Energy efficiency optimization task management method under vehicle-road cloud communication architecture and related device

Abstract

The invention discloses an energy efficiency optimization task management method under a vehicle-road cloud communication architecture and a related device, and relates to the technical field of hybrid electric vehicle energy efficiency optimizing.The method comprises the steps that the state of a hybrid electric vehicle in a target road section is obtained, and the energy efficiency optimization task of a hybrid electric vehicle is optimized through a hybrid electric system energy efficiency optimization task management strategy model; a control instruction corresponding to the hybrid power vehicle is obtained, and the control instruction is sent to the hybrid power vehicle; wherein the states comprise the engine rotating speed, the vehicle speed and the power battery charge state; the control instruction comprises an output power distribution ratio of the power battery to the engine; the hybrid power system energy efficiency optimization task management strategy model is obtained by training representative driving conditions generated according to historical speed data of a plurality of vehicles on a target road section. According to the method, energy efficiency optimization task management can be carried out on all the hybrid power vehicles running on the same road section, the efficiency and accuracy of energy optimization task management of the hybrid power system are improved, and the application range is expanded.

Description

Technical Field

[0001] The present application relates to the technical field of energy efficiency optimization of hybrid electric vehicles, and in particular to an energy efficiency optimization task management method and related devices under a vehicle-road-cloud communication architecture. Background Art

[0002] With the rapid development of automobile power systems, the proportion of "electrification" in automobile power is increasing, and hybrid power has become one of the most effective technologies to reduce fuel consumption. Hybrid technology increases the power source of the vehicle and increases the flexibility of energy management. Energy efficiency optimization task management technology based on road condition prediction has gradually attracted attention.

[0003] At present, most of the hybrid system energy efficiency optimization task management based on road condition prediction is based on the perspective of a single vehicle. For example, for a single vehicle, the historical speed data of the vehicle itself is collected, and then a network model is trained to predict the speed of the vehicle in the future based on the speed of the vehicle in the past time window, so as to perform energy efficiency optimization task management for the vehicle according to the predicted speed in the future time window. This hybrid system energy efficiency optimization task management method based on only a single vehicle is generally suitable for situations where the vehicle often travels repeatedly on a certain section of road. For sections where the vehicle does not often travel repeatedly, hybrid energy efficiency optimization task management based on road condition prediction cannot be achieved. Summary of the invention

[0004] The purpose of this application is to provide an energy efficiency optimization task management method and related devices under a vehicle-road-cloud communication architecture, which can effectively improve the efficiency and accuracy of energy efficiency optimization task management for hybrid vehicles and expand the scope of energy efficiency optimization task management for hybrid systems.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides an energy efficiency optimization task management method under a vehicle-road-cloud communication architecture, comprising:

[0007] The state of the hybrid vehicle on the target road section is obtained; the state includes engine speed, vehicle speed and power battery charge state.

[0008] According to the state of the hybrid vehicle and the hybrid power system energy efficiency optimization task management strategy model, the control instructions corresponding to the hybrid vehicle are obtained; the control instructions include the output power distribution ratio of the power battery and the engine; the hybrid power system energy efficiency optimization task management strategy model is trained based on the representative driving conditions of the target section; the representative driving conditions are generated based on the historical speed data of several vehicles on the target section.

[0009] A control command is sent to the hybrid vehicle.

[0010] In the second aspect, the present application provides an energy efficiency optimization task management system under a vehicle-road-cloud communication architecture, the system comprising: a roadside equipment RSE and a cloud platform; wherein:

[0011] The roadside equipment RSE is used to obtain historical speed data of several vehicles on a target section and the status of a hybrid vehicle; generate representative driving conditions of the target section based on the historical speed data of several vehicles on the target section; and send the representative driving conditions of the target section and the status of the hybrid vehicle to a cloud platform.

[0012] The cloud platform is used to execute the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in the first aspect.

[0013] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in the first aspect.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in the first aspect.

[0015] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in the first aspect.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] The present application provides an energy efficiency optimization task management method and related devices under a vehicle-road-cloud communication architecture, the method comprising: obtaining the state of a hybrid vehicle on a target road section; obtaining a control instruction corresponding to the hybrid vehicle according to the state of the hybrid vehicle and a hybrid system energy efficiency optimization task management strategy model; sending the control instruction to the hybrid vehicle; wherein the state includes the engine speed, the vehicle speed and the power battery charge state; the control instruction includes the output power distribution ratio of the power battery and the engine; the hybrid system energy efficiency optimization task management strategy model is obtained by training a representative driving condition generated based on the historical speed data of several vehicles on the target road section. Compared with the hybrid system energy efficiency optimization task management method for a single vehicle in the prior art, the above-mentioned scheme of the present application can realize the energy efficiency optimization task management for all hybrid vehicles traveling on the same road section, improve the efficiency and accuracy of the hybrid system energy optimization task management, and even if the hybrid vehicle has not traveled on the road section, it can still be managed for energy efficiency optimization tasks, expanding the scope of hybrid system energy efficiency optimization task management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A flowchart of an energy efficiency optimization task management method under a vehicle-road-cloud communication architecture provided in one embodiment of the present application;

[0020] Figure 2 A schematic diagram of functional modules of an energy efficiency optimization task management system under a vehicle-road-cloud communication architecture provided by an embodiment of the present application;

[0021] Figure 3 A schematic diagram of a hybrid vehicle energy efficiency optimization management system framework for a vehicle-road-cloud collaborative communication architecture provided in one embodiment of the present application;

[0022] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] The automotive industry is witnessing an era of change driven by the integration of intelligent connected vehicles and advanced communication technologies. Intelligent connected vehicles powered by vehicle-to-vehicle communications have become a promising paradigm for improving vehicle performance, safety, and efficiency. In particular, in the field of hybrid electric vehicles, the synergy between the vehicle-road-cloud communication architecture provides unprecedented opportunities for energy efficiency optimization management (also known as energy efficiency optimization task management). Through information sharing under the vehicle-road-cloud communication architecture, hybrid vehicles are able to dynamically adjust their operating parameters according to environmental conditions, traffic patterns, and road topology, thereby maximizing energy utilization and minimizing fuel consumption. Existing technologies do not fully utilize the road condition reconstruction capabilities under the vehicle-road-cloud communication architecture, which reduces the performance of energy efficiency optimization management tasks to a certain extent.

[0025] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0026] See also Figure 1 In an exemplary embodiment, a method for managing energy efficiency optimization tasks under a vehicle-road-cloud communication architecture is provided. The method for managing energy efficiency optimization tasks under a vehicle-road-cloud communication architecture comprises the following steps:

[0027] Step 101, obtaining the state of the hybrid vehicle on the target road section; the state includes engine speed, vehicle speed and power battery charge state.

[0028] Step 102, obtaining control instructions corresponding to the hybrid vehicle according to the state of the hybrid vehicle and the hybrid system energy efficiency optimization task management strategy model (referred to as strategy model or energy efficiency optimization task management strategy); the control instructions include the output power distribution ratio of the power battery and the engine; the hybrid system energy efficiency optimization task management strategy model is trained based on the representative driving conditions of the target section; the representative driving conditions (referred to as representative conditions) are generated based on the historical speed data of several vehicles on the target section.

[0029] Step 103: sending a control instruction to the hybrid vehicle.

[0030] As an optional implementation, the training process of the hybrid power system energy efficiency optimization task management strategy model specifically includes the following steps:

[0031] Step 201, according to the hybrid model, determine the reinforcement learning algorithm model and reward function; the hybrid model is used to output the state of the next moment according to the current state, control instructions and the representative driving condition; the reward function is used to calculate the environmental reward. In the training process, the change of the vehicle speed in the state output by the hybrid model conforms to the vehicle speed curve corresponding to the representative driving condition.

[0032] Step 202: Determine the environment corresponding to the reinforcement learning algorithm model according to the representative driving condition, the hybrid model and the reward function.

[0033] Step 203: train the reinforcement learning algorithm model according to the environment to obtain a trained reinforcement learning algorithm model.

[0034] Step 204 : determining a hybrid power system energy efficiency optimization task management strategy model according to the trained reinforcement learning algorithm model.

[0035] In some embodiments, the training process can be performed periodically, for example, the road condition reconstruction (i.e., generating representative driving conditions) can be performed every other week. If the road condition reconstructed this week is slightly different from the road condition last week (which can be determined by comparing the distribution of vehicle speed and acceleration), then the policy model does not need to be trained or optimized. If the reconstructed road condition changes significantly, then the policy model needs to be trained and optimized again.

[0036] As an optional implementation, during the training process of the hybrid power system energy efficiency optimization task management strategy model, a SAC (soft actor-critic) algorithm may be used to obtain the optimal strategy model according to the following process:

[0037] A2C reinforcement learning algorithm update process:

[0038] Input: s t ,a t ,r t ,s t+1 ,a t+1 ;

[0039] 1:δ t =V(s t ;w)-r t -γV(s t+1 ; w);

[0040] 2:

[0041] 3:

[0042] 4:s t ←s t+1 ,a t ←a t+1 ;

[0043] Finish.

[0044] Among them, δ represents the temporal difference value of the actor policy network, V represents the actor policy network (a nonlinear mapping from state s to action a), w represents the network parameter corresponding to the actor policy network, γ represents the discount factor (usually a constant less than 1), π represents the critic evaluation network, θ represents the parameters of the critic evaluation network, and β represents the update step size. Represents a gradient operation.

[0045] The actor strategy network obtained by training and solving the above reinforcement learning algorithm is the trained hybrid power system energy efficiency optimization task management strategy model.

[0046] As an optional implementation, the power system state equation corresponding to the hybrid model is expressed as follows:

[0047] s t+1 =f(s t ,u t );

[0048] Among them, s t+1 Indicates the state of the power system at time t+1, including engine speed, vehicle speed and power battery state of charge SoC (State-of-Charge), etc.; t represents the state of the power system at time t; u t Represents the control instruction (control parameter) at time t, that is, the output power distribution ratio of the power battery and the engine.

[0049] The above state equations are established based on the topological connection mode of the power system of the hybrid vehicle.

[0050] As an optional implementation, the reward function expression is as follows:

[0051]

[0052] Among them, r represents the environmental reward; Indicates the instantaneous fuel consumption of the engine, also known as fuel consumption rate; SoC refrepresents the reference power battery state of charge (generally a fixed value, such as set to 60%); SoC(t) represents the power battery state of charge at time t; α and β represent the weighting coefficients of the corresponding items respectively.

[0053] It can be seen from the reward function that the control goal of the hybrid system energy efficiency optimization task management strategy is to minimize the engine fuel consumption, while taking into account the power battery SoC within the reference SoC value (i.e. SoC ref ) fluctuates around.

[0054] As an optional implementation, the process of generating the representative driving condition (i.e., the process of reconstructing the road condition) specifically includes the following steps:

[0055] Step 301, calculating historical acceleration data of a plurality of vehicles according to historical speed data of a plurality of vehicles on the target road section.

[0056] Step 302: divide the historical speed data of the plurality of vehicles on the target road section and the historical acceleration data of the plurality of vehicles on the target road section into a plurality of discrete intervals.

[0057] Step 303: determine the state probability transfer matrix corresponding to each of the discrete intervals.

[0058] Step 304: Generate a representative driving condition of the target road section based on the state probability transfer matrix corresponding to each of the discrete intervals.

[0059] As an optional implementation, step 304 specifically includes:

[0060] Step 304.1, setting an initial state corresponding to the driving condition; the initial state includes the speed and acceleration corresponding to the starting time of the target road section.

[0061] Step 304.2, randomly sample the state probability transfer matrix to obtain the velocity and acceleration at the next moment.

[0062] Step 304.3, determine whether the end state corresponding to the driving condition is reached; the end state includes the speed at the end time corresponding to the target section and the total mileage corresponding to the driving condition; the total mileage corresponding to the driving condition is the mileage of the target section.

[0063] Step 304.4: If not, return to step 304.2 until the termination state is reached and the first driving condition is obtained.

[0064] Step 304.5, determining whether the first driving condition meets the characteristic value deviation requirement; if so, determining the first driving condition as the representative driving condition of the target road section.

[0065] See also Figure 2 and Figure 3 Based on the same inventive concept, the embodiment of the present application also provides an energy efficiency optimization task management system under a vehicle-road-cloud communication architecture, the system comprising a roadside equipment RSE and a cloud platform; wherein,

[0066] The roadside equipment RSE is used to obtain historical speed data of several vehicles on a target section and the status of a hybrid vehicle; generate representative driving conditions of the target section based on the historical speed data of several vehicles on the target section; and send the representative driving conditions of the target section and the status of the hybrid vehicle to a cloud platform. Figure 3 The roadside in the figure refers to the roadside equipment RSE.

[0067] The cloud platform is used to execute the steps in the above-mentioned corresponding method embodiments, namely: according to the representative driving conditions of the target road section, the hybrid power system energy efficiency optimization task management strategy model is trained to obtain a trained hybrid power system energy efficiency optimization task management strategy model; according to the trained hybrid power system energy efficiency optimization task management strategy model and the state of the hybrid vehicle, the control instruction corresponding to the hybrid vehicle is obtained; and the control instruction is sent to the hybrid vehicle.

[0068] The hybrid vehicle energy efficiency optimization task management strategy for the vehicle-road-cloud communication architecture provided in this embodiment explores the potential of vehicle-road-cloud communication information to improve the economy of hybrid vehicles by reconstructing road conditions based on historical data.

[0069] Figure 3 The overall framework of using domain control to achieve hybrid vehicle energy efficiency optimization task management under the vehicle-road-cloud collaborative communication architecture is described. First, the vehicle on the road sends the driving speed collected by the intelligent driving domain controller to the roadside equipment RSE. Then, the RSE generates a representative driving condition (i.e., the characteristic vehicle speed for a period of time) based on the received vehicle driving speed. The generation of representative conditions is also commonly called road condition reconstruction or driving condition reconstruction, and sends it to the cloud platform. Finally, the cloud platform will train the energy efficiency optimization task management strategy based on the reinforcement learning algorithm according to the generated representative driving conditions, and send the control instructions output by it (energy efficiency optimization task management strategy) to the vehicle's power domain controller for execution. The generated representative driving conditions can reflect the driving conditions of the vehicle, so that the trained energy efficiency optimization task management strategy can be more suitable for vehicles on the road near the roadside unit RSE. Two domain controllers are used in this architecture, including the intelligent driving domain controller and the power domain controller, which are used for the collection and storage of historical vehicle speed data and the execution of hybrid power distribution respectively. This method of assigning different tasks to different domain controllers is conducive to the balance of computing burden.

[0070] The energy efficiency optimization task management method under the vehicle-road-cloud communication architecture provided in this application can also be described as the following process:

[0071] Step 401 : reconstructing driving conditions to generate representative driving conditions.

[0072] The road condition reconstruction in this application is mainly based on the reconstruction of characteristic working conditions realized by the state probability transfer matrix. First, the vehicle speed and acceleration are divided into several discrete intervals with a certain degree of discreteness, and the corresponding state probability transfer matrix is ​​calculated in each corresponding speed and acceleration discrete interval; then, the initial state of the vehicle speed and acceleration is set, and the state at the next moment is obtained by randomly sampling the state probability transfer matrix until the specified terminal state is reached; then, the characteristic values ​​of the generated working condition (i.e., the first driving working condition) are calculated, and the characteristic values ​​include the mean square error of speed, the average positive acceleration, the average negative acceleration, the mean square error of positive acceleration, the number of stops per kilometer, etc.; if the above characteristic values ​​meet the deviation requirements, the generated characteristic working condition can be considered valid; otherwise, the above process based on the state probability transfer matrix can be repeated to generate a new working condition until the generated new working condition meets the characteristic value deviation requirements, thereby obtaining the characteristic working condition (i.e., the representative driving working condition).

[0073] In some embodiments, the initial states of vehicle speed and acceleration are both set to 0.

[0074] In some embodiments, the prescribed termination state is: the vehicle speed is 0, and the total mileage of the generated working condition reaches the average mileage of the working condition data set collected by the roadside unit RSE.

[0075] In some embodiments, whether the generated working condition meets the characteristic value deviation requirement is determined based on: the characteristic value of the generated working condition is within 5% of the characteristic value of the working condition data set. The characteristic value of the working condition data set is the average of the characteristic values ​​corresponding to each working condition in the working condition data set. Each working condition in the working condition data set is the speed uploaded by different vehicles and the corresponding acceleration (the acceleration can be calculated based on the speed uploaded by the vehicle).

[0076] In some other embodiments, the initial state and the final state of the vehicle speed are set to the average speed of the target road section.

[0077] Step 402, modeling the energy efficiency optimization management task problem, that is, determining the state equation and reward function of the energy efficiency optimization management task. Then, constructing a hybrid power model according to the state equation; and determining the training environment according to the representative driving conditions, the hybrid power model and the reward function.

[0078] Step 403, solving the energy efficiency optimization management task based on reinforcement learning. After determining the state equation and reward function of the energy efficiency optimization management task, any algorithm (such as A2C reinforcement learning algorithm) can be used under the reinforcement learning framework to achieve strategy solution.

[0079] The present application also provides an application scenario, which applies the energy efficiency optimization task management method under the above-mentioned vehicle-road-cloud communication architecture. Specifically: the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture provided in this embodiment can be applied to the scenario of energy efficiency optimization task management for a running hybrid vehicle.

[0080] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store relevant data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes an energy efficiency optimization task management method under a vehicle-road-cloud communication architecture.

[0081] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0082] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0083] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0084] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0085] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0086] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0087] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for energy efficiency optimization task management under a vehicle-road-cloud communication architecture, characterized in that: The energy efficiency optimization task management method under the vehicle-road-cloud communication architecture includes: Acquire the state of the hybrid vehicle on the target road section; the state includes engine speed, vehicle speed and power battery charge state; According to the state of the hybrid vehicle and the hybrid system energy efficiency optimization task management strategy model, a control instruction corresponding to the hybrid vehicle is obtained; the control instruction includes an output power distribution ratio of a power battery and an engine; the hybrid system energy efficiency optimization task management strategy model is obtained by training based on a representative driving condition of the target section; the representative driving condition is generated based on historical speed data of several vehicles on the target section; A control command is sent to the hybrid vehicle.

2. The energy efficiency optimization task management method under the vehicle-road-cloud communication architecture according to claim 1 is characterized in that: The training process of the hybrid power system energy efficiency optimization task management strategy model specifically includes: Determining a reinforcement learning algorithm model and a reward function according to a hybrid model; the hybrid model is used to output a state at a next moment according to a state at a current moment, a control instruction, and the representative driving condition; the reward function is used to calculate an environmental reward; Determining an environment corresponding to a reinforcement learning algorithm model according to the representative driving condition, the hybrid model, and the reward function; Training the reinforcement learning algorithm model according to the environment to obtain a trained reinforcement learning algorithm model; According to the trained reinforcement learning algorithm model, a hybrid power system energy efficiency optimization task management strategy model is determined.

3. The energy efficiency optimization task management method under the vehicle-road-cloud communication architecture according to claim 2 is characterized in that: The power system state equation expression corresponding to the hybrid model is as follows: s t+1 =f(s t ,u t ); Among them, s t+1 represents the state of the power system at time t+1; s t represents the state of the power system at time t; u t Represents the control instruction at time t.

4. The energy efficiency optimization task management method under the vehicle-road-cloud communication architecture according to claim 2 is characterized in that: The reward function expression is as follows: Among them, r represents the environmental reward; Indicates the instantaneous fuel consumption of the engine; SoC ref represents the state of charge of the reference power battery; SoC(t) represents the state of charge of the power battery at time t; α and β represent the weighting coefficients of the corresponding items respectively.

5. The energy efficiency optimization task management method under the vehicle-road-cloud communication architecture according to claim 1 is characterized in that: The generation process of the representative driving condition specifically includes: Calculating historical acceleration data of a plurality of vehicles according to historical speed data of a plurality of vehicles on the target road section; Dividing the historical speed data of a plurality of vehicles on the target road section and the historical acceleration data of a plurality of vehicles on the target road section into a plurality of discrete intervals; Determine the state probability transfer matrix corresponding to each of the discrete intervals; Based on the state probability transfer matrix corresponding to each of the discrete intervals, a representative driving condition of the target road section is generated.

6. The energy efficiency optimization task management method under the vehicle-road-cloud communication architecture according to claim 5 is characterized in that: The generating of the representative driving condition of the target road section based on the state probability transfer matrix corresponding to each discrete interval specifically includes: Setting an initial state corresponding to the driving condition; the initial state includes the speed and acceleration corresponding to the starting time of the target road section; Randomly sample the state probability transfer matrix to obtain the velocity and acceleration at the next moment; Determine whether the end state corresponding to the driving condition is reached; the end state includes the speed at the end time corresponding to the target section and the total mileage corresponding to the driving condition; the total mileage corresponding to the driving condition is the mileage of the target section; If not, return to the step of "randomly sampling the state probability transfer matrix to obtain the vehicle speed and vehicle acceleration at the next moment" until the terminal state is reached to obtain the first driving condition; Determine whether the first driving condition meets the characteristic value deviation requirement; if so, determine the first driving condition as a representative driving condition of the target road section.

7. An energy efficiency optimization task management system under a vehicle-road-cloud communication architecture, characterized in that: The energy efficiency optimization task management system under the vehicle-road-cloud communication architecture includes roadside equipment RSE and a cloud platform; wherein, The roadside equipment RSE is used to obtain historical speed data of several vehicles on the target road section and the status of the hybrid vehicle; generate a representative driving condition of the target road section according to the historical speed data of several vehicles on the target road section; and send the representative driving condition of the target road section and the status of the hybrid vehicle to the cloud platform; The cloud platform is used to execute the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in any one of claims 1-4.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the energy efficiency optimization task management method under the vehicle-road-cloud communication architecture described in any one of claims 1-6.

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