Vehicle body domain control dynamic power consumption scheduling method and device, vehicle and medium

Abstract

The application relates to the technical field of automobile body electronic control, in particular to a body domain control dynamic power consumption scheduling method and device, a vehicle and a medium, the method comprising the following steps: acquiring context perception information, user individualization information and vehicle energy information of a current vehicle, and identifying a current running scene according to the context perception information; determining a load priority ranking result in the current running scene based on the current running scene and the user individualization information; calculating a total power consumption quota that can be allocated to the body domain according to the vehicle energy information, and allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota. Therefore, the problems of rigid body domain power consumption scheduling strategy, lack of scene perception, fixed priority and inability to optimize in related technologies are solved, dynamic, intelligent and individualized scheduling of body domain power consumption is realized, the body power consumption is maximally reduced on the premise of guaranteeing the driving and riding experience, and the cruising range of a new energy vehicle is improved.

Description

Technical Field

[0001] This application relates to the field of automotive body electronic control technology, and in particular to a vehicle body domain control dynamic power consumption scheduling method, device, vehicle, and medium. Background Technology

[0002] With the rapid popularization of new energy vehicles, driving range has become a core indicator restricting user purchase and usage experience. In addition to the energy consumption of the power system, the power consumption of the vehicle body load (seat heating / ventilation, air conditioning, lighting, entertainment system, windows, tailgate, etc.) accounts for 15%-30% of the total power consumption of the vehicle, and can even reach more than 40% in extreme low / high temperature environments, which has a particularly significant impact on driving range.

[0003] Vehicle domain power management is essentially a problem of finite resource scheduling for multiple concurrent loads. Its core is to rationally allocate power consumption quotas for each load under the premise of limited power system output, achieving an optimal balance between driving range and driving experience. Traditional vehicle domain control systems generally adopt a static power allocation strategy: pre-assigning a fixed maximum power consumption quota to each vehicle functional module, reserving all power margins regardless of actual usage scenarios and load demands. This approach results in significant resource waste, and when multiple high-power loads start simultaneously, it can easily lead to power overload, bus voltage fluctuations, and even critical function failures, which urgently need to be addressed. Summary of the Invention

[0004] This application provides a method, device, vehicle, and medium for dynamic power consumption scheduling in the vehicle domain, which solves the problems of rigid vehicle domain power consumption scheduling strategies, lack of scene awareness, fixed priorities, and inability to coordinate optimization. It realizes dynamic, intelligent, and personalized scheduling of vehicle domain power consumption, and minimizes vehicle power consumption while ensuring the driving experience, thereby improving the driving range of new energy vehicles.

[0005] The first aspect of this application provides a vehicle body domain control dynamic power consumption scheduling method, including the following steps: Acquire the current vehicle's context-aware information, user personalization information, and vehicle energy information, and identify the current operating scenario based on the context-aware information; The load priority ranking result under the current operating scenario is determined based on the current operating scenario and the user's personalized information; The total power consumption quota that can be allocated to the vehicle body domain is calculated based on the vehicle energy information, and a corresponding power consumption quota is allocated to each load based on the load priority ranking result and the total power consumption quota.

[0006] According to one embodiment of this application, determining the load priority ranking result under the current operating scenario based on the current operating scenario and the user personalization information includes: Obtain the initial load priority matrix corresponding to the current operating scenario; The priority weights of at least one load in the initial load priority matrix are adjusted based on the user's personalized information to obtain the target load priority matrix. Based on the target load priority matrix, the load priority ranking result is determined.

[0007] According to one embodiment of this application, when allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the method further includes: Determine whether the total power consumption of the allocated loads reaches the total power consumption quota; When the total power consumption of the allocated loads reaches the total power consumption quota, power consumption allocation to the remaining loads is stopped, and the remaining loads are controlled to enter a shutdown or standby state. Based on a preset load coordination optimization mechanism, multiple associated loads with allocated power consumption are jointly adjusted.

[0008] According to one embodiment of this application, after allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the method further includes: The context-aware information and load status of the current vehicle are monitored, and if the current operating scenario changes, the load priority ranking result under the new operating scenario is re-determined according to the new operating scenario.

[0009] According to one embodiment of this application, the context-aware information includes driving context information, occupant context information, and environmental context information, and the vehicle energy information includes at least one of battery remaining charge, charging status, and power system requirements.

[0010] The vehicle domain control dynamic power consumption scheduling method provided in this application identifies the current operating scenario based on the vehicle's context-aware information; determines the load priority ranking result under the current operating scenario based on the current operating scenario and user personalized information; calculates the total power consumption quota currently available to be allocated to the vehicle domain based on the vehicle's energy information; and allocates a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota. This solves the problems of rigid vehicle domain power consumption scheduling strategies, lack of scenario awareness, fixed priorities, and inability to coordinate optimization in related technologies, achieving dynamic, intelligent, and personalized scheduling of vehicle domain power consumption. While ensuring a comfortable driving experience, it minimizes vehicle power consumption and improves the driving range of new energy vehicles.

[0011] A second aspect of this application provides a vehicle body domain control dynamic power consumption scheduling device, comprising: The identification module is used to acquire the current vehicle's contextual awareness information, user personalization information, and vehicle energy information, and to identify the current operating scenario based on the contextual awareness information. The determination module is used to determine the load priority ranking result under the current operating scenario based on the current operating scenario and the user's personalized information; The scheduling module is used to calculate the total power consumption quota that can be allocated to the vehicle body domain based on the vehicle energy information, and to allocate the corresponding power consumption quota to each load based on the load priority sorting result and the total power consumption quota.

[0012] According to one embodiment of this application, the determining module is configured to: Obtain the initial load priority matrix corresponding to the current operating scenario; The priority weights of at least one load in the initial load priority matrix are adjusted based on the user's personalized information to obtain the target load priority matrix. Based on the target load priority matrix, the load priority ranking result is determined.

[0013] According to one embodiment of this application, when allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the scheduling module is further configured to: Determine whether the total power consumption of the allocated loads reaches the total power consumption quota; When the total power consumption of the allocated loads reaches the total power consumption quota, power consumption allocation to the remaining loads is stopped, and the remaining loads are controlled to enter a shutdown or standby state. Based on a preset load coordination optimization mechanism, multiple associated loads with allocated power consumption are jointly adjusted.

[0014] According to one embodiment of this application, after allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the scheduling module is further configured to: The context-aware information and load status of the current vehicle are monitored, and if the current operating scenario changes, the load priority ranking result under the new operating scenario is re-determined according to the new operating scenario.

[0015] According to one embodiment of this application, the context-aware information includes driving context information, occupant context information, and environmental context information, and the vehicle energy information includes at least one of battery remaining charge, charging status, and power system requirements.

[0016] The vehicle domain control dynamic power consumption scheduling device provided in this application identifies the current operating scenario based on the vehicle's context-aware information; determines the load priority ranking result under the current operating scenario based on the current operating scenario and user personalized information; calculates the total power consumption quota currently available to be allocated to the vehicle domain based on the vehicle's energy information, and allocates a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota. This solves the problems of rigid vehicle domain power consumption scheduling strategies, lack of scenario awareness, fixed priorities, and inability to coordinate optimization in related technologies, achieving dynamic, intelligent, and personalized scheduling of vehicle domain power consumption. While ensuring a comfortable driving experience, it minimizes vehicle power consumption and improves the driving range of new energy vehicles.

[0017] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle body domain control dynamic power consumption scheduling method as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the vehicle domain control dynamic power consumption scheduling method as described in the above embodiments.

[0019] The present invention can achieve the following beneficial effects: (1) Significantly reduce vehicle power consumption: Through scenario-based dynamic scheduling, the average power consumption of the vehicle domain can be reduced by 20%-30%, which is particularly effective in low temperature / high temperature environments and can increase the driving range by 5%-10%.

[0020] (2) Improve the driving experience: Avoid the "one-size-fits-all" load shutdown in the traditional strategy, and maximize the satisfaction of users' personalized needs while ensuring the core functions.

[0021] (3) Improve system reliability: Through dynamic power consumption quota allocation, the problems of power overload and voltage fluctuation caused by the simultaneous start of multiple high power loads are effectively avoided.

[0022] (4) Strong scalability: It can easily connect to new vehicle body loads and new scenario types, adapting to the needs of different vehicle models and different markets.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a vehicle body domain control dynamic power consumption scheduling method provided according to an embodiment of this application; Figure 2 This is a schematic diagram of a three-dimensional context-aware model structure according to an embodiment of this application; Figure 3 This is a block diagram of dynamic load priority generation and scheduling logic according to an embodiment of this application; Figure 4 This is an overall flowchart of a three-dimensional context-aware vehicle domain control dynamic power consumption scheduling method according to an embodiment of this application; Figure 5 This is a schematic diagram of the hardware structure of a vehicle domain control dynamic power consumption scheduling system according to an embodiment of this application; Figure 6 This is a block diagram of a vehicle body domain control dynamic power consumption scheduling device according to an embodiment of this application; Figure 7 This is a schematic diagram of the vehicle structure provided in an embodiment of this application. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following description, with reference to the accompanying drawings, outlines a vehicle domain control dynamic power consumption scheduling method, apparatus, vehicle, and medium according to embodiments of this application. Addressing the problems of rigid vehicle domain power consumption scheduling strategies, lack of scene awareness, fixed priorities, and inability to coordinate optimization mentioned in the background art, this application provides a vehicle domain control dynamic power consumption scheduling method. In this method, the current operating scenario is identified based on the vehicle's context-aware information; the load priority ranking result under the current operating scenario is determined based on the current operating scenario and user personalized information; the total power consumption quota currently available to be allocated to the vehicle domain is calculated based on the vehicle's energy information; and a corresponding power consumption quota is allocated to each load based on the load priority ranking result and the total power consumption quota. This solves the problems of rigid vehicle domain power consumption scheduling strategies, lack of scene awareness, fixed priorities, and inability to coordinate optimization in related technologies, achieving dynamic, intelligent, and personalized scheduling of vehicle domain power consumption. While ensuring a comfortable driving experience, it minimizes vehicle power consumption and improves the driving range of new energy vehicles.

[0027] Specifically, Figure 1 This is a flowchart illustrating a vehicle body domain control dynamic power consumption scheduling method provided in an embodiment of this application.

[0028] like Figure 1 As shown, the vehicle body domain control dynamic power consumption scheduling method includes the following steps: In step S101, the current vehicle's context awareness information, user personalization information, and vehicle energy information are obtained, and the current operating scenario is identified based on the context awareness information.

[0029] In some embodiments, the context-aware information includes driving context information, occupant context information, and environmental context information, and the vehicle energy information includes at least one of the following: remaining battery charge, charging status, and powertrain requirements.

[0030] Specifically, such as Figure 2 As shown, the system includes three information collection sub-modules covering driving, occupant, and environment dimensions, as well as the logical relationship between these three modules in outputting data to the scene recognition module. This embodiment of the application constructs a three-dimensional context perception model to collect the following three types of contextual information in real time: Driving context: vehicle speed, acceleration, gear position, remaining battery power, charging status, and navigation route information; Occupant context: number of occupants, occupant location, occupant age / gender (identified via camera), and occupant operation history; Environmental context: outside temperature, inside temperature, light intensity, weather conditions, and geographical location.

[0031] For example, embodiments of this application can obtain contextual awareness information of the current vehicle through on-board sensors (such as vehicle speed sensors, pressure sensors, temperature sensors, radar, infrared cameras, etc.), and can obtain the remaining battery power, charging status, etc. through the vehicle's battery management system, without making specific limitations here.

[0032] Furthermore, based on contextual information, scene recognition is performed, and the vehicle's operating status is divided into N typical scenarios, including but not limited to: single-person driving for urban commuting, multi-person travel on highways, parking and resting, low-temperature charging, and low-battery emergency.

[0033] In step S102, the load priority ranking result under the current operating scenario is determined based on the current operating scenario and user personalized information.

[0034] Furthermore, in some embodiments, determining the load priority ranking result under the current operating scenario based on the current operating scenario and user personalized information includes: obtaining the initial load priority matrix corresponding to the current operating scenario; adjusting the priority weight of at least one load in the initial load priority matrix according to the user personalized information to obtain the target load priority matrix; and determining the load priority ranking result based on the target load priority matrix.

[0035] Specifically, this application embodiment dynamically adjusts the priority weight of each vehicle load based on the identified scenario and user personalized preferences to generate a load priority ranking for the current scenario. First, using the scenario identification result as an index, the initial load priority matrix corresponding to the running scenario is retrieved from a preset scenario-priority mapping relationship library. This matrix defines the baseline priority weight ranking of each vehicle load under this scenario type, reflecting the inherent emphasis of the scenario itself on different dimensions of requirements such as safety, comfort, and functionality.

[0036] Building upon this, personalized user information is introduced as a dynamic adjustment factor to modify the initial matrix: based on acquired user historical operation records, preference settings data, or real-time usage habits, incremental adjustments or reassignments are performed on the priority weights of one or more loads in the matrix. Through this personalized weighting process, a target load priority matrix adapted to user preferences is generated. Based on the weight values ​​of each load in this target load priority matrix, they are sorted in descending order to obtain the load priority ranking result that incorporates personalized user needs in the current operating scenario.

[0037] In step S103, the total power consumption quota that can be allocated to the vehicle body domain is calculated based on the vehicle energy information, and a corresponding power consumption quota is allocated to each load based on the load priority ranking result and the total power consumption quota.

[0038] Furthermore, in some embodiments, when allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the method further includes: determining whether the total power consumption of the allocated loads reaches the total power consumption quota; when the total power consumption of the allocated loads reaches the total power consumption quota, stopping the allocation of power consumption to the remaining loads and controlling the remaining loads to enter a shutdown or standby state; and jointly adjusting multiple associated loads with allocated power consumption based on a preset load coordination optimization mechanism.

[0039] Specifically, in this embodiment, the total power consumption quota currently available for allocation to the vehicle body domain is calculated based on the remaining battery capacity, charging status, and power system requirements. Power consumption quotas are then allocated to each load in descending order of load priority. When the total power consumption exceeds the available quota, the power of low-priority loads is reduced or temporarily shut down, while a load coordination optimization mechanism is activated.

[0040] First, the remaining battery power and current charging status are obtained in real time from the battery management system via the CAN bus. Simultaneously, the real-time power demand of the powertrain under current operating conditions is obtained from the vehicle controller or power domain controller. This power demand reflects the immediate occupancy of the vehicle's output power by core power components such as the drive motor, electronic control system, and high-voltage accessories.

[0041] Based on this, and taking into account the maximum sustainable output power limit of the battery reported by the battery management system, the theoretical upper limit of the remaining power available in the vehicle body domain is obtained by subtracting the current power demand of the power system and the necessary safety redundancy power from the maximum output power of the battery. The safety redundancy power is pre-calibrated according to the voltage stability requirements and dynamic response margin of the vehicle's power system.

[0042] Furthermore, the theoretical upper limit of remaining power is adjusted according to the different ranges of the remaining battery charge: when the remaining charge is in the high charge range, the theoretical upper limit is maintained as the current available quota; when the remaining charge enters the medium-low charge range, the available power is reduced in a stepwise manner according to the preset derating factor; when the remaining charge is lower than the preset emergency threshold and the vehicle is not in a charging state, the total available power consumption quota of the vehicle body domain is forcibly limited to a lower emergency power threshold than the normal scenario, so as to ensure that the power system receives priority power allocation and ensure that the vehicle can safely drive to the charging facility.

[0043] To facilitate a clearer understanding of the dynamic load priority generation and scheduling logic of this application by those skilled in the art, the following is combined with... Figure 3 Please provide a detailed explanation.

[0044] like Figure 3 As shown, the complete chain is presented, from scene recognition results and user personalized preference library input, through the priority weight calculation module to generate load priority matrix, and then the power distribution actuator to issue control commands to the vehicle load cluster. At the same time, the closed-loop feedback loop of load operation status to context acquisition module is marked.

[0045] Furthermore, in some embodiments, after allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the method further includes: monitoring the current vehicle's context awareness information and load operating status, and, if the current operating scenario changes, re-determining the load priority ranking result under the new operating scenario.

[0046] Specifically, in this embodiment, the context awareness information of the current vehicle and the actual working status of each load are continuously collected at a preset fixed period (e.g., every 100ms or every 50ms, the period length can be dynamically adjusted according to the scenario). After each collection, the multi-dimensional context feature vector at the current moment is compared with the scene features identified in the previous scheduling cycle to determine whether the context information has changed significantly.

[0047] When any of the following trigger conditions are detected, it is determined that the operating scenario has changed: (1) The change in key context parameters (vehicle speed, remaining battery power, gear, number of passengers, interior / exterior temperature, etc.) exceeds their respective preset thresholds; (2) The overall matching degree of the context feature vector deviates from the current scenario matching center value to the preset deviation range; (3) An emergency status flag (such as low battery alarm, sudden increase in power demand, etc.) is received from the battery management system or vehicle controller.

[0048] Once the scenario change is confirmed, the dynamic adjustment process of the scheduling strategy is triggered, which involves re-executing the following steps: re-identifying the current operating scenario based on the updated context-aware information; regenerating the load priority matrix based on the newly identified scenario and user personalized preference information; recalculating the total available power consumption quota of the vehicle body domain according to the latest battery remaining power, charging status and power system requirements; and re-executing the power consumption allocation and collaborative optimization of each load according to the new priority ranking results and available power consumption quota.

[0049] Therefore, this application proposes a context-aware model that includes three dimensions: driving, occupants, and environment, enabling accurate identification of vehicle operation scenarios; it adopts a dynamic priority generation algorithm to replace the traditional fixed priority strategy, allowing the scheduling strategy to adapt to different scenarios; it introduces a load collaborative optimization mechanism to achieve joint scheduling of multiple related loads, further reducing overall power consumption; and it supports user personalized preference learning, enabling automatic adjustment of scheduling logic based on user historical operations.

[0050] The following is combined with Figure 4 and Figure 5 The overall process of the vehicle domain control dynamic power consumption scheduling method based on three-dimensional context awareness and the hardware structure of the vehicle domain control dynamic power consumption scheduling system of this application are described respectively.

[0051] like Figure 4As shown, the system first collects information in real time from three dimensions: driving context, occupant context, and environmental context, using a 3D context-aware model. The collected multi-dimensional context feature vectors are matched against a pre-defined scenario library to identify the vehicle's current operating scenario. Based on the identified scenario type and combined with user history and preference settings from a user personalization preference library, a load priority matrix is ​​generated for the current scenario using priority weights to determine the power supply priority order for each vehicle load. The remaining battery power and charging status are obtained from the battery management system via the CAN bus, and combined with the current power demand of the powertrain, the total power consumption quota available for allocation to the vehicle domain at the current moment is calculated. Power is allocated to each load in descending order of load priority. Under the constraint of the total power consumption quota, high-priority loads are given priority power supply, while low-priority loads are reduced in power or shut down. Simultaneously, a load collaborative optimization mechanism is activated to jointly adjust related loads to further reduce overall power consumption. The entire process is executed cyclically at a fixed period (e.g., 100ms), achieving continuous dynamic adaptation of the power consumption scheduling strategy to changes in the vehicle's operating state.

[0052] like Figure 5 The diagram illustrates the overall hardware architecture, centered on the vehicle domain controller, integrating a main processor, storage module, CAN bus interface, and input interface clusters such as millimeter-wave radar and cameras, as well as output interface clusters such as seats, air conditioning, and lights. The vehicle domain controller houses the main processor and storage module. The main processor connects to various sensing and data source devices via the input interface cluster, including millimeter-wave radar for sensing the surrounding environment, in-vehicle cameras for identifying the number and location of occupants, temperature / light sensors for collecting temperature and light intensity data, and a battery management system connected via the CAN bus interface, thus providing hardware data support for 3D context perception. The storage module includes user preference storage and scene library storage units, used to record user historical operation preferences and preset various operating scene feature templates, respectively. The body domain controller interacts with the vehicle network via the CAN bus interface and connects to various load controllers in the vehicle via the output interface cluster. Specifically, these include the seat controller for controlling seat heating / ventilation, the air conditioning controller for regulating the interior temperature, the lighting controller for controlling various lighting fixtures, and the entertainment system controller for managing the central control screen and audio system, thereby enabling the issuance and execution of power consumption scheduling commands.

[0053] The vehicle body domain control dynamic power consumption scheduling method of the present invention will be described in detail below with reference to specific embodiments.

[0054] The first embodiment of this application is a single-person driving scenario for urban commuting, including the following steps: S1: Simultaneously collect three types of data through a 3D context perception model: The driving context module collects information such as vehicle speed (30-60km / h), remaining battery power (65%), gear position (D), and navigation route display showing 25 minutes of remaining driving time with no congested sections; The occupant context module identifies that only the driver is in the driver's seat through the in-vehicle camera, and retrieves the user's historical operation records from the storage module, showing that the user prefers seat heating over air conditioning; The environmental context module collects information such as outside temperature (-5℃), initial in-vehicle temperature (12℃), and light intensity (120 lux) through temperature and light sensors, and determines that the current situation is a sunny day in the city by combining geographical location information.

[0055] S2: The collected multi-dimensional feature vectors are matched with the preset scene library, with a matching degree of 92%, and the current scene is finally identified as "single-person driving for urban commuting".

[0056] S3: The scene recognition results and the user's personalized preference library are input into the priority weight calculation module. The priority weight of each vehicle load is calculated through a weighted algorithm to generate the load priority matrix under the current scene. The priority is sorted from high to low as follows: driver's seat heating (0.95), windshield defroster (0.92), air conditioning heating (0.88), low beam headlights (0.85), central control entertainment system (0.72), passenger seat heating (0.35), rear seat heating (0.20), and interior ambient lighting (0.10).

[0057] S4: The CAN bus interface obtains real-time battery status data from the battery management system (BMS). Combined with the current power demand of the power system of approximately 15kW and the maximum output power limit of the battery of 80kW, the total available power consumption quota that can be allocated to the vehicle body domain is calculated to be 2.5kW.

[0058] S5: The power allocation actuator allocates power consumption quotas according to the load priority matrix from high to low: 800W (level 3) for driver's seat heating, 300W for windshield defroster, 1000W for air conditioning heating, 120W for low beam headlights, and 280W for the central control entertainment system. The remaining available power consumption is 0W. Therefore, the passenger seat heating, rear seat heating, and interior ambient lighting are automatically turned off through the output interface cluster. At the same time, the load coordination optimization mechanism is activated, adjusting the air conditioning heating target temperature from the default 24℃ to 22℃. Combined with driver's seat level 3 heating, the actual power consumption of the air conditioning is reduced by approximately 200W while maintaining the same level of comfort. The total power consumption is strictly controlled within 2.5kW.

[0059] S6: The system collects context information and load operation status every 100ms and sends the data back to the context acquisition module through a feedback loop. When the vehicle arrives at its destination and is put into P gear, the context characteristics change significantly, and the system automatically triggers a new round of scheduling process, switches to the "park and rest" scenario, and readjusts the load priority and power consumption allocation.

[0060] The second embodiment of this application is a low-battery emergency driving scenario, targeting the most common low-battery range anxiety scenario for new energy vehicles, including the following steps: S1: Data is collected synchronously through a 3D context perception model: The driving context module collects the vehicle speed of 80-100km / h, the remaining battery power of 12%, the gear position of D, the navigation route shows that the nearest charging station is 18km away, and the estimated driving time is 22 minutes; the occupant context module identifies 1 driver and 1 front passenger, with no rear passengers; the environmental context module collects the outside temperature of 32℃, the initial inside temperature of 28℃, and the light intensity of 8500 lux, and combined with the geographical location information, determines that the current situation is a sunny day on a highway.

[0061] S2: The system detects that the remaining battery power is below 15% and the vehicle is not connected to a charging station while it is traveling at high speed. The feature matching degree reaches 96%, and the current scenario is identified as "emergency driving due to low battery".

[0062] S3: In this scenario, the priority weight calculation module significantly increases the weight of safety-related loads and decreases the weight of comfort loads, generating a new load priority matrix. The priority is sorted from high to low as follows: low beam headlights (0.99), turn signals / brake lights (0.99), windshield defroster (0.95), air conditioning (0.60), central control navigation system (0.55), driver's seat ventilation (0.30), passenger seat ventilation (0.25), interior ambient lighting (0.05), and central control entertainment system (audio / video) (0.02).

[0063] S4: The CAN bus interface obtains battery emergency status data from the BMS. To ensure that the vehicle can safely reach the charging station, the system significantly limits the total available power consumption of the vehicle body domain from 2.5kW in normal scenarios to 800W.

[0064] S5: The power allocation actuator allocates power according to the new priority matrix: 120W is allocated to low beam headlights, turn signals, and brake lights; 150W is reserved for standby power for the windshield defroster; 350W is allocated to air conditioning (limited to fan speed 1); 180W is allocated to the central control navigation system; and the remaining available power is 0W. Therefore, the ambient lighting, central control entertainment system (only navigation function is retained), and driver and passenger seat ventilation are automatically turned off. At the same time, the extreme condition collaborative optimization mechanism is activated: the target temperature of air conditioning is adjusted from 24℃ to 26℃, the air conditioning internal circulation is turned off and external circulation natural wind is used to assist in cooling, and the brightness of the central control screen is reduced to 50%. Through the above optimizations, the air conditioning power consumption is further reduced by about 120W, ensuring that the total power consumption does not exceed 800W.

[0065] S6: The system collects the remaining battery power and vehicle driving status every 50ms and updates the scheduling strategy in real time through the feedback loop. When the vehicle arrives at the charging station and connects to the charging gun, the context characteristics change, and the system automatically switches to the "charging" scenario, removes all power consumption limits, and restores the normal working state of all loads.

[0066] It should be noted that, in addition to the three dimensions of driving, occupants, and environment, the context dimensions in this application embodiment can include time context (morning peak / evening peak / nighttime) and event context (picking up children / business travel), further improving scene recognition accuracy. The priority algorithm can use machine learning algorithms (such as decision trees and neural networks) instead of traditional weighted algorithms, achieving more intelligent priority generation through training with a large amount of user data. Furthermore, it can deeply coordinate vehicle domain power consumption scheduling with the power domain and battery management system, dynamically adjusting the total power consumption quota of the vehicle domain based on the real-time power demand of the power system. In addition, user personalized preference data can be uploaded to the cloud to achieve preference synchronization among multiple vehicles, while utilizing cloud big data for global optimization.

[0067] The vehicle domain control dynamic power consumption scheduling method proposed in this application identifies the current operating scenario based on the vehicle's context-aware information; determines the load priority ranking result under the current operating scenario based on the current operating scenario and user personalized information; calculates the total power consumption quota currently available to be allocated to the vehicle domain based on the vehicle's energy information; and allocates a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota. This solves the problems of rigid vehicle domain power consumption scheduling strategies, lack of scenario awareness, fixed priorities, and inability to coordinate optimization in related technologies, achieving dynamic, intelligent, and personalized scheduling of vehicle domain power consumption. While ensuring a comfortable driving experience, it minimizes vehicle power consumption and improves the driving range of new energy vehicles.

[0068] Next, the vehicle body domain control dynamic power consumption scheduling device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0069] Figure 6 This is a block diagram of the vehicle body domain control dynamic power consumption scheduling device according to an embodiment of this application.

[0070] like Figure 6 As shown, the vehicle body domain control dynamic power consumption scheduling device 10 includes: an identification module 100, a determination module 200, and a scheduling module 300.

[0071] The identification module 100 is used to acquire the current vehicle's context awareness information, user personalization information, and vehicle energy information, and to identify the current operating scenario based on the context awareness information; the determination module 200 is used to determine the load priority ranking result under the current operating scenario based on the current operating scenario and user personalization information; and the scheduling module 300 is used to calculate the total power consumption quota that can be allocated to the vehicle body domain based on the vehicle energy information, and to allocate the corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota.

[0072] Furthermore, in some embodiments, the determining module 200 is used to: obtain the initial load priority matrix corresponding to the current running scenario; adjust the priority weight of at least one load in the initial load priority matrix according to the user's personalized information to obtain the target load priority matrix; and determine the load priority ranking result based on the target load priority matrix.

[0073] Furthermore, in some embodiments, when allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the scheduling module 300 is also used to: determine whether the total power consumption of the allocated loads reaches the total power consumption quota; when the total power consumption of the allocated loads reaches the total power consumption quota, stop allocating power consumption to the remaining loads and control the remaining loads to enter a shutdown or standby state; and jointly adjust multiple associated loads with allocated power consumption based on a preset load coordination optimization mechanism.

[0074] Furthermore, in some embodiments, after allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the scheduling module 300 is also used to: monitor the context awareness information and load working status of the current vehicle, and, if the current operating scenario changes, re-determine the load priority ranking result under the new operating scenario.

[0075] Furthermore, in some embodiments, the context-aware information includes driving context information, occupant context information, and environmental context information, and the vehicle energy information includes at least one of the following: remaining battery charge, state of charge, and powertrain requirements.

[0076] It should be noted that the foregoing explanation of the embodiment of the vehicle domain control dynamic power consumption scheduling method also applies to the vehicle domain control dynamic power consumption scheduling device of this embodiment, and will not be repeated here.

[0077] The vehicle domain control dynamic power consumption scheduling device proposed in this application identifies the current operating scenario based on the vehicle's context-aware information; determines the load priority ranking result under the current operating scenario based on the current operating scenario and user personalized information; calculates the total power consumption quota currently available to be allocated to the vehicle domain based on the vehicle's energy information; and allocates a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota. This solves the problems of rigid vehicle domain power consumption scheduling strategies, lack of scenario awareness, fixed priorities, and inability to coordinate optimization in related technologies, achieving dynamic, intelligent, and personalized scheduling of vehicle domain power consumption. While ensuring a comfortable driving experience, it minimizes vehicle power consumption and improves the driving range of new energy vehicles.

[0078] Figure 7 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0079] When the processor 702 executes the program, it implements the vehicle domain control dynamic power consumption scheduling method provided in the above embodiments.

[0080] Furthermore, the vehicle also includes: Communication interface 703 is used for communication between memory 701 and processor 702.

[0081] The memory 701 is used to store computer programs that can run on the processor 702.

[0082] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0083] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0084] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0085] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0086] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described vehicle domain control dynamic power consumption scheduling method.

[0087] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0088] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0089] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0090] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0091] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0092] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0093] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0094] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for dynamic power consumption scheduling in vehicle domain control, characterized in that, Includes the following steps: Acquire the current vehicle's context-aware information, user personalization information, and vehicle energy information, and identify the current operating scenario based on the context-aware information; The load priority ranking result under the current operating scenario is determined based on the current operating scenario and the user's personalized information; The total power consumption quota that can be allocated to the vehicle body domain is calculated based on the vehicle energy information, and a corresponding power consumption quota is allocated to each load based on the load priority ranking result and the total power consumption quota.

2. The method according to claim 1, characterized in that, The process of determining the load priority ranking result under the current operating scenario based on the current operating scenario and the user's personalized information includes: Obtain the initial load priority matrix corresponding to the current operating scenario; The priority weights of at least one load in the initial load priority matrix are adjusted based on the user's personalized information to obtain the target load priority matrix. Based on the target load priority matrix, the load priority ranking result is determined.

3. The method according to claim 1, characterized in that, When allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the method further includes: Determine whether the total power consumption of the allocated loads reaches the total power consumption quota; When the total power consumption of the allocated loads reaches the total power consumption quota, power consumption allocation to the remaining loads is stopped, and the remaining loads are controlled to enter a shutdown or standby state. Based on a preset load coordination optimization mechanism, multiple associated loads with allocated power consumption are jointly adjusted.

4. The method according to claim 1, characterized in that, After allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the process further includes: The context-aware information and load status of the current vehicle are monitored, and if the current operating scenario changes, the load priority ranking result under the new operating scenario is re-determined according to the new operating scenario.

5. The method according to claim 1, characterized in that, The context-aware information includes driving context information, occupant context information, and environmental context information, and the vehicle energy information includes at least one of the following: remaining battery charge, charging status, and powertrain requirements.

6. A vehicle body domain control dynamic power consumption scheduling device, characterized in that, include: The identification module is used to acquire the current vehicle's contextual awareness information, user personalization information, and vehicle energy information, and to identify the current operating scenario based on the contextual awareness information. The determination module is used to determine the load priority ranking result under the current operating scenario based on the current operating scenario and the user's personalized information; The scheduling module is used to calculate the total power consumption quota that can be allocated to the vehicle body domain based on the vehicle energy information, and to allocate the corresponding power consumption quota to each load based on the load priority sorting result and the total power consumption quota.

7. The apparatus according to claim 6, characterized in that, The determining module is used for: Obtain the initial load priority matrix corresponding to the current operating scenario; The priority weights of at least one load in the initial load priority matrix are adjusted based on the user's personalized information to obtain the target load priority matrix. Based on the target load priority matrix, the load priority ranking result is determined.

8. The apparatus according to claim 6, characterized in that, When allocating a corresponding power consumption quota to each load based on the load priority ranking result and the total power consumption quota, the scheduling module is further configured to: Determine whether the total power consumption of the allocated loads reaches the total power consumption quota; When the total power consumption of the allocated loads reaches the total power consumption quota, power consumption allocation to the remaining loads is stopped, and the remaining loads are controlled to enter a shutdown or standby state. Based on a preset load coordination optimization mechanism, multiple associated loads with allocated power consumption are jointly adjusted.

9. A vehicle, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the vehicle body domain control dynamic power consumption scheduling method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the vehicle body domain control dynamic power consumption scheduling method as described in any one of claims 1-5.

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