Energy control method and device of hybrid vehicle, vehicle and storage medium

Abstract

The invention relates to the technical field of vehicles, in particular to an energy control method and device of a hybrid vehicle, the vehicle and a storage medium. The method comprises the steps of obtaining a current road condition scene of a vehicle, determining at least one constraint demand, determining a whole vehicle power pre-control strategy according to a road condition based on a preset whole vehicle power pre-control model, and determining a self-adaptive regulation and control strategy according to the at least one constraint demand; and according to the two strategies, starting and stopping optimization control based on water depth information, battery and electric drive distribution control based on the driving requirement, energy and heat management cooperative control based on a passenger compartment and generated power adjustment control based on the power guarantee requirement are conducted on the vehicle. Therefore, the problem that power requirements and power guarantee are difficult to consider due to complex road conditions, user modification and the like is solved, multi-system efficient cooperative control such as heat management and energy management is guaranteed, and electric quantity and power guarantee is provided for driving and riding requirements of extremely complex cross-country scenes.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to an energy control method, device, vehicle, and storage medium for a hybrid vehicle. Background Technology

[0002] Given China's vast geographical and temperature range characteristics, off-road vehicles and electrification are mutually reinforcing, becoming a new growth point for comprehensive new energy transformation. With the development of electrification and the expansion of the off-road market, the user profile of hybrid vehicles, especially hybrid off-road vehicles, is becoming more diverse. They face special scenarios that are difficult for conventional vehicles to handle, such as extreme temperatures and high altitudes, slippery roads, deep snow or mud, prolonged intense driving, and ultra-high-speed driving. Furthermore, hybrid vehicles are equipped with complex engines, high-voltage components, and power systems, fitting the engine, front compartment thermal management, generator, and motor modules into an extremely compact space. This places higher demands on the coordinated control of various systems and the overall vehicle energy management, including power distribution.

[0003] In related technologies, the parameter locking for conventional and special working conditions generally requires first setting a basic value based on historical experience, and then, based on this basic value, after a long period of real vehicle calibration adjustment and verification, outputting a set of power parameters that meet the needs of one or more special scenarios.

[0004] However, the above-mentioned traditional parameter locking operation process requires a long test cycle, high cost, and is time-consuming and labor-intensive. At the same time, it is limited by the working conditions of the actual vehicle calibration. For scenarios that are not covered in the calibration but may occur in reality, it is difficult to ensure that the parameters of each system are reasonable, resulting in insufficient scenario coverage, which urgently needs to be solved. Summary of the Invention

[0005] This application provides an energy control method, device, vehicle, and storage medium for hybrid vehicles to solve the problem of balancing power demand and power supply due to complex road conditions and user modifications. It ensures efficient coordination and control of multiple systems such as thermal management and energy management, and provides power and electricity guarantees for driving needs in extremely complex off-road scenarios.

[0006] To achieve the above objectives, the first aspect of this application proposes an energy control method for a hybrid vehicle, comprising the following steps:

[0007] Obtain the current road conditions for the vehicle;

[0008] Based on the current road condition scenario, at least one constraint requirement is determined, and based on the preset vehicle power pre-control model, the vehicle power pre-control strategy is determined according to the current road condition scenario, and the vehicle adaptive control strategy is determined according to the at least one constraint requirement.

[0009] Based on the vehicle power pre-control strategy and the adaptive regulation strategy, the vehicle performs start-stop optimization control based on water depth information, battery and electric drive distribution control based on driving needs, energy and thermal management coordination control based on the passenger compartment, and power generation adjustment control based on power supply needs.

[0010] According to one embodiment of this application, before determining the vehicle's power pre-control strategy based on the current road condition scenario using a preset vehicle power pre-control model, the method further includes:

[0011] Acquire historical road spectrum data and the corresponding vehicle mode requirements, vehicle thermal management requirements, and power requirements;

[0012] Based on the historical road spectrum data, operational characteristic parameters for different scenarios are extracted, and operational characteristic constraint functions are constructed based on the operational characteristic parameters for different scenarios.

[0013] A vehicle mode constraint function is constructed based on the vehicle mode requirements corresponding to the historical road spectrum data, a vehicle thermal management constraint function is constructed based on the vehicle thermal management requirements corresponding to the historical road spectrum data, and a power constraint function is constructed based on the power requirements corresponding to the historical road spectrum data.

[0014] Based on the operating characteristic constraint function, the vehicle mode constraint function, the vehicle thermal management constraint function, and the power constraint function, a multi-constraint function model based on the historical road spectrum data is constructed, and the preset vehicle power pre-control model is established according to the multi-constraint function model.

[0015] According to one embodiment of this application, the multi-constraint function model is as follows:

[0016] SCS hty =CS f (FCN data (SCS data ),CF rd , vd V mod , vd V dmd , vd TP dmd );

[0017] Among them, SCS hty SCS is an effective data matrix under the constraint of historical road spectrum. data For historical road spectrum data matrix, FCN data CF is a function based on data transformation and processing. rd This refers to the scene features and parameter vectors related to specific operating conditions in historical road spectrum data. vd V modThis represents the vehicle driving mode and energy mode requirements and parameter vectors from historical road spectrum data. vd V dmd This is a vector of thermal management requirements and parameters for vehicle heating and cooling from historical data. vd TP dmd This is a vector of vehicle power-related requirements and parameters from historical road spectrum data.

[0018] According to one embodiment of this application, the preset vehicle dynamics pre-control model is as follows:

[0019]

[0020] Among them, SYS data To establish the basic pre-control information matrix, FCN′ data GM is a data transformation and processing function based on historical data parsing and valid data boundaries. d This refers to the vehicle's data status maturity parameter. This is a vector representing the relationship between optimal or relatively optimal requirements and operating ranges under different environmental and system boundary conditions.

[0021] According to one embodiment of this application, the current road condition scenario includes at least one of the following: sensing information from an on-board wheel speed sensor, sensing information from a gyroscope, steering wheel angle information, sensing information corresponding to a range extender, and sensing information from a wading radar.

[0022] According to the energy control method for hybrid vehicles proposed in this application, the current road condition scenario of the vehicle is obtained, at least one constraint requirement is determined, and the vehicle power pre-control strategy is determined based on the preset vehicle power pre-control model and road conditions. An adaptive control strategy is determined according to the constraint requirement. The vehicle is adjusted and controlled according to the two strategies, which solves the problem of balancing power demand and power supply due to complex road conditions and user modifications. It ensures efficient collaborative control of multiple systems such as thermal management and energy management, and provides power and energy guarantee for driving needs in extremely complex off-road scenarios.

[0023] To achieve the above objectives, a second aspect of this application provides an energy control device for a hybrid vehicle, comprising:

[0024] Acquisition module: Acquires the current road conditions of the vehicle;

[0025] Identification module: Based on the current road condition scenario, determine at least one constraint requirement, and based on the preset vehicle power pre-control model, determine the vehicle power pre-control strategy according to the current road condition scenario, and determine the vehicle's adaptive control strategy according to the at least one constraint requirement.

[0026] Control module: Based on the vehicle power pre-control strategy, adaptive regulation strategy and adaptive regulation strategy, the module performs start-stop optimization control based on water depth information, battery and electric drive distribution control based on driving needs, energy and thermal management coordination control based on passenger compartment, and power generation adjustment control based on power supply needs.

[0027] According to one embodiment of this application, before determining the vehicle's power pre-control strategy based on the current road condition scenario using the preset vehicle power pre-control model, the identification module is further configured to:

[0028] Acquire historical road spectrum data and the corresponding vehicle mode requirements, vehicle thermal management requirements, and power requirements;

[0029] Based on the historical road spectrum data, operational characteristic parameters for different scenarios are extracted, and operational characteristic constraint functions are constructed based on the operational characteristic parameters for different scenarios.

[0030] A vehicle mode constraint function is constructed based on the vehicle mode requirements corresponding to the historical road spectrum data, a vehicle thermal management constraint function is constructed based on the vehicle thermal management requirements corresponding to the historical road spectrum data, and a power constraint function is constructed based on the power requirements corresponding to the historical road spectrum data.

[0031] Based on the operating characteristic constraint function, the vehicle mode constraint function, the vehicle thermal management constraint function, and the power constraint function, a multi-constraint function model based on the historical road spectrum data is constructed, and the preset vehicle power pre-control model is established according to the multi-constraint function model.

[0032] According to one embodiment of this application, the multi-constraint function model is as follows:

[0033] SCS hty =CS f (FCN data (SCS data ),CF rd , vd V mod , vd V dmd , vd TP dmd );

[0034] Among them, SCS hty SCS is an effective data matrix under the constraint of historical road spectrum. data For historical road spectrum data matrix, FCN data CF is a function based on data transformation and processing. rd This refers to the scene features and parameter vectors related to specific operating conditions in historical road spectrum data. vdV mod This represents the vehicle driving mode and energy mode requirements and parameter vectors from historical road spectrum data. vd V dmd This is a vector of thermal management requirements and parameters for vehicle heating and cooling from historical data. vd TP dmd This is a vector of vehicle power-related requirements and parameters from historical road spectrum data.

[0035] According to one embodiment of this application, the preset vehicle dynamics pre-control model is as follows:

[0036]

[0037] Among them, SYS data Based on the pre-control information matrix, FCN′ data For data transformation and processing functions, CF rd The historical road spectrum data contains scene features and their corresponding characteristics. vd V mod This refers to the vehicle driving mode requirements and corresponding parameter vectors, and the energy mode requirements and corresponding parameter vectors in the historical road spectrum data. vd TP dmd SCS represents the vehicle power demand and corresponding parameter vector in the historical road spectrum data. hty For an effective data matrix under the constraint of historical road spectrum, GM d This refers to the vehicle's data status maturity parameter. This is the vector representing the relationship between optimal demand and operating range.

[0038] According to one embodiment of this application, the current road condition scenario includes at least one of the following: sensing information from an on-board wheel speed sensor, sensing information from a gyroscope, steering wheel angle information, sensing information corresponding to a range extender, and sensing information from a wading radar.

[0039] According to the energy control device for hybrid vehicles proposed in this application embodiment, the current road condition scenario of the vehicle is obtained, at least one constraint requirement is determined, and the vehicle power pre-control strategy is determined based on the preset vehicle power pre-control model and road conditions. An adaptive control strategy is determined according to the constraint requirement. The vehicle is adjusted and controlled according to the two strategies, which solves the problem of balancing power demand and power supply due to complex road conditions and user modifications. It ensures efficient collaborative control of multiple systems such as thermal management and energy management, and provides power and energy guarantee for driving needs in extremely complex off-road scenarios.

[0040] To achieve the above objectives, a third aspect of this application provides a vehicle comprising: 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 energy control method for a hybrid vehicle as described in the above embodiments.

[0041] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the energy control method for a hybrid vehicle as described in the above embodiments.

[0042] To achieve the above objectives, a fifth aspect of this application provides a computer program product that, when executed by a processor, implements the energy control method for hybrid vehicles as described in the above embodiments.

[0043] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. Attached Figure Description

[0044] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0045] Figure 1 This is a flowchart of an energy control method for a hybrid vehicle according to an embodiment of this application;

[0046] Figure 2 This is a flowchart illustrating an adaptive precision control according to one embodiment of this application;

[0047] Figure 3 This is a block diagram of an energy control device for a hybrid vehicle provided according to an embodiment of this application;

[0048] Figure 4 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0049] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0050] The energy control method, apparatus, vehicle, and storage medium for hybrid vehicles according to embodiments of this application will now be described with reference to the accompanying drawings. First, the energy control method for hybrid vehicles according to embodiments of this application will be described with reference to the accompanying drawings.

[0051] Figure 1 This is a flowchart of an energy control method for a hybrid vehicle according to an embodiment of this application.

[0052] like Figure 1 As shown, the energy control method for this hybrid vehicle includes the following steps:

[0053] In step S101, the current road condition scene of the vehicle is obtained.

[0054] Optionally, in some embodiments, the current road condition scenario includes at least one of the following: sensing information from the vehicle wheel speed sensor, sensing information from the gyroscope, steering wheel angle information, sensing information from the range extender, and sensing information from the wading radar.

[0055] The current road condition scenario refers to the comprehensive set of information related to the driving environment, road conditions, and the vehicle's own operating status obtained by the vehicle through various sensors and information collection devices during the current driving process, which is used to determine the specific driving situation in which the vehicle is located.

[0056] Specifically, the current road condition scenario for a vehicle may include the following information: wheel rotation speed sensed by onboard wheel speed sensors (which reflects whether slipping and driving speed, indirectly indicating whether the road surface is wet or muddy); vehicle tilt angle captured by gyroscopes (which can determine whether the vehicle is going uphill or downhill, or tilting, such as on a rugged mountain road); steering wheel angle information (which shows the steering range, such as whether the vehicle is on a sharp turn or a series of curves); range extender-related sensor information (which reflects power demand, such as frequent range extender activation indicating heavy-load road conditions such as climbing hills); and water depth detected by wading radar (which directly determines whether the vehicle is in a flooded area). It should be noted that the information constituting the current road condition scenario does not need to include all of the above (sensor information from onboard wheel speed sensors, gyroscopes, steering wheel angles, range extenders, and wading radars). As long as one, two, three, or even all of them are included, it meets the definition of the current road condition scenario.

[0057] For example, when a vehicle is in a flooded area, it may only need the sensing information from the wading radar; when a vehicle is in a sandy area, it may need the sensing information from the onboard wheel speed sensor and the corresponding sensing information from the range extender.

[0058] In step S102, based on the current road condition scenario, at least one constraint requirement is determined, and based on the preset vehicle power pre-control model, the vehicle power pre-control strategy is determined according to the current road condition scenario, and the vehicle adaptive control strategy is determined according to at least one constraint requirement.

[0059] Among these, constraint requirements refer to the limiting conditions or basic requirements that must be followed or met to ensure the safe and durable operation of each system or to meet core functions under the current road conditions of the vehicle. The preset vehicle powertrain pre-control model refers to an intelligent decision-making program designed and written into the vehicle control system by engineers before the vehicle leaves the factory, based on extensive experimental data, characteristics of different road conditions, and the operating rules of the powertrain system. The vehicle powertrain pre-control strategy refers to a basic control scheme for the powertrain system that the vehicle formulates in advance based on the preset vehicle powertrain pre-control model after identifying the current road conditions. The adaptive adjustment strategy refers to a supplementary strategy that, based on the execution of the vehicle powertrain pre-control strategy, dynamically and flexibly adjusts the powertrain control scheme according to the real-time changing constraint requirements in the current road conditions.

[0060] Specifically, sensor information from vehicle-mounted wheel speed sensors, gyroscopes, steering wheel angle, range extender-related sensors, and wading radar is effectively extracted. After numerical conversion and filtering, parameters such as vehicle speed, slope, altitude, water depth, steering wheel torque, wheel end resistance, slip ratio, ambient temperature, engine coolant temperature, and intake air temperature in the actual scenario are obtained. The current road condition scenario of the vehicle is acquired, and the vehicle calls the preset vehicle power pre-control model to determine the vehicle power pre-control strategy according to the characteristics of the scenario.

[0061] Furthermore, this application embodiment determines the vehicle's adaptive control strategy based on at least one constraint requirement. To further analyze other constraint characteristics of the vehicle in specific scenarios, parameters such as the vehicle's current driving mode (economy, comfort, sport, snow, wading, sand, rock, etc.), energy mode (fuel, pure electric, intelligent, etc.), accelerator and brake pedals, battery status, air conditioning setting, passenger compartment temperature, and actual consumption of high- and low-pressure accessories are extracted to clarify the constraints on passenger compartment thermal management, drivability, and power performance under this scenario. Specifically, air conditioning setting, passenger compartment temperature, and ambient temperature can characterize passenger compartment thermal management requirements to a certain extent. Additionally, temperature information from the electric drive and battery circuits indicates the control operation status of thermal management. Furthermore, information such as driving mode, energy mode, accelerator and brake pedals can represent the overall vehicle drivability requirements, while vehicle speed, wheel torque, motor speed, and range extender power indicate the control operation status of drivability and power performance. Based on the different vehicle modes and operating scenarios currently configured, to ensure the long-term stable operation of the vehicle under various special working conditions, different boundary requirements for power supply, energy consumption, fault warning, and post-processing safety are set for each mode to constrain the control operation of various vehicle systems. The constraint function based on the real-time operating scenario can be expressed as:

[0062]

[0063] Among them, SCS newSCS′ is an effective data matrix based on real-time scenario constraints. data For real-time scene data matrix, FCN″ data CF′ is a function based on data transformation and processing. rd This refers to the scene features and parameter vectors related to specific working conditions in real-time scene data. vd V′ mod This provides the vehicle driving mode and energy mode requirements and parameter vectors in real-time scene data. vd V′ dmd This is a vector of thermal management requirements and parameters for vehicle heating and cooling in real-time scenario data. vd TP′ dmd This refers to the vector of vehicle power-related requirements and parameters in real-time scene data. oth P dmd This is a vector array representing the constraint requirements for vehicles in real-time scene data. Pwr P dmd This represents the constraint vector for the vehicle's power supply requirements in real-time scenario data. VFH P dmd This represents the constraint vector for vehicle safety and after-processing related requirements in real-time scenario data. ped P dmd This represents the constraint vector for actual drivability-related requirements in real-time scenario data. FC P dmd This is a vector representing energy consumption-related demand constraints in real-time scenario data.

[0064] Based on the aforementioned basic pre-control, parameters are extracted from real-time road condition scenario information and adjusted according to the constraints of passenger cabin, drivability, energy consumption, power supply, safety, and power requirements under this scenario. A constraint correction coefficient CF is introduced. cor Based on this correction coefficient, the parameters in the basic data are adjusted. The closer the real-time scenario's requirements and constraints are to the historical road spectrum data results, the higher the correction coefficient CF is considered. cor The higher the value, the closer the final control parameters are to the pre-control data calculated using historical data optimization. If the demand constraints in the real-time scenario deviate more from the results of historical road spectrum data, then in order to ensure the demand in the real-time scenario, this correction coefficient CF... corThe lower the value, the closer the final control parameters are to the combined result of real-time requirements and historical optimization. A real-time, efficient, adaptive power control strategy is constructed based on the control function optimized from real-time scenario data, controlling the SOC balance point, start-stop timing, range extender power generation, drive motor power, battery power, and accessory power in different modes. Its effects are reflected in the following aspects: Start-stop optimization control based on water depth information enables real-time start-stop management at different water depths in wading scenarios; precise battery and electric drive allocation based on driving needs achieves optimal power and energy consumption under different driving requirements; efficient coordination of energy and thermal management in the passenger compartment achieves matching between the vehicle's power and the passenger compartment, ensuring temperature balance among the battery, electric drive, and passenger compartment in scenarios such as sand; and precise adjustment of power generation based on power preservation needs ensures stable driving for extended periods in various off-road scenarios. The control function optimized from real-time scenario data can be expressed as:

[0065]

[0066] Among them, SYS′ data SYS is the control parameter matrix optimized under real-time scenario constraints. new FCN″′ is based solely on the control parameter matrix of the real-time scene and boundary constraints. data This is a data transformation and processing function based on real-time scenarios and boundary constraints. This is a vector representing the relationship between optimal or relatively optimal requirements and operating ranges under different environments and system boundary conditions (real-world scenarios).

[0067] In step S103, the vehicle is subjected to start-stop optimization control based on water depth information, battery and electric drive distribution control based on driving needs, energy and thermal management coordination control based on the passenger compartment, and power generation adjustment control based on power supply needs, according to the vehicle power pre-control strategy, adaptive control strategy, and adaptive control strategy.

[0068] Among these, start-stop optimization control refers to the process by which the vehicle dynamically optimizes and regulates the start-up and shutdown states of the power source by acquiring real-time wading depth data through sensors such as wading radar and combining it with preset safety thresholds and power requirements. Passenger compartment energy refers to the energy consumed by the vehicle to meet the comfort and convenience needs of the passengers within the compartment. Thermal management refers to the process of comprehensively regulating the generation, transfer, utilization, and emission of heat from various vehicle systems (including the power system and passenger compartment). Its core is to ensure that each component and space is within a suitable temperature range, guaranteeing both safe and stable vehicle operation and passenger comfort while improving energy efficiency. Battery maintenance requirements refer to the need to maintain a certain battery charge level in specific scenarios or operating conditions to ensure the stability of the power system, the reliability of the driving range, or functional safety.

[0069] Specifically, the vehicle combines a pre-control strategy and an adaptive control strategy to operate collaboratively in different scenarios. For example, when facing flooded sections, the pre-control strategy sets the range extender's start-stop rules based on water depth thresholds (e.g., normal start-stop within 30cm, continuous operation between 30-50cm), while the adaptive strategy adjusts according to real-time water depth (e.g., immediately shutting down the range extender and switching to pure electric mode when exceeding 55cm). When the driver switches to sport mode to climb hills, the pre-control strategy allocates 60% of the battery and 40% of the motor power with redundancy, while the adaptive strategy dynamically adjusts with throttle depth, increasing the battery output to 70% when the throttle is pressed hard and reducing it when slipping. Waste and optimize torque distribution; in low-temperature winter scenarios, the pre-control strategy allocates 40% of energy to battery heating and 60% to cabin temperature heating, while the adaptive strategy fine-tunes according to real-time temperature, prioritizing battery heating once the cabin temperature reaches the target, and ensuring cabin temperature when the windows are open; when maintaining power in sandy areas, the pre-control strategy allows the range extender to replenish power at 50% power and restricts unnecessary power consumption, while the adaptive strategy adjusts power according to battery fluctuations, increasing it to 70% when it is below the target and decreasing it when it exceeds the target or the range extender overheats, ensuring a balance between power, safety, comfort and battery power.

[0070] Therefore, the current road conditions of the vehicle are obtained, at least one constraint requirement is determined, and the vehicle power pre-control strategy is determined based on the preset vehicle power pre-control model and road conditions. An adaptive control strategy is determined according to the constraint requirement. The vehicle is adjusted and controlled according to the two strategies, which solves the problem of balancing power demand and power supply caused by complex road conditions and user modifications. It ensures efficient coordination and control of multiple systems such as thermal management and energy management, and provides power and electricity guarantee for driving needs in extremely complex off-road scenarios.

[0071] Furthermore, in order to enable those skilled in the art to better understand the construction of the vehicle power pre-control model of the embodiments of this application, a detailed description is provided below in conjunction with specific embodiments.

[0072] As one possible implementation, in some embodiments, before determining the vehicle's power pre-control strategy based on the current road conditions using a preset vehicle power pre-control model, the method further includes: acquiring historical road spectrum data and the corresponding vehicle mode requirements, vehicle thermal management requirements, and power requirements; extracting operational characteristic parameters for different scenario conditions from the historical road spectrum data, and constructing operational characteristic constraint functions based on these parameters; constructing vehicle mode constraint functions based on the vehicle mode requirements corresponding to the historical road spectrum data, constructing vehicle thermal management constraint functions based on the vehicle thermal management requirements corresponding to the historical road spectrum data, and constructing power constraint functions based on the power requirements corresponding to the historical road spectrum data; constructing a multi-constraint function model based on the historical road spectrum data using the operational characteristic constraint functions, vehicle mode constraint functions, vehicle thermal management constraint functions, and power constraint functions, and establishing a preset vehicle power pre-control model based on the multi-constraint function model.

[0073] Furthermore, in some embodiments, the multi-constraint function model is as follows:

[0074] SCS hty =CS f (FCN data (SCS data ),CF rd , vd V mod , vd V dmd , vd TP dmd );

[0075] Among them, SCS hty SCS is an effective data matrix under the constraint of historical road spectrum. data For historical road spectrum data matrix, FCN data CF is a function based on data transformation and processing. rd This refers to the scene features and parameter vectors related to specific operating conditions in historical road spectrum data. vd V mod This represents the vehicle driving mode and energy mode requirements and parameter vectors from historical road spectrum data. vd V dmd This is a vector of thermal management requirements and parameters for vehicle heating and cooling from historical data. vd TP dmd This is a vector of vehicle power-related requirements and parameters from historical road spectrum data.

[0076] Furthermore, in some embodiments, the preset vehicle dynamics pre-control model is as follows:

[0077]

[0078] Among them, SYS data To establish the basic pre-control information matrix, FCN′ data GM is a data transformation and processing function based on historical data parsing and valid data boundaries. d This refers to the vehicle's data status maturity parameter. This is a vector representing the relationship between optimal or relatively optimal requirements and operating ranges under different environmental and system boundary conditions.

[0079] Historical road spectrum data refers to the set of various characteristic information related to the driving road recorded by the vehicle through sensors, control systems, etc. during past driving. Vehicle mode requirements refer to the types of modes and corresponding mode parameters that the vehicle needs to activate in specific scenarios, determined based on historical road spectrum characteristics and driving objectives. Vehicle thermal management requirements refer to the set of temperature regulation requirements of various core systems of the vehicle under specific operating conditions based on driving scenarios recorded in historical road spectrum data. Power requirements refer to the set of power output requirements of the vehicle under specific operating conditions based on driving scenario characteristics recorded in historical road spectrum data. Operational characteristic parameters are a set of key indicators extracted from historical road spectrum data that quantify the vehicle's operating state and environmental characteristics in different off-road scenarios. Operational characteristic constraint functions are a set of quantitative constraint rules constructed based on operational characteristic parameters extracted from historical road spectrum data, used to limit the boundaries of the vehicle's operating state in specific scenarios. The multi-constraint function model is a comprehensive constraint framework that integrates operational characteristic constraint functions, vehicle mode constraint functions, vehicle thermal management constraint functions, and power constraint functions based on historical road spectrum data.

[0080] Specifically, maintaining a balance between power driving demands and battery capacity is difficult in different scenarios, placing extremely high demands on the coordination of various systems. It's crucial to avoid overly restricting or protecting the systems while simultaneously preventing them from becoming unsafe, unable to operate sustainably, or causing after-sales quality issues. In rock conditions, the power system requirements of different systems are difficult to unify, necessitating power configuration tailored to specific conditions to achieve efficient system synergy. In sand conditions, a reasonable power distribution needs to be allocated to the entire system while meeting the range extender's capabilities and thermal management requirements to meet the power demands of special scenarios such as hill climbing and traversing. In snow conditions, the overall vehicle resistance is relatively low on shallow snow surfaces but high on deep snow surfaces. Simultaneously, the requirements for the battery and thermal management system are more extreme in this scenario. Providing sufficient power while meeting the needs of other systems is a critical aspect of low-temperature scenarios. In summary, it can be seen that the demands on various systems differ under different operating conditions. Directly applying strategies and parameters from conventional operating conditions is insufficient to guarantee performance under special conditions. For example, in sandy conditions, overheating and battery depletion are likely to occur, as are overcharging, over-discharging, or insufficient power at low temperatures. Therefore, it is necessary to develop control strategies that meet the needs of a single special scenario or multiple coupled scenarios in order to satisfy the requirements of different systems in that scenario.

[0081] During real-vehicle calibration, the varying operating conditions (weather, ambient temperature, terrain, etc.) can lead to insufficient scenario coverage in the calibration results. For calibrations locked in actual operating conditions, the calibration parameters of each system are better adapted and more stable across various scenarios. For operating conditions not covered in the calibration, the calibration will rely more on preset or boundary condition simulations. However, due to the complexity and variability of actual off-road scenarios, the torque, power, and battery life requirements differ across scenarios, inevitably leading to unreasonable parameters in certain scenarios. Furthermore, traditional calibration parameter locking often involves setting basic parameters based on calibration experience and undergoing lengthy real-vehicle calibration adjustments, which is time-consuming and labor-intensive. To address these issues, a basic data pre-control scheme for multi-dimensional road spectrum information in different off-road modes was created. Based on the accumulation of historical road spectrum data under different off-road scenarios, the working condition characteristics of different off-road scenarios (high and low temperatures, deep water, deep mud, shallow mud, deep snow, shallow snow, etc.) were extracted. The variation law of vehicle demand and power output under different vehicle modes such as snow, water crossing, sand, and rock under different off-road scenarios was studied. The boundary constraints of parking power generation, thermal management, vehicle start-stop, range extender control and other scenarios under different software and hardware states were clarified. The vehicle power pre-control strategy under different off-road scenarios and different constraint boundaries was constructed. The basic pre-control information such as start-stop, power generation, and power torque distribution based on historical off-road road spectrum information was effectively extracted, ensuring high coverage of parameter design for actual user needs in multiple scenarios.

[0082] Based on years of experience in off-road scenarios accumulated from previous self-developed off-road vehicle projects and benchmark models, we statistically analyzed and compiled road spectrum information from tests conducted in various special scenarios, including high and low temperatures, deep water, deep mud, shallow mud, deep snow, and shallow snow. We analyzed the vehicle's operating scenarios under different special conditions and identified characteristics, vehicle modes, vehicle thermal management and power requirements, key operating and control parameters related to these scenarios. A multi-constraint function model based on historical off-road road spectrum data was constructed to clarify its operating characteristics and constraints under different special conditions. The multi-constraint function model based on historical off-road road spectrum data is as follows:

[0083] SCS hty =CS f (FCN data (SCS data ),CF rd , vd V mod , vd V dmd , vd TP dmd );

[0084] Among them, SCS hty SCS is an effective data matrix under the constraint of historical road spectrum. data For historical road spectrum data matrix, FCN dataCF is a function based on data transformation and processing. rd This refers to the scene features and parameter vectors related to specific operating conditions in historical road spectrum data. vd V mod This represents the vehicle driving mode and energy mode requirements and parameter vectors from historical road spectrum data. vd V dmd This is a vector of thermal management requirements and parameters for vehicle heating and cooling from historical data. vd TP dmd This is a vector of vehicle power-related requirements and parameters from historical road spectrum data.

[0085] For specific scenario operating condition characteristic parameters, these parameters can, to a certain extent, represent the operational and partial constraint characteristics of various vehicle systems under that type of operating condition. For high-temperature and low-temperature scenarios, in addition to ambient temperature, other parameters include battery cell temperature, wheel end resistance, slip ratio, engine coolant temperature, intake air temperature, and torque. For other special scenarios, such as deep and shallow water scenarios, the characteristic parameters mainly include water depth, ambient temperature, wheel end resistance, engine coolant temperature, and intake air temperature. For high-altitude and mountainous road scenarios, the characteristic parameters mainly include altitude, ambient temperature, battery cell temperature, wheel end resistance, engine coolant temperature, and intake air temperature. Based on the above analysis, a special scenario operation and characteristic constraint function based on historical off-road road spectrum data can be constructed, which can be expressed as:

[0086]

[0087] in, cs EC vt For special scenario constraint vectors, cs CV vt This is a feature parameter constraint vector for a specific scenario. cs HT vt For high-temperature scenarios, constrain vectors cs HT vt For high-temperature scenarios, constrain vectors cs LT vt For low-temperature scenarios, cs HA vt For the constrained vector of the plateau scene, cs MR vt For the mountain road scene constraint vector, cs RS vt For the constraint vector of the rock scene, cs WD vt For water-related scenarios, constrained vectors cs MG vt For the constraint vector of the muddy scene, cs SN vt For the snow scene constraint vector, cs DTvt For the constraint vector of the sandy scene, [hd,T] T out This is a vector representing the temperature relationship in historical road spectrum data under different scenarios and software / hardware conditions. T T BC This is a vector representing the relationship between battery cell temperature changes under different scenarios in historical road spectrum data. [AL,CT,AT,T] E ng This is a vector representing the characteristic parameter relationships of engine coolant temperature and intake air temperature under different scenarios in historical road spectrum data, varying with altitude and ambient temperature. [AL,spd,tq,T] E ng This is a vector representing the relationship between characteristic parameters such as engine speed and torque under different scenarios in historical road spectrum data, varying with altitude and ambient temperature. [veh,tq] W hl It is a feature vector of wheel end parameters that vary with vehicle speed and torque under different scenarios in historical road spectrum data.

[0088] For parameters such as vehicle mode, overall vehicle thermal management, and power demand, these are all actively selected or operated by the user, representing to a certain extent the user's active selection of highly relevant needs in specific scenarios. These parameters mainly include overall vehicle driving mode (economy, comfort, sport, snow, wading, sand, rock, etc.), accelerator pedal opening, brake pedal, steering wheel torque and angle, air conditioning setting, and overall vehicle high and low voltage accessory consumption. As the main output control node of the vehicle, the quality of the power and thermal management system directly affects the vehicle's driving quality and experience, and the system's key operating and control parameters will affect the system's actual performance. Key system operating and control parameters mainly include vehicle speed, wheel torque, range extender power, thermal management heating / cooling level, engine fan and cooling fan speed, battery charge / discharge SOC and power, and small battery SOC and voltage. Based on the above analysis, the following constraint functions are constructed based on historical off-road road spectrum data: vehicle driving mode and energy mode requirements and parameters, overall vehicle thermal management requirements and parameters, and overall vehicle power demand and parameters. These can be expressed as:

[0089]

[0090] in, dr V md For the vehicle driving mode matrix, rev V md For the whole vehicle energy mode matrix, TM gr For thermal management heating / cooling levels, This is a vector representing the temperature parameter changes of the electric drive system under conditions such as ambient temperature, thermal management requirements, gear position, and vehicle speed. This is a vector representing the changes in the thermal management fan speed and torque parameters under conditions such as ambient temperature, thermal management requirement gear, and vehicle speed. This represents the vector of vehicle speed variation under different ambient temperature conditions, as well as changes in actual accelerator pedal opening, gradient, etc. [AL,spd,tq,T] G mot This is a vector representing the relationship between characteristic parameters such as generator speed and torque under different scenarios in historical road spectrum data, varying with altitude and ambient temperature. This is a vector representing the relationship between characteristic parameters of battery charging and discharging, drive motor output, and accessory system consumption under different scenarios in historical road spectrum data, which vary with vehicle speed and thermal management requirements. This represents the vector of small battery current and voltage changes under different scenarios and with different accessories in historical road spectrum data.

[0091] In addition to the scenarios, requirements, and control-related parameters mentioned above, it is also necessary to differentiate and identify the hardware and software status of key components in the aforementioned off-road road spectrum data, such as the status of systems like the range extender, battery pack, chassis, electric drive, and thermal management, and establish a vehicle data status maturity parameter (GM). d Its numerical value represents the reference reliability of different data under different hardware and software conditions. The better the vehicle condition and the smaller the deviation from the mass production stage, the higher the GM's reliability. d The higher the parameter value, the worse the vehicle's condition and the greater the deviation from the mass production stage, the better the GM's performance. d The smaller the parameter value, the better. For prototype vehicles in the engineering design phase, their hardware and software states differ significantly from those in the mass production phase; therefore, their requirements and control-related parameters (V...) are different. mod 、V dmd TP dmd (This information has low reference value; GM) d The smaller the value, the more important it is to refer to its scene parameters (CF). rd For prototype vehicles in the testing phase, although their hardware and software differ somewhat from those in the mass production phase, most differences are not significant. Therefore, their requirements and control-related parameters (V) are similar. mod 、V dmd TP dmd The reference value is improved compared to the engineering design stage, that is, GM d The value is higher than that in the engineering design phase; similarly, its scene parameters (CF) are also higher. rd The same applies; for prototype vehicles nearing or in the mass production stage, their hardware and software states are basically locked, therefore the various scenarios, requirements, and control-related parameters (V) of the prototype vehicles at this stage are also relevant. mod 、V dmd TP dmd CF rd This can be used as a key reference point, GM d The value is the highest among all vehicle models in all states.

[0092] Based on the selected effective parameter information, and constrained by the operating condition characteristic parameters of each special scenario, this study investigates the changing patterns of vehicle demand and power output under different off-road scenarios for different vehicle modes. In particular, it analyzes the system strategies for parking generator, thermal management control, vehicle start-stop, range extender control, and drive control under different scenario boundary conditions. Based on data references of different hardware and software states, the optimal or relatively optimal operating range of each system's control strategy under different boundary conditions is identified. Furthermore, parameters such as the boundary, demand, and control within the optimal operating range are extracted to establish a vehicle power pre-control strategy based on historical road spectrum data under different off-road scenarios and different constraint boundaries. This ensures a high-coverage parameter design for the multi-scenario needs of actual users, which can be expressed as:

[0093]

[0094] Among them, SYS data To establish the basic pre-control information matrix, FCN′ data This is a data transformation and processing function based on historical data parsing and valid data boundaries. This is a vector representing the relationship between optimal or relatively optimal requirements and operating ranges under different environmental and system boundary conditions.

[0095] like Figure 2 As shown, Figure 2 This is a flowchart of an adaptive and precise control according to an embodiment of this application. Based on the accumulation of off-road road spectrum in different modes and multiple scenarios, a basic information pre-control scheme under multiple constraint scenarios is constructed, multiple performance constraints under real-time scenarios are clarified, and a real-time power decision and adaptive control strategy is developed. This realizes the coordinated control of multiple system performances such as thermal management and energy management, and improves the adaptive control power preservation performance under multiple mode and multiple scenario constraints.

[0096] This clarified the boundary constraints under different scenarios, constructed vehicle power pre-control strategies under different off-road scenarios and different constraint boundaries, and realized the effective extraction of basic pre-control information such as start-stop, power generation, and power-torque distribution based on historical off-road road spectrum information, ensuring high coverage of parameter design for actual user needs in multiple scenarios.

[0097] According to the energy control method for hybrid vehicles proposed in this application, the current road condition scenario of the vehicle is obtained, at least one constraint requirement is determined, and the vehicle power pre-control strategy is determined based on the preset vehicle power pre-control model and road conditions. An adaptive control strategy is determined according to the constraint requirement. The vehicle is adjusted and controlled according to the two strategies, which solves the problem of balancing power demand and power supply due to complex road conditions and user modifications. It ensures efficient collaborative control of multiple systems such as thermal management and energy management, and provides power and energy guarantee for driving needs in extremely complex off-road scenarios.

[0098] Next, the energy control device for a hybrid vehicle according to an embodiment of this application is described with reference to the accompanying drawings.

[0099] Figure 3 This is a block diagram of an energy control device for a hybrid vehicle according to an embodiment of this application.

[0100] like Figure 3 As shown, the energy control device 10 of the hybrid vehicle includes: an acquisition module 100, an identification module 200, and a control module 300.

[0101] The acquisition module 100 is used to acquire the current road conditions of the vehicle.

[0102] The identification module 200 determines at least one constraint requirement based on the current road condition scenario, and determines the vehicle's overall power pre-control strategy based on the preset vehicle power pre-control model and the current road condition scenario, and determines the vehicle's adaptive control strategy based on at least one constraint requirement.

[0103] The control module 300 performs the following functions based on the vehicle's power pre-control strategy, adaptive regulation strategy, and adaptive control strategy: start-stop optimization control based on water depth information, battery and electric drive distribution control based on driving needs, energy and thermal management coordination control based on the passenger compartment, and power generation adjustment control based on power supply needs.

[0104] According to one embodiment of this application, before determining the vehicle's power pre-control strategy based on the current road conditions using a preset vehicle power pre-control model, the identification module 200 is further configured to:

[0105] Acquire historical road spectrum data and the corresponding vehicle mode requirements, vehicle thermal management requirements, and power requirements;

[0106] Operational characteristic parameters for different scenarios are extracted from historical road spectrum data, and operational characteristic constraint functions are constructed based on these parameters.

[0107] Vehicle mode constraint functions are constructed based on the vehicle mode requirements corresponding to the historical road spectrum data, vehicle thermal management constraint functions are constructed based on the vehicle thermal management requirements corresponding to the historical road spectrum data, and power constraint functions are constructed based on the power requirements corresponding to the historical road spectrum data.

[0108] A multi-constraint function model based on historical road spectrum data is constructed based on the operation characteristic constraint function, vehicle mode constraint function, vehicle thermal management constraint function and power constraint function, and a preset vehicle power pre-control model is established based on the multi-constraint function model.

[0109] According to one embodiment of this application, the multi-constraint function model is as follows:

[0110] SCShty =CS f (FCN data (SCS data ),CF rd , vd V mod , vd V dmd , vd TP dmd );

[0111] Among them, SCS hty SCS is an effective data matrix under the constraint of historical road spectrum. data For historical road spectrum data matrix, FCN data CF is a function based on data transformation and processing. rd This refers to the scene features and parameter vectors related to specific operating conditions in historical road spectrum data. vd V mod This represents the vehicle driving mode and energy mode requirements and parameter vectors from historical road spectrum data. vd V dmd This is a vector of thermal management requirements and parameters for vehicle heating and cooling from historical data. vd TP dmd This is a vector of vehicle power-related requirements and parameters from historical road spectrum data.

[0112] According to one embodiment of this application, the preset vehicle dynamics pre-control model is as follows:

[0113]

[0114] Among them, SYS data To establish the basic pre-control information matrix, FCN′ data GM is a data transformation and processing function based on historical data parsing and valid data boundaries. d This refers to the vehicle's data status maturity parameter. This is a vector representing the relationship between optimal or relatively optimal requirements and operating ranges under different environmental and system boundary conditions.

[0115] According to one embodiment of this application, the current road condition scenario includes at least one of the following: sensing information from an on-board wheel speed sensor, sensing information from a gyroscope, steering wheel angle information, sensing information corresponding to a range extender, and sensing information from a wading radar.

[0116] It should be noted that the foregoing explanation of the energy control method embodiment for hybrid vehicles also applies to the energy control device of the hybrid vehicle in this embodiment, and will not be repeated here.

[0117] According to the energy control device for hybrid vehicles proposed in this application embodiment, the current road condition scenario of the vehicle is obtained, at least one constraint requirement is determined, and the vehicle power pre-control strategy is determined based on the preset vehicle power pre-control model and road conditions. An adaptive control strategy is determined according to the constraint requirement. The vehicle is adjusted and controlled according to the two strategies, which solves the problem of balancing power demand and power supply due to complex road conditions and user modifications. It ensures efficient collaborative control of multiple systems such as thermal management and energy management, and provides power and energy guarantee for driving needs in extremely complex off-road scenarios.

[0118] Figure 4 This is a schematic diagram of a vehicle provided in an embodiment of the present invention. The vehicle may include:

[0119] The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0120] When the processor 402 executes the program, it implements the energy control method for hybrid vehicles provided in the above embodiments.

[0121] Furthermore, the vehicle also includes:

[0122] Communication interface 403 is used for communication between memory 401 and processor 402.

[0123] The memory 401 is used to store computer programs that can run on the processor 402.

[0124] The memory 401 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0125] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0126] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0127] Processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention.

[0128] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described energy control method for hybrid vehicles.

[0129] Embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above-described embodiments of the energy control method for hybrid vehicles.

[0130] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0131] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0132] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An energy control method for a hybrid vehicle, characterized in that, Includes the following steps: Obtain the current road conditions for the vehicle; Based on the current road condition scenario, at least one constraint requirement is determined, and based on the preset vehicle power pre-control model, the vehicle power pre-control strategy is determined according to the current road condition scenario, and the vehicle adaptive control strategy is determined according to the at least one constraint requirement. Based on the vehicle power pre-control strategy and the adaptive regulation strategy, the vehicle performs start-stop optimization control based on water depth information, battery and electric drive distribution control based on driving needs, energy and thermal management coordination control based on the passenger compartment, and power generation adjustment control based on power supply needs.

2. The method according to claim 1, characterized in that, Before determining the vehicle's power pre-control strategy based on the current road condition scenario using a preset vehicle power pre-control model, the method further includes: Acquire historical road spectrum data and the corresponding vehicle mode requirements, vehicle thermal management requirements, and power requirements; Based on the historical road spectrum data, operational characteristic parameters for different scenarios are extracted, and operational characteristic constraint functions are constructed based on the operational characteristic parameters for different scenarios. A vehicle mode constraint function is constructed based on the vehicle mode requirements corresponding to the historical road spectrum data, a vehicle thermal management constraint function is constructed based on the vehicle thermal management requirements corresponding to the historical road spectrum data, and a power constraint function is constructed based on the power requirements corresponding to the historical road spectrum data. Based on the operating characteristic constraint function, the vehicle mode constraint function, the vehicle thermal management constraint function, and the power constraint function, a multi-constraint function model based on the historical road spectrum data is constructed, and the preset vehicle power pre-control model is established according to the multi-constraint function model.

3. The method according to claim 2, characterized in that, The multi-constraint function model is as follows: SCS hty =CS f (FCN data (SCS data ),CF rd , vd V mod , vd V dmd , vd TP dmd ); Among them, SCS hty SCS is an effective data matrix under the constraint of historical road spectrum. data For historical road spectrum data matrix, FCN data CF is a function based on data transformation and processing. rd This refers to the scene features and parameter vectors related to specific operating conditions in historical road spectrum data. vd V mod This represents the vehicle driving mode and energy mode requirements and parameter vectors from historical road spectrum data. vd V dmd This is a vector of thermal management requirements and parameters for vehicle heating and cooling from historical data. vd TP dmd This is a vector of vehicle power-related requirements and parameters from historical road spectrum data.

4. The method according to claim 3, characterized in that, The preset vehicle power pre-control model is as follows: Among them, SYS data Based on the pre-control information matrix, FCN′ data For data transformation and processing functions, CF rd The historical road spectrum data contains scene features and their corresponding characteristics. vd V mod This refers to the vehicle driving mode requirements and corresponding parameter vectors, and the energy mode requirements and corresponding parameter vectors in the historical road spectrum data. vd TP dmd SCS represents the vehicle power demand and corresponding parameter vector in the historical road spectrum data. hty For an effective data matrix under the constraint of historical road spectrum, GM d This refers to the vehicle's data status maturity parameter. This is the vector representing the relationship between optimal demand and operating range.

5. The method according to any one of claims 1-4, characterized in that, The current road condition scenario includes at least one of the following: sensor information from the vehicle wheel speed sensor, sensor information from the gyroscope, steering wheel angle information, sensor information from the range extender, and sensor information from the wading radar.

6. An energy control device for a hybrid vehicle, characterized in that, include: The acquisition module acquires the current road conditions of the vehicle. The identification module determines at least one constraint requirement based on the current road condition scenario, and determines the vehicle's overall power pre-control strategy based on the preset vehicle power pre-control model and the current road condition scenario, and determines the vehicle's adaptive control strategy based on the at least one constraint requirement. The control module performs the following on the vehicle: start-stop optimization control based on water depth information, battery and electric drive allocation control based on driving needs, energy and thermal management coordination control based on the passenger compartment, and power generation adjustment control based on power supply needs, according to the vehicle power pre-control strategy, adaptive regulation strategy, and adaptive regulation strategy.

7. The apparatus according to claim 6, characterized in that, Before determining the vehicle's power pre-control strategy based on the preset vehicle power pre-control model and the current road condition scenario, the identification module is further configured to: Acquire historical road spectrum data and the corresponding vehicle mode requirements, vehicle thermal management requirements, and power requirements; Based on the historical road spectrum data, operational characteristic parameters for different scenarios are extracted, and operational characteristic constraint functions are constructed based on the operational characteristic parameters for different scenarios. A vehicle mode constraint function is constructed based on the vehicle mode requirements corresponding to the historical road spectrum data, a vehicle thermal management constraint function is constructed based on the vehicle thermal management requirements corresponding to the historical road spectrum data, and a power constraint function is constructed based on the power requirements corresponding to the historical road spectrum data. Based on the operating characteristic constraint function, the vehicle mode constraint function, the vehicle thermal management constraint function, and the power constraint function, a multi-constraint function model based on the historical road spectrum data is constructed, and the preset vehicle power pre-control model is established according to the multi-constraint function model.

8. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the energy control method for a hybrid vehicle as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the energy control method for hybrid vehicles as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the energy control method for hybrid vehicles as described in any one of claims 1-5.

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