second environmental parameter, the basic heating time is compensated and corrected through the second control logic to generate a target heating time; A heating device (230) includes a rear window heater and a rearview mirror heater, the heating device (230) being electrically connected to the controller (220) and operating in response to instructions from the controller (220) for the target heating duration.

10. The system according to claim 9, characterized in that, The controller (220) is also configured to: When invalid data of at least one environmental parameter provided by the multi-parameter sensing module (210) is detected, the system automatically switches to a degraded control mode and determines a default heating duration based on a preset configuration parameter to control the heating device (230).

11. The system according to claim 9, characterized in that, The system also includes: An interface for receiving environmental data from the vehicle's ADAS (240); The controller (220) is further configured to adjust the target heating duration based on the ADAS environmental data when the ADAS environmental data shows a preset severe weather type.

12. A vehicle controller, comprising a processor and a memory, wherein the memory stores a computer program, characterized in that, When the processor is configured to execute the computer program, it implements the method as described in any one of claims 1 to 8.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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