Hybrid vehicle control methods, devices, and hybrid vehicles

Abstract

This application relates to the technical field of energy management for hybrid vehicles, and discloses a hybrid vehicle control method, device, and hybrid vehicle. The hybrid vehicle control method includes: acquiring future driving path data of the vehicle from its current location to a future path segment from a high-precision map; predicting the vehicle's future energy demand data based on the future driving path data, vehicle basic data, and environmental data; determining the SOC boundary value at the current location based on the future driving path data, future energy demand data, and a SOC baseline value; determining a SOC compensation term based on the SOC boundary value and the current SOC value; determining a dynamic response term based on the rate of change of the current SOC value; and determining the engine distribution torque based on the engine base torque, the SOC compensation term, and the dynamic response term; and controlling vehicle movement based on the engine distribution torque. This method can avoid insufficient vehicle power under complex terrain conditions and improve the response speed of vehicle control.

Description

Technical Field

[0001] This application relates to the technical field of energy management for hybrid vehicles, and more particularly to a hybrid vehicle control method, device, and hybrid vehicle. Background Technology

[0002] During driving, hybrid vehicles can be propelled or braked using both battery braking energy and fuel braking energy. To prevent insufficient power when driving in complex environments, timely energy distribution between the battery and fuel braking energy is necessary. However, related technologies cannot accurately predict future driving conditions, thus failing to anticipate the vehicle's energy demands in a timely manner. This leads to untimely energy distribution, resulting in insufficient vehicle power and affecting the vehicle's control response speed. Summary of the Invention

[0003] The embodiments of this application are intended to at least partially address one of the technical problems in the related art. To this end, embodiments of this application propose a hybrid vehicle control method, apparatus, as well as a hybrid vehicle, device, and medium.

[0004] This application provides a hybrid vehicle control method, which includes: acquiring future driving path data of the vehicle from its current location to a future path from a high-precision map; predicting the vehicle's future energy demand data based on the future driving path data, vehicle basic data, and environmental data; determining the SOC boundary value at the current location based on the future driving path data, future energy demand data, and SOC benchmark value; determining a SOC compensation term based on the SOC boundary value and the current SOC value, determining a dynamic response term based on the rate of change of the current SOC value, and determining the engine distribution torque based on the engine base torque, the SOC compensation term, and the dynamic response term; and controlling the vehicle's driving based on the engine distribution torque.

[0005] In some implementations, the future driving path data includes terrain feature data and path length data; the vehicle basic data includes vehicle mass, drag coefficient, vehicle frontal area, and vehicle speed; and the environmental data includes air density. Based on the future driving path data, vehicle basic data, and environmental data, the future energy demand data of the vehicle is predicted, including: determining a first energy value for the current location based on vehicle mass and terrain feature data; obtaining a second energy value for the current location based on drag coefficient, frontal area, air density, and vehicle speed; and integrating and summing the first and second energy values ​​based on path length data to predict the future energy demand data.

[0006] In some implementations, the future driving path data includes path length data, the SOC benchmark value includes a lower SOC benchmark value and an upper SOC benchmark value, and the SOC boundary value includes a minimum SOC limit value and a maximum SOC limit value. Based on the future driving path data, future energy demand data, and the SOC benchmark value, determining the SOC boundary value at the current location includes: determining a first SOC increment based on the energy change rate of the future energy demand data at the current location and a first terrain sensitivity coefficient, and adding the first SOC increment to the lower SOC benchmark value to obtain the minimum SOC limit value; performing energy integration and accumulation on the future energy demand data within the future path length indicated by the path length data to obtain an energy accumulation result, determining a second SOC increment based on a second terrain sensitivity coefficient and the energy accumulation result, and subtracting the second SOC increment from the upper SOC benchmark value to obtain the maximum SOC limit value.

[0007] In some implementations, the SOC boundary values ​​include a minimum SOC limit and a maximum SOC limit; a SOC compensation term is determined based on the SOC boundary values ​​and the current SOC value; a dynamic response term is determined based on the rate of change of the current SOC value; and the engine distribution torque is determined based on the engine base torque, the SOC compensation term, and the dynamic response term. This includes: determining the SOC difference between the minimum SOC limit and the current SOC value, and adjusting the SOC difference based on a first gain coefficient and a nonlinear exponent to obtain the SOC compensation term; obtaining the dynamic response term based on the rate of change of the current SOC value over time and a second gain coefficient; and adding the engine base torque, the SOC compensation term, and the dynamic response term to obtain the engine distribution torque.

[0008] In some implementations, the SOC boundary value includes the maximum SOC limit; the method further includes: determining the actual regenerative braking power based on the maximum regenerative power, the maximum SOC limit, the current SOC value, the battery capacity, and the battery voltage; and performing braking energy recovery based on the actual regenerative braking power.

[0009] In some implementations, the actual regenerative braking power is determined based on the maximum regenerative power, the maximum SOC limit, the current SOC value, the battery capacity, and the battery voltage. This includes: determining the SOC difference between the maximum SOC limit and the current SOC value, and adjusting the SOC difference based on the overall efficiency of the battery capacity, battery voltage, and energy recovery path to obtain the current rechargeable power of the battery; and determining the smaller of the maximum regenerative power and the current rechargeable power of the battery as the actual regenerative braking power.

[0010] In some implementations, the method further includes: determining a regenerative braking force within the acceptable range of regenerative braking force of the battery, and applying braking control to the vehicle based on the regenerative braking force; when the regenerative braking force is insufficient, supplementing the vehicle with mechanical braking force for braking control.

[0011] In some implementations, the SOC boundary value includes the maximum SOC limit; within the range of acceptable regenerative braking force of the battery, determining the regenerative braking force includes: determining the SOC difference between the maximum SOC limit and the current SOC value, and adjusting the SOC difference based on the energy conversion ratio coefficient to obtain the acceptable regenerative braking force of the battery; determining the smaller one between the maximum regenerative braking force and the acceptable regenerative braking force of the battery as the regenerative braking force.

[0012] This application provides a hybrid vehicle control device, comprising: an acquisition module for acquiring future driving path data of the vehicle from its current location to a future path from a high-precision map; a prediction module for predicting the vehicle's future energy demand data based on the future driving path data, vehicle basic data, and environmental data; a first determination module for determining the SOC boundary value at the current location based on the future driving path data, future energy demand data, and a SOC baseline value; a second determination module for determining a SOC compensation term based on the SOC boundary value and the current SOC value, determining a dynamic response term based on the rate of change of the current SOC value, and determining the engine distribution torque based on the engine base torque, the SOC compensation term, and the dynamic response term; and a control module for controlling the vehicle's movement based on the engine distribution torque.

[0013] The present application provides a hybrid vehicle, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the method described in any of the above embodiments.

[0014] An embodiment of this application provides an electronic device, which includes: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by one or more processors, which are executed by one or more processors to cause the one or more processors to implement the steps of the method of any of the above embodiments.

[0015] Embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of any of the above embodiments. Attached Figure Description

[0016] Figure 1 A system architecture diagram provided for an embodiment of this application; Figure 2 A schematic flowchart of a hybrid vehicle control method provided for an embodiment of this application; Figure 3A schematic diagram of a hybrid vehicle control method provided for another embodiment of this application; Figure 4 A schematic diagram of a hybrid vehicle control device provided for an embodiment of this application; Figure 5 A block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0017] The embodiments of this application are described in detail below. Examples of the 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.

[0018] Traditional hybrid vehicle energy management strategies primarily rely on real-time road condition feedback and employ a fixed SOC (State of Charge) target range for control. For example, real-time feedback control can adjust energy distribution based on the vehicle's current state (such as vehicle speed and terrain feature data); and when adjusting energy distribution, it refers to a fixed SOC range, which typically requires the SOC to be maintained between 40% and 70% to ensure basic power needs; it also uses redundant engine configurations to address the risk of insufficient power, thus requiring the configuration of high-power engines.

[0019] Some technical solutions cannot predict energy demand in advance due to long-distance terrain changes (such as continuous uphill / downhill), resulting in vehicle response lag; when going downhill, SOC oversaturation causes a decrease in braking energy recovery efficiency of more than 30%, resulting in vehicle energy waste; engine power redundancy increases power system cost by 15%-20%, resulting in high cost.

[0020] It is evident that these technical solutions suffer from spatial limitations, relying solely on local real-time data (such as current terrain features and vehicle speed) and lacking a forecast of global terrain energy demands (such as long-distance continuous uphill and downhill sections). They also exhibit temporal limitations due to fixed SOC intervals. Furthermore, in uphill conditions, SOC decreases exponentially at the end of the slope, posing a risk of power interruption (when SOC ≤ 30%, the motor disengages, and torque output decreases). In downhill conditions, SOC oversaturation (SOC > 70%) leads to decreased regenerative braking energy recovery efficiency. Additionally, the system coupling is neglected, meaning the dynamic boundary conditions of mechanical braking and regenerative braking are not coordinated, resulting in increased mechanical braking load and excessive temperature rise in the friction brakes.

[0021] Therefore, this application proposes a hybrid vehicle control method that can solve the problems of insufficient power and energy waste caused by SOC control lag in complex terrain; can reduce engine power redundancy requirements and achieve cost optimization; and can improve braking energy recovery efficiency and vehicle safety.

[0022] Figure 1 A system architecture diagram provided for an embodiment of this application.

[0023] like Figure 1 As shown, the system architecture is capable of executing the hybrid vehicle control method of this application. The system architecture includes a high-precision map system, a vehicle controller HCU (Hybrid Control Unit, also known as a hybrid power control unit), an engine controller EMS (Energy Management System), a battery controller BMS (Battery Management System), and a motor controller MCU (Motor Controller Unit).

[0024] High-precision map systems include pre-loaded terrain data.

[0025] The vehicle control unit (HCU) of a hybrid electric vehicle is the core control component, serving as the decision-making and coordination center. It has functions such as energy management, torque coordination and distribution, motor and battery coordination management, safety monitoring, and regenerative braking.

[0026] The engine control system (EMS) has a power distribution function. By receiving signals from various sensors, it adjusts engine parameters such as fuel injection, ignition timing, and valve opening in real time to achieve optimal engine performance and energy economy.

[0027] The Battery Management System (BMS) can monitor the battery's State of Charge (SOC) in real time. Its main purpose is to intelligently manage and maintain each battery cell, monitor the battery's status, prevent overcharging and over-discharging, and extend the battery's lifespan.

[0028] The motor controller MCU can perform regenerative braking based on the recovered battery energy, reducing mechanical braking losses and avoiding excessive temperature rise caused by mechanical braking. For example, it can convert the DC power of the power battery into high-voltage AC power to drive the motor to output mechanical energy.

[0029] Figure 2 This is a flowchart illustrating a hybrid vehicle control method provided for an embodiment of this application.

[0030] like Figure 2 As shown, the hybrid vehicle control method 200 provided in this application includes, for example, steps S210-S250.

[0031] Step S210: Obtain the future driving path data of the vehicle from its current location to a future distance from the high-precision map.

[0032] During operation, the vehicle can obtain real-time data on its future driving path from its current location to a future distance from a high-precision map. This future driving path data includes, for example, terrain feature data, such as at least one of the following: slope, turning radius, and altitude. The future distance could be a 5-10km stretch of road the vehicle will be traveling in the future.

[0033] Step S220: Based on future driving path data, vehicle basic data, and environmental data, predict the vehicle's future energy demand data.

[0034] Vehicle baseline data includes the vehicle's current speed, vehicle mass, and other parameters. Environmental data includes air density. Since future travel path data, vehicle baseline data, and environmental data all affect vehicle energy consumption, calculations are performed on these data to predict the vehicle's future energy requirements.

[0035] Step S230: Based on future driving path data, future energy demand data, and SOC baseline value, determine the SOC boundary value at the current location.

[0036] After predicting the vehicle's future energy demand data, the future driving path data, future energy demand data, and SOC baseline value can be calculated to obtain the SOC boundary value. As the vehicle moves, the current position and the future path starting from the current position are constantly changing, so the SOC boundary value is also dynamically and adaptively adjusted in real time.

[0037] Step S240: Determine the SOC compensation term based on the SOC boundary value and the current SOC value, determine the dynamic response term based on the rate of change of the current SOC value, and determine the engine distribution torque based on the engine base torque, the SOC compensation term, and the dynamic response term.

[0038] After obtaining the SOC boundary values, the SOC compensation term and dynamic response term can be determined so that the engine torque distribution can be calculated based on the SOC compensation term and dynamic response term.

[0039] Step S250: Control the vehicle's movement based on the engine's torque distribution.

[0040] Based on the engine's torque distribution, the engine outputs driving force or braking force to control the vehicle's movement.

[0041] As can be seen, the hybrid vehicle control method of this application predicts the future energy demand data of the vehicle under complex terrain conditions, and dynamically and adaptively adjusts the SOC boundary value in real time based on the future energy demand data. The engine torque distribution is calculated based on the SOC boundary value, which avoids insufficient vehicle power under complex terrain conditions, thereby improving the response speed of vehicle control.

[0042] In one example, the vehicle's future energy demand data can be predicted using a terrain energy demand model.

[0043] For example, terrain feature data for a future 5-10km path can be extracted from a high-precision map. The terrain feature data includes at least one of the following: slope data, curvature data, and elevation data.

[0044] Specifically, the future driving path data includes terrain feature data and path length data, vehicle basic data includes vehicle mass, drag coefficient, vehicle frontal area and vehicle speed, and environmental data includes air density.

[0045] By inputting future driving path data, vehicle basic data, and environmental data into the terrain energy demand model, the future energy demand data of the vehicle is predicted. The terrain energy demand model is shown in formula (1): (1) in, For the vehicle from its current location Starting a path in the future Cumulative energy demand; θ Current location Slope data (slope angle) at the location; For vehicle quality; It is the acceleration due to gravity; This refers to the drag coefficient; The frontal area of ​​the vehicle; air density; Current location Vehicle speed at the location; It is an integral variable, representing the integral from... arrive The position of each point on the future path interval.

[0046] It is understandable that Formula (1) is calculated using slope data as an example of terrain feature data. Alternatively, the slope data in Formula (1) can be replaced with curvature or altitude data, or with the superposition of at least two of the slope, curvature, and altitude data.

[0047] See formula (1), based on vehicle mass and slope data Determine current location First energy value .

[0048] Based on drag coefficient Windward area air density and vehicle speed To obtain the second energy value at the current position. .

[0049] Based on path length data The sum of the first energy value and the second energy value By integrating and accumulating the data, future energy demand data can be predicted. .

[0050] In one example, the future driving path data includes path length data, the SOC baseline values ​​include a lower SOC baseline value and an upper SOC baseline value, and the SOC boundary values ​​include a minimum SOC limit value and a maximum SOC limit value. The SOC boundary values ​​can be calculated using a dynamic SOC corridor generation algorithm.

[0051] For example, future driving path data, future energy demand data, and SOC baseline values ​​are input into the dynamic SOC corridor generation algorithm for calculation to obtain the SOC boundary value at the current location. The dynamic SOC corridor generation algorithm includes SOC boundary adaptive rules, which are shown in formula (2): (2) in, To be at the current location The minimum permissible state of charge (SOC minimum limit). To be at the current location The highest permissible state of charge (SOC maximum limit). This is the lower limit benchmark value for SOC (e.g., the default lower limit is 20%). This is the baseline value for the upper limit of SOC (e.g., the default upper limit is 80%). , ) represents the terrain sensitivity coefficient; The predicted interval length of the future path (path length data); It is an integral variable, representing the integral from... arrive The position of each point on the future path interval.

[0052] See formula (2), based on future energy demand data. At the current location rate of energy change and the first terrain sensitivity coefficient Determine the first SOC increment and set the lower limit benchmark value of SOC Plus the first SOC increment Obtain the minimum SOC limit value Among them, the first SOC increment A positive value indicates energy consumption, while a negative value indicates energy recovery. This is the lower limit of the State of Charge (SOC) baseline. Plus the first SOC increment This is to proactively expand the SOC corridor downwards, allowing more electricity to be used in order to cope with upcoming high-energy-consuming sections (such as uphill sections).

[0053] Data on future energy demand Path length data The energy is integrated and accumulated over the indicated future path length to obtain the energy accumulation result. Based on the second terrain sensitivity coefficient and energy accumulation results Determine the second SOC increment and set the upper limit benchmark value of SOC Subtract the second SOC increment Obtain the highest limit value of SOC Among them, the second SOC increment A positive value indicates energy consumption, while a negative value indicates energy recovery. This is the baseline value for the SOC upper limit. Subtract the second SOC increment This is to proactively compress the corridor downwards, limiting the inflow of electricity and reserving space for upcoming energy recovery opportunities (such as downhill).

[0054] In one example, the SOC boundary values ​​include the minimum SOC limit and the maximum SOC limit; the engine-distributed torque can be calculated using a multi-objective optimization controller based on the engine's torque distribution strategy.

[0055] For example, through the engine's torque distribution strategy, the SOC compensation term is determined based on the SOC boundary value and the current SOC value, the dynamic response term is determined based on the rate of change of the current SOC value, and the engine's distributed torque is determined based on the engine's base torque, the SOC compensation term, and the dynamic response term. The engine's torque distribution strategy is shown in formula (3): (3) in, Distribute torque to the engine; This is the engine's base torque; This is the gain coefficient (first gain coefficient) for the SOC compensation term, used to control the intensity of the effect of electrical charge on torque; This is the gain coefficient (second gain coefficient) for the SOC change rate term, used to control the dynamic response speed; The nonlinear exponent (n is usually ≥1) is used to adjust the sensitivity of SOC compensation.

[0056] Refer to formula (3) to determine the minimum SOC limit. and the current value of SOC ( SOC difference between ), and based on the first gain coefficient and nonlinear exponent Adjusting the SOC difference ( ), to obtain SOC compensation item .

[0057] Based on the current SOC value ( rate of change over time Second gain coefficient To obtain the dynamic response item .

[0058] Engine base torque SOC compensation items and dynamic response items The engine torque distribution is obtained by performing summation calculations. .

[0059] As can be seen, this strategy dynamically adjusts the engine output torque, comprehensively considering the battery's state of charge (SOC) and its changing trend. For the SOC compensation item, based on the current battery level (…),… With minimum target ( The difference between the current charge and the engine intervention intensity is adjusted using a nonlinear coefficient (n≥1), with more significant compensation at lower charge levels. For the dynamic response term, the real-time rate of change of SOC is considered. This allows for rapid response to unexpected operating conditions and improves system stability. (Through the first gain coefficient...) Second gain coefficient The configuration enables an adaptive balance between battery maintenance and power demand.

[0060] In one example, the SOC boundary value includes the maximum SOC limit; the hybrid vehicle control method also includes coordination of brake energy recovery.

[0061] For example, the actual regenerative braking power is determined using a regenerative braking coordination algorithm based on the maximum regenerative power, the maximum SOC limit, the current SOC value, battery capacity, and battery voltage. The regenerative braking coordination algorithm is shown in formula (4): (4) in, This refers to the actual regenerative braking power (the actual usable regenerative braking power). This represents the maximum regeneration power allowed by the system. Battery capacity (Ah); For battery power (e.g., total voltage of the battery pack); The overall efficiency of the energy recovery path.

[0062] Refer to formula (4) to determine the maximum SOC limit. and the current value of SOC ( SOC difference between And based on battery capacity Battery voltage The overall efficiency of energy recovery pathways SOC difference Adjustments are made to obtain the current rechargeable power of the battery. ).

[0063] From maximum regeneration power and the current rechargeable power of the battery ( The smallest value among them is determined as the actual regenerative braking power. .

[0064] Obtain the actual regenerative braking power Subsequently, based on the actual regenerative braking power Perform braking energy recovery.

[0065] Based on a regenerative braking strategy, intelligent control of regenerative braking power is achieved. This strategy incorporates a dual-constraint approach: utilizing the maximum allowable regenerative power of the system. Compared to the current rechargeable power of the battery ( The minimum value of regenerative braking is used to avoid overcharging the battery. This regenerative braking strategy also includes dynamic adaptation, which adjusts the charging status based on real-time state of charge (SOC) and battery voltage. The energy recovery intensity is adjusted to prioritize regenerative braking and reduce mechanical braking losses. This braking energy recovery strategy also utilizes an efficiency coefficient... Compensate for energy transmission losses to ensure maximum recovery efficiency.

[0066] In one example, the recovered energy can charge the battery and, during braking, can be prioritized for regenerative braking, meaning braking is primarily based on the recovered energy. If regenerative braking is insufficient, mechanical braking can be used based on fuel or other methods.

[0067] For example, based on the principle of safety boundary constraints and the dynamic coordination method of actuators, the regenerative braking force is determined within the acceptable range of regenerative braking force of the battery, and the vehicle is braked based on the regenerative braking force. When the regenerative braking force is insufficient, supplementary mechanical braking force is used to brake the vehicle. The supplementary mechanical braking force is shown in formula (5): (5) in, The force (mechanical braking force) distributed to friction braking. The total braking force required by the driver or system; Maximum regenerative braking force; The regenerative braking force is acceptable for the battery; This is the power conversion ratio coefficient, which represents the proportion by which the power difference is converted into braking force.

[0068] See formula (5), the SOC boundary value includes the highest SOC limit value. Within the acceptable range of regenerative braking force for the battery, determine the regenerative braking force, including: Determine the maximum SOC limit and the current value of SOC ( SOC difference between And based on the power conversion ratio coefficient SOC difference Adjustments were made to obtain an acceptable regenerative braking force for the battery. ).

[0069] From maximum regenerative braking force and the battery's acceptable regenerative braking force ( The smallest one among them is selected as the regenerative braking force. .

[0070] It is evident that this strategy can intelligently allocate braking force, including the concept of prioritizing regenerative braking, i.e., within the acceptable range of battery capacity. It maximizes the use of regenerative braking force; it also includes the concept of safety redundancy, that is, when regenerative braking is insufficient (exceeding the limit)... When battery capacity is limited, automatically supplement mechanical braking force. Through the power conversion ratio coefficient Dynamically matching battery charging capacity with braking demand ensures dual optimization of braking performance and battery safety, while reducing the burden on mechanical braking, thereby reducing the temperature rise of friction brakes.

[0071] Figure 3 A schematic diagram of a hybrid vehicle control method provided for another embodiment of this application.

[0072] like Figure 3As shown, the hybrid vehicle control method can be implemented by combining high-precision maps with a dynamic programming HEV (Hybrid Electric Vehicle) energy management control system.

[0073] The high-precision map data preprocessing module can be used to extract slope, curvature, and elevation data for the next 5-10km, generating terrain feature vectors, which may include, for example, slope vectors. Curvature vector Altitude vector .

[0074] Future energy demand data are calculated using a terrain-based energy demand prediction model.

[0075] Based on the dynamic SOC corridor generation algorithm, the SOC boundary value is adjusted in real time.

[0076] Torque distribution is based on engine torque distribution strategy.

[0077] Regenerative braking is prioritized based on the regenerative braking control strategy.

[0078] Based on the principles of safety boundary constraints and actuator dynamic coordination, mechanical braking coordination and dynamic allocation of engine / electrode commands are performed for braking.

[0079] Vehicle status feedback closed-loop update means that parameters such as SOC, vehicle speed, gradient, and brake temperature can be updated in real time.

[0080] In addition, global path energy management can be achieved through cloud-based collaborative optimization. For example, relevant data can be collected and calculated in the cloud, and the results can be sent to the hybrid vehicle. For instance, high-precision map data can be collected in the cloud, and calculations can be performed based on any of the formulas mentioned above to obtain the results, which can then be sent to the hybrid vehicle.

[0081] In summary, this application, based on a high-precision map-based dynamic range prediction method for SOC, a terrain energy demand prediction model and multi-objective optimization algorithm, a dynamic coordination mechanism for braking energy recovery and mechanical braking, and a vehicle control system for engine power distribution, improves the vehicle's SOC control response speed and energy recovery efficiency, reduces engine power demand redundancy and braking system temperature, improves vehicle operating performance, and reduces costs. For details, please refer to Table 1 below.

[0082] Table 1

[0083] Figure 4 A schematic diagram of a hybrid vehicle control device provided in an embodiment of this application.

[0084] like Figure 4 As shown, the hybrid vehicle control unit 400 includes: The acquisition module 410 is used to acquire the future driving path data of the vehicle from its current location to a future distance from the high-precision map; The prediction module 420 is used to predict the future energy demand data of the vehicle based on future driving path data, vehicle basic data and environmental data; The first determining module 430 is used to determine the SOC boundary value at the current location based on future driving path data, future energy demand data, and SOC benchmark value. The second determining module 440 is used to determine the SOC compensation term based on the SOC boundary value and the current SOC value, determine the dynamic response term based on the rate of change of the current SOC value, and determine the engine distribution torque based on the engine base torque, the SOC compensation term and the dynamic response term. Control module 450 is used to control vehicle movement based on engine torque distribution.

[0085] For example, the future driving path data includes terrain feature data and path length data, the vehicle basic data includes vehicle mass, drag coefficient, vehicle frontal area and vehicle speed, and the environmental data includes air density; the prediction module 420 is also used to: determine a first energy value for the current location based on vehicle mass and terrain feature data; obtain a second energy value for the current location based on drag coefficient, frontal area, air density and vehicle speed; and integrate and accumulate the first energy value and the second energy value based on path length data to predict future energy demand data.

[0086] For example, the future driving path data includes path length data, the SOC benchmark value includes a lower SOC benchmark value and an upper SOC benchmark value, and the SOC boundary value includes a minimum SOC limit value and a maximum SOC limit value; the first determining module 430 is further configured to: determine a first SOC increment based on the energy change rate of the future energy demand data at the current location and a first terrain sensitivity coefficient, and add the first SOC increment to the lower SOC benchmark value to obtain the minimum SOC limit value; perform energy integration accumulation on the future energy demand data within the future path length indicated by the path length data to obtain an energy accumulation result, determine a second SOC increment based on a second terrain sensitivity coefficient and the energy accumulation result, and subtract the second SOC increment from the upper SOC benchmark value to obtain the maximum SOC limit value.

[0087] For example, the SOC boundary values ​​include a minimum SOC limit and a maximum SOC limit; the second determining module 440 is further configured to: determine the SOC difference between the minimum SOC limit and the current SOC value, and adjust the SOC difference based on a first gain coefficient and a nonlinear exponent to obtain a SOC compensation term; obtain a dynamic response term based on the rate of change of the current SOC value over time and a second gain coefficient; and add the engine base torque, the SOC compensation term, and the dynamic response term to obtain the engine distribution torque.

[0088] For example, the SOC boundary value includes the maximum SOC limit value; the hybrid vehicle control device 400 further includes: a third determining module for determining the actual regenerative braking power based on the maximum regenerative power, the maximum SOC limit value, the current SOC value, the battery capacity, and the battery voltage; and an energy recovery module for performing braking energy recovery based on the actual regenerative braking power.

[0089] For example, the third determining module is further configured to: determine the SOC difference between the maximum SOC limit and the current SOC value, and adjust the SOC difference based on the combined efficiency of battery capacity, battery voltage and energy recovery path to obtain the current rechargeable power of the battery; and determine the smaller one between the maximum regenerative power and the current rechargeable power of the battery as the actual regenerative braking power.

[0090] For example, the hybrid vehicle control device 400 further includes: a first braking module for determining the regenerative braking force within the range of acceptable regenerative braking force of the battery, and for braking control of the vehicle based on the regenerative braking force; and a second braking module for supplementing the mechanical braking force to brake the vehicle when the regenerative braking force is insufficient.

[0091] For example, the first braking module is further configured to: determine the SOC difference between the maximum SOC limit and the current SOC value, and adjust the SOC difference based on the energy conversion ratio coefficient to obtain an acceptable regenerative braking force for the battery; and determine the smaller one between the maximum regenerative braking force and the acceptable regenerative braking force for the battery as the regenerative braking force.

[0092] It is understood that the specific functions of the hybrid vehicle control device 400 can be referred to the hybrid vehicle control method above, and will not be repeated here.

[0093] This application provides a hybrid vehicle including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0094] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0095] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0096] This application provides a computer program product that includes instructions that, when executed by a processor of a computer device, enable the computer device to perform the steps of the method described in any of the above embodiments.

[0097] Figure 5 A block diagram of an electronic device provided in an embodiment of this application.

[0098] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method in any of the above embodiments.

[0099] like Figure 5 As shown, for ease of understanding, an embodiment of this application illustrates a specific electronic device 500.

[0100] Electronic device 500 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0101] like Figure 5 As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 502 or a computer program loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0102] Multiple components in electronic device 500 are connected to I / O interface 505. These components include: input unit 506, such as a keyboard or mouse; output unit 507, such as various types of displays or speakers; storage unit 508, such as a disk or optical disk; and communication unit 509, such as a network interface card (NIC), modem, or wireless transceiver. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0103] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods described above. For example, in some embodiments, any one or more of the methods described above can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of any one or more of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform any one or more of the methods described above by any other suitable means (e.g., by means of firmware).

[0104] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this application, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0105] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0106] In the description of this application, 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 application, 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.

[0107] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0108] Furthermore, the terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this application can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this application, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly and specifically defined in the embodiments.

[0109] In this application, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific implementation.

[0110] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

Claims

1. A hybrid vehicle control method characterized by, The method includes: Obtain the vehicle's future driving path data from its current location to a future distance from the high-precision map; Based on the future driving path data, vehicle basic data, and environmental data, predict the vehicle's future energy demand data; Based on the future driving path data, the future energy demand data, and the SOC baseline value, determine the SOC boundary value at the current location; The SOC compensation term is determined based on the SOC boundary value and the current SOC value, the dynamic response term is determined based on the rate of change of the current SOC value, and the engine distribution torque is determined based on the engine base torque, the SOC compensation term, and the dynamic response term. The vehicle's movement is controlled based on the torque distributed by the engine. The future driving path data includes path length data; the SOC benchmark value includes a lower SOC benchmark value and an upper SOC benchmark value; and the SOC boundary value includes a minimum SOC limit value and a maximum SOC limit value. Determining the SOC boundary value at the current location based on the future driving path data, the future energy demand data, and the SOC benchmark value includes: Based on the energy change rate at the current location and the first terrain sensitivity coefficient of the future energy demand data, a first SOC increment is determined, and the first SOC increment is added to the lower limit benchmark value of SOC to obtain the minimum limit value of SOC. The future energy demand data is integrated and accumulated within the future path length indicated by the path length data to obtain the energy accumulation result. The second SOC increment is determined based on the second terrain sensitivity coefficient and the energy accumulation result. The SOC upper limit benchmark value is subtracted from the second SOC increment to obtain the maximum SOC limit value. The SOC boundary values ​​include a minimum SOC limit and a maximum SOC limit. The process of determining a SOC compensation term based on the SOC boundary values ​​and the current SOC value, determining a dynamic response term based on the rate of change of the current SOC value, and determining the engine distribution torque based on the engine base torque, the SOC compensation term, and the dynamic response term includes: Determine the SOC difference between the minimum SOC limit and the current SOC value, and adjust the SOC difference based on the first gain coefficient and the nonlinear exponent to obtain the SOC compensation term; The dynamic response term is obtained based on the rate of change of the current SOC value over time and the second gain coefficient; The engine's base torque, the SOC compensation term, and the dynamic response term are added together to calculate the engine's distributed torque.

2. The method according to claim 1, characterized in that, The future driving path data includes terrain feature data and path length data; the vehicle basic data includes vehicle mass, drag coefficient, vehicle frontal area, and vehicle speed; and the environmental data includes air density. The prediction of the vehicle's future energy demand based on the future driving path data, vehicle basic data, and environmental data includes: Based on the vehicle mass and the terrain feature data, a first energy value for the current location is determined; Based on the drag coefficient, the frontal area, the air density, and the vehicle speed, a second energy value for the current location is obtained; Based on the path length data, the first energy value and the second energy value are integrated and accumulated to predict the future energy demand data.

3. The method according to claim 1, characterized in that, The SOC boundary values ​​include the highest SOC limit value; The method further includes: The actual regenerative braking power is determined based on the maximum regenerative power, the maximum SOC limit, the current SOC value, the battery capacity, and the battery voltage. Braking energy recovery is performed based on the actual regenerative braking power.

4. The method according to claim 3, characterized in that, The determination of actual regenerative braking power based on maximum regenerative power, the highest SOC limit, the current SOC value, battery capacity, and battery voltage includes: The SOC difference between the maximum SOC limit and the current SOC value is determined, and the SOC difference is adjusted based on the combined efficiency of the battery capacity, battery voltage, and energy recovery path to obtain the current rechargeable power of the battery. The smaller of the maximum regenerative power and the current rechargeable power of the battery is determined as the actual regenerative braking power.

5. The method according to claim 1, characterized in that, The method further includes: Within the acceptable range of regenerative braking force of the battery, the regenerative braking force is determined, and the vehicle braking control is performed based on the regenerative braking force. When the regenerative braking force is insufficient, supplementary mechanical braking force is used to control the vehicle's braking.

6. The method according to claim 5, characterized in that, The SOC boundary values ​​include the highest SOC limit value; Determining the regenerative braking force within the acceptable range of regenerative braking force for the battery includes: The SOC difference between the maximum SOC limit and the current SOC value is determined, and the SOC difference is adjusted based on the power conversion ratio coefficient to obtain the acceptable regenerative braking force of the battery. The smaller of the maximum regenerative braking force and the regenerative braking force acceptable to the battery is determined as the regenerative braking force.

7. A hybrid vehicle control device, characterized in that, The device includes: The acquisition module is used to obtain the vehicle's future driving path data from its current location to a future distance from the high-precision map; The prediction module is used to predict the vehicle's future energy demand data based on the future driving path data, vehicle basic data, and environmental data. The first determining module is used to determine the SOC boundary value at the current location based on the future driving path data, the future energy demand data, and the SOC benchmark value; wherein the future driving path data includes path length data, the SOC benchmark value includes a lower SOC benchmark value and an upper SOC benchmark value, and the SOC boundary value includes a minimum SOC limit value and a maximum SOC limit value; determining the SOC boundary value at the current location based on the future driving path data, the future energy demand data, and the SOC benchmark value includes: determining a first SOC increment based on the energy change rate of the future energy demand data at the current location and a first terrain sensitivity coefficient, and adding the first SOC increment to the lower SOC benchmark value to obtain the minimum SOC limit value; performing energy integration and accumulation on the future energy demand data within the future path length indicated by the path length data to obtain an energy accumulation result, determining a second SOC increment based on a second terrain sensitivity coefficient and the energy accumulation result, and subtracting the second SOC increment from the upper SOC benchmark value to obtain the maximum SOC limit value; The second determining module is used to determine a SOC compensation term based on the SOC boundary value and the current SOC value, determine a dynamic response term based on the rate of change of the current SOC value, and determine the engine distribution torque based on the engine base torque, the SOC compensation term, and the dynamic response term; wherein, the SOC boundary value includes a minimum SOC limit value and a maximum SOC limit value; the step of determining the SOC compensation term based on the SOC boundary value and the current SOC value, determining the dynamic response term based on the rate of change of the current SOC value, and determining the engine distribution torque based on the engine base torque, the SOC compensation term, and the dynamic response term includes: determining the SOC difference between the minimum SOC limit value and the current SOC value, and adjusting the SOC difference based on a first gain coefficient and a nonlinear exponent to obtain the SOC compensation term; obtaining the dynamic response term based on the rate of change of the current SOC value over time and a second gain coefficient; and adding the engine base torque, the SOC compensation term, and the dynamic response term to obtain the engine distribution torque; The control module is used to control the vehicle's movement based on the torque distributed by the engine.

8. A hybrid vehicle, characterized in that, The hybrid vehicle includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the method of any one of claims 1-6.

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