Vehicle control method, computer readable storage medium and vehicle

Abstract

This application discloses a vehicle control method, a computer-readable storage medium, and a vehicle, relating to the field of vehicle energy consumption optimization technology. The vehicle control method includes: solving an objective function based on constraints to obtain multiple speed planning curves corresponding to the optimization objective of matching vehicle energy consumption and ideal vehicle speed; determining the speed planning curve with the minimum vehicle energy consumption as the target speed planning curve, and determining the vehicle speed at state (k+1) based on the target speed planning curve; determining whether state (k+1) is the endpoint; if state (k+1) is not the endpoint, using the next prediction time domain as the new current prediction time domain and updating k+1 to k, and then proceeding to the step of solving the objective function based on constraints to obtain multiple speed planning curves, where the next prediction time domain is from state (k+2) to state (k+1).

Description

Technical Field

[0001] This application belongs to the field of vehicle energy consumption optimization technology, and particularly relates to a vehicle control method, a computer-readable storage medium, and a vehicle. Background Technology

[0002] Currently, vehicle energy consumption is typically optimized to reduce overall vehicle energy consumption and increase driving range. Existing vehicle energy consumption optimization methods mainly fall into four categories: improving drive system efficiency, optimizing vehicle design parameters, reducing accessory power consumption, and optimizing vehicle control strategies.

[0003] Among these methods, optimizing vehicle control strategies can improve vehicle energy consumption without altering the vehicle's structure and hardware. This method has less impact on the vehicle and is less costly compared to other methods. Existing methods for optimizing vehicle control strategies only optimize energy consumption during acceleration or braking, failing to achieve optimal energy consumption across the entire driving cycle. Summary of the Invention

[0004] This application provides a vehicle control method, a computer-readable storage medium, and a vehicle, which can optimize the overall vehicle energy consumption from the perspective of the entire driving phase, and achieve the optimization of global driving energy consumption.

[0005] A first aspect of this application provides a vehicle control method, comprising: constructing an objective function based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain, the objective function being used to calculate the overall vehicle energy consumption and ideal speed matching degree in the current prediction time domain, the control quantity including driving torque and braking torque, and the state quantity including vehicle speed and displacement; solving the objective function based on constraints to obtain multiple vehicle speed planning curves corresponding to the optimization objective of the vehicle energy consumption and ideal speed matching degree, the constraints including: determining the state from state moment (k+1) to state moment (k+2) in the current prediction time domain based on a state prediction model and the state quantity and control quantity at state moment (k). The state variables and control variables at each state time point between state time points, where k is a positive integer. This is used to represent the prediction time step; the vehicle speed planning curve with the minimum vehicle energy consumption is determined as the target vehicle speed planning curve, and the vehicle speed at the (k+1)th state time is determined based on the target vehicle speed planning curve; it is determined whether the (k+1)th state time is the endpoint time; if the (k+1)th state time is not the endpoint time, the next prediction time domain is used as the new current prediction time domain and k is updated to k+1, and the objective function is solved based on the constraint conditions to obtain the multiple vehicle speed planning curves. The next prediction time domain is from the (k+2)th state time to the (k+1)th state time. State moment.

[0006] In an optional embodiment of the first aspect, constructing the objective function based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain includes: constructing a cost function based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain; constructing a time penalty function based on the vehicle speed at each state moment in the current prediction time domain; constructing a traffic signal penalty function based on the vehicle displacement at each state moment in the current prediction time domain; and constructing the objective function based on the cost function, the time penalty function, and the traffic signal penalty function.

[0007] In an optional embodiment of the first aspect, constructing the cost function based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain includes: based on Construct the cost function. Used to represent the cost function. and Used to represent weights, Functions used to represent the control quantity. Functions used to represent the state variables.

[0008] In an optional embodiment of the first aspect, constructing the time penalty function based on the vehicle speed at each state time in the current prediction time domain includes: based on Construct the time penalty function, the Used to represent the time penalty function. Used to represent weights, Used to represent the vehicle speed at state j. This represents the expected vehicle speed at state j, where j starts taking values ​​from k+1 and continues to k+1. .

[0009] In an optional embodiment of the first aspect, constructing the traffic signal penalty function based on the vehicle's displacement at each state time within the current prediction time domain includes: based on Construct the traffic signal penalty function. Used to represent the traffic signal penalty function. Used to represent weights, This is used to represent the predicted distance between the vehicle's position at state j and the traffic light at state i. This represents the expected distance between the vehicle's position at state j and the traffic light at state i, where i is a positive integer and j starts from k+1 and continues to k+1. .

[0010] In an optional embodiment of the first aspect, before constructing the objective function based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain, the method further includes: obtaining the expected time interval of the vehicle at the initial state moment; based on the expected time interval of the initial state moment, forward recursively calculating the forward expected time interval of the vehicle passing through each intersection on the driving route, wherein the forward expected time interval is the expected time interval of the corresponding intersection obtained by forward recursion; based on the reference time interval of a first target intersection, backward recursively calculating the backward expected time interval of the vehicle passing through each intersection, wherein the first target intersection is the last intersection in the forward recursion process where the forward expected time interval is not an empty set, the reference time interval is determined based on the forward expected time interval of the first target intersection, and the backward expected time interval is the expected time interval of the corresponding intersection obtained by backward recursion; based on the backward expected time interval of a second target intersection, determining the expected vehicle speed of the vehicle at each state moment on the driving route, wherein the second target intersection is the last intersection in the backward recursion process where the backward expected time interval is not an empty set.

[0011] In an optional embodiment of the first aspect, the determination of the state from state (k+1) to state (k+) in the current prediction time domain based on the state prediction model and the state and control variables at state time (k-th state) is performed. The state variables and control variables at each state time point between state times include: constructing the state prediction model based on the state variables and control variables of the vehicle; discretizing the state prediction model to obtain the model prediction equation; and substituting the control variables and state variables of the vehicle at the k-th state time into the model prediction equation. The forward recursion yields the state from the (k+1)th state in the current prediction time domain to the (k+)th state. The state variables and control variables at each state moment between state moments. The state quantity used to represent the state at the (k+1)th state time. The state quantity used to represent the state at time k. Used to indicate the sampling time interval Functions used to represent the state and control variables related to the k-th state time.

[0012] In an optional embodiment of the first aspect, determining the vehicle speed planning curve with the minimum vehicle energy consumption as the target vehicle speed planning curve includes: for each vehicle speed planning curve, determining the driving energy consumption value and braking energy consumption value corresponding to each vehicle speed planning curve; determining the vehicle energy consumption corresponding to each vehicle speed planning curve based on the driving energy consumption value and the braking energy consumption value; and determining the vehicle speed planning curve with the minimum vehicle energy consumption as the target vehicle speed planning curve.

[0013] A second aspect of the present application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the vehicle control method provided in any aspect of the present application.

[0014] A third aspect of this application provides a vehicle including an electronic control unit. The electronic control unit performs the vehicle control method provided in any aspect of this application.

[0015] In the vehicle control method provided in this application embodiment, the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain are used to construct an objective function for calculating the overall vehicle energy consumption and ideal speed matching degree in the current prediction time domain. Based on the state prediction model and the state quantity and control quantity at the k-th state moment, the state from the (k+1)-th state moment to the (k+)-th state moment in the current prediction time domain is determined. The state variables and control variables at each state time point between states can be used to determine the state variables from state (k+1) to state (k+2). The state variables and control variables at each state time point serve as constraints. Solving the objective function using these constraints yields multiple speed planning curves corresponding to the optimized matching degree of vehicle energy consumption and ideal speed. Among these speed planning curves, the speed at state time (k+1) can be determined based on the speed planning curve with the minimum vehicle energy consumption, i.e., the target speed planning curve. If state time (k+1) is not the endpoint, the above method is used to predict the next target speed planning curve in the prediction time domain. This rolling optimization continues until the driving task is completed. This ensures that the vehicle energy consumption corresponding to the speed at each state time point is minimized and optimal throughout the entire driving process. This achieves optimization of overall vehicle energy consumption from the perspective of the entire driving stage, and optimizes vehicle driving energy consumption, overcoming the shortcomings of existing vehicle control strategies that only optimize energy consumption in local driving stages and lack global optimization for the entire driving stage. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of a vehicle control method provided in one embodiment of this application;

[0018] Figure 2 This is a schematic diagram illustrating the determination of the predicted distance between a vehicle and an intersection according to an embodiment of this application;

[0019] Figure 3 This is a schematic diagram illustrating the calculation of vehicle energy consumption by a vehicle energy management module according to an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of the current prediction time domain and the next prediction time domain provided in one embodiment of this application;

[0021] Figure 5 This is a schematic diagram of the last prediction time domain provided in one embodiment of this application;

[0022] Figure 6 This is a schematic diagram of a process for determining the desired vehicle speed on a driving route according to an embodiment of this application;

[0023] Figure 7 This is a schematic diagram of a vehicle control method using an intelligent connected vehicle as an example, provided in one embodiment of this application;

[0024] Figure 8 This is a schematic diagram of a vehicle control device provided in one embodiment of this application;

[0025] Figure 9 This is a schematic diagram of an electronic control unit provided in one embodiment of this application.

[0026] The above figures include the following reference numerals:

[0027] 800. Vehicle control device; 810. Construction module; 820. Speed ​​planning main program module; 830. Vehicle energy management module; 840. Judgment module; 850. Cyclic prediction module; 901. Processor; 902. Memory; 903. Communication interface; 910. Bus. Detailed Implementation

[0028] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0030] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0031] Energy consumption is a major concern for pure electric vehicles. Typically, the vehicle's energy management module monitors energy consumption distribution and vehicle health. Furthermore, energy consumption can be optimized to reduce overall vehicle energy consumption and increase driving range.

[0032] Existing methods for optimizing vehicle energy consumption mainly include: improving drive system efficiency, optimizing vehicle design parameters, reducing accessory power consumption, and optimizing vehicle control strategies, etc.

[0033] Among these methods, optimizing vehicle control strategies can improve vehicle energy consumption without altering the vehicle's structure and hardware. Compared to other optimization methods, this approach has less impact on the vehicle and is less costly. Common methods for optimizing vehicle control strategies include optimizing the vehicle's acceleration curve and increasing energy recovery intensity. However, these methods only optimize energy consumption during the acceleration or braking phases and cannot optimize energy consumption across the entire driving process.

[0034] In view of this, this application provides a vehicle control method, a computer-readable storage medium, and a vehicle. The vehicle control method provided in this application utilizes the control and state variables of the vehicle at each state moment within the current prediction time domain to construct an objective function for calculating the overall vehicle energy consumption and ideal speed matching degree within the current prediction time domain. Furthermore, based on the state prediction model and the state and control variables at the k-th state moment, it determines the state from the (k+1)-th state moment to the (k+)-th state moment within the current prediction time domain. The state variables and control variables at each state time point between states can be used to determine the state variables from state (k+1) to state (k+2). The state variables and control variables at each state time point serve as constraints. Solving the objective function using these constraints yields multiple speed planning curves corresponding to the optimized matching degree of vehicle energy consumption and ideal vehicle speed. Among these speed planning curves, the vehicle speed at state time (k+1) is determined based on the speed planning curve with the minimum energy consumption, i.e., the target speed planning curve. If state time (k+1) is not the endpoint, the above method is used to predict the next target speed planning curve in the prediction time domain. This rolling optimization continues until the driving task is completed. This ensures that the vehicle energy consumption corresponding to the speed at each state time point is minimized and optimal throughout the entire driving process. This achieves optimization of overall vehicle energy consumption from the perspective of the entire driving stage and global vehicle energy consumption optimization, overcoming the shortcomings of existing vehicle control strategies that only optimize energy consumption in local driving stages and lack global optimization for the entire driving stage.

[0035] For example, the vehicle control method provided in this application can be applied to intelligent connected vehicles such as buses and port transport vehicles that have specific routes and perform fixed transportation tasks. In practical applications, based on the driving routes and road information of intelligent connected vehicles (e.g., buses and port transport vehicles) in a specific area, the vehicle control method of this application can calculate multiple speed planning curves in real time, and... Figure 3The vehicle energy management module shown calculates multiple input speed planning curves to obtain the target speed planning curve. Based on the target speed planning curve, the vehicle speed at the next state time after the current state time can be determined. This rolling optimization continues until the intelligent connected vehicle's driving task is completed.

[0036] It should be noted that the application scenarios described in the above embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will understand that with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems. The vehicle control method provided by the embodiments of this application can be applied to various application scenarios for optimizing vehicle energy consumption.

[0037] The vehicle control method provided in the embodiments of this application will be described below. In practical applications, the vehicle control method in the embodiments of this application can be executed by an electronic control unit.

[0038] The following describes specific embodiments of the vehicle control method, computer-readable storage medium, and vehicle provided in this application. First, the vehicle control method will be introduced.

[0039] Figure 1 A schematic flowchart of a vehicle control method according to an embodiment of this application is shown. Figure 1 As shown, the method includes steps S101 to S105.

[0040] S101. Based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain, an objective function is constructed. The objective function is used to calculate the overall vehicle energy consumption and ideal speed matching degree in the current prediction time domain. The control quantity includes driving torque and braking torque, and the state quantity includes vehicle speed and displacement.

[0041] The current prediction time domain is from the (k+1)th state time to the (k+)th state time. The prediction time domain consists of state moments. In practical applications, the vehicle's journey from its origin to its destination includes N state moments. The prediction time domain is defined as the period from state (k+1) to state (k+)... A state moment is a subset of N state moments.

[0042] For example, the k-th state time can be the initial time. That is, the vehicle control method of this application can predict the vehicle speed from the initial time to the terminal time.

[0043] For example, ideal speed matching degree is the degree of matching between the predicted speed and the desired speed.

[0044] In constructing the objective function, in order to better predict the vehicle speed at each state moment in the current prediction time domain based on the constructed objective function, in some embodiments, a cost function is constructed based on the control and state variables of the vehicle at each state moment in the current prediction time domain; a time penalty function is constructed based on the vehicle speed at each state moment in the current prediction time domain; a traffic signal penalty function is constructed based on the vehicle displacement at each state moment in the current prediction time domain; and the objective function is constructed based on the cost function, the time penalty function, and the traffic signal penalty function.

[0045] In this embodiment, a cost function is constructed based on control and state variables. This cost function comprehensively considers various factors during vehicle control, enabling efficient and safe vehicle operation. A time penalty function is constructed based on vehicle speed to ensure the vehicle maintains a suitable speed during travel, avoiding unnecessary acceleration and / or deceleration. A traffic signal penalty function is constructed based on displacement, allowing the vehicle to pass through intersections at a certain speed instead of stopping and waiting. Based on the cost function, time penalty function, and traffic signal penalty function, a target function is constructed to ensure its reasonableness. Subsequently, a target speed planning curve is obtained based on the target function. Following this curve, the vehicle can not only complete its driving task but also reduce energy consumption and maintain a reasonable speed, resulting in higher overall vehicle safety.

[0046] For example, the objective function can be flexibly set according to different needs. For instance, if there are no requirements for vehicle operating efficiency, the objective function can be constructed using only a cost function and a traffic signal penalty function. Similarly, if there are no requirements for vehicle braking performance, the objective function can also be constructed using only a cost function and a time penalty function. Of course, in some cases, to ensure smoother vehicle operation and higher vehicle safety, additional penalty functions such as acceleration change penalty function, distance penalty function, and lane departure penalty function can be incorporated into the objective function.

[0047] To better evaluate the matching degree between vehicle energy consumption and ideal vehicle speed, in one embodiment, a cost function is constructed based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain, including: based on Construct the cost function. Used to represent the cost function and Used to represent weights, Used to represent functions related to control quantities. Used to represent functions related to state variables.

[0048] This cost function considers the control and state variables of the vehicle at each state time within the current prediction time domain. This allows for a comprehensive consideration of various factors in the vehicle control process, and also enables the relatively quick finding of the optimal control variable while the vehicle meets various constraints. Furthermore, weights are also set in the cost function. and By adjusting the weights and This not only balances the importance of control and state variables in the cost function, but also allows for the adjustment of their proportions according to actual needs, thus satisfying different requirements.

[0049] In one embodiment, a time penalty function is constructed based on the vehicle speed at each state time in the current prediction time domain, including: based on Construct a time penalty function. Used to represent a time penalty function. Used to represent weights, Used to represent the vehicle speed at state j. This represents the expected vehicle speed at state j, where j starts taking values ​​from k+1 and continues to k+1. .

[0050] When vehicle operating efficiency is a requirement, it is generally desirable that the vehicle speed at each state moment within the current prediction time domain is the same as the expected vehicle speed preset in the electronic control unit for that state moment. However, in practical applications, due to the influence of road information, traffic information, etc., the predicted vehicle speed and the expected vehicle speed at each state within the current prediction time domain cannot be exactly the same. Given this situation, to maximize vehicle operating efficiency, it is necessary to minimize the difference between the predicted vehicle speed at state j (where state j is a state moment within the current prediction time domain) and the expected vehicle speed at state j. Therefore, constructing a time penalty function using the square of the difference between the predicted and expected vehicle speeds at state j can more significantly highlight the deviation between the predicted and expected speeds. Furthermore, setting weights allows for flexible adjustment of the deviation between the predicted and expected speeds to meet different needs.

[0051] For example, the time penalty function is not limited to being constructed based on the predicted and expected vehicle speeds at time j; it can also be constructed based on the predicted and expected displacements at time j. This application does not limit this.

[0052] In one embodiment, a traffic signal penalty function is constructed based on the vehicle's displacement at each state time within the current prediction time domain, including: based on Construct a traffic signal penalty function. Used to represent traffic signal penalty functions. Used to represent weights, This is used to represent the predicted distance between the vehicle's position at state j and the traffic light at state i. This represents the expected distance between the vehicle's position at state j and the traffic light at state i, where i is a positive integer and j starts from k+1 and continues to k+1. .

[0053] To optimize vehicle energy consumption, it's often desirable for vehicles to pass through intersections at a certain speed rather than stopping, thus reducing excessive energy consumption from braking and starting from a standstill. Therefore, to minimize vehicle energy consumption, it's necessary to reduce the difference between the predicted distance and the expected distance between the vehicle at state j and the traffic light at position i as much as possible. Constructing a traffic signal penalty function using the square of the difference between the predicted and expected distances at state j and the traffic light at position i can more significantly highlight the deviation between the predicted and expected distances. Furthermore, setting weights allows for flexible adjustment of the proportion of this deviation in the overall objective function, thereby ensuring that vehicles pass through intersections at a certain speed rather than stopping.

[0054] like Figure 2 As shown, within the current prediction time domain, a vehicle traveling on the road needs to pass through two intersections, each equipped with traffic lights. For each state time within the current prediction time, such as state time k+1, the predicted distance between the vehicle and the first traffic light is the first predicted distance, and the predicted distance between the vehicle and the second traffic light is the second predicted distance. At state time k+1, the traffic signal penalty function can be composed of the square of the difference between the first predicted distance and the first expected distance, and the square of the difference between the second predicted distance and the second expected distance. Within the current prediction time domain, the calculation rules for other state times are the same as in the above embodiment, and will not be repeated here.

[0055] S102, based on the constraints, solve the objective function to obtain multiple vehicle speed planning curves corresponding to the optimization objective of vehicle energy consumption and ideal vehicle speed matching. The constraints include: based on the state prediction model and the state and control variables at state k, determine the time from state k+1 to state k+ in the current prediction time domain. The state variables and control variables at each state time point between state time points, where k is a positive integer. Used to indicate the prediction time step.

[0056] in, It can be a positive integer.

[0057] Based on the state variables and control variables at state k, and the state prediction model, we can obtain the state from state (k+1) to state (k+1) within the current prediction time domain. Control and state variables between state moments. Transferring the control and state variables from state (k+1) to state (k+... The control and state variables between state moments are input into the objective function. Through continuous iteration and adjustment, multiple sets of control and state variables can be obtained to satisfy the optimization objective. This allows for the generation of multiple vehicle speed planning curves while satisfying the optimization objective, thus enabling the efficient and rapid generation of multiple vehicle speed planning curves.

[0058] For example, a nonlinear solver can be used to solve the objective function based on constraints. For instance, if the objective function value is less than a preset value, or if the solution time exceeds a preset time, the solution results are output, yielding multiple speed planning curves corresponding to the optimization objective of matching vehicle energy consumption and ideal vehicle speed. In an optional embodiment, the nonlinear solver can be an MPC (Model Predictive Control) solver.

[0059] For example, during the process of solving the objective function, the weights and cost functions in the objective function can be adjusted multiple times, thereby obtaining multiple different speed planning curves.

[0060] In one embodiment, based on the state prediction model and the state and control variables at state k, the state from state (k+1) to state (k+) within the current prediction time domain is determined. The state variables and control variables at each state time point between state times include: constructing a state prediction model based on the vehicle's state variables and control variables; discretizing the state prediction model to obtain the model prediction equation; and substituting the vehicle's control variables and state variables at the k-th state time into the model prediction equation. The forward recursion yields the state from the (k+1)th state in the current prediction time domain to the (k+)th state. The state variables and control variables at each state time point between state time points. Used to represent the state quantity at state k+1. Used to represent the state quantity at time k. Used to indicate the sampling time interval Functions used to represent state and control variables related to the k-th state time.

[0061] in, Used to represent the control quantity at state k.

[0062] In this embodiment, the state prediction model is discretized, resulting in a model prediction equation that accurately describes the dynamic characteristics of the vehicle system. Then, the control and state variables at state k are substituted into the model prediction equation, and the predictions from state (k+1) to state (k+2) are recursively derived. The state variables and control variables at each state moment provide an accurate predictive basis for vehicle control.

[0063] Taking a vehicle as an example, the longitudinal dynamics equation can accurately describe the changes in the vehicle's motion state during acceleration, deceleration, and hill climbing. It can also accurately describe the influence of driving force, rolling resistance, air resistance, and gradient resistance on the vehicle's motion, thus enabling accurate prediction of the vehicle's longitudinal motion state. In one optional embodiment of this application, a state prediction model is constructed based on the longitudinal dynamics equation, the vehicle's control variables, and state variables. This state prediction model can be expressed as:

[0064]

[0065] in, Used to indicate vehicle speed Used to represent displacement Used to indicate drive torque Used to indicate braking torque Used to indicate the overall vehicle weight. Used to indicate the transmission ratio Used to indicate the efficiency of a transmission system. Used to represent gravitational acceleration Used to represent the rolling resistance coefficient Used to indicate the slope angle of the road surface Used to indicate air density Used to represent the air drag coefficient The projected area used to represent the direction of vehicle travel. Used to indicate the effective radius of a wheel Used to represent acceleration, Used to represent speed. Simplified to its general form, it can be expressed as: ,in, Used to represent state variables, i.e. , Used to represent control quantities, i.e. .

[0066] To reduce computational complexity, the state prediction model is discretized to obtain the model prediction equation. ,in, Used to represent the state quantity at time k. Used to represent the state quantity at time k+1. It is a function relating to the state and control variables at time k.

[0067] Set prediction time step and control time steps Based on the state variables and control variables at state k, we can obtain the values ​​from state (k+1) to state (k+2). The state variables and control variables at each state moment can be specifically represented as follows:

[0068]

[0069] S103, determine the vehicle speed planning curve with the minimum vehicle energy consumption as the target vehicle speed planning curve, and determine the vehicle speed at state k+1 based on the target vehicle speed planning curve.

[0070] To quickly determine the target vehicle speed planning curve corresponding to the minimum energy consumption value from multiple vehicle speed planning curves, in one embodiment, the vehicle speed planning curve with the minimum energy consumption is determined as the target vehicle speed planning curve. This includes: for each vehicle speed planning curve, determining the driving energy consumption value and braking energy consumption value corresponding to each vehicle speed planning curve; based on the driving energy consumption value and braking energy consumption value, determining the vehicle energy consumption corresponding to each vehicle speed planning curve; and determining the vehicle speed planning curve with the minimum energy consumption as the target vehicle speed planning curve.

[0071] For example, such as Figure 3As shown, in the vehicle energy management module, the specific process of predicting drive energy consumption based on the drive system can include: inputting the predicted vehicle speed corresponding to the speed planning curve predicted by the speed planning main program module into the driver control module. In the driver control module, the predicted vehicle speed corresponding to the speed planning curve is calculated to obtain the throttle opening, and the throttle opening is sent to the VCU control module. In the VCU control module, when the driver's throttle opening indicates an acceleration intention, a torque command is issued to the drive motor model. In the drive motor model, considering the current vehicle drive mode (single motor drive mode, fixed torque distribution ratio mode between front and rear motors, dynamic torque distribution mode between front and rear motors), the current state of charge (SOC) of the vehicle's power battery, and the current state of power (SOP) of the drive motor, the motor torque value and output power are obtained and sent to the power battery model. In the power battery model, based on the current SOC state of the battery and the required output power, the latest power battery SOC state, SOP state, or internal resistance (IR) state is obtained. The calculated drive power and torque of the drive motor are simultaneously output to the vehicle's longitudinal dynamics model. In the longitudinal dynamics model, factors such as frictional resistance, rolling resistance, slope resistance, and wind resistance are introduced to calculate the vehicle's actual speed, which is then sent to the driver control module. Throughout the driving process, the real-time energy consumption prediction process and its corresponding predicted value can be obtained by observing the SOC state in the power battery model.

[0072] For example, such as Figure 3As shown, in the vehicle energy management module, the specific process of predicting braking energy consumption based on the braking system can include: inputting the predicted vehicle speed corresponding to the speed planning curve predicted by the speed planning main program module to the driver control module. In the driver control module, the predicted vehicle speed corresponding to the speed planning curve is used to calculate the throttle opening, and the throttle opening is sent to the VCU control module. When the VCU control module detects that the driver intends to brake, it sends a braking command to the braking system model. The braking system model comprehensively considers whether the vehicle's kinetic energy recovery mode is activated (when kinetic energy recovery mode is off, all driving braking torque is provided by mechanical braking torque; when kinetic energy recovery mode is on, it is necessary to further calculate the distribution ratio of mechanical braking torque and regenerative braking torque separately, and determine whether the regenerative braking torque meets the requirements of battery charging power and the peak torque that can be provided at the current motor speed). When the regenerative braking mode is off, the braking system model inputs the requested braking torque value into the air pump motor model to calculate the braking power required for braking, and the air pump motor model then inputs the braking power into the power battery model. When the regenerative braking mode is on, the braking system model calculates the regenerative power of the regenerated kinetic energy and inputs it into the power battery model to simulate the power battery charging process. The power battery model calculates the current real-time SOC, SOP, or IR state of the power battery based on the real-time requested braking output power and the regenerative braking function. At the same time, the braking system model also inputs the requested braking torque into the vehicle longitudinal dynamics model, obtains the current actual vehicle speed based on the current vehicle state and external resistance state, and sends it to the driver control module. By monitoring the air pump motor model, the mechanical power consumed during braking can be obtained, and by monitoring the power battery model, the comprehensive braking energy consumption value of the entire vehicle can be obtained.

[0073] S104, determine whether the (k+1)th state time is the end time.

[0074] S105, if the (k+1)th state time is not the endpoint, the next prediction time domain is used as the new current prediction time domain, and k is updated to k+1. Then, based on the constraints, the objective function is solved to obtain multiple vehicle speed planning curves. The next prediction time domain is from state time k+2 to k+1+1. State moment.

[0075] like Figure 4 As shown, assume that the k-th state time is the initial time, and =5, then at state k, we can predict the time from state (k+1) to state (k+5). The control and state variables at the current state time (i.e., the current prediction time domain). Then, during the vehicle's operation, the vehicle's movement can be controlled based on the predicted vehicle speed at state time k+1, and at state time k+1, the speed from k+2 to k+1 can be predicted. Control and state variables at the state time (i.e., the next prediction time domain).

[0076] like Figure 5 As shown, assume the last prediction time domain is generated by the N-th... The state is composed of states from the Nth state to the Nth state. After the prediction time domain, consisting of state time up to state time N, is completed, the remaining time no longer satisfies the prediction time step. The number of steps. Then, in the N-th step... State prediction is based on the (N+1)th time step. State time up to the N-th The prediction time domain formed by the state time, and the N+1th time... State prediction is based on the (N+2)th time. State time up to the N-th The prediction time domain, consisting of state moments, is then applied sequentially until only the N-th state is considered. The predicted time domain, which is formed by the state, completes the prediction of the position.

[0077] As can be seen from the above one or more embodiments, the vehicle control method provided by this application optimizes the energy consumption of the entire vehicle from the perspective of the entire driving stage, achieves the optimization of global driving energy consumption, and makes up for the shortcomings of the existing vehicle control strategy which only optimizes the energy consumption of local driving stages and lacks global optimization of the entire driving stage.

[0078] The vehicle control method of this application, such as Figure 6 As shown, before constructing the objective function based on the control and state variables of the vehicle at each state moment in the current prediction time domain, the vehicle control method of this application further includes:

[0079] S106, obtain the expected time interval of the vehicle at the initial state time, and based on the expected time interval at the initial state time, recursively calculate the expected time interval of the vehicle passing through each intersection on the driving route. The expected time interval is the expected time interval of the corresponding intersection obtained by forward recursion.

[0080] S107, based on the reference time interval of the first target intersection, reverse the expected time interval of the vehicle passing through each intersection. The first target intersection is the intersection in the forward recursion process where the last forward expected time interval is not an empty set. The reference time interval is determined based on the forward expected time interval of the first target intersection, and the reverse expected time interval is the expected time interval of the corresponding intersection obtained by reverse recursion.

[0081] S108. Based on the reverse expected time interval of the second target intersection, determine the expected vehicle speed at each state moment on the driving route. The second target intersection is the intersection in the reverse recursion process where the last reverse expected time interval is not an empty set.

[0082] This embodiment uses a bidirectional recursive process of forward and reverse recursion to accurately and efficiently calculate the expected vehicle speed when the vehicle passes through an intersection on the driving route without stopping.

[0083] For ease of understanding, this paper uses an intelligent connected vehicle as an example to introduce the vehicle control method of this application. Figure 7 The diagram shown is a schematic representation of a vehicle control method provided in an embodiment of this application.

[0084] First, before implementing the vehicle control method of this application, the driving route and vehicle position of the intelligent connected vehicle are obtained based on GPS, and road information (such as traffic light phases) is obtained from the cloud. Through forward and backward recursion, the expected time intervals for the intelligent connected vehicle to pass through each intersection are determined. Then, based on the expected time intervals for each intersection, the optimal expected speed curve is determined, thereby obtaining the expected speed of the intelligent connected vehicle at each state time. This expected speed can be used to construct the objective function in each prediction time domain.

[0085] Subsequently, during the journey of the intelligent connected vehicle from its origin to its destination, the vehicle control method described in this application can be used to control the vehicle's movement. For example, starting from the initial state time (assuming the initial state time is the k-th state time), based on information such as the position and speed of the preceding vehicle obtained from the cloud and the vehicle speed obtained from ESP, the MPC controller is used to predict the control and state variables in the current prediction time domain, thereby obtaining multiple vehicle speed planning curves. These multiple vehicle speed planning curves, the driving route, and the position of the intelligent connected vehicle are input into the vehicle energy management module to obtain the vehicle energy consumption corresponding to each speed planning curve. The speed planning curve with the lowest energy consumption is determined as the target speed planning curve.

[0086] Finally, based on the target speed planning curve, the predicted speed of the intelligent connected vehicle at state k+1 is determined. At state k+1, the intelligent connected vehicle is controlled to move based on the predicted speed at state k+1. Then, at state k+1, the control and state variables for the next prediction time domain are predicted using the above method. This rolling optimization continues until the intelligent connected vehicle reaches its destination.

[0087] Based on vehicle control methods, this application also provides specific embodiments of vehicle control devices.

[0088] like Figure 8 As shown, the vehicle control device 800 provided in this application embodiment includes a construction module 810, a speed planning main program module 820, a vehicle energy management module 830, a judgment module 840, and a cycle prediction module 850.

[0089] The construction module 810 is used to construct an objective function based on the control and state variables of the vehicle at each state moment in the current prediction time domain. The objective function is used to calculate the overall vehicle energy consumption and ideal speed matching degree in the current prediction time domain. The control variables include driving torque and braking torque, and the state variables include vehicle speed and displacement.

[0090] The speed planning main program module 820 is used to solve the objective function based on constraints, obtaining multiple speed planning curves corresponding to the optimization objective of matching vehicle energy consumption and ideal vehicle speed. The constraints include: determining the time from state (k+1) to state (k+2) within the current prediction time domain based on the state prediction model and the state and control variables at state k. The state variables and control variables at each state time point between state time points, where k is a positive integer. Used to indicate the prediction time step.

[0091] The vehicle energy management module 830 is used to determine the vehicle speed planning curve with the minimum energy consumption as the target vehicle speed planning curve, and to determine the vehicle speed at the (k+1)th state time based on the target vehicle speed planning curve.

[0092] The judgment module 840 is used to determine whether the (k+1)th state time is the end time.

[0093] The cyclic prediction module 850, when the (k+1)th state time is not the endpoint, takes the next prediction time domain as the new current prediction time domain, updates k to k+1, and enters the step of solving the objective function based on constraints to obtain multiple vehicle speed planning curves. The next prediction time domain is from the (k+2)th state time to k+1+1. State moment.

[0094] As an optional embodiment, the construction module is used to construct a cost function based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain; construct a time penalty function based on the vehicle speed at each state moment in the current prediction time domain; construct a traffic signal penalty function based on the vehicle displacement at each state moment in the current prediction time domain; and construct an objective function based on the cost function, the time penalty function, and the traffic signal penalty function.

[0095] As an optional embodiment, a building module is used for... Construct the cost function. Used to represent the cost function and Used to represent weights, Used to represent functions related to control quantities. Used to represent functions related to state variables.

[0096] in, for The corresponding weights for The corresponding weights and They can be the same or different; this application does not specify. and The specific values ​​can be restricted, but in practical applications, they can be flexibly set according to actual needs.

[0097] As an optional embodiment, a building module is used for... Construct a time penalty function. Used to represent a time penalty function. Used to represent weights, Used to represent the vehicle speed at state j. This represents the expected vehicle speed at state j, where j starts taking values ​​from k+1 and continues to k+1. .

[0098] This application does not address The specific value is not restricted; in practical applications, it can be flexibly set according to actual needs. Furthermore, this application does not restrict the specific value of the parameter. and and Size restrictions between them.

[0099] As an optional embodiment, a building module is used for... Construct a traffic signal penalty function. Used to represent traffic signal penalty functions. Used to represent weights, This is used to represent the predicted distance between the vehicle's position at state j and the traffic light at state i. This represents the expected distance between the vehicle's position at state j and the traffic light at state i, where i is a positive integer and j starts from k+1 and continues to k+1. .

[0100] As an optional embodiment, the vehicle control device further includes a desired vehicle speed determination module. This desired vehicle speed determination module is used to obtain the desired time interval of the vehicle at the initial state moment; based on the desired time interval at the initial state moment, it forward-calculates the desired time interval for the vehicle to pass through each intersection on the driving route, where the forward desired time interval is the desired time interval for the corresponding intersection obtained through forward calculation; based on the reference time interval of a first target intersection, it reverse-calculates the desired time interval for the vehicle to pass through each intersection, where the first target intersection is the intersection whose last forward desired time interval is not an empty set during the forward calculation process; the reference time interval is determined based on the forward desired time interval of the first target intersection, and the reverse desired time interval is the desired time interval for the corresponding intersection obtained through reverse calculation; based on the reverse desired time interval of a second target intersection, it determines the desired vehicle speed at each state moment on the driving route, where the second target intersection is the intersection whose last reverse desired time interval is not an empty set during the reverse calculation process.

[0101] As an optional embodiment, the speed planning main program module is used to construct a state prediction model based on the vehicle's state variables and control variables; discretize the state prediction model to obtain the model prediction equation; and substitute the vehicle's control variables and state variables at state k into the model prediction equation. The forward recursion yields the state from the (k+1)th state in the current prediction time domain to the (k+)th state. The state variables and control variables at each state time point between state time points. Used to represent the state quantity at state k+1. Used to represent the state quantity at time k. Used to indicate the sampling time interval Functions used to represent state and control variables related to the k-th state time.

[0102] In one optional embodiment, the vehicle energy management module is used to determine the driving energy consumption value and braking energy consumption value corresponding to each vehicle speed planning curve; based on the driving energy consumption value and braking energy consumption value, determine the vehicle energy consumption corresponding to each vehicle speed planning curve; and determine the vehicle speed planning curve with the minimum vehicle energy consumption as the target vehicle speed planning curve.

[0103] Based on the vehicle control method, this application also provides specific embodiments of a vehicle for the vehicle control method. The vehicle includes an electronic control unit that includes a vehicle control device capable of executing the vehicle control method of this application.

[0104] Figure 9 A schematic diagram of the hardware structure of the electronic control unit provided in an embodiment of this application is shown.

[0105] The electronic control unit may include a processor 901 and a memory 902 storing computer program instructions.

[0106] Specifically, the processor 901 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0107] Memory 902 may include mass storage for data or instructions. For example, and not limitingly, memory 902 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 902 may include removable or non-removable (or fixed) media. Where appropriate, memory 902 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 902 is non-volatile solid-state memory.

[0108] The processor 901 implements any of the vehicle control methods described in the above embodiments by reading and executing computer program instructions stored in the memory 902.

[0109] In one example, the electronic control unit may also include a communication interface 903 and a bus 910. Wherein, as... Figure 9 As shown, the processor 901, memory 902, and communication interface 903 are connected through bus 910 and complete communication with each other.

[0110] The communication interface 903 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0111] Bus 910 includes hardware, software, or both that couple the components together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 910 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0112] Furthermore, in conjunction with the vehicle control methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the vehicle control methods in the above embodiments.

[0113] In addition, in conjunction with the vehicle control method in the above embodiments, this application embodiment can provide a computer program product for implementation. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device performs the vehicle control method as provided in any aspect of the above embodiments of this application.

[0114] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0115] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0116] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0117] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0118] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A vehicle control method, characterized in that, include: Based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain, an objective function is constructed. The objective function includes a time penalty function. The objective function is used to calculate the overall vehicle energy consumption and ideal speed matching degree in the current prediction time domain. The control quantity includes driving torque and braking torque, and the state quantity includes vehicle speed and displacement. Based on the constraints, the objective function is solved to obtain multiple vehicle speed planning curves corresponding to the optimization objective of matching vehicle energy consumption and ideal vehicle speed. The constraints include: determining the state from state (k+1) to state (k+2) within the current prediction time domain based on the state prediction model and the state and control variables at state k. The state variables and control variables at each state time point between state time points, where k is a positive integer. Used to indicate the prediction time step; The vehicle speed planning curve that minimizes vehicle energy consumption is determined as the target vehicle speed planning curve, and the vehicle speed at the (k+1)th state time is determined based on the target vehicle speed planning curve. Determine whether the (k+1)th state time is the end time; If the (k+1)th state time is not the endpoint time, the next prediction time domain is used as the new current prediction time domain, and k is updated to k+1. Then, based on the constraints, the objective function is solved to obtain the multiple vehicle speed planning curves. The next prediction time domain is from the (k+2)th state time to the (k+1)th state time. State moment; Obtain the expected time interval of the vehicle at the initial state time. Based on the expected time interval of the initial state time, recursively calculate the expected time interval of the vehicle passing through each intersection on the driving route. The expected time interval is the expected time interval of the corresponding intersection obtained by forward recursion. Based on the reference time interval of the first target intersection, the reverse expected time interval of the vehicle passing through each intersection is recursively calculated. The first target intersection is the last intersection in the forward recursion process where the forward expected time interval is not an empty set. The reference time interval is determined based on the forward expected time interval of the first target intersection, and the reverse expected time interval is the expected time interval of the corresponding intersection obtained by the reverse recursion. Based on the reverse expected time interval of the second target intersection, the expected vehicle speed of the vehicle at each state moment on the driving route is determined. The second target intersection is the last intersection in the reverse recursion process where the reverse expected time interval is not an empty set.

2. The method according to claim 1, characterized in that, The objective function also includes a cost function and a traffic signal penalty function; The objective function is constructed based on the control and state variables of the vehicle at each state moment in the current prediction time domain, including: Based on the control quantity and state quantity of the vehicle at each state time in the current prediction time domain, a cost function is constructed. Based on the vehicle speed at each state time in the current prediction time domain, a time penalty function is constructed; Based on the vehicle's displacement at each state time within the current prediction time domain, a traffic signal penalty function is constructed; The objective function is constructed based on the cost function, the time penalty function, and the traffic signal penalty function.

3. The method according to claim 2, characterized in that, The cost function is constructed based on the control quantity and state quantity of the vehicle at each state moment in the current prediction time domain, including: based on Construct the cost function. Used to represent the cost function. and Used to represent weights, Functions used to represent the control quantity. Functions used to represent the state variables.

4. The method according to claim 2, characterized in that, The step of constructing a time penalty function based on the vehicle speed at each state time within the current prediction time domain includes: based on Construct the time penalty function, the Used to represent the time penalty function. Used to represent weights, Used to represent the vehicle speed at state j. This represents the expected vehicle speed at state j, where j starts taking values ​​from k+1 and continues to k+1. .

5. The method according to claim 2, characterized in that, The step of constructing a traffic signal penalty function based on the vehicle's displacement at each state time within the current prediction time domain includes: based on Construct the traffic signal penalty function. Used to represent the traffic signal penalty function. Used to represent weights, This is used to represent the predicted distance between the vehicle's position at state j and the traffic light at state i. This represents the expected distance between the vehicle's position at state j and the traffic light at state i, where i is a positive integer and j starts from k+1 and continues to k+1. .

6. The method according to claim 1, characterized in that, Based on the state prediction model and the state and control variables at state time k, the state from state time (k+1) to state time (k+) in the current prediction time domain is determined. The state variables and control variables at each state moment between state moments include: Based on the vehicle's state variables and control variables, the state prediction model is constructed. The state prediction model is discretized to obtain the model prediction equation. ; Substitute the control quantity and state quantity of the vehicle at the k-th state time into the model prediction equation. The forward recursion yields the state from the (k+1)th state in the current prediction time domain to the (k+)th state. The state variables and control variables at each state moment between state moments. The state quantity used to represent the state at the (k+1)th state time. The state quantity used to represent the state at time k. Used to indicate the sampling time interval Functions used to represent the state and control variables related to the k-th state time.

7. The method according to claim 1, characterized in that, The step of determining the vehicle speed planning curve that minimizes vehicle energy consumption as the target vehicle speed planning curve includes: For each of the vehicle speed planning curves, determine the driving energy consumption value and braking energy consumption value corresponding to each vehicle speed planning curve; Based on the driving energy consumption value and the braking energy consumption value, determine the vehicle energy consumption corresponding to each of the vehicle speed planning curves; The vehicle speed planning curve that minimizes the vehicle's energy consumption is determined as the target vehicle speed planning curve.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the vehicle control method as described in any one of claims 1 to 7.

9. A vehicle, characterized in that, The vehicle includes an electronic control unit that performs the vehicle control method as described in any one of claims 1 to 7.

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