Hybrid vehicle control method and device

Abstract

A hybrid vehicle control method and device are provided. The method includes obtaining a vehicle travel sample set, the vehicle travel sample set including a plurality of vehicle travel samples and label data corresponding to each vehicle travel sample, the vehicle travel sample representing the vehicle's historical driving data in a historical driving stage, and the label data representing the vehicle's optimal power allocation strategy in the historical driving stage; using the vehicle travel sample set to train an initial power allocation neural network model to obtain a target power allocation neural network model; establishing a vehicle speed prediction model based on the vehicle's historical driving data corresponding to the optimal power allocation strategy in the historical driving stage; determining a predicted vehicle speed at a predicted time point based on the predicted time point and the vehicle speed prediction model; inputting the vehicle speed and the predicted vehicle speed in the current driving stage into the target power allocation neural network model to obtain a target power allocation strategy corresponding to the predicted vehicle speed, so as to control the vehicle according to the target power allocation strategy.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicle control technology, and more specifically, to a hybrid vehicle control method, a hybrid vehicle control device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] As the world's energy crisis becomes increasingly severe and environmental pollution worsens, people are paying more and more attention to efficient and clean power systems, and hybrid vehicles have become a trend in vehicle development. Hybrid vehicles add an electric drive system to the traditional fuel vehicle power system. The main components of the electric drive include batteries, generators, and drive motors. The speed and torque adjustment function of the motor increases the probability of the engine operating in the high-efficiency zone. The battery's energy storage function can also recover energy in individual working conditions, greatly increasing fuel economy.

[0003] Since the power system of a hybrid vehicle includes not only an electric drive system but also a fuel vehicle power system, if the power of the engine and the electric motor of the hybrid vehicle cannot be reasonably distributed, the fuel economy of the vehicle will be reduced. Summary of the invention

[0004] In view of this, embodiments of the present disclosure provide a hybrid vehicle control method, a hybrid vehicle control device, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] One aspect of an embodiment of the present disclosure provides a hybrid vehicle control method, including:

[0006] Acquire a vehicle travel sample set, wherein the vehicle travel sample set includes a plurality of vehicle travel samples and label data corresponding to each of the vehicle travel samples, the vehicle travel samples represent vehicle historical driving data of the vehicle in a historical driving stage, the vehicle historical driving data include vehicle speed sets corresponding to different time periods of the vehicle in the historical driving stage, the vehicle speed set includes a first vehicle speed and a second vehicle speed, wherein the first vehicle speed and the second vehicle speed are vehicle speeds separated by a preset time period in the same time period, and the label data represent an optimal power allocation strategy of the vehicle in the historical driving stage;

[0007] Using the vehicle travel sample set to train an initial power allocation neural network model to obtain a target power allocation neural network model, wherein the initial power allocation neural network model is established based on test cycle optimal power allocation data corresponding to the test cycle data, and the test cycle data represents vehicle driving data of the vehicle under standard working conditions;

[0008] Establishing a vehicle speed prediction model based on the vehicle historical driving data corresponding to the above optimal power allocation strategy in the historical driving stage;

[0009] Determine the predicted vehicle speed at the predicted time point according to the predicted time point and the vehicle speed prediction model;

[0010] The vehicle speed in the current driving phase and the predicted vehicle speed are input into the target power allocation neural network model to obtain a target power allocation strategy corresponding to the predicted vehicle speed, so as to control the vehicle according to the target power allocation strategy.

[0011] According to an embodiment of the present disclosure, the optimal power allocation strategy in the above historical driving stage is determined by the following steps:

[0012] The above-mentioned vehicle historical driving data in the above-mentioned historical driving stage is processed by a dynamic programming algorithm to obtain the optimal power allocation strategy in the above-mentioned historical driving stage, wherein the above-mentioned optimal power allocation strategy includes: optimal values ​​of engine output torque, generator output torque and motor output torque.

[0013] According to an embodiment of the present disclosure, the above-mentioned initial power allocation neural network model is established according to the test cycle optimal power allocation data corresponding to the test cycle data, including:

[0014] Processing the test cycle data using a dynamic programming algorithm to obtain optimal power allocation data for the test cycle;

[0015] The initial power allocation neural network model is established based on the test cycle data and the test cycle optimal power allocation data.

[0016] According to an embodiment of the present disclosure, the initial power allocation neural network model includes a BP neural network model, and the initial power allocation neural network model includes an input layer, at least one hidden layer and an output layer.

[0017] According to an embodiment of the present disclosure, the above-mentioned vehicle historical driving data in the above-mentioned historical driving stage also includes the total vehicle mileage as shown in the first formula, and the above-mentioned vehicle status data in the current driving stage includes the vehicle mileage, real-time vehicle speed and required power in the current driving stage. The calculation of the vehicle mileage in the current driving stage is shown in the second formula:

[0018]

[0019]

[0020] Among them, Dis n is the total mileage of the vehicle in the historical driving stage, Dis n-1is the total reference mileage of the vehicle in the driving phase before the historical driving phase, Dis cur is the total reference mileage of the vehicle in the historical driving stage, m is the update step of the historical travel data, and L n is the vehicle mileage in the current driving phase, L n-1 It is the vehicle mileage during the historical driving stage.

[0021] According to an embodiment of the present disclosure, the target power distribution strategy includes: an output torque signal of the engine of the vehicle, an output torque signal of the generator and an output torque signal of the drive motor, wherein the generator is used to convert mechanical energy into electrical energy;

[0022] The above-mentioned vehicle historical driving data also includes: vehicle battery remaining power, gear position signal, throttle signal and brake signal.

[0023] According to an embodiment of the present disclosure, the vehicle speed prediction model is established based on the vehicle historical driving data corresponding to the optimal power allocation strategy in the historical driving stage, including:

[0024] Determine the vehicle speed state matrix according to the optimal power allocation strategy in the above historical driving stage;

[0025] According to the above vehicle speed state matrix and vehicle speed frequency matrix, a state transition probability matrix is ​​determined;

[0026] The vehicle speed prediction model is determined according to the state transition probability matrix, wherein the vehicle speed prediction model includes a Markov chain vehicle speed prediction model.

[0027] According to an embodiment of the present disclosure, the above-mentioned vehicle trip sample set is used to train the initial power allocation neural network model to obtain the target power allocation neural network model, including:

[0028] Input the vehicle driving sample into the initial power allocation neural network model, and output a predicted power allocation strategy, wherein the predicted power allocation strategy includes the predicted output torque of the engine, the output torque of the generator, and the output torque of the drive motor;

[0029] According to the above-mentioned predicted power allocation strategy and the above-mentioned label data calculation function, a loss result is obtained;

[0030] The parameters of the initial power allocation neural network model are iteratively adjusted according to the loss result to generate the target power allocation neural network model.

[0031] According to an embodiment of the present disclosure, the above method further includes:

[0032] When the mileage of the vehicle in the current driving phase meets the preset update step length, obtaining the optimal power allocation strategy for the current driving phase after the update step length;

[0033] The optimal power allocation strategy of the current driving stage after the above-mentioned updated step size and the corresponding vehicle historical driving data are used as new label data and new vehicle travel samples to train the above-mentioned target power allocation neural network model to obtain a new target power allocation neural network model;

[0034] Establishing a new vehicle speed prediction model based on the vehicle historical driving data corresponding to the optimal power allocation strategy in the current driving stage;

[0035] Determine a new predicted vehicle speed at a new predicted time point in a future driving phase according to the new predicted time point and the new vehicle speed prediction model;

[0036] The vehicle speed in the future driving stage and the new predicted vehicle speed are input into the new target power distribution neural network model to obtain a new target power distribution strategy corresponding to the new predicted vehicle speed, so as to control the vehicle according to the new target power distribution strategy.

[0037] Another aspect of the disclosed embodiment provides a hybrid vehicle control device, comprising:

[0038] an acquisition module, configured to acquire a vehicle travel sample set, wherein the vehicle travel sample set includes a plurality of vehicle travel samples and label data corresponding to each of the vehicle travel samples, wherein the vehicle travel samples represent vehicle historical travel data of the vehicle in a historical travel stage, wherein the vehicle historical travel data includes vehicle speed sets corresponding to different time periods of the vehicle in the historical travel stage, wherein the vehicle speed set includes a first vehicle speed and a second vehicle speed, wherein the first vehicle speed and the second vehicle speed are vehicle speeds separated by a preset time period in the same time period, and wherein the label data represent an optimal power allocation strategy of the vehicle in the historical travel stage;

[0039] A training module, for training an initial power allocation neural network model using the vehicle travel sample set to obtain a target power allocation neural network model, wherein the initial power allocation neural network model is established based on test cycle optimal power allocation data corresponding to the test cycle data, and the test cycle data represents vehicle driving data of the vehicle under standard working conditions;

[0040] An establishing module, for establishing a vehicle speed prediction model according to the vehicle historical driving data corresponding to the above-mentioned optimal power allocation strategy in the historical driving stage;

[0041] A vehicle speed prediction module, used to determine the predicted vehicle speed at the predicted time point according to the predicted time point and the vehicle speed prediction model; and

[0042] The power distribution prediction module is used to input the vehicle speed of the current driving stage and the above-mentioned predicted vehicle speed into the above-mentioned target power distribution neural network model, and obtain the target power distribution strategy corresponding to the above-mentioned predicted vehicle speed, so as to control the above-mentioned vehicle according to the above-mentioned target power distribution strategy.

[0043] Another aspect of an embodiment of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0044] Another aspect of an embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method described above when executed.

[0045] Another aspect of an embodiment of the present disclosure provides a computer program product, wherein the computer program product includes computer executable instructions, and the instructions are used to implement the method described above when executed.

[0046] According to an embodiment of the present disclosure, an initial power distribution neural network model is trained by using historical vehicle driving data and an optimal power distribution strategy in a historical driving stage to obtain a target power distribution neural network model. The target power distribution neural network model is used to process the vehicle speed in the current driving stage and the predicted vehicle speed, so as to obtain a target power distribution strategy corresponding to the predicted vehicle speed, and then the vehicle is controlled according to the target power distribution strategy. Since the target power distribution neural network model learns about the optimal power distribution strategy in historical data during the training process, controlling the vehicle using the predicted target power distribution strategy can reduce the economy of the vehicle, thereby solving the problem of poor economy of hybrid vehicles during use. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0048] Figure 1 An exemplary system architecture for applying a hybrid vehicle control method according to an embodiment of the present disclosure is schematically shown;

[0049] Figure 2 A flow chart schematically shows a hybrid vehicle control method according to an embodiment of the present disclosure;

[0050] Figure 3 A training flow chart of a target power allocation neural network model according to an embodiment of the present disclosure is schematically shown;

[0051] Figure 4 A block diagram schematically shows a hybrid vehicle control device according to an embodiment of the present disclosure; and

[0052] Figure 5 A block diagram of an electronic device for implementing a hybrid vehicle control method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0053] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0054] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0055] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0056] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0057] The existing hybrid vehicle control methods mainly include rule-based control methods, dynamic programming algorithms, model prediction algorithms, and equivalent fuel consumption minimum methods. At present, these methods have their own advantages and disadvantages. For example, the rule-based control method has good robustness and small calculation amount, but it cannot obtain the optimal result. The dynamic programming algorithm can obtain the optimal result but the calculation amount is large and cannot be used for real-time control. Using a single control method cannot meet the optimal control of the variable actual vehicle driving process, which makes it difficult for hybrid vehicles to achieve the best fuel economy.

[0058] In view of this, an embodiment of the present disclosure provides a hybrid vehicle control method and device. The method may include obtaining a vehicle travel sample set, wherein the vehicle travel sample set may include multiple vehicle travel samples and label data corresponding to each vehicle travel sample, the vehicle travel sample represents the vehicle historical driving data of the vehicle in the historical driving stage, and the label data represents the optimal power allocation strategy of the vehicle in the historical driving stage; using the vehicle travel sample set to train an initial power allocation neural network model to obtain a target power allocation neural network model, wherein the initial power allocation neural network model is established based on the test cycle optimal power allocation data corresponding to the test cycle data, and the test cycle data represents the vehicle driving data of the vehicle under standard working conditions; according to the vehicle historical driving data corresponding to the optimal power allocation strategy in the historical driving stage, a vehicle speed prediction model is established; according to the prediction time point and the vehicle speed prediction model, a predicted vehicle speed at the prediction time point is determined; the vehicle speed of the current driving stage and the predicted vehicle speed are input into the target power allocation neural network model to obtain a target power allocation strategy corresponding to the predicted vehicle speed, so as to control the vehicle according to the target power allocation strategy.

[0059] Figure 1 The exemplary system architecture 100 to which the hybrid vehicle control method according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0060] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal vehicles 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal vehicles 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0061] The user can use the terminal vehicles 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various vehicle data collection applications can be installed on the terminal vehicles 101, 102, 103. In an exemplary embodiment, the terminal vehicle 101 can also obtain the target power distribution neural network model on other terminal vehicles.

[0062] The terminal vehicles 101 , 102 , and 103 may be hybrid vehicles having independent computing devices or transmitting and receiving devices.

[0063] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for the vehicle history data transmitted by the terminal vehicles 101, 102, and 103. The background management server may analyze the received vehicle history data and feed back the processing results (such as a target power allocation neural network model generated according to the vehicle history data) to the terminal vehicle.

[0064] It should be noted that the hybrid vehicle control method provided in the embodiment of the present disclosure can generally be executed by the terminal vehicle 101, 102, or 103, or can also be executed by other terminal vehicles different from the terminal vehicles 101, 102, or 103. Accordingly, the hybrid vehicle control system provided in the embodiment of the present disclosure can generally be arranged in the terminal vehicle 101, 102, or 103, or can be arranged in other terminal vehicles different from the terminal vehicles 101, 102, or 103. Alternatively, the hybrid vehicle control method provided in the embodiment of the present disclosure can also be executed by the server 105 and the final result can be transmitted to the terminal vehicle 101, 102, or 103. Accordingly, the hybrid vehicle control system provided in the embodiment of the present disclosure can generally be arranged in the server 105. The hybrid vehicle control method provided in the embodiment of the present disclosure can also be executed by a server or server cluster different from the server 105 and capable of communicating with the terminal vehicles 101, 102, 103 and / or the server 105. Accordingly, the hybrid vehicle control system provided by the embodiment of the present disclosure may also be disposed in a server or server cluster that is different from the server 105 and is capable of communicating with the terminal vehicles 101 , 102 , 103 and / or the server 105 .

[0065] It should be understood that Figure 1 The number of terminal vehicles, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided as required.

[0066] Figure 2 The flowchart of the hybrid vehicle control method according to the embodiment of the present disclosure is schematically shown.

[0067] like Figure 2 As shown, the hybrid vehicle control method may include operations S201-S205.

[0068] In operation S201, a vehicle travel sample set is obtained, wherein the vehicle travel sample set may include multiple vehicle travel samples and label data corresponding to each vehicle travel sample, the vehicle travel sample represents the vehicle's historical driving data in a historical driving stage, the vehicle's historical driving data may include vehicle speed sets corresponding to different time periods in the historical driving stage, the vehicle speed set may include a first vehicle speed and a second vehicle speed, wherein the first vehicle speed and the second vehicle speed are vehicle speeds separated by a preset time period in the same time period, and the label data represents an optimal power allocation strategy for the vehicle in the historical driving stage.

[0069] In operation S202, an initial power distribution neural network model is trained using a vehicle travel sample set to obtain a target power distribution neural network model, wherein the initial power distribution neural network model is established based on test cycle optimal power distribution data corresponding to the test cycle data, and the test cycle data represents vehicle driving data under standard operating conditions.

[0070] In operation S203, a vehicle speed prediction model is established according to the historical driving data of the vehicle corresponding to the optimal power allocation strategy in the historical driving stage.

[0071] In operation S204, a predicted vehicle speed at the predicted time point is determined according to the predicted time point and the vehicle speed prediction model.

[0072] In operation S205, the vehicle speed and the predicted vehicle speed in the current driving phase are input into the target power distribution neural network model to obtain a target power distribution strategy corresponding to the predicted vehicle speed, so as to control the vehicle according to the target power distribution strategy.

[0073] According to an embodiment of the present disclosure, the test cycle data is configured by the vehicle manufacturer at the factory, which is data corresponding to the vehicle type measured under standard operating conditions. The data may include different optimal power distribution strategies corresponding to different vehicle speeds under standard operating conditions.

[0074] According to an embodiment of the present disclosure, the optimal power distribution strategy represents a distribution scheme of the output power of the generator and the engine in the hybrid vehicle, and the distribution scheme can be used to control the operation of the vehicle.

[0075] According to an embodiment of the present disclosure, a hybrid vehicle may include a vehicle that uses both fossil fuel and electric energy as energy sources, such as a common gasoline-electric hybrid vehicle on the market.

[0076] According to an embodiment of the present disclosure, the historical driving stage is different from the current driving stage. For example, when the total mileage of the vehicle in the current driving stage is 111 kilometers, the historical driving stage may refer to 0-110 kilometers. Preferably, the historical driving stage of the present disclosure may refer to 100-110 kilometers. The historical driving stage closest to the current driving stage can be determined according to the update step size. For example, when the update step size is 20 kilometers, when the total mileage of the vehicle is 111 kilometers, the 11 kilometers of the current stage does not meet the update requirements. Therefore, preferably, the historical driving stage may be a total mileage of the vehicle of 80-100 kilometers.

[0077] According to an embodiment of the present disclosure, the predicted time point may be a time point that is separated from the current moment by a preset time length. For example, if the current moment is 0:0:0:0 and the preset time length is 1 second, then the predicted time point is 0:0:0:01. It should be noted that the preset time length may be specifically set by a vehicle engineer based on actual conditions.

[0078] In an embodiment of an embodiment, a vehicle travel sample set in a historical driving stage closest to the current driving stage (for example, the above-mentioned 100-110 kilometers or 80-100 kilometers) is used to train an initial power distribution neural network model established using test cycle optimal power distribution data corresponding to the test cycle data, so that a trained target power distribution neural network model can be obtained.

[0079] According to an embodiment of the present disclosure, the data of the closest historical driving stage is used as a vehicle trip sample, which can enable the target power distribution neural network model to learn the driving characteristics that are closest to the driver's current driving state.

[0080] According to an embodiment of the present disclosure, a vehicle speed prediction model is established based on historical driving data, and it can predict the vehicle speed according to the prediction time point, thereby obtaining a predicted vehicle speed corresponding to the prediction time point.

[0081] According to an embodiment of the present disclosure, the vehicle speed of the current driving stage (for example, the vehicle speed at the current moment) and the predicted vehicle speed are input into the trained target power allocation neural network model, so that the target power allocation strategy corresponding to the predicted vehicle speed can be obtained. For example, if the vehicle requires a total power output of 110kw, the target power allocation strategy can be 80kw for the engine output and 30kw for the motor output. It should be noted that the above examples are only for illustration and are not intended to limit the present disclosure.

[0082] According to an embodiment of the present disclosure, an initial power distribution neural network model is trained using historical vehicle driving data and an optimal power distribution strategy within a historical driving stage to obtain a target power distribution neural network model. The target power distribution neural network model is used to process the vehicle speed and predicted vehicle speed in the current driving stage, thereby obtaining a target power distribution strategy corresponding to the predicted vehicle speed, and then controlling the vehicle according to the target power distribution strategy. Since the target power distribution neural network model learns about the optimal power distribution strategy in historical data during the training process, controlling the vehicle using the predicted target power distribution strategy can reduce the economy of the vehicle, thereby solving the problem of poor economy of hybrid vehicles during use.

[0083] According to an embodiment of the present disclosure, the optimal power allocation strategy in the historical driving stage is determined by the following steps:

[0084] The dynamic programming algorithm is used to process the historical driving data of the vehicle in the historical driving stage to obtain the optimal power allocation strategy in the historical driving stage, wherein the optimal power allocation strategy may include: optimal values ​​of engine output torque, generator output torque and motor output torque.

[0085] According to the embodiments of the present disclosure, in multi-stage decision problems, the decisions taken at each stage are generally related to time. The decisions depend on the current state and immediately cause the state to shift. A decision sequence is generated in the changing state. This process of solving the optimization of multi-stage decisions is called a dynamic programming method.

[0086] According to an embodiment of the present disclosure, after obtaining the historical driving data of the vehicle, a dynamic programming algorithm is used to perform offline optimization calculations on the above data, so as to obtain the optimal power allocation strategy corresponding to the historical driving data, such as the optimal values ​​of the engine output torque, generator output torque and motor output torque corresponding to a certain vehicle speed in the historical driving data.

[0087] According to an embodiment of the present disclosure, the initial power allocation neural network model is established according to the test cycle optimal power allocation data corresponding to the test cycle data, and may include the following operations:

[0088] The test cycle data is processed by a dynamic programming algorithm to obtain the test cycle optimal power allocation data. An initial power allocation neural network model is established based on the test cycle data and the test cycle optimal power allocation data.

[0089] According to an embodiment of the present disclosure, a dynamic programming algorithm is used to process the test cycle data of the vehicle factory configuration, so as to obtain the test cycle optimal power distribution data corresponding to the test cycle data, which may include the optimal values ​​of the engine output torque, the generator output torque and the motor output torque corresponding to a certain vehicle speed under standard working conditions. An initial power distribution neural network model is established based on the test cycle data and the test cycle optimal power distribution data obtained above.

[0090] According to an embodiment of the present disclosure, the initial power allocation neural network model may include a BP neural network model, and the initial power allocation neural network model may include an input layer, at least one hidden layer and an output layer.

[0091] According to the embodiment of the present disclosure, the input layer is used to extract features from the input vehicle travel samples, the hidden layer trains the model based on the extracted features, and outputs the corresponding predicted power allocation strategy through the output layer. The number of hidden layers can be specifically set according to actual needs, for example, one or two layers.

[0092] According to an embodiment of the present disclosure, the historical driving data of the vehicle in the historical driving stage may further include the total mileage of the vehicle as shown in formula (1), and the vehicle status data in the current driving stage may include the mileage of the vehicle in the current driving stage, the real-time vehicle speed and the required power. The calculation of the mileage of the vehicle in the current driving stage is shown in formula (2):

[0093]

[0094]

[0095] Among them, Dis n is the total mileage of the vehicle in the historical driving stage, Dis n-1 is the total reference mileage of the vehicle in the driving phase before the historical driving phase, Dis cur is the total reference mileage of the vehicle in the historical driving stage, m is the update step of the historical travel data, and L n is the vehicle mileage in the current driving phase, L n-1 It is the vehicle mileage during the historical driving stage.

[0096] According to the embodiments of the present disclosure, the value of the update step size can be set by the vehicle engineer according to the specific situation. For example, the update step size mentioned above is 20 kilometers. The purpose of setting the update step size is to use the driving data within a certain distance as a training sample to update and train the target power allocation neural network model after the vehicle has traveled a certain distance, so that the target power allocation neural network model can learn the latest driving characteristics of the driver in a timely manner, thereby obtaining a target power allocation strategy with better economy.

[0097] According to an embodiment of the present disclosure, the target power distribution strategy may include: an output torque signal of an engine of the vehicle, an output torque signal of a generator, and an output torque signal of a drive motor, wherein the generator is used to convert mechanical energy into electrical energy.

[0098] The vehicle's historical driving data may also include: vehicle battery remaining power, gear position signal, throttle signal and brake signal.

[0099] According to an embodiment of the present disclosure, the power source of a portion of the vehicle is in the form of converting mechanical energy generated by an engine into electrical energy, so that the electric motor outputs corresponding kinetic energy to drive the vehicle.

[0100] According to the embodiments of the present disclosure, the driver controls the vehicle through multiple components such as gear, throttle, brake, etc. according to actual conditions while the vehicle is driving. Therefore, during the training process, comprehensive training can be performed on the vehicle's battery remaining power, gear signal, throttle signal, brake signal, etc., so that a more accurate target power distribution neural network model can be obtained.

[0101] According to an embodiment of the present disclosure, when predicting the target power distribution strategy corresponding to the predicted vehicle speed, the current vehicle speed, predicted vehicle speed, vehicle battery remaining power, gear signal, throttle signal and brake signal can be simultaneously input into the target power distribution neural network model to obtain the target power distribution strategy.

[0102] According to an embodiment of the present disclosure, establishing a vehicle speed prediction model based on the vehicle historical driving data corresponding to the optimal power allocation strategy in the historical driving stage may include the following steps:

[0103] According to the optimal power allocation strategy in the historical driving stage, a vehicle speed state matrix is ​​determined. According to the vehicle speed state matrix and the vehicle speed frequency matrix, a state transition probability matrix is ​​determined. According to the state transition probability matrix, a vehicle speed prediction model is determined, wherein the vehicle speed prediction model may include a Markov chain vehicle speed prediction model.

[0104] According to the embodiments of the present disclosure, the Markov chain vehicle speed prediction model is essentially a Markov chain prediction model. The solution direction of the Markov chain is a probability prediction method for the occurrence of an event, which is a prediction method for predicting the changes in the future at various moments or periods based on the current state.

[0105] According to an embodiment of the present disclosure, the vehicle speed state matrix is ​​constructed by the vehicle speed at each historical time point in the historical driving stage. The vehicle speed frequency matrix is ​​composed of the proportion of each vehicle speed in the total number of vehicle speeds. State transition probability matrix: the probability of changing from one vehicle speed to another, a matrix composed of multiple probabilities.

[0106] Figure 3 The training flowchart of the target power allocation neural network model according to an embodiment of the present disclosure is schematically shown.

[0107] like Figure 3 As shown, using the vehicle travel sample set to train the initial power allocation neural network model to obtain the target power allocation neural network model may include steps S301 to S303:

[0108] In step S301, a vehicle driving sample is input into an initial power distribution neural network model, and a predicted power distribution strategy is output, wherein the predicted power distribution strategy may include predicted output torque of the engine, output torque of the generator and output torque of the drive motor.

[0109] In step S302, a function is calculated according to the predicted power allocation strategy and the label data to obtain a loss result.

[0110] In step S303, the parameters of the initial power allocation neural network model are iteratively adjusted according to the loss result to generate a target power allocation neural network model.

[0111] According to the embodiments of the present disclosure, during the training process, each vehicle driving sample input into the initial power allocation neural network model can output a predicted power allocation strategy, and the loss results of the power allocation strategy and the actual label data are calculated, so that the parameters of the initial power allocation neural network model are adjusted according to the loss results. If the loss results meet the convergence conditions, it can be determined as the target power allocation neural network model.

[0112] According to an embodiment of the present disclosure, the method may further include the following operations:

[0113] When the mileage of the vehicle in the current driving phase meets the preset update step length, the optimal power allocation strategy for the current driving phase after the update step length is obtained.

[0114] The optimal power allocation strategy of the current driving stage after the updated step size and the corresponding vehicle historical driving data are used as new label data and new vehicle travel samples to train the target power allocation neural network model to obtain a new target power allocation neural network model.

[0115] A new vehicle speed prediction model is established based on the vehicle's historical driving data corresponding to the optimal power allocation strategy in the current driving stage.

[0116] According to the new prediction time point and the new vehicle speed prediction model, a new predicted vehicle speed at a new prediction time point in the future driving phase is determined.

[0117] The vehicle speed in the future driving stage and the new predicted vehicle speed are input into the new target power allocation neural network model to obtain a new target power allocation strategy corresponding to the new predicted vehicle speed, so as to control the vehicle according to the new target power allocation strategy.

[0118] In an exemplary embodiment, for example, the preset update step size is 20km, and the total mileage of the vehicle at the current time t is 111km. It can be seen that the mileage in the current driving stage does not meet the preset update step size. The vehicle continues to drive, and at the next time t+1, the total mileage of the vehicle is 120km. At this time, the mileage at the next time t+1 meets the preset update step size. Therefore, the optimal power allocation strategy of the vehicle in the 100km~120km stage and the corresponding vehicle historical driving data can be used as new label data and new vehicle travel samples to update and train the target power allocation neural network model.

[0119] At the same time, the vehicle speed prediction model is re-established based on the vehicle's historical driving data corresponding to the optimal power allocation strategy in the 100km~120km stage, and then the predicted vehicle speed is predicted based on the new prediction time point t+2. Finally, the vehicle speed at time t+1 and the predicted vehicle speed at time t+2 are input into the above updated and trained target power allocation neural network model to obtain the target power allocation strategy at time t+2.

[0120] According to an embodiment of the present disclosure, the above-mentioned use of the data of the closest historical driving stage as a vehicle trip sample for update training can enable the target power distribution neural network model to learn the driving characteristics that are closest to the driver's current driving state, thereby improving the vehicle's economic efficiency.

[0121] Figure 4 A block diagram of a hybrid vehicle control device according to an embodiment of the present disclosure is schematically shown.

[0122] like Figure 4 As shown, the hybrid vehicle control device 400 may include an acquisition module 401 , a training module 402 , a building module 403 , a vehicle speed prediction module 404 and a power distribution prediction module 405 .

[0123] The acquisition module 401 is used to acquire a vehicle travel sample set, wherein the vehicle travel sample set may include multiple vehicle travel samples and label data corresponding to each vehicle travel sample, the vehicle travel sample represents the vehicle's historical driving data in a historical driving stage, the vehicle's historical driving data may include vehicle speed sets corresponding to different time periods in the historical driving stage, the vehicle speed set may include a first vehicle speed and a second vehicle speed, wherein the first vehicle speed and the second vehicle speed are vehicle speeds separated by a preset time period in the same time period, and the label data represents the optimal power allocation strategy of the vehicle in the historical driving stage.

[0124] The training module 402 is used to train the initial power distribution neural network model using the vehicle travel sample set to obtain the target power distribution neural network model, wherein the initial power distribution neural network model is established based on the test cycle optimal power distribution data corresponding to the test cycle data, and the test cycle data represents the vehicle driving data under standard working conditions.

[0125] The establishing module 403 is used to establish a vehicle speed prediction model according to the historical driving data of the vehicle corresponding to the optimal power allocation strategy in the historical driving stage.

[0126] The vehicle speed prediction module 404 is used to determine the predicted vehicle speed at the predicted time point according to the predicted time point and the vehicle speed prediction model.

[0127] The power distribution prediction module 405 is used to input the vehicle speed and the predicted vehicle speed in the current driving stage into the target power distribution neural network model to obtain a target power distribution strategy corresponding to the predicted vehicle speed, so as to control the vehicle according to the target power distribution strategy.

[0128] According to an embodiment of the present disclosure, an initial power distribution neural network model is trained using historical vehicle driving data and an optimal power distribution strategy within a historical driving stage to obtain a target power distribution neural network model. The target power distribution neural network model is used to process the vehicle speed and predicted vehicle speed in the current driving stage, thereby obtaining a target power distribution strategy corresponding to the predicted vehicle speed, and then controlling the vehicle according to the target power distribution strategy. Since the target power distribution neural network model learns about the optimal power distribution strategy in historical data during the training process, controlling the vehicle using the predicted target power distribution strategy can reduce the economy of the vehicle, thereby solving the problem of poor economy of hybrid vehicles during use.

[0129] According to an embodiment of the present disclosure, the hybrid vehicle control device 400 may further include a calculation module.

[0130] The calculation module is used to process the vehicle's historical driving data in the historical driving stage using a dynamic programming algorithm to obtain an optimal power allocation strategy in the historical driving stage, wherein the optimal power allocation strategy may include: optimal values ​​of the engine output torque, the generator output torque and the motor output torque.

[0131] According to an embodiment of the present disclosure, the hybrid vehicle control device 400 may further include a processing module and a building module.

[0132] The processing module is used to process the test cycle data using a dynamic programming algorithm to obtain the test cycle optimal power allocation data.

[0133] A building module is used to establish an initial power allocation neural network model according to the test cycle data and the test cycle optimal power allocation data.

[0134] According to an embodiment of the present disclosure, the initial power allocation neural network model may include a BP neural network model, and the initial power allocation neural network model may include an input layer, at least one hidden layer and an output layer.

[0135] According to an embodiment of the present disclosure, the target power distribution strategy may include: an output torque signal of an engine of the vehicle, an output torque signal of a generator, and an output torque signal of a drive motor, wherein the generator is used to convert mechanical energy into electrical energy.

[0136] The vehicle's historical driving data may also include: vehicle battery remaining power, gear position signal, throttle signal and brake signal.

[0137] According to an embodiment of the present disclosure, the establishing module 403 may include a first determining unit, a second determining unit, and a third determining unit.

[0138] The first determination unit is used to determine the vehicle speed state matrix according to the optimal power allocation strategy in the historical driving stage.

[0139] The second determining unit is used to determine the state transition probability matrix according to the vehicle speed state matrix and the vehicle speed frequency matrix.

[0140] The third determining unit is used to determine a vehicle speed prediction model according to the state transition probability matrix, wherein the vehicle speed prediction model may include a Markov chain vehicle speed prediction model.

[0141] According to an embodiment of the present disclosure, the training module 402 may include an input unit, a calculation unit, and an iteration unit.

[0142] An input unit is used to input vehicle driving samples into an initial power distribution neural network model and output a predicted power distribution strategy, wherein the predicted power distribution strategy may include predicted output torque of the engine, output torque of the generator and output torque of the drive motor.

[0143] The computing unit is used to calculate the function according to the predicted power allocation strategy and the label data to obtain the loss result.

[0144] The iteration unit is used to iteratively adjust the parameters of the initial power allocation neural network model according to the loss result to generate a target power allocation neural network model.

[0145] According to an embodiment of the present disclosure, the hybrid vehicle control device 400 may further include an update module, an update training module, a second establishment module, a second vehicle speed prediction module, and a second power distribution prediction module.

[0146] An updating module, used for obtaining an optimal power allocation strategy for the current driving phase after the update step length when the mileage of the vehicle in the current driving phase meets a preset update step length;

[0147] The update training module is used to train the target power allocation neural network model by using the optimal power allocation strategy of the current driving stage after the update step size and the corresponding vehicle historical driving data as new label data and new vehicle travel samples, so as to obtain a new target power allocation neural network model.

[0148] The second establishing module is used to establish a new vehicle speed prediction model according to the vehicle historical driving data corresponding to the optimal power allocation strategy in the current driving stage.

[0149] The second vehicle speed prediction module is used to determine a new predicted vehicle speed at a new predicted time point in a future driving phase according to a new predicted time point and a new vehicle speed prediction model.

[0150] The second power distribution prediction module is used to input the vehicle speed in the future driving stage and the new predicted vehicle speed into the new target power distribution neural network model to obtain a new target power distribution strategy corresponding to the new predicted vehicle speed, so as to control the vehicle according to the new target power distribution strategy.

[0151] According to the modules and units of the embodiments of the present disclosure, any multiple or at least part of the functions of any multiple thereof can be implemented in one module. According to the modules and units of the embodiments of the present disclosure, any one or more can be split into multiple modules for implementation. According to the modules and units of the embodiments of the present disclosure, any one or more can be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System on a Chip, a System on a Substrate, a System on a Package, an Application Specific Integrated Circuit (ASIC), or can be implemented by hardware or firmware of any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, according to the modules and units of the embodiments of the present disclosure, one or more can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0152] For example, any multiple of the acquisition module 401, the training module 402, the establishment module 403, the vehicle speed prediction module 404, and the power allocation prediction module 405 can be combined in one module / unit for implementation, or any one of the modules / units can be split into multiple modules / units. Alternatively, at least part of the functions of one or more of these modules / units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit. According to an embodiment of the present disclosure, at least one of the acquisition module 401, the training module 402, the establishment module 403, the vehicle speed prediction module 404, and the power allocation prediction module 405 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the acquisition module 401, the training module 402, the establishment module 403, the vehicle speed prediction module 404 and the power distribution prediction module 405 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0153] It should be noted that the hybrid vehicle control device part in the embodiment of the present disclosure corresponds to the hybrid vehicle control method part in the embodiment of the present disclosure. The description of the hybrid vehicle control device part specifically refers to the hybrid vehicle control method part and will not be repeated here.

[0154] Figure 5 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0155] like Figure 5 As shown, the electronic device 500 according to the embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0156] In RAM 503, various programs and data required for the operation of electronic device 500 are stored. Processor 501, ROM 502 and RAM 503 are connected to each other via bus 504. Processor 501 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0157] According to an embodiment of the present disclosure, the electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The system 500 may further include one or more of the following components connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0158] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0159] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0160] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM (Erasable Programmable Read Only Memory, EPROM) or flash memory), a portable compact disk read-only memory (Computer Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

[0161] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 502 and / or the RAM 503 described above and / or one or more memories other than the ROM 502 and the RAM 503 .

[0162] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the hybrid vehicle control method provided by the embodiment of the present disclosure.

[0163] When the computer program is executed by the processor 501, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0164] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0165] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0166] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0167] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A hybrid vehicle control method, comprising: Acquire a vehicle travel sample set, wherein the vehicle travel sample set includes a plurality of vehicle travel samples and label data corresponding to each of the vehicle travel samples, the vehicle travel samples represent vehicle historical travel data of the vehicle in a historical travel stage, the vehicle historical travel data include vehicle speed sets corresponding to different time periods of the vehicle in the historical travel stage, the vehicle speed set includes a first vehicle speed and a second vehicle speed, wherein the first vehicle speed and the second vehicle speed are vehicle speeds separated by a preset time period in the same time period, and the label data represent an optimal power allocation strategy of the vehicle in the historical travel stage; Using the vehicle travel sample set to train an initial power allocation neural network model to obtain a target power allocation neural network model, wherein the initial power allocation neural network model is established based on test cycle optimal power allocation data corresponding to test cycle data, and the test cycle data represents vehicle driving data of the vehicle under standard working conditions; Determining a vehicle speed state matrix according to an optimal power allocation strategy during the historical driving stage; Determine a state transition probability matrix according to the vehicle speed state matrix and the vehicle speed frequency matrix, wherein the state transition probability matrix is ​​a probability of changing from one vehicle speed to another vehicle speed, and is a matrix composed of multiple probabilities; Determining a vehicle speed prediction model according to the state transition probability matrix, wherein the vehicle speed prediction model includes a Markov chain vehicle speed prediction model; Determining a predicted vehicle speed at the predicted time point according to the predicted time point and the vehicle speed prediction model; The vehicle speed in the current driving phase and the predicted vehicle speed are input into the target power allocation neural network model to obtain a target power allocation strategy corresponding to the predicted vehicle speed, so as to control the vehicle according to the target power allocation strategy.

2. The method according to claim 1, wherein: The optimal power allocation strategy in the historical driving stage is determined by the following steps: The vehicle historical driving data in the historical driving stage is processed by a dynamic programming algorithm to obtain an optimal power allocation strategy in the historical driving stage, wherein the optimal power allocation strategy includes: optimal values ​​of engine output torque, generator output torque and motor output torque.

3. The method according to claim 1, wherein: The initial power allocation neural network model is established according to the test cycle optimal power allocation data corresponding to the test cycle data, and includes: Processing the test cycle data using a dynamic programming algorithm to obtain optimal power allocation data for the test cycle; The initial power allocation neural network model is established according to the test cycle data and the test cycle optimal power allocation data.

4. The method according to claim 3, wherein: The initial power allocation neural network model comprises a BP neural network model, and the initial power allocation neural network model comprises an input layer, at least one hidden layer and an output layer.

5. The method according to claim 1, wherein: The vehicle historical driving data in the historical driving stage also includes the total vehicle mileage as shown in formula (1), and the vehicle status data in the current driving stage includes the vehicle mileage, real-time vehicle speed and required power in the current driving stage. The calculation of the vehicle mileage in the current driving stage is shown in formula (2): （1） （2） in, is the total vehicle mileage during the historical driving phase, is the total reference mileage of the vehicle in the driving phase before the historical driving phase, is the total reference mileage of vehicles in the historical driving stage, is the update step of historical travel data, is the vehicle mileage in the current driving phase, It is the vehicle mileage during the historical driving stage.

6. The method according to claim 5, wherein: The target power distribution strategy includes: an output torque signal of the engine of the vehicle, an output torque signal of the generator and an output torque signal of the drive motor, wherein the generator is used to convert mechanical energy into electrical energy; The vehicle historical driving data also includes: vehicle battery remaining power, gear position signal, throttle signal and brake signal.

7. The method according to claim 1, wherein: The method of using the vehicle travel sample set to train the initial power allocation neural network model to obtain the target power allocation neural network model includes: Inputting the vehicle travel sample into the initial power distribution neural network model, and outputting a predicted power distribution strategy, wherein the predicted power distribution strategy includes predicted output torque of the engine, output torque of the generator, and output torque of the drive motor; Obtaining a loss result according to the predicted power allocation strategy and the label data calculation function; The parameters of the initial power allocation neural network model are iteratively adjusted according to the loss result to generate the target power allocation neural network model.

8. The method according to any one of claims 1 to 7, further comprising: When the mileage of the vehicle in the current driving phase satisfies a preset update step length, obtaining an optimal power allocation strategy for the current driving phase after the update step length; The optimal power allocation strategy of the current driving stage after the step size update and the corresponding vehicle historical driving data are used as new label data and new vehicle travel samples to train the target power allocation neural network model to obtain a new target power allocation neural network model; Establishing a new vehicle speed prediction model according to the vehicle historical driving data corresponding to the optimal power allocation strategy in the current driving stage; Determining a new predicted vehicle speed at a new predicted time point in a future driving phase according to the new predicted time point and the new vehicle speed prediction model; The vehicle speed in the future driving stage and the new predicted vehicle speed are input into the new target power distribution neural network model to obtain a new target power distribution strategy corresponding to the new predicted vehicle speed, so as to control the vehicle according to the new target power distribution strategy.

9. A hybrid vehicle control device, comprising: an acquisition module, configured to acquire a vehicle travel sample set, wherein the vehicle travel sample set includes a plurality of vehicle travel samples and label data corresponding to each of the vehicle travel samples, wherein the vehicle travel samples represent vehicle historical travel data of the vehicle in a historical travel stage, wherein the vehicle historical travel data includes vehicle speed sets corresponding to different time periods of the vehicle in the historical travel stage, wherein the vehicle speed set includes a first vehicle speed and a second vehicle speed, wherein the first vehicle speed and the second vehicle speed are vehicle speeds separated by a preset time period in the same time period, and wherein the label data represent an optimal power allocation strategy of the vehicle in the historical travel stage; a training module, configured to train an initial power allocation neural network model using the vehicle travel sample set to obtain a target power allocation neural network model, wherein the initial power allocation neural network model is established based on test cycle optimal power allocation data corresponding to test cycle data, wherein the test cycle data represents vehicle driving data of the vehicle under standard operating conditions; The establishment module is used to establish a vehicle speed prediction model according to the historical driving data of the vehicle corresponding to the optimal power allocation strategy in the historical driving stage, including: A first determination unit is used to determine a vehicle speed state matrix according to an optimal power allocation strategy in a historical driving stage; A second determining unit is used to determine a state transition probability matrix according to the vehicle speed state matrix and the vehicle speed frequency matrix, wherein the state transition probability matrix is ​​a probability of changing from one vehicle speed to another vehicle speed, and is a matrix composed of multiple probabilities; A third determining unit, configured to determine a vehicle speed prediction model according to the state transition probability matrix, wherein the vehicle speed prediction model may include a Markov chain vehicle speed prediction model; a vehicle speed prediction module, used to determine a predicted vehicle speed at the predicted time point according to the predicted time point and the vehicle speed prediction model; and The power distribution prediction module is used to input the vehicle speed in the current driving stage and the predicted vehicle speed into the target power distribution neural network model to obtain a target power distribution strategy corresponding to the predicted vehicle speed, so as to control the vehicle according to the target power distribution strategy.

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