Vehicle control method, storage medium and vehicle

Abstract

The invention relates to a vehicle control method, a storage medium and a vehicle, and relates to the field of vehicles, the method comprises the steps that multi-domain data in the vehicle driving process are acquired, and the multi-domain data comprise power domain data, vehicle body domain data, automatic driving domain data and network connection domain data; according to the multi-domain data, predicting load electricity utilization total power and generator generation power of the vehicle at multiple moments within a set time range; the electricity utilization mode of the vehicle is determined according to the total electricity utilization power of the loads and the electricity generation power of the generator, the loads started in different electricity utilization modes are different, the electricity utilization power of all the loads is different, and / or power sources for supplying power to all the loads are different; and adjusting the power supply of each load in the vehicle according to the power utilization mode. The power utilization mode of the vehicle can be adjusted through the multi-domain data of the vehicle, the comfort and convenience of a user can be improved, and the energy efficiency of the vehicle is improved.

Description

Technical Field

[0001] This disclosure relates to the field of vehicles, and more specifically, to a vehicle control method, a storage medium, and a vehicle. Background Technology

[0002] With the continuous improvement of vehicle intelligence and connectivity, the number of loads and power consumption of automotive low-voltage electronic and electrical systems have increased dramatically. Traditional power management systems, consisting of body controllers, battery management sensors, and independent fuse / relay boxes, are gradually revealing many shortcomings when facing increasingly complex power usage scenarios.

[0003] The power supply to each load in a vehicle is usually adjusted based on the battery status of the vehicle's battery. This approach can only passively manage each load after the battery status deteriorates to a threshold, and cannot make forward-looking dynamic adjustments to each load based on the vehicle's power demand in the future. This affects the user's comfort and convenience, and leads to lower energy efficiency of the vehicle. Summary of the Invention

[0004] The purpose of this disclosure is to provide a device vehicle control method, a storage medium, and a vehicle.

[0005] According to a first aspect of the present disclosure, a vehicle control method is provided, comprising:

[0006] Acquire multi-domain data during vehicle operation, including: power domain data, body domain data, autonomous driving domain data, and connectivity domain data; Based on the multi-domain data, predict the total load power consumption and generator power of the vehicle at multiple moments within a set time range; The power consumption mode of the vehicle is determined based on the total power consumption of the loads and the power generation of the generator. Different loads are activated under different power consumption modes, the power consumption of each load is different, and / or the power supply for each load is different. The power supply to each load in the vehicle is adjusted according to the power consumption mode.

[0007] Optionally, predicting the total load power consumption and generator power of the vehicle at multiple moments within a set time range based on the multi-domain data includes: The total power consumption of the load at the multiple moments is predicted based on the vehicle body domain data, the autonomous driving domain data, and the connected vehicle domain data. The generator power output at the multiple times is predicted based on the power domain data and the autonomous driving domain data.

[0008] Optionally, the power consumption mode includes: a balanced mode, a surplus mode, and a power deficit mode. Determining the vehicle's power consumption mode based on the total power consumption of the load and the generator's power output includes: Obtain the difference between the total power consumption of the load and the power generation of the generator at each of the plurality of time points, and determine the average value of the difference; When the average value is within a preset power range, the vehicle's power consumption mode is determined to be the balanced mode; When the average value is greater than the upper limit of the power range, the power consumption mode of the vehicle is determined to be the surplus mode. When the average value is less than the lower limit of the power range, the vehicle's power consumption mode is determined to be the power depletion mode.

[0009] Optionally, adjusting the power supply to each load in the vehicle according to the power consumption mode includes: When the vehicle's power consumption mode is balanced, the vehicle's generator supplies power to each load. When the power consumption mode is surplus mode, the generator supplies power to each load and charges the vehicle's battery. When the power consumption mode is the power shortage mode, the generator and the battery supply power to the loads.

[0010] Optionally, adjusting the power supply to each load in the vehicle according to the power consumption mode includes: When the vehicle's power consumption mode is a low-power mode, the SOC at each of the multiple time points is predicted based on the total power consumption of the load and the power generated by the generator at the multiple time points. When the SOC is less than a preset SOC threshold at any of the multiple times, the power consumption of each load in the vehicle is adjusted according to the generator power and the battery output power of the vehicle.

[0011] Optionally, adjusting the power consumption of each load in the vehicle based on the generator output power and the battery output power of the vehicle includes: The priority of each load is dynamically adjusted using the multi-domain data; The power consumption of each load in the vehicle is adjusted according to the priority of each load, the power output of the generator, and the output power of the battery.

[0012] Optionally, adjusting the power consumption of each load in the vehicle according to the priority of each load, the power output of the generator, and the output power of the battery includes: Based on multi-objective optimization, the power consumption of each load is adjusted according to the priority of each load, the power generation of the generator, and the output power of the battery. The multi-objectives include: startup objective, comfort objective, and energy consumption objective.

[0013] Optionally, adjusting the power supply to each load in the vehicle according to the power consumption mode includes: Obtain the power consumption of each load; The power distribution unit of the vehicle controls the power channels corresponding to each load, and outputs the power consumption corresponding to each load to supply power to each load.

[0014] According to a second aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the vehicle control method described in the first aspect of the present disclosure.

[0015] According to a third aspect of the present disclosure, a vehicle is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to implement the steps of the vehicle control method described in the first aspect of this disclosure.

[0016] The above technical solution acquires multi-domain data during vehicle operation, including power domain data, body domain data, autonomous driving domain data, and connectivity domain data. Based on this multi-domain data, the total power consumption of the loads and the generator output power of the vehicle at multiple moments within a set time range are predicted. The vehicle's power consumption mode is determined based on this total power consumption and generator output power. Different loads are activated under different power consumption modes, each load has a different power consumption, and / or the power source supplying each load is different. The power supply to each load in the vehicle is adjusted according to this power consumption mode. This ability to adjust the vehicle's power consumption mode using multi-domain data improves user comfort and convenience, and enhances the vehicle's energy efficiency.

[0017] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0019] Figure 2 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0020] Figure 3 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0021] Figure 4 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0022] Figure 5 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0023] Figure 6 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0024] Figure 7 This is a schematic diagram illustrating a vehicle control system according to an exemplary embodiment.

[0025] Figure 8 This is a schematic diagram of a vehicle control device 800 according to an exemplary embodiment.

[0026] Figure 9 This is a schematic diagram of an electronic device 900 according to an exemplary embodiment. Detailed Implementation

[0027] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0028] In the following description, the words "first" and "second" are used only to distinguish the purpose of the description and should not be interpreted as indicating or implying relative importance or order.

[0029] In related technologies, the power supply to various loads in a vehicle is typically adjusted based on the battery state. This method can only passively manage loads after the battery state deteriorates to a threshold, and cannot proactively and dynamically adjust loads based on the vehicle's future power demand. For example, insufficient battery power may cause some loads in the vehicle system (such as air conditioning and multimedia) to suddenly shut down, affecting user comfort and convenience. Furthermore, it cannot effectively charge the battery when the vehicle's alternator is generating excessive power (such as on long downhill sections), leading to lower vehicle energy efficiency.

[0030] To address the aforementioned problems, this disclosure provides a vehicle control method, storage medium, and vehicle. This method acquires multi-domain data during vehicle operation, including power domain data, body domain data, autonomous driving domain data, and connectivity domain data. Based on this multi-domain data, it predicts the total power consumption of the loads and the generator power output of the vehicle at multiple moments within a set time range. Based on the total power consumption of the loads and the generator power output, it determines the vehicle's power consumption mode, where different loads are activated, the power consumption of each load is different, and / or the power supply to each load is different under different power consumption modes. The method then adjusts the power supply to each load in the vehicle according to the power consumption mode. This method can adjust the power supply to each load of the vehicle based on the vehicle's multi-domain data, effectively considering the impact of different domain parameters on the power consumption of each load, improving user comfort and convenience, and enhancing the vehicle's energy efficiency.

[0031] Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps: In step S11, multi-domain data during vehicle operation is acquired, including: power domain data, body domain data, autonomous driving domain data, and connectivity domain data.

[0032] For example, traditional vehicles typically employ a distributed ECU architecture, which consists of hundreds of independent ECUs (Electronic Control Units), each controlling a specific function (such as windows, engine, audio, etc.). This not only leads to complex wiring harnesses, high costs, and heavy weight during vehicle design, but also results in limited and non-shared computing power for each ECU, making it difficult to support high-performance requirements such as autonomous driving. In contrast, the vehicle described in this disclosure integrates hundreds of distributed ECUs into several corresponding functional domains through functional aggregation and hardware centralization. Each domain is managed uniformly by a high-performance domain controller. It is understood that the functional domain divisions may differ between different vehicles, and this disclosure does not impose any limitations on this. In one possible embodiment, the multiple functional domains of the vehicle may include: a powertrain domain, a body domain, an autonomous driving domain, and a connectivity domain. The powertrain domain controller provides real-time vehicle engine status (running, off, starting), engine speed, load rate, generator magnetic field duty cycle (indirectly reflecting generator output current), and start-stop system status (about to start or stop). This data can be used to predict the generator's power generation capacity. The body domain controller provides the status of various loads in the vehicle and user settings, such as air conditioning set temperature, fan speed, compressor operating status, seat heating / ventilation settings, steering wheel heating status, door / window / sunroof status, and interior / exterior lighting status. This data can be used to predict the vehicle's power demand. The autonomous driving domain controller provides high-precision navigation path information, including future road gradient (uphill / downhill), road type (highway, city, rural), expected speed curve, and real-time traffic congestion information. This data may directly affect the vehicle's engine operating condition, generator operating condition, and auxiliary system power consumption (such as cooling fan, electric power steering, etc.). The connectivity domain controller provides external environmental information, such as obtaining accurate weather forecasts (ambient temperature, solar intensity) for a future driving route. Ambient temperature affects the power consumption of the vehicle's air conditioning system. In summary, multi-domain data during vehicle operation can be obtained through the controller corresponding to the functional domain. This multi-domain data can include: power domain data, body domain data, autonomous driving domain data, and connectivity domain data.

[0033] In step S12, the total load power and generator power of the vehicle at multiple moments within a set time range are predicted based on the multi-domain data.

[0034] For example, after acquiring the multi-domain data, the total load power consumption and generator power consumption of the vehicle at multiple moments within a future period (i.e., a set time range) can be predicted based on the multi-domain data. The total load power consumption at each moment is the sum of the power demands of each electrical load in the vehicle at that moment, predicted based on the multi-domain data during vehicle operation. The generator power consumption at each moment is the generator power consumption of the vehicle at that moment, predicted based on the multi-domain data during vehicle operation. The total load power consumption at multiple moments reflects the changes in the total load power consumption within the set time range, and the generator power consumption at multiple moments reflects the changes in the generator power consumption within the set time range. Furthermore, this disclosure does not limit the size of the set time range or the time interval between the multiple moments; for example, the set time range can be 10 minutes, and the time interval between the multiple moments can be 1 minute.

[0035] In step S13, the power consumption mode of the vehicle is determined based on the total power consumption of the load and the power generation of the generator. Different loads are activated under different power consumption modes, the power consumption of each load is different, and / or the power supply for each load is different.

[0036] For example, to ensure stable vehicle operation and a good driving experience for users, multiple power consumption modes can be set for the vehicle. These modes can be determined based on the total power consumption of the load and the generator's output power. For instance, at any given time, if the total power consumption of the load is less than or equal to the generator's output power, the power consumption mode at that time can be determined as the first power consumption mode. In this first mode, the generator can supply power to all active loads. Alternatively, at any given time, if the total power consumption of the load is greater than the generator's output power, the power consumption mode at that time can be determined as the second power consumption mode. In this second mode, the generator cannot meet the power demands of all active loads. In this case, the total generator output power can be balanced with the total load power consumption by turning off some lower-priority loads (such as ambient lighting and audio functions), reducing the power supply to some loads (such as air conditioning compressors and seat heaters), or by using the battery for compensation.

[0037] Optionally, when the vehicle's power consumption mode is balanced, the vehicle's generator supplies power to each load. When the power consumption mode is surplus mode, the generator supplies power to each load and charges the vehicle's battery. When the power consumption mode is a power shortage mode, the generator and the battery supply power to each load.

[0038] For example, the vehicle may include multiple power consumption modes, such as a balanced mode, a surplus mode, and a power deficit mode. The balanced mode refers to a state where the generator's output power and the total load power consumption are relatively balanced within a set time period. The generator's output power can basically meet the power needs of each load and will not affect vehicle operation or user experience. The surplus mode refers to a state where the generator's output power is much greater than the total load power consumption within a set time period. The generator's output power can fully meet the power needs of each load, but there is a waste of generator power. The power deficit mode refers to a state where the generator's output power is much less than the total load power consumption within a set time period. The generator's output power cannot fully meet the power needs of each load, which may affect vehicle operation and user experience.

[0039] When the vehicle's power consumption mode is in balanced mode, the generator's output power and the power consumption of each load are relatively balanced, allowing the generator to supply power to these loads. Secondly, to avoid wasting generator energy in surplus mode, the generator can simultaneously charge the vehicle's battery using its remaining power. During charging, the battery's State of Charge (SOC) is monitored in real-time, and charging stops when the SOC equals the battery's maximum capacity. Furthermore, to prevent impacts on vehicle operation and user experience in depleted mode, both the battery and generator can supply power to each load simultaneously, maintaining stable operation, minimizing impact on vehicle performance, and improving the user experience.

[0040] In step S14, the power supply to each load in the vehicle is adjusted according to the power consumption mode.

[0041] For example, after determining the power consumption mode of the vehicle, power can be supplied to each load in the vehicle according to the power supply method corresponding to the power consumption mode.

[0042] The above technical solution acquires multi-domain data during vehicle operation, including power domain data, body domain data, autonomous driving domain data, and connectivity domain data. Based on this multi-domain data, the total power consumption of the loads and the generator output power of the vehicle at multiple moments within a set time range are predicted. The vehicle's power consumption mode is determined based on this total power consumption and generator output power. Different loads are activated under different power consumption modes, each load has a different power consumption, and / or the power source supplying each load is different. The power supply to each load in the vehicle is adjusted according to this power consumption mode. This ability to adjust the vehicle's power consumption mode using multi-domain data improves user comfort and convenience, and enhances the vehicle's energy efficiency.

[0043] Figure 2 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 2 As shown, step S12 may include the following steps: In step S121, the total power consumption of the load at multiple times is predicted based on the vehicle body domain data, the autonomous driving domain data, and the network connectivity domain data.

[0044] For example, based on a pre-set total load power prediction model in the vehicle, the corresponding vehicle body domain data, the autonomous driving domain data, and the network connectivity domain data can be input to predict the total load power at multiple times. For instance, the autonomous driving domain data (such as navigation path, road slope, predicted vehicle speed), the network connectivity domain data (such as ambient temperature, light intensity, etc.), and the vehicle body domain data (such as air conditioning settings, seat heating settings, etc.) can be input into the total load power prediction model to predict the total load power of the vehicle at multiple times. Furthermore, the total load power prediction model can contain different load power consumption models. For example, the load power consumption model can be an air conditioning power consumption model, which can comprehensively consider ambient temperature, target interior temperature, sunlight intensity, wind speed, etc., to calculate the theoretical power consumption of the compressor required to maintain the user-set target temperature. When the navigation indicates that a congested section will be entered in 5 minutes, the vehicle speed will decrease, the engine compartment cooling capacity will decrease, and the condenser pressure will increase. The model will predict that the compressor power consumption will increase by 10% from the current level. For example, the load power consumption model can be a seat heating power consumption model. The seat heating power consumption is directly related to the heating level set by the user, so the heating power consumption of the seat can be predicted based on the heating level. In summary, this total load power consumption prediction model can add up the power consumption of all controllable loads in the vehicle at each moment to generate the total load power consumption at each moment within the set time range.

[0045] In step S122, the generator power output at the multiple moments is predicted based on the power domain data and the autonomous driving domain data.

[0046] For example, engine data from the powertrain domain, and future vehicle speed and gradient from the intelligent driving domain. Prediction process: internal to the system. The vehicle can store the engine's universal characteristic diagram and the generator's external characteristic diagram. Based on these diagrams, and combined with engine data from the powertrain domain and future vehicle speed and gradient from the intelligent driving domain, the generator's output power at various times can be predicted. For example, during a long downhill slope, the engine is in a fuel-cut or low-load state, the generator has more available torque redundancy, resulting in stronger power generation and higher output power. Conversely, during rapid acceleration or in congested traffic, the engine's own load is high, limiting the generator's output and reducing its power generation capacity, resulting in lower output power.

[0047] Figure 3 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 3 As shown, step S13 may include the following steps: In step S131, the power consumption mode includes a balanced mode, a surplus mode, and a power deficit mode. The difference between the total power consumption of the load and the power generation of the generator at each of the multiple time points is obtained, and the average value of the difference is determined.

[0048] In step S132, when the average value is within a preset power range, the vehicle's power consumption mode is determined to be the balanced mode.

[0049] In step S133, when the average value is greater than the upper limit of the power range, the power consumption mode of the vehicle is determined to be the surplus mode.

[0050] In step S134, when the average value is less than the lower limit of the power range, the vehicle's power consumption mode is determined to be the power depletion mode.

[0051] For example, the difference between the total power consumption of the load and the generator power output at each moment within the set time range is calculated. This difference at each moment reflects whether the generator power output can meet the power demand of each load. Therefore, the differences across multiple moments can reflect whether the generator power output can meet the power demand of each load within the set time range. It is understood that determining the vehicle's power consumption mode based on these differences may lead to frequent switching of the vehicle's power consumption mode, causing instability in the vehicle system and increased energy consumption. Therefore, the power consumption mode within the set time range can be determined based on the average of these multiple differences. For example, if the average value is within a preset power range, it indicates that the generator's power generation and the load's power consumption are relatively balanced within the set time range, and the vehicle's power supply mode can be determined to be a balanced mode. If the average value is greater than the upper limit of the power range, it indicates that the generator's power generation within the set time range can fully meet the load's power consumption, and the vehicle's power supply mode can be determined to be a surplus mode. If the average value is less than the lower limit of the power range, it indicates that the generator's power generation within the set time range cannot meet the load's power consumption, and the vehicle's power supply mode can be determined to be a power deficit mode.

[0052] Figure 4 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 4 As shown, step S14 may include the following steps: In step S141, when the vehicle's power consumption mode is a power shortage mode, the SOC at each of the multiple time points is predicted based on the total power consumption of the load and the power generation of the generator at the multiple time points.

[0053] In step S142, when the SOC is less than a preset SOC threshold at any of the multiple times, the power consumption of each load in the vehicle is adjusted according to the generator power and the battery output power of the vehicle.

[0054] For example, when the vehicle's power consumption mode is in a low-power mode, the generator and battery simultaneously supply power to various loads. During this power supply process, the vehicle battery's State of Charge (SOC) decreases as the power supply duration increases. However, if the vehicle battery's SOC drops below a preset SOC threshold, it may affect vehicle starting and load power consumption. Therefore, in this low-power mode, the battery's SOC can be predicted at each of the multiple time points based on the total load power consumption and generator power generation. When the SOC is less than the preset SOC threshold, the power consumption of each load in the vehicle can be dynamically adjusted to reduce the battery's output power, thereby avoiding the problem of the vehicle failing to start when the battery's SOC is below the SOC threshold. During vehicle operation, there may be multiple different operating modes, such as driving mode, idle start-stop mode, parking power consumption mode, and deep sleep mode. Each operating mode may have different management strategies and corresponding SOC thresholds. For example, in driving mode, the system can more actively manage the load; while in parking power mode, protecting the battery and preventing it from running out of power is the only priority. Therefore, the SOC threshold can be determined based on the vehicle's operating mode.

[0055] Furthermore, the difference between the generator's power output and the total power consumed by the load at each moment can be used to represent the power that the battery needs to compensate for in this power shortage mode. Therefore, based on the equivalent circuit model of the battery, the SOC of the battery at each moment can be predicted according to the difference and the battery's state data (such as battery temperature, voltage, current, internal resistance, etc.).

[0056] In one possible embodiment, when adjusting the power consumption of each load in the vehicle according to the generator power and the battery output power of the vehicle, the vehicle can set different priorities for each load. The higher the load priority, the higher the importance of the load during vehicle operation. Then, the lower priority load can be turned off or the output power of the lower priority load can be reduced to prevent the battery SOC from dropping below the corresponding SOC threshold and affecting vehicle starting.

[0057] Figure 5 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 5 As shown, step S142 may include the following steps: In step S1421, the priority of each load is dynamically adjusted through the multi-domain data.

[0058] In step S1422, the power consumption of each load in the vehicle is adjusted according to the priority of each load, the power generation of the generator, and the output power of the battery.

[0059] For example, the importance of each load may vary depending on the user settings and the vehicle's usage conditions. Therefore, in a low-battery mode, if the priority of each load is fixed, and the power consumption of each load is adjusted based on this fixed priority, the generator's output power, and the battery's output power, it may directly affect the user's comfort and convenience. For instance, in a hot summer, the air conditioning compressor might suddenly shut off due to insufficient generator and battery output power, causing a rapid increase in the vehicle's interior temperature and resulting in strong user dissatisfaction. Therefore, in one possible embodiment, the priority of each load can be dynamically adjusted based on multi-domain data, abandoning the fixed list and dynamically adjusting the priority of each load through multi-domain data (such as ambient temperature, vehicle speed, and light sensor status). For example, in cold nights or rainy weather, to ensure driving safety, the priority of rear window defrosting and headlights can be increased; due to low ambient temperatures, to ensure user driving comfort, the priority of seat heating can be increased; and to prevent the battery's SOC from falling below the SOC threshold, the priority of the audio system can be decreased. After dynamically adjusting the priority of each load, the generator's power output and the battery's output power can be used to meet the power requirements of each load in descending order of priority. For example, for core loads with high priority that must be guaranteed (such as ECUs and fuel pumps), the full required power can be allocated. For high-priority comfort loads (such as air conditioning and seat heating), a percentage of their required power can be allocated (for example, reducing the duty cycle of the air conditioning compressor from 95% to 75%. This will slightly reduce the cooling capacity, but the user will hardly notice it). For low-priority loads (such as multimedia systems and windshield wipers), only the minimum maintenance power can be allocated or no power can be allocated temporarily.

[0060] Optionally, step S1422 may include: adjusting the power consumption of each load based on multi-objective optimization according to the priority of each load, the power generation of the generator and the output power of the battery, wherein the multi-objective includes: startup objective, comfort objective and energy consumption objective.

[0061] For example, when the vehicle's power consumption mode is in a low-battery mode, in addition to meeting the vehicle's starting target (i.e., SOC greater than the SOC threshold), user comfort and vehicle energy consumption also need to be considered to ensure the user's driving experience and operating costs. Therefore, when adjusting the power consumption of each load based on its priority, the generator's output power, and the battery's output power, multi-objective optimization can be used. While ensuring the adjustment scheme meets the starting target, comfort and energy consumption targets are considered simultaneously. The comfort target can be achieved by minimizing a "user discomfort function," which assigns a "discomfort weight" to each load's intervention behavior (such as shutting down or downshifting). The user's discomfort is determined by the discomfort weight of each load and its corresponding intervention behavior. For example, completely shutting down the air conditioner has the highest discomfort weight, while slightly reducing the compressor's duty cycle has a very low discomfort weight. Therefore, the user's discomfort can be the sum of the discomfort weights of each load. The energy consumption target can be minimizing fuel consumption, which can be determined by the adjusted power demand of each load. This multi-objective optimization enables the intelligent search for the optimal balance between user experience and energy efficiency while ensuring vehicle safety.

[0062] Figure 6 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 6 As shown, step S14 may further include the following steps: In step S143, the power consumption of each load is obtained.

[0063] In step S144, the power distribution unit of the vehicle is controlled to output the power corresponding to each load through the power channels corresponding to each load, so as to supply power to each load.

[0064] For example, after determining the adjusted power demand of each load, the power demand can be used as the power consumption of that load, and the vehicle's power distribution unit can supply power to the corresponding load using that power consumption. The power distribution unit can internally consist of multiple parallel intelligent power channels, each of which can be responsible for one or a group of loads. Therefore, the power channels in the vehicle's power distribution unit corresponding to each load can be controlled to output the power consumption corresponding to each load, thus supplying power to each load.

[0065] Figure 7 This is a vehicle control system illustrated according to an exemplary embodiment, such as Figure 7 As shown, the system includes: a multi-domain data acquisition unit, an intelligent power manager, and a power distribution unit; The multi-domain data acquisition unit can be used to acquire multi-domain data, including: power domain data (engine status, engine load, start-stop status, etc.), body domain data (air conditioning settings, seat heating, door and window status, etc.), autonomous driving domain data (navigation route, traffic information, ADAS status, etc.) and connected domain data (weather, road conditions, etc.). The intelligent power manager is communicatively connected to the multi-domain data acquisition unit, and the communication method may include (CAN FD / Ethernet vehicle network). The intelligent power manager includes: a multi-domain information fusion and prediction module, a dynamic strategy decision engine, and an actuator control interface. The multi-domain information fusion and prediction module is used to predict the total power consumption of the load, the generator power output, and the battery SOC at each moment within a set time range based on the multi-domain data. The dynamic strategy decision engine is used to allocate the power consumption of each load based on the prediction results of the multi-domain information fusion and prediction module, using multi-objective optimization and dynamic priority. The actuator control interface is used to transmit the dynamic strategy decision... The power allocation result of the engine is parsed into specific, executable hardware instructions, that is, the power consumption of any load is converted into specific control commands for the power allocation unit; for example, "limit the power of the air conditioner compressor to 800W" is parsed into "output PWM signal, duty cycle 65%" to control the power channel of the compressor; and data frames conforming to the LIN bus or private CAN protocol are generated and sent to the power allocation unit in sequence; or advanced communication with the generator controller is carried out through the CAN bus to send request instructions, such as "please increase the output voltage to 14.8V in the next 60 seconds to achieve fast charging".

[0066] This power distribution unit can be composed of multiple parallel intelligent power channels. Each power channel can be responsible for one or a group of loads. Based on control commands output from the actuator control interface, it controls the corresponding power channel to supply power to the load at the corresponding power consumption. Each power channel can include a high-power MOSFET, a voltage / current detection circuit, and a drive and protection circuit. The high-power MOSFET acts as a solid-state switch, replacing mechanical relays, enabling extremely high-frequency switching control. This allows for linear or stepped adjustment of load power, rather than simply "on" or "off". The voltage / current detection circuit can accurately measure the current and voltage flowing through the channel in real time, calculating the real-time power. This allows the system to not only control individual loads but also sense the actual operating status of the loads, achieving closed-loop control. The drive and protection circuit ensures that the MOSFETs in the corresponding channels can switch quickly and safely, and integrates overcurrent, overvoltage, short-circuit, and overheat protection functions to ensure vehicle electrical safety. Furthermore, the power distribution unit can communicate with the intelligent power manager via a LIN bus or a dedicated private CAN network, receiving fine-grained control commands and reporting the real-time status of each channel (current, voltage, temperature, fault codes, etc.). In summary, this system enables the vehicle control method described in this embodiment, improving user comfort and convenience, and enhancing vehicle energy efficiency.

[0067] Figure 8 This is a schematic diagram of a vehicle control device 800 according to an exemplary embodiment, as shown below. Figure 8 As shown, the device 800 includes: an acquisition module 810, a prediction module 820, a determination module 830, and a control module 840.

[0068] The acquisition module 810 is used to acquire multi-domain data during vehicle operation, including: power domain data, body domain data, autonomous driving domain data and connectivity domain data; The prediction module 820 is used to predict the total load power and generator power of the vehicle at multiple moments within a set time range based on the multi-domain data. The determining module 830 is used to determine the power consumption mode of the vehicle based on the total power consumption of the load and the power generation of the generator. Different loads are turned on under different power consumption modes, the power consumption of each load is different, and / or the power supply for each load is different. The control module 840 is used to adjust the power supply to each load in the vehicle according to the power consumption mode.

[0069] Optionally, the prediction module 820 includes: a first prediction submodule and a second prediction submodule; The first prediction submodule is used to predict the total power consumption of the load at multiple times based on the vehicle body domain data, the autonomous driving domain data and the network connectivity domain data. The second prediction submodule is used to predict the generator power output at multiple times based on the power domain data and the autonomous driving domain data.

[0070] Optionally, the power consumption mode includes: a balanced mode, a surplus mode, and a deficit mode. The determining module 830 is used for: Obtain the difference between the total power consumption of the load and the power generated by the generator at each of the multiple time points, and determine the average value of the difference; When the average value is within the preset power range, the vehicle's power consumption mode is determined to be the balanced mode; When the average value is greater than the upper limit of the power range, the vehicle's power consumption mode is determined to be the surplus mode. When the average value is less than the lower limit of the power range, the vehicle's power consumption mode is determined to be the low-power mode.

[0071] Optionally, the control module 840 is used for: When the vehicle's power consumption mode is balanced, the vehicle's generator supplies power to each load. When the power consumption mode is surplus mode, the generator supplies power to each load and charges the vehicle's battery. When the power consumption mode is a power shortage mode, the generator and the battery supply power to each load.

[0072] Optionally, the control module 840 includes a third prediction submodule; The third prediction submodule is used to predict the SOC at each of the multiple time points based on the total power consumption of the load and the power generation of the generator when the vehicle's power consumption mode is in a power shortage mode. The control module 840 is also used to adjust the power consumption of each load in the vehicle according to the generator power and the battery output power of the vehicle when the SOC is less than a preset SOC threshold at any of the multiple times.

[0073] Optionally, the control module 840 further includes: a determination submodule; This determination submodule is used to dynamically adjust the priority of each load based on the multi-domain data; The control module 840 is also used to adjust the power consumption of each load in the vehicle according to the priority of each load, the power generation of the generator and the output power of the battery.

[0074] Optionally, the control module 840 is also used to adjust the power consumption of each load based on multi-objective optimization, according to the priority of each load, the power generation of the generator and the output power of the battery, the multi-objectives including: startup objective, comfort objective and energy consumption objective.

[0075] Optionally, the control module 840 further includes: an acquisition submodule; This acquisition submodule is used to acquire the power consumption of each load; The control module 840 is also used to control the power channels in the power distribution unit of the vehicle that correspond to each load, and output the power consumption corresponding to each load to supply power to each load.

[0076] Figure 9 This is a schematic diagram illustrating an electronic device 900 according to an exemplary embodiment. For example... Figure 9 As shown, the electronic device 900 may include a processor 901 and a memory 902. The electronic device 900 may also include one or more of a multimedia component 903, an input / output (I / O) interface 904, and a communication component 905.

[0077] The processor 901 controls the overall operation of the electronic device 900 to complete all or part of the steps in the exhaust emission control method described above. The memory 902 stores various types of data to support the operation of the electronic device 900. This data may include, for example, instructions for any application or method operating on the electronic device 900, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 902 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 903 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 902 or transmitted via communication component 905. The audio component also includes at least one speaker for outputting audio signals. I / O interface 904 provides an interface between processor 901 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 905 is used for wired or wireless communication between the electronic device 900 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 9G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 905 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0078] In an exemplary embodiment, the electronic device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the exhaust emission control method described above.

[0079] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the exhaust emission control method described above. For example, the computer-readable storage medium may be the memory 902 including program instructions, which may be executed by the processor 901 of the electronic device 900 to complete the exhaust emission control method described above.

[0080] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the exhaust emission control method described above when executed by the programmable device.

[0081] In another exemplary embodiment, a vehicle is also provided, which includes the electronic device 900 described in the above embodiments.

[0082] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0083] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0084] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A vehicle control method, characterized in that, include: Acquire multi-domain data during vehicle operation, including: power domain data, body domain data, autonomous driving domain data, and connectivity domain data; Based on the multi-domain data, predict the total load power consumption and generator power of the vehicle at multiple moments within a set time range; The power consumption mode of the vehicle is determined based on the total power consumption of the loads and the power generation of the generator. Different loads are activated under different power consumption modes, the power consumption of each load is different, and / or the power supply for each load is different. The power supply to each load in the vehicle is adjusted according to the power consumption mode.

2. The method according to claim 1, characterized in that, The step of predicting the total power consumption of the vehicle's load and the generator's power output at multiple moments within a set time range based on the multi-domain data includes: The total power consumption of the load at the multiple moments is predicted based on the vehicle body domain data, the autonomous driving domain data, and the connected vehicle domain data. The generator power output at the multiple times is predicted based on the power domain data and the autonomous driving domain data.

3. The method according to claim 1, characterized in that, The power consumption modes include: balanced mode, surplus mode, and power deficit mode. Determining the vehicle's power consumption mode based on the total power consumption of the load and the generator's power output includes: Obtain the difference between the total power consumption of the load and the power generation of the generator at each of the plurality of time points, and determine the average value of the difference; When the average value is within a preset power range, the vehicle's power consumption mode is determined to be the balanced mode; When the average value is greater than the upper limit of the power range, the power consumption mode of the vehicle is determined to be the surplus mode. When the average value is less than the lower limit of the power range, the vehicle's power consumption mode is determined to be the power depletion mode.

4. The method according to claim 1, characterized in that, The adjustment of power supply to each load in the vehicle according to the power consumption mode includes: When the vehicle's power consumption mode is balanced, the vehicle's generator supplies power to each load. When the power consumption mode is surplus mode, the generator supplies power to each load and charges the vehicle's battery. When the power consumption mode is the power shortage mode, the generator and the battery supply power to the loads.

5. The method according to claim 1, characterized in that, The adjustment of power supply to each load in the vehicle according to the power consumption mode includes: When the vehicle's power consumption mode is a low-power mode, the SOC at each of the multiple time points is predicted based on the total power consumption of the load and the power generated by the generator at the multiple time points. When the SOC is less than a preset SOC threshold at any of the multiple times, the power consumption of each load in the vehicle is adjusted according to the generator power and the battery output power of the vehicle.

6. The method according to claim 5, characterized in that, The method of adjusting the power consumption of each load in the vehicle based on the generator power output and the battery output of the vehicle includes: The priority of each load is dynamically adjusted using the multi-domain data; The power consumption of each load in the vehicle is adjusted according to the priority of each load, the power output of the generator, and the output power of the battery.

7. The method according to claim 6, characterized in that, The method of adjusting the power consumption of each load in the vehicle according to the priority of each load, the power generation of the generator, and the output power of the battery includes: Based on multi-objective optimization, the power consumption of each load is adjusted according to the priority of each load, the power generation of the generator, and the output power of the battery. The multi-objectives include: startup objective, comfort objective, and energy consumption objective.

8. The method according to claim 1, characterized in that, The adjustment of power supply to each load in the vehicle according to the power consumption mode includes: Obtain the power consumption of each load; The power distribution unit of the vehicle controls the power channels corresponding to each load, and outputs the power consumption corresponding to each load to supply power to each load.

9. 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 steps of the method according to any one of claims 1 to 8.

10. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the steps of the method according to any one of claims 1-8.

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