Drive control method, electronic device, and vehicle

Abstract

The application provides a driving control method, an electronic device and a vehicle, which can dynamically determine the minimum reserved power in different scenarios according to the navigation end point of the navigation path and the user driving habit represented by the historical behavior data, and guarantee the subsequent vehicle use demand of the user after reaching the navigation end point. Under the constraint of the maximum limit torque, the torque demand of the vehicle in different road sections is predicted according to the path information of the navigation path, and a demand torque sequence is obtained, which provides data support for subsequent driving planning. Under the constraint of the minimum reserved power, the minimum comprehensive energy consumption is taken as the target, the corresponding optimal driving mode sequence is determined according to the demand torque sequence, and the most suitable driving mode is allocated for different road sections of the navigation path, so that the differentiated driving control is realized according to the different navigation end points while the low energy consumption is guaranteed, and the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a drive control method, electronic equipment, and vehicle. Background Technology

[0002] In related technologies, the energy management strategies of plug-in hybrid electric vehicles generally adopt static threshold rules, which make local decisions based only on real-time vehicle speed, battery status and driving style, completely ignoring the differences in the urgency of the navigation destination, resulting in a poor user experience. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a drive control method, electronic device and vehicle, which performs vehicle drive control by constructing the optimal drive mode sequence corresponding to the minimum reserved power, so as to achieve differentiated drive control according to different navigation destinations while ensuring low energy consumption and improving user experience.

[0004] To achieve the above objectives, this application provides a drive control method, comprising:

[0005] Determine the minimum reserve battery power based on the navigation destination and historical driving data; The required torque sequence of the vehicle is determined based on the preset maximum limit torque and the path information of the navigation path; The optimal drive mode sequence with the minimum overall energy consumption is determined based on the required torque sequence and the minimum reserved power, and the motor and engine are controlled to drive the vehicle according to the optimal drive mode sequence.

[0006] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method described above when executing the computer program.

[0007] Based on the same inventive concept, this application also provides a vehicle including the electronic equipment described above.

[0008] As can be seen from the above, the drive control method, electronic device, and vehicle provided in this application can determine the minimum reserved power based on the navigation destination and historical driving data; determine the vehicle's required torque sequence based on the preset maximum limit torque and the navigation path information; determine the optimal drive mode sequence with the minimum overall energy consumption based on the required torque sequence and the minimum reserved power; and control the motor and engine to drive the vehicle according to the optimal drive mode sequence. Depending on the navigation destination and combined with user driving habits represented by historical behavior data, the minimum reserved power is dynamically determined for different scenarios to ensure the user's subsequent driving needs after reaching the navigation destination. Under the constraint of the maximum limit torque, the torque demand of the vehicle on different road segments is predicted based on the navigation path information to obtain the required torque sequence, providing data support for subsequent drive planning. Under the constraint of the minimum reserved power, with the goal of minimizing overall energy consumption, the corresponding optimal drive mode sequence is determined based on the required torque sequence, allocating the most suitable drive mode to different road segments of the navigation path. This ensures low energy consumption while achieving differentiated drive control based on different navigation destinations, improving the user experience. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of the driving control method according to an embodiment of this application; Figure 2 This is a schematic diagram of the drive control device according to an embodiment of this application; Figure 3 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0012] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] In this article, it is important to understand that any number of elements in the accompanying figures is for illustrative purposes and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0014] Based on the above background description, the following situations also exist in the related technologies: In related technologies, the energy management strategies of plug-in hybrid electric vehicles generally adopt static threshold rules, making local decisions based solely on real-time vehicle speed, battery status, and driving style. This completely ignores the differences in urgency levels inherent in navigation destinations, resulting in the inability to identify the urgency attributes of destinations (e.g., hospital emergency rooms are high urgency, while suburban residences are low urgency). Consequently, a uniform power reserve strategy is adopted, failing to achieve differentiated energy management. Especially in critical scenarios, failing to reserve sufficient power in advance before high-urgency destinations (e.g., restricted traffic areas, emergency charging stations) can easily force vehicles to switch to fuel mode, leading to environmental fines or charging failures. On the other hand, excessive power is reserved for low-urgency destinations (e.g., suburbs without charging facilities), resulting in energy waste, reduced fuel efficiency, and increased overall energy consumption.

[0015] The drive control method, electronic device, and vehicle provided in this application can determine the minimum reserved power based on the navigation destination and historical driving data; determine the vehicle's required torque sequence based on the preset maximum limit torque and the navigation path information; determine the optimal drive mode sequence with the minimum overall energy consumption based on the required torque sequence and the minimum reserved power; and control the motor and engine to drive the vehicle according to the optimal drive mode sequence. Depending on the navigation destination and combined with user driving habits represented by historical behavior data, the minimum reserved power is dynamically determined for different scenarios to ensure the user's subsequent driving needs after reaching the navigation destination. Under the constraint of the maximum limit torque, the torque demand of the vehicle on different road segments is predicted based on the navigation path information to obtain the required torque sequence, providing data support for subsequent drive planning. Under the constraint of the minimum reserved power, with the goal of minimizing overall energy consumption, the corresponding optimal drive mode sequence is determined based on the required torque sequence, allocating the most suitable drive mode to different road segments of the navigation path. This ensures low energy consumption while achieving differentiated drive control based on different navigation destinations, improving the user experience.

[0016] The driving control method provided by the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0017] In some embodiments, such as Figure 1 As shown, the drive control method includes steps 101-103.

[0018] Step 101: Determine the minimum reserve battery power based on the navigation destination of the navigation route and historical driving data.

[0019] In practice, the minimum reserved battery capacity is the minimum amount of battery power the vehicle needs to retain after reaching the navigation destination. Determining the minimum reserved battery capacity is used to address the problem of blind battery allocation caused by hybrid vehicles' inability to recognize destination attributes.

[0020] First, the system obtains the user-defined navigation destination information via the in-vehicle T-BOX or vehicle-to-everything (V2X) module. This information includes latitude and longitude coordinates, the name of the Point of Interest (POI), and a unique identifier. Then, it retrieves the user's historical driving data stored on a cloud server or local encrypted storage unit. This data records the vehicle's driving trajectory, dwell time, and destination charging behavior over a specific period (e.g., one year). The geographical location information of the current navigation destination is then spatiotemporally matched with the historical data, with a geofence radius of 50 meters and a time window of 24 hours. If more than a preset threshold (e.g., 3 times) of historical dwell records are detected within this geofence, the navigation destination is determined to be a high-frequency activity point for the user.

[0021] At this point, the system will further correlate the user's subjective evaluation tags for the location in the historical data (such as "home," "company," or "hospital" selected via voice assistant or manual operation) or the average state of charge (SOC) at that location during historical trips. For example, if a user consistently travels to a location every Monday and Friday afternoon, and that location consistently requires a pure electric arrival (i.e., SOC > 25% upon arrival) in historical driving data, the system will automatically learn and increase the urgency weight of that location. Conversely, if the SOC at that location is frequently below 5% in historical data and has not triggered user complaints or traffic restrictions, the system will determine that the location is not sensitive to battery power. Based on the above historical behavior analysis and a comprehensive judgment of destination attributes (such as whether it is a residential area or whether there are charging stations), the minimum reserved battery power for the navigation destination is finally determined.

[0022] If the urgency level of the navigation destination is determined to be high (e.g., hospital, airport), the minimum reserved battery capacity is set to 25% of the total battery capacity; if the urgency level is determined to be medium (e.g., shopping mall, office building), the minimum reserved battery capacity is set to 15% of the total battery capacity; and if the urgency level is determined to be low (e.g., suburban park, residence without charging facilities), the minimum reserved battery capacity is set to 5% of the total battery capacity. This dynamic minimum reserved battery capacity retention mechanism ensures that the battery reserve strategy is not only based on the objective classification of map data but also incorporates the user's personalized driving habits, realizing a shift from static planning to personalized and differentiated customized navigation planning.

[0023] For high-urgency navigation destinations, the corresponding driving scenarios include hospital emergency rooms, public fast charging stations, airport arrival levels, and traffic-restricted areas. Users traveling to these locations either have an urgent need for time that is "not a minute's delay" or are concerned about being unable to move due to a lack of power (gasoline vehicles are prohibited from entering restricted areas). High-urgency navigation destinations should be marked as the highest priority, and a large minimum reserve of battery power (e.g., at least 25%) should be reserved in advance to ensure that the vehicle can still operate on pure electric power upon arrival at the navigation destination.

[0024] For navigation destinations with medium to high urgency, the corresponding driving scenarios include shopping in commercial centers, working in office areas, and charging at ordinary charging stations. Users have some time pressure or charging needs when traveling to these locations, but the margin for error is relatively high. Therefore, a balanced strategy will be adopted to find a dynamic balance between fuel consumption and battery reserve, and an appropriate minimum reserve battery level will be set (which needs to be less than the minimum reserve battery level under high urgency, for example, at least 15% of the battery level).

[0025] For navigation destinations with low urgency, the corresponding driving scenarios include suburban residences, older communities without charging stations, and leisure parks. Users go to these locations to relax, and there may not even be charging stations available. There is no need to constantly monitor the battery level. Allowing the battery to drop to as low as 5% and using fuel can maximize overall fuel economy and avoid the problem of wasting fuel in order to save electricity. Therefore, a minimum reserve battery level should be set (which needs to be less than the minimum reserve battery level under medium urgency, for example, at least 5% battery level).

[0026] Step 102: Determine the required torque sequence of the vehicle based on the preset maximum limit torque and the path information of the navigation path.

[0027] In practical implementation, the required torque sequence of the vehicle is determined to transform the macroscopic navigation path into a microscopic vehicle dynamics control command sequence. By calling a high-precision map API interface, the entire path information from the starting point to the destination is obtained, including road curvature, slope (based on a DEM digital elevation model), road surface friction coefficient, speed limit zones, and traffic light distribution. The maximum limiting torque parameter of the vehicle's power system is preset, which is constrained by the motor's peak torque, the battery's maximum discharge power, and the transmission gear position to prevent power system overload. A sliding window algorithm is used to segment the path, cutting the continuous path into discrete segments of a preset distance (e.g., 100 meters). For each segment, based on its path type (e.g., uphill, downhill, straight, curve) and preset vehicle mass, air resistance coefficient, and rolling resistance coefficient, the initial required torque for that segment is calculated using the longitudinal dynamics equation. The calculation formula is: Treq=(Mgsinθ+MgCrcosθ+1 / 2CdAρv) 2 +Ma)×r, where M is the vehicle mass, g is the gravitational acceleration, θ is the slope angle, Cr is the rolling resistance coefficient, Cd is the wind resistance coefficient, A is the frontal area, ρ is the air density, v is the vehicle speed, a is the acceleration, and r is the wheel radius.

[0028] After calculating the initial torque demand, it is compared with the preset maximum torque limit. If the initial torque demand is greater than the maximum torque limit (e.g., under full-load steep incline conditions), a torque limiting strategy is implemented to forcibly clamp the demand torque to the maximum torque limit value, ensuring driving safety and preventing damage to the powertrain. If the initial torque demand is less than or equal to the maximum torque limit, the calculated value of the initial torque demand is directly used. By traversing all segmented paths, the discrete torque demand for each segment is sequentially arranged according to the direction of travel, thus constructing a demand torque sequence that covers the entire journey and changes over time. This demand torque sequence serves as the input for subsequent energy management strategies, precisely describing the vehicle's specific power requirements during future driving.

[0029] Step 103: Determine the optimal drive mode sequence with the minimum overall energy consumption based on the required torque sequence and the minimum reserved power, and control the motor and engine to drive the vehicle according to the optimal drive mode sequence.

[0030] In practice, the minimum reserved power ensures the power demand when reaching the navigation destination. While meeting the hard constraint of the minimum reserved power, the optimal drive mode sequence with the minimum comprehensive energy consumption is determined through energy optimization to reduce the overall energy consumption. The optimal drive mode sequence with the minimum comprehensive energy consumption is determined by the idea of ​​model predictive control (MPC) and the optimal control quantity is solved in the rolling time domain.

[0031] First, the current battery level reported by the current battery management system is read, and the current available battery level is calculated, which is the difference between the current battery level and the minimum reserved battery level. Then, based on the demand torque sequence and combined with the vehicle's dynamic model (including engine fuel consumption rate map, motor efficiency map, and battery internal resistance model), forward simulation prediction is performed for each possible driving mode (including pure electric drive, series drive, parallel drive, driving charging, and regenerative braking).

[0032] For each segment of the navigation path, after determining the required torque for that segment, the remaining battery charge and corresponding fuel consumption are calculated after driving in different drive modes. The prediction process strictly adheres to a hard constraint of minimum reserved battery charge; any combination of drive modes that results in the predicted battery charge at the navigation endpoint being lower than the minimum reserved battery charge will be eliminated.

[0033] After completing the multi-mode traversal prediction of the entire path, a multi-dimensional candidate solution set is constructed, and an optimization objective function is introduced, namely, minimizing the comprehensive energy consumption of the entire navigation path. The calculation of comprehensive energy consumption includes not only fuel consumption, but also converts electrical energy consumption into equivalent fuel consumption (e.g., based on the grid carbon emission factor or the conversion ratio of electricity price to oil price), or fuel consumption can be equivalent to the corresponding equivalent electrical energy consumption. All drive mode combinations that satisfy the minimum reserved power hard constraint are screened out, and the group with the minimum comprehensive energy consumption is selected as the optimal drive mode sequence.

[0034] For example, in high-urgency destination scenarios, hybrid mode is selected in the early stages to save fuel, while switching to pure electric mode as the destination approaches to meet the power reserve requirements; in low-urgency scenarios, direct fuel drive is allowed in the middle and later stages to improve thermal efficiency.

[0035] Finally, the calculated optimal driving mode sequence (e.g., [Road 1: pure electric, Road 2: hybrid, Road 3: pure electric...]) is sent to the vehicle controller in real time. Based on this optimal driving mode sequence, the vehicle controller precisely adjusts the engine's fuel injection quantity and ignition timing, as well as the electric motor's torque request and the battery's output power limit. This enables dynamic and seamless switching of the vehicle's driving modes, ensuring optimal global energy consumption while meeting the destination's power requirements.

[0036] In summary, the drive control method, electronic device, and vehicle provided in this application can dynamically determine the minimum reserved power in different scenarios based on the different navigation destinations of the navigation path and the user's driving habits represented by historical behavior data, ensuring the user's subsequent driving needs after reaching the navigation destination. Under the constraint of maximum torque limit, the torque demand of the vehicle in different road segments is predicted based on the path information of the navigation path, resulting in a demand torque sequence, which provides data support for subsequent drive planning. Under the constraint of minimum reserved power, with the goal of minimizing overall energy consumption, the corresponding optimal drive mode sequence is determined based on the demand torque sequence, allocating the most suitable drive mode to different road segments of the navigation path. While ensuring low energy consumption, differentiated drive control is achieved based on different navigation destinations, improving the user experience.

[0037] In some embodiments, determining the minimum reserved battery power based on the navigation destination of the navigation route and historical driving data includes: The urgency level of the navigation destination is determined based on the navigation destination and historical driving data; In response to an emergency level of Level 1, the preset maximum safe power level is set as the minimum reserved power level; In response to an emergency level of Level 2, the preset default safe power level is set as the minimum reserved power level; In response to an emergency level of Level 3, the preset minimum safe power level is set as the minimum reserved power level. The first emergency level is more urgent than the second emergency level, and the second emergency level is more urgent than the third emergency level; the default safe battery level is greater than the minimum safe battery level but less than the maximum safe battery level.

[0038] In practice, a strict hierarchical mapping relationship needs to be established for the determination of the minimum reserved power. Based on the acquired navigation destination attributes and historical data, the destination is divided into three distinct urgency levels.

[0039] The first emergency level corresponds to scenarios involving life safety, legal regulations, or critical mission assurance. Typical locations include "hospital emergency rooms," "airport departure levels," "fuel-restricted areas," and "public fast-charging stations (where the current battery level is insufficient to reach the destination)." For such navigation destinations, a preset maximum safe battery level (e.g., 25% of the total battery capacity) is set as the minimum reserved battery level. This means that upon arrival at the destination, the system must forcibly retain at least 25% battery power to ensure that the vehicle can operate on pure electric power within restricted areas, or to provide quiet, zero-emission mobility in emergency situations.

[0040] The second emergency level corresponds to daily commutes and business activities, with typical locations including "business centers," "office parks," and "regular charging stations." For these navigation destinations, a preset default safe battery level (e.g., 15% of the total battery capacity) is set as the minimum reserved battery level. This level is sufficient to cover the pure electric driving needs in most urban congested traffic conditions, while also taking into account fuel economy for long-distance travel.

[0041] The third emergency level corresponds to leisure and recreational scenarios where there is no need for charging. Typical locations include "suburban residences," "older communities without charging stations," "forest parks," or "remote rural areas." For such navigation destinations, a preset minimum safe battery level (e.g., 5% of the total battery capacity, i.e., the lower limit of discharge allowed by the battery management system) is determined as the minimum reserved battery level. In these scenarios, instead of forcibly retaining a large amount of redundant battery power, deep battery depletion is allowed to maximize energy utilization, reduce unnecessary engine starts, and lower overall travel costs. Through this tiered and quantifiable mechanism, it is possible to accurately match users' psychological expectations and actual vehicle usage needs in different scenarios.

[0042] In some embodiments, determining the urgency level of the navigation destination based on the navigation destination and historical driving data includes: In response to the existence of a historical navigation destination that is the same as the navigation destination in the historical driving data, the number of records of the historical navigation destination is determined; In response to a number of records being greater than or equal to a preset threshold, the historical urgency level corresponding to the historical navigation endpoint is determined as the urgency level. If the number of records is less than a preset threshold, the urgency level is determined based on the attribute information of the navigation destination.

[0043] In practice, the process of dynamically determining the urgency level of a destination involves several steps. First, the system searches the user's historical driving database, both locally and in the cloud. When the current navigation destination coincides with a historical navigation destination in the database (with an error of less than 50 meters), the system counts the number of records associated with that destination within a preset period (e.g., 90 days). If this number is greater than or equal to a preset threshold (e.g., 3 times), the location is considered the user's usual address (e.g., home or workplace). At this point, the system no longer relies solely on the general classification of map POIs but directly uses the historical urgency level associated with that destination as the current urgency level. For example, even if a user navigates to a "factory" (which might be classified as an industrial building on the map, typically low urgency), if the user's historical visits to the factory were always for emergency equipment repairs (historical records show frequent use of the external discharge function and short stays), the system will learn to raise its real-time urgency level to the highest urgency level. Conversely, if the number of records is less than the preset threshold, it indicates the user rarely visits this location, classifying it as an unfamiliar place, and the urgency level is determined based on the navigation destination's attribute information. This mechanism effectively solves the problems of lagging map data updates and differences in users' personalized needs, and realizes personalized and intelligent determination of urgency.

[0044] The urgency level is determined based on the attribute information of the navigation destination, including: Extract semantic labels and map classification codes for navigation destinations from attribute information; In response to the presence of preset available keywords in semantic tags, the urgency level is determined based on the mapping relationship between the keywords and the preset keyword levels. If no preset available keywords are found in the semantic tags, the urgency level is determined based on the map classification code and the preset classification code table.

[0045] In practice, the urgency determination algorithm for unfamiliar locations that users don't frequently navigate to relies primarily on deep semantic mining of map data. The navigation engine obtains destination attribute information, including POI names (semantic tags) and the navigation map's category code. A built-in keyword ranking mapping table determines the corresponding urgency level. This table predefines keywords with high urgency attributes, such as "emergency room," "first aid," "ICU," "airport," "traffic restriction," and "fast charging." First, the semantic tags are segmented and matched one by one against keywords in the mapping table. If the semantic tag contains a pre-defined available keyword (e.g., the destination name is "XX Emergency Rescue Center"), it is directly determined as the highest urgency level based on the keyword ranking mapping. If the semantic tag does not contain a specific keyword (e.g., the destination name is "XX Park"), a backup determination logic is used, which determines the urgency based on the map category code.

[0046] Map classification codes are typically 6 digits, such as "090100" representing a general hospital and "010100" representing a residential area. Using a built-in classification code table, the code prefix is ​​linked to the level of urgency (e.g., "09" represents medical facilities, classified as the highest urgency level; "01" represents residential areas, classified as the third highest urgency level). The classification code prefix of the navigation destination is extracted, and the classification code table is consulted to determine the urgency level. This dual verification mechanism of "semantic + coding" accurately identifies the urgency attributes of most unfamiliar locations, ensuring the accuracy and robustness of the strategy execution.

[0047] For example, if the voice tag includes available keywords such as "emergency room," "first aid," "ICU," "airport departure," "airport arrival," and "restricted area," then the first emergency level is directly determined as the emergency level. If no available keywords are found in the voice tag, category mapping is used to determine the emergency level. Mapping based on map classification codes can be done as follows: First emergency level: Medical and health facilities (090000), transportation hubs (150000), public charging stations (180500).

[0048] Second emergency level: Commercial and office (070000), Science, education and culture (080000).

[0049] Third emergency level: residential areas (010000), natural scenery (220000).

[0050] Map classification codes are typically 6 digits and use a hierarchical structure. The first two digits indicate the major category (e.g., 01 represents residential, 07 represents commercial, and 09 represents medical).

[0051] The middle two digits indicate the category (e.g., 0901 represents a general hospital, and 0902 represents a specialized hospital).

[0052] The last two digits indicate the sub-category (e.g., 090101 represents a Grade III Class A hospital, and 090102 represents a community health service center).

[0053] Optionally, dynamic road conditions and environmental factors can be incorporated for real-time dynamic transformation calculations. When a user initiates a navigation request, the system calls the navigation API to perform path coupling analysis on static labels, combining real-time data. The navigation API is called to obtain route_info (including congestion_index and slope). If the navigation destination is a "commercial area," and the current route's congestion index is >0.8 (extremely congested), and the current battery level is <40%, the urgency level is increased because congestion significantly increases power consumption, requiring more power to be reserved for continued driving.

[0054] Simultaneously, policy fence detection is performed on the navigation destination, querying the "Low Emission Zone (LEZ)" or "Restricted Area" polygon data in the GIS database. If the navigation destination falls within a restricted area, regardless of the POI, it is forcibly locked to the highest emergency level.

[0055] In some embodiments, determining the vehicle's required torque sequence based on a preset maximum limit torque and path information of the navigation path includes: The navigation path is segmented based on the path information to obtain at least one segmented path and the path type of each segmented path. For each segmented path, the required torque for the corresponding segment is determined based on the path type and the maximum torque limit. The required torque for multiple road segments is sorted according to the spatial position of the corresponding segment path in the navigation path to obtain the required torque sequence.

[0056] In practice, road segments can be divided based on the navigation API in the route information, such as uphill segments, normal and stable segments, downhill segments, congested segments, and construction segments.

[0057] The path information includes detailed road details from the starting point to the destination. An adaptive segmentation algorithm can also be used to process the path based on navigation information. In areas with high curvature, drastic gradient changes, or frequent speed limit changes (such as mountain roads or urban overpasses), the system automatically shortens the segment length (e.g., 50 meters per segment); on straight highways with uniform road conditions, the system appropriately extends the segment length (e.g., 200 meters per segment). For each segment, path type characteristics are extracted, including average gradient, design speed, and road surface material. Based on these characteristics, a pre-defined torque demand mapping table is consulted. This table, established through offline simulation and real-vehicle calibration, stores the baseline torque values ​​required to maintain a constant vehicle speed under different gradients and speeds. For example, at a 5% gradient and a speed of 80 km / h, the baseline torque is 150 Nm. This baseline torque is used as the initial torque demand.

[0058] Then, the vehicle's current load status (obtained via suspension height or load cells) and additional resistance (such as wind resistance added by the roof rack) are incorporated to correct the initial torque demand. The corrected torque value is compared with the preset maximum torque limit. The maximum torque limit is dynamically calculated based on the current battery temperature, current battery charge, and motor thermal management status. If the corrected torque exceeds the maximum torque limit, a torque limiting command is issued, and the maximum torque limit is used as the final torque demand for the road segment to prevent motor overheating or overcurrent protection. Otherwise, the corrected torque is output directly.

[0059] Finally, the required torques of all segmented paths are connected in series according to the topological order of the paths to form a complete, timestamped sequence of required torques, providing an accurate input reference for the subsequent MPC optimization controller.

[0060] In some embodiments, determining the required torque for a corresponding road segment based on the path type and the maximum limit torque includes: The initial required torque for the corresponding segment path should be determined based on the path type and the preset required torque. In response to the initial demand torque being greater than the maximum limiting torque, the maximum limiting torque is determined as the segment demand torque; In response to the initial demand torque being less than or equal to the maximum limiting torque, the initial demand torque is determined as the segment demand torque.

[0061] In practical implementation, the calculation process for the required torque of a single road segment begins by determining the dominant path type for each independently segmented path. The criteria for determining the path type include: road classification (expressway, national highway, urban road, etc.) extracted from map data, geometric features (radius of curvature, gradient), and real-time traffic flow data. For example, if the absolute gradient of a road segment is greater than 5%, it is classified as an "uphill path" or a "downhill path"; if the radius of curvature is less than 100 meters, it is classified as a "curved path." The corresponding path type is determined using a built-in table of required torque benchmarks for different path types. For straight road segments, the required torque is primarily used to overcome rolling resistance and air resistance; for uphill road segments, the required torque needs to include an additional component to overcome gravity; for downhill road segments, the required torque may be negative (i.e., engine braking or energy recovery).

[0062] Based on the determined path type, the initial required torque for the corresponding path segment is obtained by looking up the required torque baseline table. Then, the vehicle's overall controller is invoked to obtain the real-time maximum limiting torque of the current powertrain. The maximum limiting torque is a dynamically changing parameter, constrained by the battery's maximum discharge power, the motor's peak torque characteristics, and the transmission's input torque limit. Logical judgment is then executed: if the initial required torque is greater than the current maximum limiting torque, it indicates that the current powertrain cannot provide the required driving force; in this case, the torque is cut off, and the maximum limiting torque is determined as the actual required torque for the road segment, while simultaneously illuminating the "Power Limited" warning light on the instrument panel. If the initial required torque is less than or equal to the maximum limiting torque, it indicates that the powertrain is capable of meeting the demand; in this case, the initial required torque is determined as the final required torque for the road segment. This approach ensures that the vehicle can adapt to various complex road conditions while ensuring that the powertrain operates within safe boundaries.

[0063] After traversing all segmented paths, the discrete torque demand for each segment is sequentially arranged according to the direction of travel, thus constructing a time-varying torque demand sequence covering the navigation path. This sequence serves as input for subsequent energy management strategies, precisely describing the vehicle's specific power requirements during future driving.

[0064] In some embodiments, determining the optimal drive mode sequence with minimum overall energy consumption based on the demand torque sequence and the minimum reserved power includes: The difference between the current battery level and the minimum reserved battery level is determined as the current available battery level; Predict the total energy consumption and remaining power of each segment after driving in different driving modes, based on the demand torque sequence and the current available power. The drive mode with remaining power less than or equal to the minimum reserved power is determined as the available drive mode; The drive mode with the lowest total energy consumption among the available drive modes is determined as the optimal drive mode. The optimal driving modes are sorted according to their spatial position in the navigation path to obtain the optimal driving mode sequence.

[0065] In practice, determining the optimal drive mode sequence is the core optimization process of the energy management strategy, which is solved using dynamic programming (DP) or model predictive control (MPC). First, the current battery charge OCcurrent reported by the BMS is obtained, and the difference between the current battery charge and the minimum reserved charge SOCmin is determined as the current available charge SOCavailable, i.e., SOCavailable = SOCcurrent. SOCmin. Then, based on the demand torque sequence, the future driving process is simulated and predicted. The simulation process adopts a rolling time-domain strategy: starting from the current moment, predict the next N control steps (e.g., N=10, corresponding to 5 kilometers in the future). Within each control step, the system enumerates all possible driving modes: Mode 0 (pure electric), Mode 1 (series hybrid), Mode 2 (parallel hybrid), and Mode 3 (regenerative braking). Depending on the vehicle architecture, the fuel driving mode includes series hybrid and parallel hybrid modes. In the series hybrid mode (e.g., range-extended hybrid), the engine drives the generator. In the series hybrid structure, there is no mechanical connection between the engine and the wheels. The only function of the engine is to drive the generator to generate electricity, and the electrical energy output by the generator is either directly supplied to the drive motor or stored in the power battery. The wheels are entirely driven by the electric motor.

[0066] In parallel hybrid systems (such as P2 / P3 architecture hybrids), the engine directly drives the wheels. Mechanically, it retains the gearbox and driveshaft of a gasoline vehicle while integrating the electric motor into the powertrain, creating two independent physical drive paths. Path one is fuel chemical energy → engine mechanical energy → wheels; path two is electrical energy → electric motor mechanical energy → wheels. The engine and motor can output power in parallel, acting together on the drive shaft through mechanical coupling mechanisms (such as clutches, planetary gear sets, or direct coupling). Under different operating conditions, they can drive the wheels independently (e.g., low-speed pure electric, high-speed pure gasoline) or simultaneously (e.g., jointly outputting torque during rapid acceleration).

[0067] For each mode, the vehicle dynamics model is invoked to calculate the remaining battery power, fuel consumption, and total energy consumption of the route at the end of that step. During the calculation, the battery power trajectory is strictly monitored to ensure that the remaining battery power at the navigation endpoint is not lower than the minimum reserved battery power SOCmin.

[0068] Simultaneously, an objective function J = ∑(Fuel_consumption + λ × Elec_consumption) is constructed, where λ is the fuel-electricity equivalence factor, Fuel_consumption is fuel consumption, Elec_consumption is electricity consumption, and λ × Elec_consumption represents the equivalent fuel consumption. Among all mode combinations that satisfy the minimum reserved energy constraint, the mode sequence that minimizes the objective function J is sought.

[0069] For example, when a long downhill section is predicted ahead, a driving mode for charging is selected in advance, utilizing the downhill potential energy to charge the battery for use in subsequent urban traffic congestion. When a high-speed cruising section is predicted ahead, a series hybrid mode with engine direct drive is selected, allowing the engine to operate in its most efficient range. Ultimately, the optimal mode switching sequence is obtained, and the first control variable of this sequence (i.e., the driving mode to be adopted at the current moment) is sent to the actuator. Then, the above optimization process is repeated at the next sampling moment (e.g., after 1 second), achieving rolling optimization control.

[0070] In some embodiments, the drive control method further includes: In response to a path type that is uphill and an uphill gradient that is greater than or equal to a preset first gradient threshold, the fuel-driven mode is determined as the optimal driving mode. In response to a path type that is a downhill path and a downhill gradient that is greater than or equal to a preset second gradient threshold, the braking energy recovery mode is determined as the optimal driving mode.

[0071] In practical implementation, a rule-based supplementary optimization strategy is introduced for special road conditions to cope with extreme situations. The gradient information of the navigation path is monitored in real time. When the system determines that the current path is uphill and the gradient is greater than a preset first gradient threshold (e.g., 25°), the MPC (Multi-Purpose Control) economy optimization logic will be temporarily disabled, forcibly executing the fuel-driven mode, including parallel or series drive modes. This is because, under uphill conditions, the vehicle needs to overcome a significant gravitational force. If only pure electric mode is used, it will cause the battery to discharge at high power, resulting in a sudden voltage drop and rapid battery temperature rise, which may trigger thermal runaway protection or cause power interruption in severe cases. Forcing the use of the engine or a combination of engine and motor power can effectively share the battery load and ensure continuous power output. Conversely, when the current path is determined to be downhill and the gradient is greater than a preset threshold (e.g., -25°), the regenerative braking mode will be automatically activated, using the reverse torque of the motor to provide braking force, while simultaneously converting the vehicle's potential energy into electrical energy stored back in the battery. This not only reduces wear on mechanical brake pads but also significantly increases battery capacity, reserving energy for subsequent flat or uphill sections. Among these, the rule-based control strategy has the highest priority; when it conflicts with the MPC optimization results, the aforementioned rules take precedence, thus ensuring the vehicle's safety and reliability under extreme conditions.

[0072] In summary, the drive control method provided in this application fundamentally changes the passive energy management logic of hybrid systems in related technologies by constructing a dynamic drive mode control strategy for plug-in hybrid electric vehicles based on the urgency level of the navigation destination. This achieves a technological breakthrough from blindly conservative power conservation to precise, on-demand power distribution. It produces the following significant technical effects in terms of safety, economy, intelligence, and user experience: First, a robust three-tiered battery protection system has been established, completely eliminating compliance risks and range anxiety in high-urgency scenarios. In related technologies, hybrid strategies often fail to recognize destination attributes, forcing the engine to start due to battery depletion at critical moments such as hospitals and airports. This leads to users facing fines in restricted areas or disrupting emergency environments due to noise and emissions issues. This application's implementation establishes a quantitative grading mechanism for high urgency (≥25% minimum reserved battery), medium urgency (≥15% minimum reserved battery), and low urgency (≥5% minimum reserved battery), coupled with adaptive weight adjustments based on historical behavior data, setting a high battery red line for high-urgency destinations. Especially in scenarios such as "hospital emergency rooms" or "airport departure levels," MPC model predictive control enforces pure electric drive and maximum energy recovery, ensuring the vehicle always maintains sufficient pure electric range upon reaching the navigation destination. This not only guarantees user compliance at the legal and regulatory level but also eliminates users' psychological concerns about the reliability of critical mission travel, significantly improving the user experience.

[0073] Secondly, it breaks through the energy consumption bottleneck of static thresholds, achieving optimal comprehensive energy consumption throughout the entire life cycle. Related technologies, to prevent battery depletion, often start the engine prematurely or maintain a high battery level (e.g., always retaining 20%), resulting in a large amount of unused energy being wasted, leading to uneconomical fuel consumption in an attempt to save electricity. This application's embodiment introduces a minimum reserved battery level as the sole hard constraint boundary, releasing all battery space outside the constraint boundary for optimizing economy. In low-urgency destinations (such as suburban residences or areas without charging facilities), the reserved battery level is allowed to drop to 5%, maximizing the depth of energy utilization. Simultaneously, by combining path gradient, congestion prediction, and vehicle dynamics models, it can intelligently identify low-energy-consumption driving modes, such as using the engine's high-efficiency range for direct drive on high-speed cruising sections, maximizing kinetic energy recovery on downhill sections, and utilizing the motor's efficient response in congested urban areas. This peak-shaving and valley-filling dynamic mode allocation avoids energy conversion losses caused by ineffective battery protection, significantly reducing overall vehicle fuel and electricity consumption while ensuring travel needs are met.

[0074] Third, it achieves personalized drive control. Related technologies interpret map POIs only at the name and address level, failing to understand the user's personalized needs. This application innovatively integrates cloud-based deep neural network learning capabilities, establishing a dynamic mapping model of "user behavior - urgency weight" by mining users' historical driving big data (such as high-frequency destinations, battery usage habits, parking duration, etc.). For example, it can automatically identify specific locations a user visits three times a week. Even if the location is classified as a typical "company area" on the map, the urgency level will be dynamically upgraded from "moderately urgent" to "highly urgent" due to the user's high-frequency need for battery power. This energy management strategy, with its memory and evolutionary capabilities, allows the vehicle to proactively adapt to the user's lifestyle, improving user experience.

[0075] Fourth, a forward-looking control architecture based on multi-source heterogeneous data fusion has been established, significantly improving power response and safety redundancy under complex road conditions. Many related technologies rely on real-time sensor data and lack the ability to predict road conditions ahead. This application's embodiment constructs a four-dimensional prediction model of "road segment-urgency-mode-energy consumption" by deeply fusing terrain data from high-precision maps, real-time traffic flow data, and vehicle dynamics models. When facing extreme conditions such as long uphill or downhill sections, it no longer passively waits for insufficient power or brake overheating, but instead plans the driving mode several kilometers in advance. For example, when a long uphill section is detected and the destination is of high urgency, the engine will be started in advance in the middle of the path for charging or parallel driving, avoiding voltage drops caused by high-power battery discharge; on long downhill sections, energy recovery mode is activated. This forward-looking torque distribution and mode switching mechanism not only optimizes energy consumption but also effectively avoids overcharging and over-discharging of the power battery, extending battery life, while reducing wear on the mechanical braking system and improving the overall vehicle safety and durability.

[0076] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0077] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a drive control device.

[0079] refer to Figure 2 The drive control device includes: The battery level determination module 10 is configured to determine the minimum reserved battery level based on the navigation destination of the navigation route and historical driving data. The torque sequence prediction module 20 is configured to determine the vehicle's required torque sequence based on the preset maximum limit torque and the path information of the navigation path. The drive sequence prediction module 30 is configured to: determine the optimal drive mode sequence with the minimum overall energy consumption based on the demand torque sequence and the minimum reserved power, and control the motor and engine to drive the vehicle according to the optimal drive mode sequence.

[0080] In one embodiment, the power level determination module 10 is further configured to: The urgency level of the navigation destination is determined based on the navigation destination and historical driving data; In response to an emergency level of Level 1, the preset maximum safe power level is set as the minimum reserved power level; In response to an emergency level of Level 2, the preset default safe power level is set as the minimum reserved power level; In response to an emergency level of Level 3, the preset minimum safe power level is set as the minimum reserved power level. The first emergency level is more urgent than the second emergency level, and the second emergency level is more urgent than the third emergency level; the default safe battery level is greater than the minimum safe battery level but less than the maximum safe battery level.

[0081] In one embodiment, the power level determination module 10 is further configured to: In response to the existence of a historical navigation destination that is the same as the navigation destination in the historical driving data, the number of records of the historical navigation destination is determined; In response to a number of records being greater than or equal to a preset threshold, the historical urgency level corresponding to the historical navigation endpoint is determined as the urgency level. If the number of records is less than a preset threshold, the urgency level is determined based on the attribute information of the navigation destination.

[0082] In one embodiment, the power level determination module 10 is further configured to: Extract semantic labels and map classification codes for navigation destinations from attribute information; In response to the presence of preset available keywords in semantic tags, the urgency level is determined based on the mapping relationship between the keywords and the preset keyword levels. If no preset available keywords are found in the semantic tags, the urgency level is determined based on the map classification code and the preset classification code table.

[0083] In one embodiment, the torque sequence prediction module 20 is further configured to: The navigation path is segmented based on the path information to obtain at least one segmented path and the path type of each segmented path. For each segmented path, the required torque for the corresponding segment is determined based on the path type and the maximum torque limit. The required torque for multiple road segments is sorted according to the spatial position of the corresponding segment path in the navigation path to obtain the required torque sequence.

[0084] In one embodiment, the torque sequence prediction module 20 is further configured to: The initial required torque for the corresponding segment path should be determined based on the path type and the preset required torque. In response to the initial demand torque being greater than the maximum limiting torque, the maximum limiting torque is determined as the segment demand torque; In response to the initial demand torque being less than or equal to the maximum limiting torque, the initial demand torque is determined as the segment demand torque.

[0085] In one embodiment, the driving sequence prediction module 30 is further configured to: The difference between the current battery level and the minimum reserved battery level is determined as the current available battery level; Predict the total energy consumption and remaining power of each segment after driving in different driving modes, based on the demand torque sequence and the current available power. The drive mode with remaining power less than or equal to the minimum reserved power is determined as the available drive mode; The drive mode with the lowest total energy consumption among the available drive modes is determined as the optimal drive mode. The optimal driving modes are sorted according to their spatial position in the navigation path to obtain the optimal driving mode sequence.

[0086] In one embodiment, the driving sequence prediction module 30 is further configured to: In response to the path type being an uphill path, the fuel-powered driving mode is determined as the optimal driving mode; In response to the path type being an uphill path, the energy recovery mode in pure electric drive mode is determined as the optimal drive mode. For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0087] The apparatus of the above embodiments is used to implement the corresponding drive control method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0088] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides an electronic device, 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 drive control method described in any of the above embodiments.

[0089] Figure 3This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0090] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0091] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0092] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0093] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0094] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0095] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0096] The electronic devices described above are used to implement the corresponding drive control methods in any of the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0097] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the drive control method as described in any of the above embodiments.

[0098] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0099] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the drive control method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0100] Based on the same concept, corresponding to the methods of any of the above embodiments, this application also provides a computer program product, including computer program instructions. When the computer program instructions are run on a computer, the computer causes the computer to execute the drive control method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0101] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a vehicle, including the electronic device or drive control device of the above embodiments, and executes the drive control method as described in any of the above embodiments through the electronic device or drive control device of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0102] It is understood that before using the technical solutions of the various embodiments in this application, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0103] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations described in this application.

[0104] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0105] It is understood that the above notification and user authorization process is merely illustrative and does not limit the implementation of this application. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this application.

[0106] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0107] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0108] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0109] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A drive control method, characterized in that, include: Determine the minimum reserve battery power based on the navigation destination and historical driving data; The required torque sequence of the vehicle is determined based on the preset maximum limit torque and the path information of the navigation path; The optimal drive mode sequence with the minimum overall energy consumption is determined based on the required torque sequence and the minimum reserved power, and the motor and engine are controlled to drive the vehicle according to the optimal drive mode sequence.

2. The drive control method according to claim 1, characterized in that, The process of determining the minimum reserved battery power based on the navigation destination and historical driving data includes: The urgency level of the navigation destination is determined based on the navigation destination and the historical driving data; In response to the emergency level being the first emergency level, the preset maximum safe power level is determined as the minimum reserved power level; In response to the emergency level being the second emergency level, the preset default safe power level is determined as the minimum reserved power level; In response to the emergency level being the third emergency level, the preset minimum safe power level is determined as the minimum reserved power level; Wherein, the urgency level of the first emergency level is greater than that of the second emergency level, and the urgency level of the second emergency level is greater than that of the third emergency level; the default safe power level is greater than the minimum safe power level and less than the maximum safe power level.

3. The drive control method according to claim 2, characterized in that, The step of determining the urgency level of the navigation destination based on the navigation destination and the historical driving data includes: In response to the existence of a historical navigation destination that is the same as the navigation destination in the historical driving data, the number of records of the historical navigation destination is determined; In response to the number of records being greater than or equal to a preset threshold number, the historical urgency level corresponding to the historical navigation endpoint is determined as the urgency level; In response to the number of records being less than a preset threshold, the urgency level is determined based on the attribute information of the navigation destination.

4. The drive control method according to claim 3, characterized in that, Determining the urgency level based on the attribute information of the navigation destination includes: Extract the semantic label and map classification code of the navigation destination from the attribute information; In response to the presence of preset available keywords in the semantic tags, the urgency level is determined according to the available keywords and the preset keyword level mapping relationship; In response to the absence of preset available keywords in the semantic tags, the urgency level is determined based on the map classification code and the preset classification code table.

5. The drive control method according to claim 1, characterized in that, The step of determining the vehicle's required torque sequence based on the preset maximum torque limit and the path information of the navigation path includes: The navigation path is segmented based on the path information to obtain at least one segmented path and the path type of each segmented path; For each segmented path, the required torque for the corresponding segment is determined based on the path type and the maximum limit torque. The required torque for multiple road segments is sorted according to the spatial position of the corresponding segmented paths in the navigation path to obtain the required torque sequence.

6. The drive control method according to claim 5, characterized in that, The step of determining the required torque for the corresponding road segment based on the path type and the maximum limit torque includes: The initial required torque for the corresponding segmented path should be determined based on the path type and the preset required torque. In response to the initial required torque being greater than the maximum limiting torque, the maximum limiting torque is determined as the required torque for the road segment; In response to the initial required torque being less than or equal to the maximum limiting torque, the initial required torque is determined as the required torque for the road segment.

7. The drive control method according to claim 1, characterized in that, The process of determining the optimal drive mode sequence with the lowest overall energy consumption based on the required torque sequence and the minimum reserved power includes: The difference between the current battery level and the minimum reserved battery level is determined as the current available battery level; Based on the required torque sequence and the current available power, predict the total energy consumption and remaining power of the road segment after driving through each segment path in different driving modes; The driving mode in which the remaining power is less than or equal to the minimum reserved power is determined as the available driving mode; The driving mode with the lowest total energy consumption of the road segment among the available driving modes is determined as the optimal driving mode. The optimal driving modes are sorted according to their spatial positions in the navigation path based on the corresponding segmented paths to obtain the optimal driving mode sequence.

8. The drive control method according to claim 5, characterized in that, Also includes: In response to the path type being an uphill path and the uphill gradient being greater than or equal to a preset first gradient threshold, the fuel-driven mode is determined as the optimal driving mode. In response to the path type being a downhill path and the downhill gradient being greater than or equal to a preset second gradient threshold, the braking energy recovery mode is determined as the optimal driving mode.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, Including the electronic device as described in claim 9.

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