Vehicle control method and vehicle

Abstract

The application provides a vehicle control method and a vehicle, and relates to the technical field of vehicle control. The method comprises the following steps: in the case that it is detected that the vehicle is running on a slope road, predicting a slope change rate corresponding to a next moment of a current moment of the vehicle; determining a target control parameter of the next moment of the current moment based on the slope change rate; and pre-controlling the vehicle with the target control parameter at the current moment. Based on the above scheme, the timeliness of vehicle control can be improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and more particularly to a vehicle control method and a vehicle in the field of vehicle control technology. Background Technology

[0002] When a vehicle is driving on a slope, its control parameters need to be constantly adjusted to meet the control requirements of driving on such a slope. If the vehicle's control parameters lag, it will lead to vehicle control issues and hinder normal driving.

[0003] Therefore, improving the timeliness of vehicle control is an urgent problem that needs to be solved. Summary of the Invention

[0004] This application provides a vehicle control method and a vehicle, which can improve the timeliness of vehicle control.

[0005] In a first aspect, this application provides a vehicle control method, the method comprising: When a vehicle is detected traveling on a slope, predict the rate of change of the slope for the vehicle at the current moment and the next moment. Based on the slope change rate, determine the target control parameters for the next time step from the current time step; At the current moment, the vehicle is pre-controlled using the target control parameters.

[0006] In this embodiment of the application, when it is detected that the vehicle is traveling on a slope, the slope change rate of the vehicle at the current time is predicted first. Then, the control parameters (i.e. target control parameters) for pre-controlling the vehicle at the current time are determined by the predicted slope change rate of the vehicle at the current time. The target control parameters are then used to pre-control the vehicle at the current time. It is evident that when controlling the vehicle, real-time control is not based directly on the road slope at the vehicle's current location. Instead, the slope change rate at the next moment is predicted. The target control parameters for pre-controlling the vehicle at the current moment are then determined based on this predicted slope change rate. This pre-determination of target control parameters avoids the lag in control parameter output caused by real-time parameter determination, thus preventing vehicle control lag. Furthermore, after determining the target control parameters, they are not used to control the vehicle at the next moment, but rather pre-controlled at the current moment. This early intervention of the target control parameters effectively saves the time required for the control parameters to take effect, further preventing vehicle control lag. Therefore, this application embodiment solves the problem of real-time vehicle control lag by predicting the slope change rate and pre-controlling the vehicle using target control parameters determined by the slope change rate, effectively improving the timeliness of vehicle control.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the above-mentioned determination of the target control parameters for the next time step based on the slope change rate includes: Based on the rate of change of gradient and the vehicle's gear at the current moment, determine the target shift speed for the next moment; and / or, Based on the rate of change of slope and the vehicle's base torque, determine the target torque for the next time step from the current moment; and / or, Based on the slope change rate and the energy recovery intensity at the current moment, determine the target energy recovery intensity for the next moment. The target shift speed, target torque, and / or target energy recovery intensity are determined as the target control parameters.

[0008] In this embodiment, the target control parameters are determined by combining the vehicle's current gear, base torque, or energy recovery intensity with the slope change rate corresponding to the next time step. This allows for the determination of control parameters that better reflect the vehicle's actual driving conditions, improving the accuracy of the control parameters. Furthermore, the accuracy of vehicle pre-control is enhanced based on more accurate control parameters.

[0009] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the determination of the target shift speed for the next moment based on the gradient change rate and the vehicle's gear at the current moment includes: Determine the initial shift speed based on the vehicle's current gear position; When the gradient change rate is greater than or equal to the first preset change rate, the initial shift speed is increased to obtain the increased shift speed; the increased shift speed is determined as the target shift speed. If the gradient change rate is less than the second preset change rate, the initial shift speed is reduced to obtain the reduced shift speed; the reduced shift speed is determined as the target shift speed. The first preset rate of change is a positive threshold, and the second preset rate of change is a negative threshold.

[0010] In this embodiment, the initial shift speed corresponding to the vehicle's gear at the current moment is adjusted by increasing or decreasing the rate of change of the gradient corresponding to the next moment. This makes the adjusted shift speed more closely match the rate of change of the gradient corresponding to the next moment, improving the accuracy of the adjusted shift speed. Based on the improved accuracy of the adjusted shift speed, the accuracy of vehicle pre-control is further enhanced.

[0011] The adjusted shift speed refers to either the increased or decreased shift speed.

[0012] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the determination of the target torque for the next moment based on the rate of change of slope and the vehicle's base torque includes: Based on the slope change rate, determine the target correction torque for the next time step from the current time step; The base torque is corrected based on the target torque to obtain the target torque.

[0013] In this embodiment, the vehicle's base torque is corrected by using the correction torque corresponding to the rate of change of slope at the current moment in the next moment. This corrected target torque more realistically matches the slope change, improving the accuracy of the target torque. Based on a more accurate target torque, the accuracy of vehicle pre-control is further improved.

[0014] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the determination of the target correction torque for the next moment based on the slope change rate includes: Based on the slope change rate, vehicle gravity, and sensing distance, determine the initial correction amount for the next moment from the current moment. The target correction factor is determined based on the vehicle's remaining battery power and maximum torque margin. Based on the target correction coefficient, the initial correction amount is corrected to obtain the target correction torque.

[0015] In this embodiment, the target correction torque is determined through multi-dimensional features to avoid the limitations of determining the target correction torque using a single feature, thereby improving the accuracy of the target correction torque. Based on the improved accuracy of the target correction torque, the accuracy of the target torque itself is further enhanced.

[0016] The multidimensional features include the vehicle's gradient change rate at the current moment and the next moment, the vehicle's gravity, sensing distance, remaining battery power, and maximum torque margin.

[0017] Combining the first aspect and the above-described implementation methods, in some implementation methods of the first aspect, the target correction coefficient is determined based on the vehicle's remaining battery power and the vehicle's maximum torque margin, including: Determine the first correction factor based on the remaining battery power; The second correction factor is determined based on the maximum torque margin; The target correction factor is determined based on the first correction factor and the second correction factor.

[0018] In this embodiment, the target correction coefficient is determined by combining the vehicle's remaining battery power and maximum torque margin. This makes the target correction coefficient more consistent with the actual situation of the vehicle's battery and torque margin, thus improving the accuracy of the target correction coefficient. Based on the improved accuracy of the target correction coefficient, the accuracy of the target correction torque is further improved.

[0019] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the determination of the target energy recovery intensity for the next moment based on the slope change rate and the energy recovery intensity at the current moment includes: When the slope change rate decreases, the energy recovery intensity at the current moment is increased to obtain the increased energy recovery intensity; the increased energy recovery intensity is determined as the target energy recovery intensity. When the slope change rate increases, the energy recovery intensity at the current moment is reduced to obtain the reduced energy recovery intensity; the reduced energy recovery intensity is determined as the target energy recovery intensity.

[0020] In this embodiment, the energy recovery intensity at the current moment is adjusted by increasing or decreasing the rate of change of the slope corresponding to the next moment, which makes the adjusted energy recovery intensity more closely match the rate of change of the slope corresponding to the next moment, thus improving the accuracy of the adjusted energy recovery intensity. Based on the improved accuracy of the adjusted energy recovery intensity, the accuracy of vehicle pre-control is further improved.

[0021] The adjusted energy recovery intensity refers to either the increased energy recovery intensity or the decreased energy recovery intensity.

[0022] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the predicted rate of change of gradient for the vehicle at the current moment in the next moment includes: In the target binding relationship, the control parameters corresponding to the slope change rate are determined based on the slope change rate. The target binding relationship is used to represent the binding relationship between the slope change rate and the control parameters corresponding to the slope change rate. The control parameter corresponding to the slope change rate is determined as the target control parameter.

[0023] In this embodiment, a precise mapping between the slope change rate and the vehicle's control parameters is achieved through a binding relationship, thereby determining control parameters with higher accuracy and improving the accuracy of the control parameters. Based on more accurate control parameters, the accuracy of vehicle pre-control is further improved.

[0024] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the predicted rate of change of gradient for the vehicle at the current moment in the next moment includes: Based on vehicle operation data at multiple times, the gradient values ​​at multiple times are determined, where multiple times include the current time and historical times that are sequentially continuous with the current time; Based on the mileage step size, the slope values ​​at multiple times are converted to obtain multiple slope values ​​corresponding to the mileage. Based on multiple slope values ​​corresponding to mileage, the slope change rate is predicted.

[0025] In this embodiment, by converting time-based slope values ​​(i.e., slope values ​​at multiple times) into mileage-based slope values ​​(i.e., multiple slope values ​​corresponding to a mileage), the interference of different vehicle speeds on the slope change rate can be avoided, thus improving the accuracy of slope change rate prediction. Based on the more accurate slope change rate prediction, the accuracy of vehicle pre-control is further improved.

[0026] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the predicted rate of change of gradient for the vehicle at the current moment in the next moment includes: Based on the current slope change rate corresponding to the vehicle at the current moment and the historical slope change rate corresponding to the previous moment, where both the current slope change rate and the historical slope change rate are true values. Based on the change between the current slope change rate and the historical slope change rate, and the current slope change rate, predict the slope change rate.

[0027] In this embodiment, the slope change rate fluctuation trend (i.e., change amount) is obtained by comparing the actual current slope change rate with the historical slope change rate. This trend, along with the current slope change rate, is used to predict the short-term slope change rate, ensuring consistency between the prediction results and the actual road slope change trend, thereby improving the accuracy of slope change rate prediction. Based on this improved accuracy in slope change rate prediction, the accuracy of vehicle pre-control is further enhanced.

[0028] Secondly, this application provides a vehicle control device, the device comprising: The prediction module is used to predict the rate of change of the slope of the vehicle at the next moment when the vehicle is detected to be traveling on a slope. The processing module is used to determine the target control parameters for the next time step based on the slope change rate; and to perform pre-control on the vehicle at the current time step using the target control parameters.

[0029] Thirdly, this application provides a controller, including a storage module and a processing module. The storage module is used to store executable program code, and the processing module is used to call and run the executable program code from the storage module, causing the controller to execute the methods in the first aspect or any possible implementation of the first aspect.

[0030] Fourthly, this application provides a vehicle including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods described in the first aspect or any possible implementation thereof.

[0031] Fifthly, this application provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0032] Sixthly, this application provides a computer-readable storage medium storing computer program code that, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of a scenario for the vehicle control method provided in an embodiment of this application; Figure 2 This is a schematic flowchart of a vehicle control method provided in an embodiment of this application; Figure 3 This is a schematic diagram of the distance provided in the embodiments of this application; Figure 4 This is another schematic flowchart of a vehicle control method provided in an embodiment of this application; Figure 5 This is a schematic diagram of the architecture of the prediction model provided in the embodiments of this application; Figure 6 This is a schematic diagram of the vehicle control device provided in the embodiments of this application; Figure 7 This is a schematic diagram of the controller provided in an embodiment of this application; Figure 8 This is a schematic diagram of the vehicle structure provided in the embodiments of this application. Detailed Implementation

[0034] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0035] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0036] When a vehicle is driving on a slope, its control parameters need to be constantly adjusted to meet the control requirements of driving on such a slope. If the vehicle experiences control parameter lag, it will lead to vehicle control delays, which is detrimental to normal vehicle operation. These control parameters may include at least one of the following: the vehicle's output torque, the vehicle's shift speed, and the vehicle's energy recovery intensity. For ease of understanding, output torque will be used as an example below.

[0037] For example, with the rapid development of new energy vehicles and intelligent driving technologies, the torque control of a vehicle's powertrain affects its energy efficiency, power response, driving smoothness, and the durability of the transmission and battery systems. When a vehicle is driving on a slope, its driving demands constantly change, requiring real-time adjustments to the vehicle's output torque to meet the power requirements. However, adjusting the output torque in real-time based on the road gradient can easily lead to torque control lag, which is detrimental to normal vehicle operation. The vehicle powertrain refers to the collective device that generates power and converts that power into the mechanical energy required for vehicle movement. It can include the engine, drive motor, battery, transmission, and other related devices. The vehicle powertrain is related to the vehicle's power source; different vehicles generally have different powertrains depending on their power source. For example, if a vehicle's power source is an engine, then the corresponding vehicle powertrain consists of an engine, a gearbox, and a transmission system; if a vehicle's power source is a drive motor, then the corresponding vehicle powertrain consists of a drive motor, a battery, a gearbox, and a transmission system; if a vehicle's power source is both an engine and a drive motor, then the corresponding vehicle powertrain consists of an engine, a drive motor, a battery, a gearbox, and a transmission system.

[0038] In related technologies, the output torque of a vehicle is generally determined by the slope value of the road where the vehicle is located in real time. However, this real-time detected slope value often has acquisition delays and errors, which can easily lead to torque control lag.

[0039] Figure 1 This is a schematic diagram of a vehicle control method provided in an embodiment of this application.

[0040] For example, such as Figure 1 As shown, Figure 1 This includes vehicle 101 and sloping road 102. Vehicle 101 travels on sloping road 102.

[0041] When vehicle 101 is traveling on a sloped road 102, if the output torque of vehicle 101 is determined in real time based on the slope value of the road where the vehicle is located, the torque of vehicle 101 will not be output in time, resulting in a lag problem.

[0042] In view of this, this application proposes a vehicle control method and a vehicle. Through the embodiments of this application, when a vehicle is detected traveling on a slope, on the one hand, the slope change rate corresponding to the vehicle at the current moment and the next moment is predicted. The vehicle's control parameters are determined based on this predicted slope change rate, avoiding the control parameter output lag caused by real-time determination of control parameters, thus preventing vehicle control lag. On the other hand, the determined control parameters are used for pre-control of the vehicle, avoiding the vehicle control lag caused by the time the control parameters take effect. In other words, by predicting the slope change rate and pre-controlling the vehicle, the problem of vehicle control lag is minimized, effectively improving the timeliness of vehicle control.

[0043] The following is combined Figures 2 to 5 The vehicle control method provided in the embodiments of this application will be described in detail.

[0044] Figure 2 This is a flowchart illustrating a vehicle control method provided in an embodiment of this application. The method can be controlled by a vehicle (e.g., Figure 1 The vehicle 101 in the vehicle executes the command, or the controller in the vehicle executes the command.

[0045] For example, such as Figure 2 As shown, the method 200 includes the following implementation process: S210, when a vehicle is detected traveling on a slope, predicts the rate of change of the slope of the vehicle at the next moment.

[0046] For example, when a vehicle is powered on, it can be detected whether the vehicle is moving. While the vehicle is moving, the road gradient of the road it is currently traveling on can be obtained. Based on the road gradient, it can be determined whether the vehicle is traveling on a slope. When the vehicle is traveling on a slope, the gradient change rate for the next moment can be predicted, allowing for pre-control of the vehicle at the current moment based on this gradient change rate. Here, the gradient change rate for the next moment represents the rate of change of the road gradient at the vehicle's location at the next moment compared to the current location. The gradient change rate indicates how quickly the road gradient changes.

[0047] When the vehicle is not moving, i.e., when the vehicle is parked, it can detect in real time whether the vehicle is moving. Also, when the vehicle is not traveling on a sloped road, such as a flat road, no vehicle pre-control related to the rate of change of slope is performed.

[0048] For example, the relationship between the road gradient of the road where the vehicle is traveling and a preset gradient threshold is used to determine whether the vehicle is traveling on a sloping road. When the road gradient of the road where the vehicle is traveling is less than or equal to the preset gradient threshold, it indicates that the road gradient is relatively small and flat, belonging to a flat road. When the road gradient of the road where the vehicle is traveling is greater than the preset gradient threshold, it indicates that the road gradient is large and not flat, belonging to a sloping road.

[0049] Among them, the slope of a road refers to the longitudinal inclination of the road, which is the ratio of the vertical distance to the horizontal distance between two points along the road. For example... Figure 3 As shown, the vertical distance between road point A and road point B is H, and the horizontal distance is L. The percentage of the ratio of H to L is determined as the road slope between road point A and road point B, i.e., road slope = Furthermore, a preset slope threshold is used to represent the maximum slope value for a road to be flat, such as 0.2% or 0.5%, etc., which is not limited in this embodiment. The preset slope threshold can be obtained through pre-configuration or actual vehicle testing and can be pre-stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0050] Optionally, the road slope of the road where the vehicle is located can be collected in real time by an on-board slope sensor (e.g., a gyroscope) installed in the vehicle, or the vehicle's position data can be collected in real time by a positioning device installed in the vehicle, and then the road slope of that position data, i.e., the road slope of the road where the vehicle is located, can be determined from map data. The map data can include any one of high-precision maps, navigation maps, etc. The positioning device can include at least one of an inertial measurement unit (IMU), a wheel odometer, a global positioning system (GPS), etc. It should be understood that on-board cameras, on-board slope sensors, and positioning devices are common vehicle configurations, and no additional impact on the vehicle is required to implement the embodiments of this application, thus reducing the implementation cost of the embodiments of this application.

[0051] S220, based on the slope change rate, determines the target control parameters for the next time step from the current time step.

[0052] For example, when the slope change rate of the vehicle at the next moment is obtained, the control parameters (which can be called "target control parameters") of the vehicle at the next moment can be determined by the slope change rate of the vehicle at the next moment. The target control parameters are used to pre-control the vehicle at the current moment, and the target control parameters may include at least one of the following: target shift speed, target torque, target energy recovery intensity, etc.

[0053] S230, at the current moment, pre-controls the vehicle with the target control parameters.

[0054] For example, at the current moment, the vehicle is pre-controlled using target control parameters to ensure timely vehicle control.

[0055] When the target control parameter is the target shift speed, the vehicle's shift speed at the next moment can be pre-controlled based on the target shift speed at the current moment.

[0056] When the target control parameter is the target torque, the output torque of the vehicle at the next moment can be pre-controlled based on the target torque at the current moment.

[0057] When the target control parameter is the target energy recovery intensity, the energy recovery of the vehicle at the next moment can be pre-controlled based on the target energy recovery intensity at the current moment.

[0058] In such Figure 2In the method 200 shown, when it is detected that the vehicle is traveling on a slope, the slope change rate of the vehicle at the current time is first predicted, and then the control parameters (i.e. target control parameters) for pre-controlling the vehicle at the current time are determined by the predicted slope change rate of the vehicle at the current time. The target control parameters are then used to pre-control the vehicle at the current time. It is evident that when controlling the vehicle, real-time control is not based directly on the road slope at the vehicle's current location. Instead, the slope change rate at the next moment is predicted. The target control parameters for pre-controlling the vehicle at the current moment are then determined based on this predicted slope change rate. This pre-determination of target control parameters avoids the lag in control parameter output caused by real-time parameter determination, thus preventing vehicle control lag. Furthermore, after determining the target control parameters, they are not used to control the vehicle at the next moment, but rather pre-controlled at the current moment. This early intervention of the target control parameters effectively saves the time required for the control parameters to take effect, further preventing vehicle control lag. Therefore, this application embodiment solves the problem of real-time vehicle control lag by predicting the slope change rate and pre-controlling the vehicle using target control parameters determined by the slope change rate, effectively improving the timeliness of vehicle control.

[0059] Optionally, if the rate of change of the slope is greater than or equal to a preset change threshold, a step of pre-controlling the vehicle with the target control parameters at the current moment is executed.

[0060] For example, when obtaining the slope change rate of the vehicle at the next moment corresponding to the current moment, it is also possible to determine the relationship between the slope change rate of the vehicle at the next moment corresponding to the current moment and a preset change threshold. Based on the relationship between the slope change rate of the vehicle at the next moment corresponding to the current moment and the preset change threshold, it is determined whether to execute S230.

[0061] If the rate of change of the slope of the vehicle at the next moment is greater than or equal to the preset change threshold, it means that the predicted slope change of the vehicle at the next moment is large. If the vehicle is still controlled in real time based on the real-time road slope, it may lead to vehicle control lag. Therefore, in order to avoid vehicle control lag, the target control parameters can be used to intervene in advance at the current moment to pre-control the vehicle.

[0062] If the rate of change of the slope of the vehicle at the current moment is less than the preset change threshold, it means that the predicted slope change of the vehicle at the current moment is small. The vehicle can be controlled in real time based on the real-time road slope without the need for pre-control of the vehicle. This avoids the problem of large fluctuations in vehicle operation caused by frequent switching of control parameters, which leads to large fluctuations in vehicle driving on sloping roads, thereby improving the smoothness of vehicle driving on sloping roads.

[0063] The preset change threshold represents the minimum change value at which the vehicle needs to be pre-controlled. It can be obtained through pre-configuration or real vehicle testing and stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0064] Optionally, if the slope change rate is greater than or equal to a preset change threshold, the step of determining the target control parameters for the next moment based on the slope change rate is performed.

[0065] If the rate of change of the slope of the vehicle at the next moment is greater than or equal to the preset change threshold, in order to avoid vehicle control lag, S220 can be executed to determine the target control parameters for pre-controlling the vehicle.

[0066] If the rate of change of the slope of the vehicle at the next moment is less than the preset change threshold, S220 can be skipped since there is no need to pre-control the vehicle.

[0067] In this embodiment of the application, the relationship between the slope change rate of the vehicle at the current moment and the preset change threshold is used as a pre-triggered condition. This can avoid ineffective control when the slope change rate of the vehicle at the current moment is small, reduce unnecessary occupation of control resources, and save control resources.

[0068] It should be noted that S210~S230 above is a simplified description of the vehicle control method provided in the embodiments of this application. The following is a more detailed explanation of... Figure 2 The specific implementation methods shown in the embodiments are described in detail below: When executing S220, the above-mentioned determination of the target control parameters for the next moment based on the gradient change rate includes: determining the target shift speed for the next moment based on the gradient change rate and the vehicle's gear at the current moment; and / or, determining the target torque for the next moment based on the gradient change rate and the vehicle's base torque; and / or, determining the target energy recovery intensity for the next moment based on the gradient change rate and the energy recovery intensity at the current moment; and defining the target shift speed, target torque, and / or target energy recovery intensity as target control parameters.

[0069] For example, when determining the target shift speed based on the rate of change of the gradient corresponding to the next moment of the current moment, the vehicle's current gear can also be obtained. The target shift speed is then determined by combining the rate of change of the gradient corresponding to the next moment of the current moment with the vehicle's current gear. By combining the rate of change of the gradient corresponding to the next moment of the current moment with the vehicle's current gear to determine the target shift speed, a target shift speed that more closely reflects the vehicle's actual driving conditions can be determined, improving the accuracy of the target shift speed. This improved accuracy of the target shift speed further enhances the accuracy of vehicle pre-control.

[0070] Shift speed refers to the vehicle speed at which the transmission shifts up or down. The vehicle's gear position is determined in real-time by a Hall effect sensor on the shifter. Alternatively, pulse speed signals are collected in real-time by input and output shaft speed sensors in the transmission, and the vehicle analyzes these signals to determine the current gear position. Another method is to determine the current gear position based on the vehicle speed and throttle opening. It should be understood that a correspondence exists between vehicle speed / throttle opening and gear position. This correspondence can be pre-configured or obtained through real-vehicle testing and pre-stored in the vehicle's storage unit or a cloud server connected to the vehicle for easy retrieval.

[0071] For example, when determining the target torque using the rate of change of the gradient corresponding to the next moment of the current time, the vehicle's base torque can also be obtained. The target torque is then determined by combining the rate of change of the gradient corresponding to the next moment of the current time with the vehicle's base torque. By combining the rate of change of the gradient corresponding to the next moment of the current time with the vehicle's current gear to determine the target torque, a target torque that more closely reflects the vehicle's actual driving conditions can be determined, improving the accuracy of the target torque. This improved accuracy of the target torque further enhances the accuracy of vehicle pre-control.

[0072] The vehicle's base torque is the standard output torque calculated at the current moment based on the road gradient of the road where the vehicle is located.

[0073] For example, when determining the target energy recovery intensity based on the slope change rate corresponding to the vehicle's gradient at the current moment and the next moment, the vehicle's energy recovery intensity at the current moment can also be obtained. The target energy recovery intensity is then determined by combining the slope change rate corresponding to the vehicle's gradient at the current moment and the energy recovery intensity at the current moment. By combining the slope change rate corresponding to the vehicle's gradient at the current moment with the energy recovery intensity at the current moment to determine the target energy recovery intensity, a target energy recovery intensity that more closely reflects the vehicle's actual driving conditions can be determined, improving the accuracy of the target energy recovery intensity. This improved accuracy of the target energy recovery intensity further enhances the accuracy of vehicle pre-control.

[0074] Among them, Regenerative Braking Intensity refers to the magnitude of the vehicle's braking energy recovery force, and the energy recovery intensity is positively correlated with the braking energy recovery force. Furthermore, the energy recovery intensity is mapped in real-time to the energy recovery level signal within the vehicle. This energy recovery level signal can be obtained in real-time through the braking energy recovery paddle shifters, or triggered by the user actively setting the energy recovery level on the central control screen.

[0075] Therefore, once the target shift speed, target torque, and / or target energy recovery intensity are obtained, they can be determined as target control parameters.

[0076] Optionally, the target control parameters are determined based on at least one of the following: the rate of change of the slope at the current moment and the next moment; the vehicle's gear at the current moment; the vehicle's base torque; and the energy recovery intensity at the current moment.

[0077] In this embodiment, the target control parameters are determined by combining the vehicle's current gear, base torque, or energy recovery intensity with the slope change rate corresponding to the next time step. This allows for the determination of control parameters that better reflect the vehicle's actual driving conditions, improving the accuracy of the control parameters. Furthermore, the accuracy of vehicle pre-control is enhanced based on more accurate control parameters.

[0078] In one implementation, determining the target shift speed for the next moment based on the gradient change rate and the vehicle's gear at the current moment includes: determining an initial shift speed based on the vehicle's gear at the current moment; increasing the initial shift speed if the gradient change rate is greater than or equal to a first preset change rate to obtain an increased shift speed; determining the increased shift speed as the target shift speed; decreasing the initial shift speed if the gradient change rate is less than a second preset change rate to obtain a decreased shift speed; and determining the decreased shift speed as the target shift speed.

[0079] For example, upon obtaining the vehicle's current gear, the initial shift speed corresponding to that gear can be determined. Furthermore, the relationship between the rate of change of gradient at the next moment and a first preset rate of change can be assessed. The target shift speed is then determined based on this relationship.

[0080] Optionally, the initial shift speed is determined by the vehicle's gear at the current moment; the target shift speed for the next moment is determined by determining the initial shift speed and the rate of change of the gradient corresponding to the vehicle at the next moment.

[0081] When the rate of change of gradient corresponding to the next moment of the current time is greater than or equal to a first preset rate of change, the initial shift speed is increased to obtain the increased shift speed, which is then determined as the target shift speed. This is because when the rate of change of gradient corresponding to the next moment of the current time is greater than or equal to the first preset rate of change, it indicates that the predicted gradient increase is significant, and the driving resistance of the vehicle will increase accordingly. To avoid insufficient power for climbing due to higher gears, the shift speed threshold can be appropriately increased, meaning that upshifting can only be performed at a higher speed, allowing the vehicle to maintain a lower gear and ensuring sufficient torque output for climbing, thus guaranteeing adequate power for climbing. Therefore, the initial shift speed is increased to obtain the increased shift speed, which is then determined as the target shift speed.

[0082] For example, when increasing the initial shift speed, a target gain coefficient can be determined first, and the initial shift speed can be increased using this target gain coefficient to obtain the increased shift speed. Specifically, the product of the target gain coefficient and the initial shift speed is determined as the increased shift speed.

[0083] Optionally, a preset gain coefficient can be set as the target gain coefficient. To avoid the target gain coefficient being too large or too small, the preset gain coefficient should be within a reasonable range. It should be understood that the preset gain coefficient and reasonable range can be obtained through pre-configuration or real-vehicle testing and can be pre-stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0084] Optionally, the target gain coefficient corresponding to the initial shift speed can be determined based on the initial shift speed. It should be understood that there is a correspondence between the initial shift speed and the gain coefficient. This correspondence can be obtained through pre-configuration or real-vehicle testing and can be pre-stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0085] Optionally, within a preset time period before the rate of change of the slope corresponding to the next moment of the current time is greater than or equal to a first preset rate of change, it is determined whether the number of positive and negative changes in the rate of change of the slope corresponding to the next moment of the current time is less than or equal to a preset number. If the number of positive and negative changes in the rate of change of the slope corresponding to the next moment of the current time is less than or equal to the preset number, it indicates that the road slope where the vehicle is located does not have frequent alternations between uphill and downhill, and the change trend is relatively stable, reducing the probability of incorrect adjustment of the shift speed. At this time, the initial shift speed can be increased to obtain the increased shift speed, and the increased shift speed is determined as the target shift speed. However, if the number of positive and negative changes in the rate of change of the slope corresponding to the next moment of the current time is greater than the preset number, it indicates that the road slope where the vehicle is located has frequent alternations between uphill and downhill, and the change trend is unstable, which easily increases the probability of incorrect adjustment of the shift speed. At this time, the initial shift speed can be kept unchanged to avoid frequent adjustment of the shift speed causing the vehicle to jerk. By judging the number of positive and negative changes in the rate of change of the gradient of the vehicle at the current moment to the next moment, frequent gear shifting can be avoided, which would lead to frequent upshifts and downshifts. This improves the smoothness of power output and reduces wear on the transmission, thereby extending the service life of the transmission.

[0086] The preset duration can be 30 seconds or 40 seconds, etc., and can be obtained through pre-configuration or actual vehicle testing, and pre-stored in the vehicle's storage unit, or stored in a cloud server connected to the vehicle for easy retrieval. The preset number of times can represent the maximum number of times the road gradient where the vehicle is located does not frequently alternate between uphill and downhill, for example, 2 times or 1 time, etc., and this application does not limit this. The preset number of times can be obtained through pre-configuration or actual vehicle testing, and pre-stored in the vehicle's storage unit, or stored in a cloud server connected to the vehicle for easy retrieval.

[0087] It should be noted that the number of positive and negative changes in the slope change rate (which can be called the "sign_change_count") represents the total number of times the slope change rate switches back and forth between positive and negative directions. For example, the total number of times the slope change rate switches from + to -, then from - to +, and then from + to - is 3.

[0088] Alternatively, when obtaining the increased shift speed, it is not necessary to directly determine the increased shift speed as the target shift speed. Instead, the increased shift speed can be limited first to obtain the limited speed, and then the limited speed can be determined as the target shift speed. Specifically, the increased shift speed is limited using the initial shift speed to obtain the limited speed.

[0089] For example, the maximum speed limit for shifting gears is first determined by the initial shifting speed. This maximum speed limit is then used to limit the increased shifting speed, resulting in the limited speed. For instance, if the increased shifting speed is less than or equal to the maximum speed limit, the increased shifting speed can be determined as the limited speed. If the increased shifting speed is greater than the maximum speed limit, the maximum speed limit can be determined as the limited speed. That is, the limited speed is always less than or equal to the maximum speed limit, and the limited speed is always greater than the initial shifting speed. Furthermore, the maximum speed limit for shifting gears is determined by a first adjustment coefficient and the initial shifting speed. Specifically, the product of the first adjustment coefficient and the initial shifting speed is determined as the maximum speed limit. The first adjustment coefficient can be pre-configured or obtained through real-vehicle testing and pre-stored in the vehicle's storage unit, or it can be stored in a cloud server connected to the vehicle for easy retrieval.

[0090] When the rate of change of gradient corresponding to the next moment of the current time is less than the second preset rate of change, the initial shift speed is reduced to obtain the reduced shift speed, which is then determined as the target shift speed. This is because when the rate of change of gradient corresponding to the next moment of the current time is less than the second preset rate of change, it indicates that the predicted gradient descent is significant, and the vehicle will tend to coast and accelerate. To avoid the problem of low energy recovery efficiency due to low gear, the shift speed threshold can be appropriately lowered, meaning that the vehicle can shift gears at a lower speed, allowing the vehicle to maintain a higher gear and thus remain in the high-efficiency energy recovery range as much as possible, thereby maximizing energy recovery and improving the vehicle's energy recovery efficiency. Therefore, the initial shift speed is reduced to obtain the reduced shift speed, which is then determined as the target shift speed.

[0091] For example, when reducing the initial shift speed, a target attenuation coefficient can be determined first, and the initial shift speed can be reduced using this target attenuation coefficient to obtain the reduced shift speed. Specifically, the product of the target attenuation coefficient and the initial shift speed is determined as the reduced shift speed.

[0092] Optionally, a preset attenuation coefficient can be set as the target attenuation coefficient. To avoid the target attenuation coefficient being too large or too small, the preset attenuation coefficient should be within a reasonable range. It should be understood that the preset attenuation coefficient and reasonable range can be obtained through pre-configuration or real-vehicle testing and can be pre-stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0093] Optionally, the target attenuation coefficient corresponding to the initial shift speed can be determined based on the initial shift speed. It should be understood that there is a correspondence between the initial shift speed and the attenuation coefficient. This correspondence can be obtained through pre-configuration or real-vehicle testing and pre-stored in the vehicle's storage unit, or it can be stored in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0094] Optionally, within a preset time period before the rate of change of the slope corresponding to the next moment of the current time is less than a second preset rate of change, it is determined whether the number of positive and negative changes in the rate of change of the slope corresponding to the next moment of the current time is less than or equal to a preset number. If the number of positive and negative changes in the rate of change of the slope corresponding to the next moment of the current time is less than or equal to the preset number, it indicates that the road slope where the vehicle is located does not have frequent alternations between uphill and downhill, and the change trend is relatively stable, reducing the probability of incorrect adjustment of the shift speed. At this time, the initial shift speed can be reduced to obtain the reduced shift speed, and the reduced shift speed is determined as the target shift speed. However, if the number of positive and negative changes in the rate of change of the slope corresponding to the next moment of the current time is greater than the preset number, it indicates that the road slope where the vehicle is located has frequent alternations between uphill and downhill, and the change trend is unstable, which easily increases the probability of incorrect adjustment of the shift speed. At this time, the initial shift speed can be kept unchanged to avoid frequent adjustment of the shift speed causing the vehicle to jerk.

[0095] The first preset rate of change is a positive threshold, and the second preset rate of change is a negative threshold; that is, the first preset rate of change is positive, and the second preset rate of change is negative. The absolute values ​​of the first and second preset rates of change may be equal or unequal. Both the first and second preset rates of change can be obtained through pre-configuration or real-vehicle testing and pre-stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval. The initial shift speeds corresponding to different vehicle gears may be different or the same.

[0096] There is a correspondence between vehicle gears and initial shift speeds. This correspondence can be obtained through pre-configuration or real-vehicle testing and pre-stored in the vehicle's storage unit, or it can be stored in a cloud server connected to the vehicle for easy retrieval at any time. The initial shift speeds corresponding to different vehicle gears may be different or the same.

[0097] Alternatively, when obtaining the reduced shift speed, it is not necessary to directly determine the reduced shift speed as the target shift speed. Instead, the reduced shift speed can be limited first to obtain the limited speed, and then the limited speed can be determined as the target shift speed. Specifically, the reduced shift speed is limited using the initial shift speed to obtain the limited speed.

[0098] For example, a minimum speed limit for the shifting speed is first determined based on the initial shifting speed. This minimum speed limit is then used to limit the reduced shifting speed, resulting in the limited speed. For instance, if the reduced shifting speed is less than or equal to the minimum speed limit, the minimum speed limit can be used as the limited speed. If the reduced shifting speed is greater than the minimum speed limit, the reduced shifting speed can also be used as the limited speed. That is, the limited speed is always greater than or equal to the minimum speed limit, and the limited speed is always less than the initial shifting speed. Furthermore, the minimum speed limit for the shifting speed is determined using a second adjustment coefficient and the initial shifting speed. Specifically, the product of the second adjustment coefficient and the initial shifting speed is used as the minimum speed limit. The second adjustment coefficient can be pre-configured or obtained through real-vehicle testing and pre-stored in the vehicle's storage unit, or it can be stored in a cloud server connected to the vehicle for easy retrieval.

[0099] In this embodiment, the initial shift speed corresponding to the vehicle's gear at the current moment is adjusted by increasing or decreasing the rate of change of the gradient corresponding to the next moment. This makes the adjusted shift speed more closely match the rate of change of the gradient corresponding to the next moment, improving the accuracy of the adjusted shift speed. Based on the improved accuracy of the adjusted shift speed, the accuracy of vehicle pre-control is further improved. Here, the adjusted shift speed is either an increased or decreased shift speed.

[0100] In one implementation, determining the target torque for the next moment based on the slope change rate and the vehicle's base torque includes: determining the target corrected torque for the next moment based on the slope change rate; and correcting the base torque based on the target corrected torque to obtain the target torque.

[0101] For example, by determining the rate of change of the slope corresponding to the vehicle at the next moment from the current moment, the corrected torque corresponding to that rate of change is determined (which can be called the "target corrected torque"). Then, the vehicle's base torque is corrected using this target corrected torque to obtain the corrected torque; and this corrected torque is determined as the target torque. Specifically, the sum of the target corrected torque and the vehicle's base torque is determined as the corrected torque.

[0102] Optionally, the target torque can be determined by the target correction torque and the vehicle's base torque.

[0103] In this embodiment, the vehicle's base torque is corrected by using the correction torque corresponding to the rate of change of slope at the current moment in the next moment. This corrected target torque more realistically matches the slope change, improving the accuracy of the target torque. Based on a more accurate target torque, the accuracy of vehicle pre-control is further improved.

[0104] Furthermore, the above-mentioned determination of the target correction torque for the next moment based on the slope change rate includes: determining the initial correction amount for the next moment based on the slope change rate, the vehicle's gravity, and the sensing distance; determining the target correction coefficient based on the vehicle's remaining battery power and maximum torque margin; and correcting the initial correction amount based on the target correction coefficient to obtain the target correction torque.

[0105] For example, when obtaining the slope change rate corresponding to the next moment of the current moment, the vehicle's gravity and sensing distance can also be obtained, as well as the vehicle's current remaining battery charge (State of Charge, SOC) and maximum torque margin. Then, using the slope change rate corresponding to the next moment of the current moment, the vehicle's gravity and sensing distance, as well as the SOC and maximum torque margin, the target correction torque can be determined together.

[0106] For example, when determining the weight of a vehicle, it is necessary to first obtain the vehicle's mass, and then determine the vehicle's weight by multiplying the vehicle's mass by gravitational acceleration. Specifically, the product of the vehicle's mass and gravitational acceleration is determined as the vehicle's weight.

[0107] For example, when determining the sensing distance, the vehicle's current speed and a preset time window can be used to determine the distance of the road ahead that the vehicle can perceive within the preset time window, and this distance is then defined as the sensing distance. Specifically, the product of the vehicle's current speed and the preset time window is determined as the sensing distance (also known as the "pre-aiming distance"). Figure 3 As shown, Figure 3In this context, L represents the future travel distance that the vehicle can perceive within a preset time window; therefore, L is the perception distance. The perception distance can represent the distance of the road ahead that the vehicle can currently perceive. The vehicle's current speed can be obtained in real time through wheel speed sensors or acceleration sensors on the vehicle.

[0108] Among them, SOC can represent the percentage of the current remaining charge of the power battery relative to its total capacity, i.e., the state of charge of the power battery. And, maximum torque margin (also known as "maximum torque tolerance") represents the remaining available drive torque reserve of the vehicle at the current moment; it is the difference between the maximum drive torque that the vehicle is allowed to output and the torque currently being output.

[0109] It should be understood that the vehicle's quality and preset time window (which can be denoted as "H") can be obtained through pre-configuration or real vehicle testing and stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval at any time.

[0110] For example, the initial correction amount for the next moment is first determined using the vehicle's gradient change rate, weight, and sensing distance. Specifically, the initial correction amount is determined by multiplying the gradient change rate, weight, and sensing distance. Then, target correction coefficients are determined using the State of Charge (SOC) and maximum torque margin. The initial correction amount is then corrected using these target correction coefficients to obtain the corrected torque, which is then determined as the target corrected torque. Specifically, the corrected torque is determined by multiplying the target correction coefficient and the initial correction amount. Finally, the vehicle's weight is determined using its mass and gravitational acceleration. The vehicle's weight is then determined by multiplying its mass and gravitational acceleration.

[0111] It should be understood that the target correction coefficient, in addition to correcting the initial correction amount, can also perform dimensional transformation on the initial correction amount. That is, the target correction coefficient is both a correction coefficient and a dimensional transformation coefficient.

[0112] Optionally, the target correction torque can be obtained by combining the target correction coefficient and the initial correction amount.

[0113] Alternatively, when obtaining the corrected torque, it may not be directly determined as the target corrected torque. Instead, the corrected torque may first be limited to obtain the limited torque, and then the limited torque may be determined as the target corrected torque. Specifically, the corrected torque is limited by the vehicle's current torque to obtain the limited torque.

[0114] For example, first, a torque limit range is determined based on the vehicle's current torque. Then, the corrected torque is limited within this range to obtain the limited torque. For instance, if the corrected torque is greater than the maximum upper limit of the torque limit range, that maximum upper limit can be determined as the limited torque. If the corrected torque is greater than the minimum upper limit of the torque limit range but less than the maximum upper limit, the corrected torque can be determined as the limited torque. If the corrected torque is less than the minimum upper limit of the torque limit range, that minimum upper limit can be determined as the limited torque. In other words, the limited torque is always greater than or equal to the minimum upper limit and less than or equal to the maximum upper limit.

[0115] Furthermore, the torque adjustment amount is determined by using a third adjustment coefficient and the vehicle's current torque. This torque adjustment amount is then used to increment or decrement the vehicle's current torque to obtain the torque limit range. Specifically, the product of the third adjustment coefficient and the vehicle's current torque is determined as the torque adjustment amount. This torque adjustment amount is then used to increment or decrement the vehicle's current torque to obtain the torque limit range. In other words, the sum of the vehicle's current torque and the torque adjustment amount is determined as the maximum upper limit of the torque limit range, and the difference between the vehicle's current torque and the torque adjustment amount is determined as the minimum upper limit of the torque limit range. The third adjustment coefficient can be pre-configured or obtained through real-vehicle testing and can be pre-stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval.

[0116] In this embodiment, the target correction torque is determined through multi-dimensional features to avoid the limitations of determining the target correction torque using a single feature, thereby improving the accuracy of the target correction torque. Building upon this improved accuracy, the overall accuracy of the target torque is further enhanced. The multi-dimensional features include the vehicle's gradient change rate at the current moment and the next moment, the vehicle's gravity, sensing distance, remaining battery power, and maximum torque margin.

[0117] Furthermore, the above-mentioned determination of the target correction coefficient based on the vehicle's remaining battery power and maximum torque margin includes: determining a first correction coefficient based on the remaining battery power; determining a second correction coefficient based on the maximum torque margin; and determining the target correction coefficient based on the first and second correction coefficients.

[0118] For example, a correction factor (which can be called the "first correction factor") is determined based on the State of Charge (SOC), and a correction factor (which can be called the "second correction factor") is determined based on the maximum torque margin. The sum of the first and second correction factors is then determined as the target correction factor, or the product of the first and second correction factors is determined as the target correction factor.

[0119] Specifically, the State of Charge (SOC) is normalized to obtain a normalized SOC, and the normalized SOC is determined as the first correction coefficient. Specifically, the difference between the SOC and the minimum safe charge capacity allowed by the power battery (denoted as "charge difference A") is first calculated, and the difference between the maximum safe charge capacity allowed by the power battery and the minimum safe charge capacity allowed by the power battery (denoted as "charge difference B") is calculated. The ratio of charge difference A to charge difference B is then determined as the first correction coefficient. In this embodiment, the minimum safe charge capacity allowed by the power battery can be denoted as "SOCmin," and the maximum safe charge capacity allowed by the power battery can be denoted as "SOCmax." For example, SOCmin can be 10% or 15%, and SOCmax can be 100% or 98%, etc. This embodiment does not limit this. SOCmin and SOCmax can be obtained through pre-configuration or power battery testing and pre-stored in the vehicle's storage unit, or stored in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0120] Furthermore, the maximum torque margin is normalized to obtain a normalized torque margin, and this normalized torque margin is determined as the second correction coefficient. Specifically, the ratio between the maximum torque margin and the maximum torque that the drive motor can output at the current speed is first calculated, and then the difference between 1 and this ratio is determined as the second correction coefficient.

[0121] In this embodiment, the target correction coefficient is determined by combining the vehicle's remaining battery power and maximum torque margin. This makes the target correction coefficient more consistent with the actual situation of the vehicle's battery and torque margin, thus improving the accuracy of the target correction coefficient. Based on the improved accuracy of the target correction coefficient, the accuracy of the target correction torque is further improved.

[0122] In one implementation, determining the target energy recovery intensity for the next moment based on the slope change rate and the energy recovery intensity at the current moment includes: increasing the energy recovery intensity at the current moment when the slope change rate decreases to obtain an increased energy recovery intensity; determining the increased energy recovery intensity as the target energy recovery intensity; decreasing the energy recovery intensity at the current moment when the slope change rate increases to obtain a decreased energy recovery intensity; and determining the decreased energy recovery intensity as the target energy recovery intensity.

[0123] For example, when obtaining the slope change rate of the vehicle at the next moment from the current moment, the current slope change rate of the vehicle at the current moment can be obtained, and the change of the slope change rate of the vehicle at the next moment from the current moment can be determined. By comparing the change of the slope change rate of the vehicle at the next moment from the current moment to the current slope change rate, the target energy recovery intensity at the next moment can be determined.

[0124] If the rate of change of gradient for the vehicle decreases in the next time step (i.e., the rate of change of gradient for the next time step is less than the rate of change of gradient for the current time step), the energy recovery intensity for the current time step is increased to obtain the increased energy recovery intensity. This increased energy recovery intensity is then determined as the target energy recovery intensity. This is because a decrease in the rate of change of gradient for the vehicle in the next time step indicates that the vehicle is continuously traveling downhill. To improve energy recovery efficiency, the energy recovery intensity can be appropriately increased to fully utilize the downhill potential energy and recover as much energy as possible, thereby improving the vehicle's energy recovery efficiency. Furthermore, by pre-controlling the vehicle's energy recovery based on the target energy recovery intensity, the vehicle can enter the energy recovery preparation state in advance, ensuring timely energy recovery and avoiding kinetic energy loss due to delayed energy recovery.

[0125] For example, if the energy recovery intensity at the current moment is level 1, and the rate of change of the slope decreases at the next moment, the target energy recovery intensity can be determined to be level 2.

[0126] Specifically, the increased energy recovery intensity is greater than the energy recovery intensity at the current moment. Furthermore, the greater the decrease in the rate of change of the slope at the next moment, the greater the increase in energy recovery intensity. In other words, the larger the difference between the rate of change of the slope at the next moment and the energy recovery intensity at the current moment, the greater the target energy recovery intensity. This can be understood as a positive correlation between the decrease in the rate of change of the slope at the next moment and the increase in the target energy recovery intensity; that is, the difference between the rate of change of the slope at the next moment and the energy recovery intensity at the current moment is positively correlated with the target energy recovery intensity.

[0127] If the rate of change of gradient for the vehicle increases from the current moment to the next moment (i.e., the rate of change of gradient for the vehicle from the current moment to the next moment is greater than the rate of change of gradient for the vehicle at the current moment), the energy recovery intensity at the current moment is reduced, resulting in a reduced energy recovery intensity. This reduced energy recovery intensity is then determined as the target energy recovery intensity. This is because an increase in the rate of change of gradient for the vehicle from the current moment to the next moment indicates that the vehicle is continuously traveling uphill. To avoid insufficient climbing power due to excessive energy recovery, the energy recovery intensity can be appropriately reduced to minimize the impact of energy recovery on the vehicle's power output, thereby ensuring that the vehicle can output sufficient torque for climbing and guaranteeing adequate climbing power.

[0128] For example, if the energy recovery intensity at the current moment is level 2, and the slope change rate increases at the next moment, the target energy recovery intensity can be determined to be level 1.

[0129] Specifically, the reduced energy recovery intensity is less than the energy recovery intensity at the current moment. Furthermore, the greater the increase in the rate of change of slope at the next moment, the greater the decrease in energy recovery intensity. In other words, the larger the difference between the current rate of change of slope at the current moment and the energy recovery intensity at the next moment, the smaller the target energy recovery intensity. This can be understood as a positive correlation between the increase in the rate of change of slope at the next moment and the decrease in the target energy recovery intensity, and a negative correlation between the difference between the current rate of change of slope at the current moment and the energy recovery intensity at the next moment.

[0130] It's important to note that the current slope change rate at the current moment represents the rate of change of the road slope at the vehicle's current location compared to the road slope at the vehicle's location at the previous moment. Furthermore, this current slope change rate is calculated using actual road slope data collected at both the vehicle's current and previous locations; it is a real value, not a prediction. This can be understood as a positive correlation between the decrease in the slope change rate at the next moment and the increase in the target energy recovery intensity; that is, the difference between the slope change rate at the next moment and the current energy recovery intensity is positively correlated with the target energy recovery intensity.

[0131] In this embodiment, the energy recovery intensity at the current moment is adjusted by increasing or decreasing the rate of change of the slope corresponding to the next moment, which makes the adjusted energy recovery intensity more closely match the rate of change of the slope corresponding to the next moment, thus improving the accuracy of the adjusted energy recovery intensity. Based on the improved accuracy of the adjusted energy recovery intensity, the accuracy of vehicle pre-control is further improved. Here, the adjusted energy recovery intensity refers to either an increased energy recovery intensity or a decreased energy recovery intensity.

[0132] Optionally, the above-mentioned prediction of the slope change rate of the vehicle at the next moment at the current moment includes: in the target binding relationship, determining the control parameter corresponding to the slope change rate based on the slope change rate; and determining the control parameter corresponding to the slope change rate as the target control parameter.

[0133] The target binding relationship is used to represent the binding relationship between the slope change rate and the control parameters corresponding to the slope change rate. This target binding relationship can be obtained through pre-configuration or real vehicle testing and can be pre-stored in the vehicle's storage unit or in a cloud server that communicates with the vehicle so that the vehicle can retrieve it at any time.

[0134] For example, when obtaining the slope change rate of the vehicle at the next moment from the current moment, the control parameters corresponding to the slope change rate of the vehicle at the next moment from the current moment can be queried in the target binding relationship. The queried control parameters corresponding to the slope change rate of the vehicle at the next moment from the current moment are then determined as the target control parameters.

[0135] For example, in a target binding relationship, if the control parameter corresponding to the slope change rate A of the vehicle at the current time to the next time is control parameter A, then control parameter A can be determined as the target control parameter.

[0136] It should be understood that the control parameters corresponding to the slope change rate at the next moment can be different or the same for different vehicles, and this application embodiment does not limit this. For example, the control parameter corresponding to the slope change rate A at the next moment is control parameter A, and the control parameter corresponding to the slope change rate B at the next moment is control parameter B. Alternatively, the control parameter corresponding to both the slope change rate A and the slope change rate B at the next moment can be control parameter A.

[0137] In this embodiment, a precise mapping between the slope change rate and the vehicle's control parameters is achieved through a binding relationship, thereby determining control parameters with higher accuracy and improving the accuracy of the control parameters. Based on more accurate control parameters, the accuracy of vehicle pre-control is further improved.

[0138] Figure 4 This is another schematic flowchart illustrating a vehicle control method provided in this application. The method can be implemented by a vehicle (e.g., Figure 1 The vehicle 101 in the vehicle executes the command, or the controller in the vehicle executes the command.

[0139] For example, such as Figure 4 As shown, the method 400 includes the following implementation process: S410, when it is detected that the vehicle is traveling on a slope, predicts the rate of change of the slope of the vehicle at the next moment.

[0140] In one implementation, the above-mentioned prediction of the slope change rate of the vehicle at the next moment corresponding to the current moment includes: determining the slope values ​​at multiple moments based on vehicle operation data at multiple moments; converting the slope values ​​at multiple moments based on the mileage step to obtain multiple slope values ​​corresponding to the mileage; and predicting the slope change rate based on the multiple slope values ​​corresponding to the mileage.

[0141] Among them, multiple moments include the current moment and historical moments that are sequentially continuous with the current moment.

[0142] For example, when a vehicle is detected traveling on a slope, multiple onboard sensors can collect multi-dimensional operational data of the vehicle in real time. This multi-dimensional operational data (i.e., vehicle operational data at multiple moments) can then be used to predict the rate of change of the slope at the next moment. The multiple onboard sensors include at least one of an Inertial Measurement Unit (IMU), a barometric pressure sensor, a wheel speed sensor, and an acceleration sensor. The multi-dimensional operational data includes at least one of the following: vehicle pitch angle, vehicle longitudinal acceleration, ambient air pressure at the vehicle's location, vehicle speed, and mileage. Furthermore, the multi-dimensional operational data can refer to the operational data of the vehicle at its current location as well as the operational data of the vehicle at multiple historical locations prior to the current moment, with these historical moments sequentially linked to the current moment.

[0143] For example, the slope at different times is estimated by using the ambient air pressure at the vehicle's location. Then, the road slope at different times (i.e., slope values ​​at multiple times) is determined by using the vehicle's pitch angle and the estimated slope at different times. Determining the road slope at the vehicle's location using both the vehicle pitch angle and the estimated slope avoids the slope calculation error caused by inertial acceleration interference during vehicle acceleration by the IMU, thus improving the accuracy of slope calculation. It is important to ensure that the timestamps of the vehicle pitch angle and the estimated slope are aligned to guarantee data synchronization.

[0144] Specifically, the vehicle pitch angle and estimated slope are fused to obtain the road slope at the vehicle's location. First, the vehicle's longitudinal acceleration is used to determine the first weight corresponding to the vehicle pitch angle and the second weight corresponding to the estimated slope. Then, the first weight and the vehicle pitch angle, the second weight and the estimated slope are weighted and fused to obtain the road slope at the vehicle's location. First, the product between the first weight and the vehicle pitch angle (which can be denoted as "product 1") and the product between the second weight and the estimated slope (which can be denoted as "product 2") are calculated. Then, the sum of product 1 and product 2 is used to determine the road slope at the vehicle's location.

[0145] It should be understood that the sum of the first and second weights is 1, meaning the second weight is the difference between 1 and the first weight. Furthermore, the road slope at the vehicle's location (which can be denoted as "θ(t)") carries a timestamp and the vehicle's mileage within that timestamp, used for subsequent prediction of the slope change rate. For example, if the timestamp is 10:00, and the vehicle travels 50m between 10:00 and 10:01, then the road slope at the vehicle's location carries the timestamp of 10:00 and the mileage traveled is 50m.

[0146] For example, when determining the first weight and the second weight, the magnitudes of the vehicle's longitudinal acceleration and a preset acceleration threshold are first determined. The first weight and the second weight are then determined based on the magnitudes of the vehicle's longitudinal acceleration and the preset acceleration threshold.

[0147] When the absolute value of the vehicle's longitudinal acceleration is less than or equal to a preset acceleration threshold, it indicates that the vehicle is in a smooth driving state and its driving is relatively stable (also known as "steady state"). In this case, the vehicle pitch angle data collected by the IMU is the primary factor, with a first weight greater than a second weight, for example, the first weight is 0.8 and the second weight is 0.2. When the absolute value of the vehicle's longitudinal acceleration is greater than the preset acceleration threshold, it indicates that the vehicle is not in a smooth driving state and its driving is unstable (also known as "instability"). In this case, the estimated slope estimated by ambient air pressure is the primary factor, with a second weight greater than the first weight, for example, the first weight is 0.3 and the second weight is 0.7. The first weight is used to adjust the proportion of the vehicle pitch angle in the fusion of the vehicle pitch angle and the estimated slope, and the second weight is used to adjust the proportion of the estimated slope in the fusion of the vehicle pitch angle and the estimated slope. The preset acceleration threshold can represent the maximum steady-state acceleration of the vehicle. It can be obtained through pre-configuration or real-vehicle testing and pre-stored in the vehicle's storage unit, or it can be stored in a cloud server connected to the vehicle for easy retrieval at any time.

[0148] Optionally, the vehicle pitch angle and longitudinal acceleration are collected in real time using an IMU (Insulated Measurement Unit), with a sampling frequency of 100 Hz. The ambient air pressure at the vehicle's location is collected in real time using a barometric pressure sensor, with a sampling frequency of 10 Hz. The mileage can be calculated in real time by accumulating the vehicle speed using the trapezoidal integral method. The output frequency for the road gradient at the vehicle's location is 10 Hz. It should be understood that the sampling frequency and output frequency are merely illustrative examples and are not intended to limit the scope of this application.

[0149] For example, when the road slope of multiple vehicle locations is obtained, multiple road slopes can be sampled by a sliding window using a mileage step size to obtain multiple sets of mileage data at equidistant mileage points. Then, using an interpolation algorithm, the road slope corresponding to each equidistant mileage point (which can be denoted as "θi") is calculated in the road slope of the aforementioned vehicle locations. That is, multiple slope values ​​corresponding to the mileage. The multiple sets of mileage data and the θi corresponding to each set of mileage data (which can be denoted as "si") in the multiple sets of mileage data are combined into multiple sets of sequence data, namely "(si,θi)".

[0150] Furthermore, upon obtaining (si, θi), a preset mileage can be used as the window length to perform fitting calculations on all (si, θi) within the window length to obtain the slope change rate corresponding to all (si, θi) within the window length. Specifically, the least squares method is used to perform a first polynomial fitting on all (si, θi) within the window length to obtain the first fitting polynomial. The slope of the first fitting polynomial is determined as the slope change rate corresponding to all (si, θi) within the window length. Here, the first fitting polynomial can be expressed as θ(s1) = k1 × s1 + b1, where θ(s1) represents the road slope corresponding to the window length, s1 represents the driving mileage of the window length, k1 represents the slope of the first fitting polynomial (which can be denoted as "feature_dθdx"), and s1 represents the constant term of the first fitting polynomial.

[0151] Furthermore, a polynomial fitting is also performed on the end portion of the window length, that is, the entire (si, θi) of the end portion is fitted to obtain the slope change rate corresponding to the entire (si, θi) of the end portion. Specifically, the least squares method is used to perform a polynomial fitting on the entire (si, θi) of the end portion to obtain a second fitting polynomial. The slope of the second fitting polynomial is determined as the slope change rate corresponding to the entire (si, θi) of the end portion. Here, the second fitting polynomial can be expressed as θ(s2) = k2 × s2 + b2, where θ(s2) represents the road slope corresponding to the end portion, s2 represents the driving distance of the end portion, k2 represents the slope of the second fitting polynomial (which can be denoted as "feature_dθdx_recent"), and s2 represents the constant term of the second fitting polynomial. k3 can reflect the latest local change trend of the road slope, which is used to detect abrupt changes in road slope in small road segments, such as bridge approach ramps, to avoid the overall slope change masking the local slope change, thus improving the accuracy of slope change rate prediction. Furthermore, the end portion may represent the last 20% of the mileage interval of the window length, or the last 30% of the mileage interval of the window length, etc., but this application embodiment does not limit this.

[0152] Optionally, when multiple k1s are obtained, the number of positive and negative switching of k1 can be counted to reduce the probability of incorrect adjustment of the shift speed by counting the number of positive and negative switching of k1.

[0153] Alternatively, polynomial fitting can be achieved using a digital filter (e.g., Savitzky-Golay). This digital filter can be any of the following: Savitzky-Golay Filter (SG Filter), Locally Estimated Scatterplot Smoothing (LOESS) Filter, or Polynomial Interpolation Resampling Filter.

[0154] Wherein, (si,θi) is independent of the timestamp t, and corresponds only to the mileage and road slope, thus eliminating the adverse effects of different vehicle speeds on the slope change rate. Furthermore, the mileage step size can be 5m or 10m, etc., and this application embodiment does not limit this. The mileage step size can be obtained through pre-configuration or actual vehicle testing, and can be pre-stored in the vehicle's storage unit, or stored in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time. Also, the preset mileage can be 50m or 100m, etc., and this application embodiment does not limit this. The preset mileage can be obtained through pre-configuration or actual vehicle testing, and can be pre-stored in the vehicle's storage unit, or stored in a cloud server connected to the vehicle for easy retrieval by the vehicle at any time.

[0155] Furthermore, after obtaining θ(t), k1, k2, sign_change_count, vehicle speed, vehicle gear, and SOC, feature vectors can be assembled from θ(t), k1, k2, sign_change_count, vehicle speed, vehicle gear, and SOC to obtain the assembled feature vector (which can be denoted as "F"). F=[θ(t),k1,k2,sign_change_count,vehicle speed,vehicle gear,SOC].

[0156] When you get F, you can input F as follows: Figure 5 The prediction model 500 shown predicts the slope change rate corresponding to F. Specifically, F is used as the input data of the input layer 510, processed by the network layer 520 to predict the slope change rate corresponding to F, and the slope change rate corresponding to F is used as the output data of the output layer 530. It should be understood that the prediction model 500 is a trained model with the function of predicting the slope change rate.

[0157] In this embodiment, by converting time-based slope values ​​(i.e., slope values ​​at multiple times) into mileage-based slope values ​​(i.e., multiple slope values ​​corresponding to a mileage), the interference of different vehicle speeds on the slope change rate can be avoided, thus improving the accuracy of slope change rate prediction. Based on the more accurate slope change rate prediction, the accuracy of vehicle pre-control is further improved.

[0158] Optionally, the above-mentioned prediction of the slope change rate corresponding to the next moment of the current moment of the vehicle includes: based on the current slope change rate corresponding to the current moment of the vehicle and the historical slope change rate corresponding to the previous moment of the current moment; and based on the change between the current slope change rate and the historical slope change rate and the current slope change rate, predicting the slope change rate.

[0159] Both the current gradient change rate and the historical gradient change rate are actual values. The current gradient change rate corresponding to the vehicle at the current moment is calculated using the actual road gradients collected at the vehicle's current location and the vehicle's location at the previous moment, and is therefore an actual value. Similarly, the historical gradient change rate of the vehicle at the previous moment is also calculated using the actual road gradients collected at the vehicle's location at the previous moment and the location at the moment before that point, and is therefore an actual value. Both the current gradient change rate and the historical gradient change rate can be stored in the vehicle's storage unit or in a cloud server connected to the vehicle for easy retrieval at any time.

[0160] For example, when the current slope change rate and the historical slope change rate are obtained, the difference between the current slope change rate and the historical slope change rate can be calculated first. This difference can then be defined as the change between the current and historical slope change rates, reflecting the fluctuation trend of the slope change rate between adjacent time points. Based on this change, and combined with the current slope change rate, the slope change rate corresponding to the vehicle at the next time point can be predicted.

[0161] Optionally, the change amount and the current slope change rate are input into the slope prediction model to output the slope change rate corresponding to the vehicle at the next moment. It should be understood that the slope prediction model is a pre-trained model with the function of predicting the slope change rate, and the slope prediction model is mounted on the vehicle or a cloud server that is connected to the vehicle.

[0162] Optionally, the current slope change rate and the sum of the change rates can be determined as the slope change rate corresponding to the vehicle at the next moment.

[0163] In this embodiment, the slope change rate fluctuation trend (i.e., change amount) is obtained by comparing the actual current slope change rate with the historical slope change rate. This trend, along with the current slope change rate, is used to predict the short-term slope change rate, ensuring consistency between the prediction results and the actual road slope change trend, thereby improving the accuracy of slope change rate prediction. Based on this improved accuracy in slope change rate prediction, the accuracy of vehicle pre-control is further enhanced.

[0164] Figure 5 This is a schematic diagram of the architecture of the prediction model provided in the embodiments of this application.

[0165] For example, such as Figure 5 As shown, the prediction model 500 may include an input layer 510, a network layer 520, and an output layer 530. The training data F serves as the input data, and the predicted slope change rate serves as the output data.

[0166] The input layer 510 receives training data and transmits the training data to the network layer 520. The number of training data is at least one, for example, F corresponding to roads already traveled by the vehicle.

[0167] Network layer 520 is used to process the training data to predict the slope change rate corresponding to the training data.

[0168] Network layer 520 may include a first network layer and a second network layer.

[0169] The first network layer includes a hidden layer with 64 hidden units, layer normalization (LayerNorm), and a regularization rule (Dropout) with a deactivation rate of 0.2.

[0170] The second network layer includes a hidden layer with 32 hidden units, layer normalization (LayerNorm), and a regularization rule (Dropout) with a deactivation rate of 0.2.

[0171] Optionally, the hidden layer with 64 hidden units can be any one of GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and TCN (Temporal Convolutional Network).

[0172] Output layer 530 is used to output the predicted slope change rate corresponding to the training data. Output layer 530 can map the 32 hidden features output from the second network layer to a single predicted slope change rate value.

[0173] Optionally, when predicting the slope change rate corresponding to the training data, a loss function can be used to constrain the predicted slope change rate to improve the stability and accuracy of the prediction results.

[0174] The loss function is calculated using the mean squared error loss, directional loss, and smoothing loss. Specifically, the loss function is determined by summing the products of the mean squared error loss, directional loss, and their corresponding weight coefficients, as well as the smoothing loss and its corresponding weight coefficients.

[0175] The mean squared error loss is the basic loss term, used to constrain the deviation between the predicted slope change rate and the actual slope change rate. The direction loss is the consistency loss between the predicted and actual slope change rates, used to penalize samples where the rising and falling trends of the predicted slope change rate are opposite to those of the actual slope change rate, thus avoiding control errors caused by incorrect prediction direction. The smoothing loss penalizes drastic jumps in the predicted slope change rate, ensuring its stability. Furthermore, the weighting coefficients for the direction loss and the smoothing loss can be continuously adjusted through training; for example, the weighting coefficient for the direction loss is 0.3 and the weighting coefficient for the smoothing loss is 0.05, but this embodiment does not limit this.

[0176] Optionally, in order to improve the accuracy of slope change rate prediction, training samples can be collected in different environments (e.g., plains, hills, mountains, etc.) to obtain multiple F values ​​under different environments. The prediction model 500 can be trained using these multiple F values ​​to improve its generalization ability and robustness.

[0177] Optionally, when training the prediction model 500, the optimizer Adam (Adaptive Moment Estimation) can be selected, with an initial learning rate (which can be denoted as "lr") of 1×10. 3 The training process employs a cosine annealing strategy to decay the temperature to 1×10⁻⁶. 5The batch size is 256, and the early stop strategy (which can be denoted as "patience") is 15. Furthermore, the training samples are grouped according to different speed segments, and sub-models for different speed segments are trained separately to adapt to the needs of slope change rate prediction at different speeds. When predicting the slope change rate, the current vehicle speed is used as the basis for selecting the sub-model within the speed segment corresponding to the vehicle's current speed to avoid interference from vehicle speed and improve the accuracy of slope change rate prediction. For example, different speed segments can include 0km / h-30km / h, 30km / h-60km / h, 60km / h-90km / h, and >90km / h. Adam is an adaptive learning rate optimization algorithm based on first-order moment estimation (i.e., gradient mean) and second-order moment estimation (i.e., gradient variance).

[0178] S420 determines the target control parameters for the next moment based on the rate of change of the gradient corresponding to the vehicle at the current moment.

[0179] S430: Determine whether the rate of change of the gradient of the vehicle at the next time step is greater than or equal to a preset threshold. If yes, proceed to S440; otherwise, proceed to S410.

[0180] For example, if it is determined that the rate of change of the slope of the vehicle at the next moment is greater than or equal to a preset change threshold, S440 can be executed. If it is determined that the rate of change of the slope of the vehicle at the next moment is less than the preset change threshold, then S410 is executed repeatedly.

[0181] S440, at the current moment, pre-controls the vehicle with the target control parameters.

[0182] It should be noted that, Figure 4 The relevant steps have been completed. Figure 2 and Figure 3 The corresponding embodiments are described in detail, and will not be repeated here.

[0183] It should be understood that the above examples are provided to help those skilled in the art understand the embodiments of this application, and are not intended to limit the embodiments of this application to the specific values ​​or scenarios exemplified. Those skilled in the art can obviously make various equivalent modifications or variations based on the above examples, and such modifications or variations also fall within the scope of the embodiments of this application.

[0184] The above text combined Figures 1 to 5 The vehicle control method provided in the embodiments of this application has been described in detail; the following will be combined with Figure 6 and Figure 8The apparatus embodiments of this application are described in detail below. It should be understood that the apparatus in the embodiments of this application can perform the various methods described in the foregoing embodiments of this application, that is, the specific working processes of the various products described below can be referred to the corresponding processes in the foregoing method embodiments.

[0185] Figure 6 This is a schematic diagram of the vehicle control device provided in the embodiments of this application.

[0186] For example, such as Figure 6 As shown, the device 600 includes: The prediction module 610 is used to predict the rate of change of the slope of the vehicle at the next moment when the vehicle is detected to be traveling on a slope. The processing module 620 is used to determine the target control parameters for the next time step based on the slope change rate; and to pre-control the vehicle using the target control parameters at the current time step.

[0187] In one possible implementation, the processing module 620 is specifically used for: Based on the rate of change of gradient and the vehicle's gear at the current moment, determine the target shift speed for the next moment; and / or, Based on the rate of change of slope and the vehicle's base torque, determine the target torque for the next time step from the current moment; and / or, Based on the slope change rate and the energy recovery intensity at the current moment, determine the target energy recovery intensity for the next moment. The target shift speed, target torque, and / or target energy recovery intensity are determined as the target control parameters.

[0188] In one possible implementation, the processing module 620 is specifically used for: Determine the initial shift speed based on the vehicle's current gear position; When the gradient change rate is greater than or equal to the first preset change rate, the initial shift speed is increased to obtain the increased shift speed; the increased shift speed is determined as the target shift speed. If the gradient change rate is less than the second preset change rate, the initial shift speed is reduced to obtain the reduced shift speed; the reduced shift speed is determined as the target shift speed. The first preset rate of change is a positive threshold, and the second preset rate of change is a negative threshold.

[0189] In one possible implementation, the processing module 620 is specifically used for: Based on the slope change rate, determine the target correction torque for the next time step from the current time step; The base torque is corrected based on the target torque to obtain the target torque.

[0190] In one possible implementation, the processing module 620 is specifically used for: Based on the slope change rate, vehicle gravity, and sensing distance, determine the initial correction amount for the next moment from the current moment. The target correction factor is determined based on the vehicle's remaining battery power and maximum torque margin. Based on the target correction coefficient, the initial correction amount is corrected to obtain the target correction torque.

[0191] In one possible implementation, the processing module 620 is specifically used for: Determine the first correction factor based on the remaining battery power; The second correction factor is determined based on the maximum torque margin; The target correction factor is determined based on the first correction factor and the second correction factor.

[0192] In one possible implementation, the processing module 620 is specifically used for: When the slope change rate decreases, the energy recovery intensity at the current moment is increased to obtain the increased energy recovery intensity; the increased energy recovery intensity is determined as the target energy recovery intensity. When the slope change rate increases, the energy recovery intensity at the current moment is reduced to obtain the reduced energy recovery intensity; the reduced energy recovery intensity is determined as the target energy recovery intensity.

[0193] In one possible implementation, the processing module 620 is specifically used for: In the target binding relationship, the control parameters corresponding to the slope change rate are determined based on the slope change rate. The target binding relationship is used to represent the binding relationship between the slope change rate and the control parameters corresponding to the slope change rate. The control parameter corresponding to the slope change rate is determined as the target control parameter.

[0194] In one possible implementation, the processing module 620 is specifically used for: Based on vehicle operation data at multiple times, the gradient values ​​at multiple times are determined, where multiple times include the current time and historical times that are sequentially continuous with the current time; Based on the mileage step size, the slope values ​​at multiple times are converted to obtain multiple slope values ​​corresponding to the mileage. Based on multiple slope values ​​corresponding to mileage, the slope change rate is predicted.

[0195] In one possible implementation, the processing module 620 is specifically used for: Based on the current slope change rate corresponding to the vehicle at the current moment and the historical slope change rate corresponding to the previous moment, where both the current slope change rate and the historical slope change rate are true values. Based on the change between the current slope change rate and the historical slope change rate, and the current slope change rate, predict the slope change rate.

[0196] It should be noted that the aforementioned device 600 is embodied in the form of a functional module. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0197] For example, a "module" can be a software program, hardware circuit, or a combination of both that implements the above functions. Hardware circuits may include application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or combined processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functions.

[0198] Therefore, the modules of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0199] Figure 7 This is a schematic diagram of the controller provided in the embodiments of this application.

[0200] For example, such as Figure 7 As shown, the vehicle includes a controller 700, which includes a storage module 710 and a processing module 720. The storage module 710 stores executable program code 7101, and the processing module 720 is used to call and execute the executable program code 7101 to perform a vehicle control method.

[0201] Figure 8 This is a schematic diagram of the vehicle structure provided in the embodiments of this application.

[0202] For example, such as Figure 8 As shown, the vehicle 800 includes a memory 810 and a processor 820, wherein the memory 810 stores executable program code 8101, and the processor 820 is used to call and execute the executable program code 8101 to perform a vehicle control method.

[0203] This application can divide the vehicle into functional modules based on the above method example. For example, each module can correspond to a separate function module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0204] When each functional module is divided according to its corresponding function, the vehicle may include: a prediction module and a processing module, etc. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0205] The vehicle provided in this application is used to execute the vehicle control method described above, and thus can achieve the same effect as the above implementation method.

[0206] When using integrated units, the vehicle may include a processing module and a storage module. The processing module is used to control and manage the vehicle's movements. The storage module is used to support the vehicle in executing relevant program code and data.

[0207] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and microprocessors, etc., and the storage module may be a memory.

[0208] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs (Digital Video Discs), CD-ROMs (Compact Disc Read-Only Memory), microdrives, magneto-optical disks, ROMs (Read-Only Memory), RAMs (Random Access Memory), EPROMs (Erasable Programmable Read-Only Memory), EEPROMs (Electrically Erasable Programmable Read Only Memory), DRAMs (Dynamic Random Access Memory), VRAMs (Video Random Access Memory), flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0209] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement a vehicle control method as described in the above embodiments.

[0210] In addition, the vehicle provided in the embodiments of this application may specifically be a chip, component or module. The vehicle may include a connected processor and a memory. The memory is used to store instructions. When the vehicle is running, the processor may call and execute the instructions to make the chip execute a vehicle control method in the above embodiments.

[0211] The vehicle, computer-readable storage medium, computer program product or chip provided in this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0212] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0213] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0214] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle control method, characterized in that, The method includes: If a vehicle is detected to be traveling on a slope, predict the rate of change of the slope of the vehicle at the next time step; Based on the slope change rate, determine the target control parameters for the next time step at the current time step; At the current moment, the vehicle is pre-controlled using the target control parameters.

2. The method according to claim 1, characterized in that, The determination of the target control parameters for the next time step based on the slope change rate includes: Based on the slope change rate and the vehicle gear at the current moment, determine the target shift speed for the next moment; and / or, Based on the slope change rate and the vehicle's base torque, determine the target torque for the next moment at the current moment; and / or, Based on the slope change rate and the energy recovery intensity at the current moment, determine the target energy recovery intensity for the next moment. The target shift speed, the target torque, and / or the target energy recovery intensity are determined as the target control parameters.

3. The method according to claim 2, characterized in that, Determining the target shift speed for the next moment based on the slope change rate and the vehicle gear at the current moment includes: Determine the initial shift speed based on the vehicle's current gear position; When the slope change rate is greater than or equal to the first preset change rate, the initial shift speed is increased to obtain the increased shift speed; the increased shift speed is determined as the target shift speed. If the slope change rate is less than the second preset change rate, the initial shift speed is reduced to obtain the reduced shift speed; the reduced shift speed is determined as the target shift speed. Wherein, the first preset rate of change is a positive threshold, and the second preset rate of change is a negative threshold.

4. The method according to claim 2, characterized in that, Determining the target torque for the next moment based on the slope change rate and the vehicle's base torque includes: Based on the slope change rate, determine the target correction torque for the next moment at the current moment; The base torque is corrected based on the target corrected torque to obtain the target torque.

5. The method according to claim 4, characterized in that, Determining the target correction torque for the next time step based on the slope change rate includes: Based on the slope change rate, the vehicle's gravity, and the sensing distance, determine the initial correction amount for the next moment of the current moment; Based on the vehicle's remaining battery power and maximum torque margin, a target correction coefficient is determined; Based on the target correction coefficient, the initial correction amount is corrected to obtain the target correction torque.

6. The method according to claim 5, characterized in that, The determination of the target correction coefficient based on the vehicle's remaining battery power and maximum torque margin includes: Based on the remaining power, determine the first correction coefficient; Based on the maximum torque margin, a second correction coefficient is determined; The target correction coefficient is determined based on the first correction coefficient and the second correction coefficient.

7. The method according to claim 2, characterized in that, Determining the target energy recovery intensity for the next time step based on the slope change rate and the energy recovery intensity at the current time includes: When the slope change rate decreases, the energy recovery intensity at the current moment is increased to obtain the increased energy recovery intensity; the increased energy recovery intensity is determined as the target energy recovery intensity. When the slope change rate increases, the energy recovery intensity at the current moment is reduced to obtain the reduced energy recovery intensity; the reduced energy recovery intensity is determined as the target energy recovery intensity.

8. The method according to claim 1, characterized in that, The prediction of the slope change rate of the vehicle at the current moment in the next moment includes: In the target binding relationship, based on the slope change rate, the control parameter corresponding to the slope change rate is determined, wherein the target binding relationship is used to represent the binding relationship between the slope change rate and the control parameter corresponding to the slope change rate; The control parameter corresponding to the slope change rate is determined as the target control parameter.

9. The method according to any one of claims 1 to 8, characterized in that, The prediction of the slope change rate of the vehicle at the current moment in the next moment includes: Based on vehicle operation data at multiple times, the gradient values ​​at multiple times are determined, wherein the multiple times include the current time and historical times that are sequentially continuous with the current time; Based on the mileage step size, the slope values ​​at multiple times are converted to obtain multiple slope values ​​corresponding to the mileage. The slope change rate is predicted based on multiple slope values ​​corresponding to the mileage.

10. The method according to any one of claims 1 to 8, characterized in that, The prediction of the slope change rate of the vehicle at the current moment in the next moment includes: Based on the current slope change rate of the vehicle at the current moment and the historical slope change rate at the previous moment, wherein the current slope change rate and the historical slope change rate are both true values; The slope change rate is predicted based on the change between the current slope change rate and the historical slope change rate, and the current slope change rate.

11. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 10.

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