Method and device for adjusting driving mode of hybrid vehicle, hybrid vehicle and medium

Abstract

The invention relates to the technical field of hybrid vehicles, in particular to a driving mode adjusting method and device of a hybrid vehicle, the hybrid vehicle and a medium, and the method comprises the steps that the remaining electric quantity, navigation data and weather data of the current hybrid vehicle are obtained; calculating the comprehensive energy consumption of the hybrid vehicle based on the navigation data and the weather data; and adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the residual electric quantity. Therefore, the problems that in the prior art, the hybrid vehicle driving mode switching logic is simple, and flexibility is lacked are solved.

Description

Technical Field

[0001] The present application relates to the technical field of hybrid vehicles, and in particular to a method and device for adjusting a driving mode of a hybrid vehicle, a hybrid vehicle, and a medium. Background Art

[0002] The widespread use of plug-in hybrid vehicles, which have two power sources, namely internal combustion engines and electric motors. The battery of a hybrid vehicle is much smaller than that of a pure electric vehicle, about 20% of that of a pure electric vehicle. With the continuous development of hybrid vehicle technology, how to optimize energy consumption according to different driving conditions under limited battery capacity has become a key issue in improving energy efficiency.

[0003] Existing hybrid vehicles usually rely on simple rules to choose the switch between electric drive and internal combustion engine drive, such as using pure electric drive when the vehicle speed is less than 60km / h, using internal combustion engine drive or manual switching by the user when the vehicle speed is greater than 60km / h. It does not consider the factors affecting energy consumption from multiple aspects and lacks flexibility. Summary of the invention

[0004] The present application provides a hybrid vehicle driving mode adjustment method, device, hybrid vehicle and medium to solve the problems of simple switching logic and lack of flexibility of hybrid vehicle driving mode in the prior art.

[0005] The first aspect of the present application provides a method for adjusting the driving mode of a hybrid vehicle, comprising the following steps: obtaining the remaining power, navigation data and weather data of the current hybrid vehicle; calculating the comprehensive energy consumption of the hybrid vehicle based on the navigation data and weather data; and adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power.

[0006] Optionally, in one embodiment of the present application, the weather data includes temperature and weather type, the navigation data includes road condition type and navigation mileage, the road condition types include: high-speed road conditions, low-speed road conditions and congested road conditions, and before adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and remaining power, it also includes: identifying the navigation mileage of each road condition type in the navigation data; calculating the mileage energy consumption of each road condition type based on the navigation mileage, temperature and weather type of each road condition type; calculating the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption of each road condition type.

[0007] Optionally, in one embodiment of the present application, the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption and the remaining power, including: if the remaining power is greater than the comprehensive energy consumption, the driving mode of the hybrid vehicle is adjusted to motor drive; otherwise, the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type.

[0008] Optionally, in one embodiment of the present application, the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type, including: if the remaining power is less than the comprehensive energy consumption and greater than the mileage energy consumption of congested road conditions, then the driving mode of the hybrid vehicle is adjusted to motor drive in sections with congested road conditions; if the remaining power minus the mileage energy consumption of congested road conditions is greater than the mileage energy consumption of low-speed road conditions, then the driving mode of the hybrid vehicle is adjusted to motor drive in sections with low-speed road conditions; if the road condition types are all high-speed road conditions, or the mileage energy consumption of congested road conditions is greater than the remaining power, or the mileage energy consumption of low-speed road conditions is greater than the remaining power, then the driving mode of the hybrid vehicle is adjusted based on the target algorithm model.

[0009] Optionally, in one embodiment of the present application, before adjusting the driving mode of a hybrid vehicle based on a target algorithm model, the method includes: obtaining a data set of the target algorithm model, wherein the data set includes at least one of remaining battery power, temperature, weather, navigation mileage, road conditions, and driving mode; dividing the data set into a training data set, a test data set, and a verification data set; training the target algorithm model using the training data set, adjusting parameters of the target algorithm model using the verification data set, and testing the target algorithm model using the test data set, until the prediction accuracy of the target algorithm model reaches a preset standard and the training is stopped.

[0010] Optionally, in one embodiment of the present application, the target algorithm model is a recurrent neural network model.

[0011] The second aspect of the present application provides a driving mode adjustment device for a hybrid vehicle, including: an acquisition module, used to obtain the remaining power, navigation data and weather data of the current hybrid vehicle; a calculation module, used to calculate the comprehensive energy consumption of the hybrid vehicle based on the navigation data and weather data; an adjustment module, used to adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power.

[0012] Optionally, in one embodiment of the present application, the weather data includes temperature and weather type, the navigation data includes road condition type and navigation mileage, the road condition types include: high-speed road conditions, low-speed road conditions and congested road conditions, and also includes: an identification module for identifying the navigation mileage of each road condition type in the navigation data before adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and remaining power; calculating the mileage energy consumption of each road condition type based on the navigation mileage, temperature and weather type of each road condition type; and calculating the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption of each road condition type.

[0013] Optionally, in one embodiment of the present application, the adjustment module is further used to: if the remaining power is greater than the comprehensive energy consumption, adjust the driving mode of the hybrid vehicle to motor drive; otherwise, adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type.

[0014] Optionally, in one embodiment of the present application, the adjustment module is further used to: if the remaining power is less than the comprehensive energy consumption and greater than the mileage energy consumption of congested road conditions, then adjust the driving mode of the hybrid vehicle to motor drive in the section with congested road conditions; if the remaining power minus the mileage energy consumption of congested road conditions is greater than the mileage energy consumption of low-speed road conditions, then adjust the driving mode of the hybrid vehicle to motor drive in the section with low-speed road conditions; if the road conditions are all high-speed conditions, or the mileage energy consumption of congested road conditions is greater than the remaining power, or the mileage energy consumption of low-speed road conditions is greater than the remaining power, then adjust the driving mode of the hybrid vehicle based on the target algorithm model.

[0015] Optionally, in one embodiment of the present application, it also includes: a training module, which is used to obtain a data set of the target algorithm model before adjusting the driving mode of the hybrid vehicle based on the target algorithm model, wherein the data set includes at least one of the remaining battery power, temperature, weather, navigation mileage, road conditions, and driving mode; divide the data set into a training data set, a test data set, and a verification data set; train the target algorithm model using the training data set, adjust the parameters of the target algorithm model using the verification data set, and test the target algorithm model using the test data set, until the prediction accuracy of the target algorithm model reaches a preset standard and the training is stopped.

[0016] Optionally, in one embodiment of the present application, the target algorithm model is a recurrent neural network model.

[0017] A third aspect of the present application provides a hybrid vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to perform a driving mode adjustment method for the hybrid vehicle as described in the above embodiment.

[0018] A fourth aspect of the present application provides a computer-readable storage medium having a computer program or instructions stored thereon, and the computer program or instructions are executed by a processor to perform a method for adjusting the driving mode of a hybrid vehicle as described in the above embodiment.

[0019] A fifth aspect of the present application provides a computer program product, including a computer program or instructions. When the computer program or instructions are executed, a method for adjusting the driving mode of a hybrid vehicle as described in the above embodiment is implemented.

[0020] Therefore, this application has at least the following beneficial effects:

[0021] The embodiment of the present application can calculate the comprehensive energy consumption of the hybrid vehicle according to the current navigation data and weather data of the hybrid vehicle, and adjust the driving mode of the hybrid vehicle according to the remaining power and the comprehensive energy consumption, making full use of the data during the vehicle's driving process to automatically adjust the driving mode, with high flexibility, and no need for the user to manually switch, and can reduce the energy consumption of the vehicle and increase the driving mileage. Therefore, the technical problems of the prior art hybrid vehicle driving mode switching logic being simple and lacking flexibility are solved.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 A flowchart of a method for adjusting a driving mode of a hybrid vehicle provided according to an embodiment of the present application;

[0025] Figure 2 A schematic diagram of a sequence-to-sequence conversion and generation process of an RNN (Recurrent Neural Network) model provided according to an embodiment of the present application;

[0026] Figure 3 A schematic diagram of system control of a driving mode adjustment method for a hybrid vehicle provided in an embodiment of the present application;

[0027] Figure 4 A specific flow chart of a method for adjusting a driving mode of a hybrid vehicle provided according to an embodiment of the present application;

[0028] Figure 5 This is an example diagram of a driving mode adjustment device for a hybrid vehicle provided according to an embodiment of the present application;

[0029] Figure 6 Schematic diagram of the structure of a hybrid vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes the driving mode adjustment method, device, hybrid vehicle and medium of the hybrid vehicle of the embodiment of the present application with reference to the accompanying drawings. In view of the fact that the existing hybrid vehicles mentioned in the above background technology usually rely on simple rules to select the switch between electric drive and internal combustion engine drive, such as using pure electric drive when the vehicle speed is less than 60km / h, using internal combustion engine drive or manual switching by the user when the vehicle speed is greater than 60km / h, and not considering the factors affecting energy consumption from multiple aspects, and lacking flexibility, the present application provides a driving mode adjustment method for a hybrid vehicle, in which the comprehensive energy consumption of the hybrid vehicle can be calculated according to the current navigation data and weather data of the hybrid vehicle, and the driving mode of the hybrid vehicle can be adjusted according to the remaining power and the comprehensive energy consumption, and the driving mode can be automatically adjusted by making full use of the data during the vehicle driving process, with high flexibility, and no need for manual switching by the user, and the energy consumption of the vehicle can be reduced, and the driving mileage can be increased. Therefore, the problems of simple switching logic and lack of flexibility of the driving mode of hybrid vehicles in the prior art are solved.

[0032] Specifically, Figure 1 A schematic flow chart of a method for adjusting the driving mode of a hybrid vehicle provided in an embodiment of the present application.

[0033] like Figure 1 As shown, the driving mode adjustment method of the hybrid vehicle includes the following steps:

[0034] In step S101 , the remaining power, navigation data and weather data of the current hybrid vehicle are obtained.

[0035] Among them, weather data includes temperature and weather type, such as rain, snow, sunny, etc. Navigation data includes road condition type and navigation mileage, and road condition type includes congested road conditions, low-speed road conditions, high-speed road conditions, etc. Hybrid vehicles are hybrid vehicles that can be driven by internal combustion engines and / or electric motors.

[0036] In step S102 , the comprehensive energy consumption of the hybrid vehicle is calculated based on the navigation data and the weather data.

[0037] It is understandable that the embodiment of the present application can calculate the comprehensive energy consumption of the hybrid vehicle based on the navigation data and weather data, wherein the comprehensive energy consumption is the sum of the energy consumption required for the hybrid vehicle to travel under each road condition in the navigation mileage.

[0038] In an embodiment of the present application, before calculating the comprehensive energy consumption of the hybrid vehicle based on the remaining power, navigation data and weather data, it also includes: identifying the navigation mileage of each road condition type in the navigation data; calculating the mileage energy consumption of each road condition type based on the navigation mileage, temperature and weather type of each road condition type; and calculating the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption of each road condition type.

[0039] It can be understood that the embodiments of the present application can identify the navigation mileage, temperature and weather type of each road condition type in the navigation data to calculate the mileage energy consumption of each road condition, and then calculate the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption under each road condition type.

[0040] Specifically, high-speed mileage energy consumption N1 = high-speed mileage K1 * 100 km energy consumption under ideal conditions / 100KM * temperature weight T1 * weather weight C1;

[0041] Low-speed mileage energy consumption N2 = low-speed mileage K2 * 100 km energy consumption under ideal conditions / 100 km * temperature weight T2 * weather weight C2;

[0042] Congestion mileage energy consumption N3 = congestion mileage K3 * 100 km energy consumption under ideal conditions / 100 km * temperature weight T3 * weather weight C3;

[0043] Comprehensive energy consumption = N = N1 + N2 + N3.

[0044] In step S103, the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption and the remaining power.

[0045] It can be understood that the embodiments of the present application can adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power, and make full use of the data during the vehicle's driving process to automatically adjust the driving mode without the need for manual switching by the user, and can reduce the vehicle's energy consumption and increase driving mileage.

[0046] In an embodiment of the present application, the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption and the remaining power, including: if the remaining power is greater than the comprehensive energy consumption, the driving mode of the hybrid vehicle is adjusted to motor drive; otherwise, the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type.

[0047] It can be understood that the embodiment of the present application can adjust the driving mode by comparing the relationship between the comprehensive energy consumption and the remaining power and the mileage energy consumption of each road condition type. The specific adjustment method is as follows.

[0048] In an embodiment of the present application, road condition types include: high-speed road conditions, low-speed road conditions and congested road conditions, and the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type, including: if the remaining power is less than the comprehensive energy consumption and greater than the mileage energy consumption of congested road conditions, then the driving mode of the hybrid vehicle is adjusted to motor drive in the section with congested road conditions; if the remaining power minus the mileage energy consumption of congested road conditions is greater than the mileage energy consumption of low-speed road conditions, then the driving mode of the hybrid vehicle is adjusted to motor drive in the section with low-speed road conditions; if the road condition types are all high-speed road conditions, or the mileage energy consumption of congested road conditions is greater than the remaining power, or the mileage energy consumption of low-speed road conditions is greater than the remaining power, then the driving mode of the hybrid vehicle is predicted based on the target algorithm model.

[0049] The target algorithm model may be an RNN, but is not limited to this model, and may also be an LSTM or GRU model, for example.

[0050] Specifically, when the remaining power is greater than the comprehensive energy consumption, it indicates that the battery has enough power to support the entire journey, so the driving mode of the hybrid vehicle is adjusted to motor drive, thereby reducing the operating cost and reducing;

[0051] When the remaining power is less than the comprehensive energy consumption and greater than the mileage energy consumption under congested road conditions, the hybrid vehicle is adjusted to be driven by the motor in congested sections because the engine efficiency is low at low speed or at rest.

[0052] When the remaining power minus the mileage energy consumption under congested road conditions is greater than the mileage energy consumption under low-speed road conditions, the hybrid vehicle is also adjusted to drive the motor under low-speed conditions to fully utilize the advantages of the electric mode without affecting the cruising range;

[0053] When all road conditions are high-speed, or the mileage energy consumption in congested sections is greater than the remaining power, or the mileage energy consumption in low-speed conditions is greater than the remaining power, the driving mode of the hybrid vehicle is predicted based on the target algorithm model, including but not limited to internal combustion engine main drive + motor auxiliary drive, internal combustion engine auxiliary drive + motor main drive, and pure internal combustion engine drive.

[0054] In an embodiment of the present application, before predicting the driving mode of a hybrid vehicle based on a target algorithm model, the method includes: obtaining a data set of the target algorithm model, wherein the data set includes at least one of remaining battery power, temperature, weather, navigation mileage, road conditions, and driving mode; dividing the data set into a training data set, a test data set, and a verification data set; training the target algorithm model using the training data set, adjusting parameters of the target algorithm model using the verification data set, and testing the target algorithm model using the test data set, until the prediction accuracy of the target algorithm model reaches a preset standard and the training is stopped.

[0055] It can be understood that the embodiments of the present application can obtain a data set of the target algorithm, divide the data set into a training data set, a test data set and a verification data set to realize the training of the target algorithm model, and facilitate the use of the target algorithm model in the implementation process.

[0056] The specific training process of the target algorithm model is as follows:

[0057] Step 1: Dataset preparation: 1) Collect the top 100 data of low-speed road conditions, high-speed road conditions and low-speed + high-speed road conditions with low energy consumption in the user's driving log (as shown in Table 1), and then use the time series data of relatively low energy consumption of automobiles (as shown in Table 2), the dimensions include vehicle VIN code, time, remaining battery power, temperature, weather, navigation mileage, road conditions, driving mode; 2) Data preprocessing, such as cleaning, exception handling, data labeling, etc.; 3) Divide the data set into 70% training data set, 20% test data set and 10% validation data set; 4) Convert the multi-dimensional time series data in Table 2 into RNN format through time series conversion, normalization processing, and sequence filling processing so that it can be effectively understood and processed. Among them, Table 2 is the relevant data of vehicles with model T1-TX (two-wheel drive), departure place A town group, and destination A town market.

[0058] Table 1

[0059]

[0060] Table 2

[0061]

[0062]

[0063] Step 2: Training model: Input the prepared training data into the RNN model for data training and learning, capture the long-term dependency characteristics of time series data, and use the test set and validation set data to adjust the model parameters. During the training process, adjust the parameters to optimize the model and improve the model accuracy. The sequence-to-sequence conversion and generation process of the RNN model is as follows: Figure 2 shown.

[0064] Step 3: Test and evaluate the model: Use the test set to test the model and calculate the accuracy of the model prediction to evaluate the performance of the model and select the best model parameters.

[0065] Step 4: Model deployment and monitoring: Deploy the trained model to the cloud server, monitor the performance and prediction results, and make fine-tuning based on the actual results.

[0066] In summary, the driving mode adjustment method of the hybrid vehicle in the embodiment of the present application combines the navigation service system, the battery service system, the intelligent control system and the driving controller. The specific system control is as follows: Figure 3 As shown, by making full use of different driving conditions to select low-energy driving modes, the navigation mileage, road conditions, weather, and remaining battery power can be collected in real time, and the system of predicting and controlling the driving mode of the plug-in hybrid can be achieved through an algorithm model, thereby reducing energy consumption and improving driving mileage.

[0067] Among them, the specific functions of each control system are as follows:

[0068] The temperature and weather conditions during driving determine the driving mode of the car and the energy consumption of other devices such as air conditioners, headlights, and wipers. Therefore, the weather service system obtains the temperature and weather conditions along the navigation route in real time.

[0069] The battery service system obtains the remaining battery power in real time, understands the relationship between the remaining power and mileage, and decides whether to use the electric motor or the internal combustion engine to drive more points. For example, if the mileage is 30km and the remaining power is 30km, the electric motor can drive more points, and the internal combustion engine can assist in driving.

[0070] The navigation service system obtains the user's navigation mileage and road condition data in real time. It is more appropriate to use electric motor drive at low speed or in congested road conditions, but the remaining power should also be considered; it is more appropriate to use internal combustion engine drive at high speed. If the remaining power is sufficient, the motor can also assist in driving to reduce fuel consumption.

[0071] Intelligent control system, when the user presses the accelerator pedal, 1) the system calculates the comprehensive energy consumption according to the navigation mileage, road conditions and weather; 2.1) when the remaining battery power is greater than the comprehensive energy consumption, the motor is used to drive 100% and output instructions; 2.2) when the remaining battery power is less than the comprehensive energy consumption, when the remaining battery power is greater than the energy consumption of the congested mileage, the motor is used to drive 100% in congested sections and output instructions; 2.3) when the remaining battery power (remaining battery power - energy consumption of congested mileage) is greater than low speed (vehicle speed is less than 60k m / h) mileage, use the motor drive in the low-speed section and use the motor 100% drive, and output instructions; 2.4) When the remaining power (battery remaining power - energy consumption of congested mileage - energy consumption of low-speed mileage) or power is insufficient, the system uses the model to predict the driving mode, inputs multiple maintenance data, and predicts the driving mode through the model, such as internal combustion engine drive 100%, internal combustion engine main drive a% + motor auxiliary drive b%, internal combustion engine auxiliary drive a% + motor main drive b%), and outputs instructions, among which the calculation process of the intelligent control system is as follows Figure 4 shown.

[0072] The drive controller drives the internal combustion engine and the electric motor to drive and adjust the power according to the input instructions.

[0073] Specifically, the implementation process of the driving mode adjustment method of the hybrid vehicle in the embodiment of the present application is as follows:

[0074] Step 1: After the user gets in the car and starts the vehicle, the vehicle computer automatically starts the battery service system, weather service system, navigation service system and intelligent control system;

[0075] Step 2: The navigation service system obtains the current traffic data in real time, and counts the high-speed mileage, low-speed mileage, and congested mileage of the navigation destination, and outputs it to the intelligent control system.

[0076] Step 3: The weather service system obtains real-time weather data on temperature and mileage, such as rain, snow, low visibility, etc., and outputs it to the intelligent control system.

[0077] Step 4: The battery service system obtains the current remaining power D1 in real time and outputs it to the intelligent control system.

[0078] Step 5: 1) High-speed mileage energy consumption N1 = high-speed mileage K1 * energy consumption per 100 kilometers under ideal conditions / 100KM * temperature weight T1 * weather weight C1; low-speed mileage energy consumption N2 = low-speed mileage K2 * energy consumption per 100 kilometers under ideal conditions / 100KM * temperature weight T2 * weather weight C2; congested mileage energy consumption N3 = congested mileage K3 * energy consumption per 100 kilometers under ideal conditions / 100KM * temperature weight T3 * weather weight C3.

[0079] Step 6: Comprehensive energy consumption N=N1+N2+N3. If the remaining battery power D1 is greater than the comprehensive energy consumption N, use the motor to drive 100% and output the instruction to execute step 10; otherwise, execute step 7.

[0080] Step 7: Congestion mileage energy consumption N3. If the remaining battery power D1 is greater than the congestion mileage energy consumption N3, when the vehicle reaches the congested section, the motor is driven 100% and an instruction is output to execute step 10; otherwise, step 8 is executed.

[0081] Step 8: Obtain the low-speed mileage energy consumption N2, and calculate the remaining battery power D2 = D1-N3. If the remaining battery power D2 is greater than the low-speed mileage energy consumption N2, when the vehicle reaches the low-speed section, the motor is used to drive 100%, and an instruction is output to execute step 10; otherwise, execute step 9.

[0082] Step 9: When driving at high speed, or the energy consumption N3 in congested mileage is greater than the remaining battery power D1, or the energy consumption N2 in low-speed mileage is greater than the remaining battery power D2, the input multi-maintenance data is used to predict the driving mode (internal combustion engine main drive a% + motor auxiliary drive b% / internal combustion engine drive a% / internal combustion engine auxiliary drive a% + motor main drive b%) through the algorithm model, and output instructions.

[0083] Step 10: The drive controller drives the internal combustion engine and the motor drive and the power size according to the drive mode and ratio output by the algorithm control system.

[0084] According to the driving mode adjustment method for a hybrid vehicle proposed in an embodiment of the present application, the comprehensive energy consumption of the hybrid vehicle can be calculated based on the current navigation data and weather data of the hybrid vehicle, and the driving mode of the hybrid vehicle can be adjusted based on the remaining power and the comprehensive energy consumption. The driving mode can be automatically adjusted by making full use of the data during the vehicle's driving process. The method has high flexibility and does not require manual switching by the user. It can also reduce the energy consumption of the vehicle and increase the driving mileage.

[0085] Next, a driving mode adjustment device for a hybrid vehicle according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0086] Figure 5 It is a block diagram of a driving mode adjustment device for a hybrid vehicle according to an embodiment of the present application.

[0087] like Figure 5 As shown, the hybrid vehicle driving mode adjustment device 10 includes: an acquisition module 100 , a calculation module 200 and an adjustment module 300 .

[0088] Among them, the acquisition module 100 is used to obtain the remaining power, navigation data and weather data of the current hybrid vehicle; the calculation module 200 is used to calculate the comprehensive energy consumption of the hybrid vehicle based on the navigation data and weather data; the adjustment module 300 is used to adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power.

[0089] In the embodiment of the present application, the weather data includes temperature and weather type, the navigation data includes road condition type and navigation mileage, and the road condition type includes: high-speed road condition, low-speed road condition and congested road condition.

[0090] In the embodiment of the present application, the device 10 of the embodiment of the present application further includes: an identification module.

[0091] Among them, the identification module is used to identify the navigation mileage of each road condition type in the navigation data before adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power; calculate the mileage energy consumption of each road condition type based on the navigation mileage, temperature and weather type of each road condition type; calculate the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption of each road condition type.

[0092] In the embodiment of the present application, the adjustment module 300 is further used to: if the remaining power is greater than the comprehensive energy consumption, adjust the driving mode of the hybrid vehicle to motor drive; otherwise, adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type.

[0093] In the embodiment of the present application, the adjustment module 300 is further used for: if the remaining power is less than the comprehensive energy consumption and greater than the mileage energy consumption of congested road conditions, then adjusting the driving mode of the hybrid vehicle to motor drive in the section with congested road conditions; if the remaining power minus the mileage energy consumption of congested road conditions is greater than the mileage energy consumption of low-speed road conditions, then adjusting the driving mode of the hybrid vehicle to motor drive in the section with low-speed road conditions; if the road conditions are all high-speed conditions, or the mileage energy consumption of congested road conditions is greater than the remaining power, or the mileage energy consumption of low-speed road conditions is greater than the remaining power, then adjusting the driving mode of the hybrid vehicle based on the target algorithm model.

[0094] In the embodiment of the present application, the device 10 of the embodiment of the present application further includes: a training module.

[0095] Among them, the training module is used to obtain a data set of the target algorithm model before adjusting the driving mode of the hybrid vehicle based on the target algorithm model, wherein the data set includes at least one of the remaining battery power, temperature, weather, navigation mileage, road conditions, and driving mode; divide the data set into a training data set, a test data set, and a verification data set; use the training data set to train the target algorithm model, use the verification data set to adjust the parameters of the target algorithm model, and use the test data set to test the target algorithm model until the prediction accuracy of the target algorithm model reaches a preset standard and stops the training.

[0096] In an embodiment of the present application, the target algorithm model is a recurrent neural network model.

[0097] It should be noted that the above explanation of the embodiment of the method for adjusting the driving mode of a hybrid vehicle is also applicable to the device for adjusting the driving mode of a hybrid vehicle of this embodiment, and will not be repeated here.

[0098] According to the driving mode adjustment device for a hybrid vehicle proposed in the embodiment of the present application, the comprehensive energy consumption of the hybrid vehicle can be calculated based on the current navigation data and weather data of the hybrid vehicle, and the driving mode of the hybrid vehicle can be adjusted based on the remaining power and the comprehensive energy consumption. The driving mode can be automatically adjusted by making full use of the data during the vehicle's driving process. The device has high flexibility and does not require manual switching by the user. It can also reduce the energy consumption of the vehicle and increase the driving mileage.

[0099] Figure 6 A schematic diagram of the structure of a hybrid vehicle provided in an embodiment of the present application. The hybrid vehicle may include:

[0100] A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0101] When the processor 602 executes the program, the driving mode adjustment method of the hybrid vehicle provided in the above embodiment is implemented.

[0102] Furthermore, the hybrid vehicle further comprises:

[0103] The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0104] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0105] The memory 601 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0106] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0107] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0108] The processor 602 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0109] An embodiment of the present application further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the driving mode adjustment method of a hybrid vehicle as described above is implemented.

[0110] The embodiment of the present application also provides a computer program product, including a computer program or instructions, which, when executed, implements the above hybrid vehicle driving mode adjustment method.

[0111] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0112] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0113] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0114] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

Claims

1. A method for adjusting the driving mode of a hybrid vehicle, characterized in that: The following steps are involved: Obtain the remaining power, navigation data and weather data of the current hybrid vehicle; calculating a comprehensive energy consumption of the hybrid vehicle based on the navigation data and the weather data; The driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption and the remaining power.

2. The driving mode adjustment method of a hybrid vehicle according to claim 1, characterized in that: The weather data includes temperature and weather type, the navigation data includes road condition type and navigation mileage, the road condition type includes: high-speed road condition, low-speed road condition and congested road condition, and before adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power, the method further includes: Identify the navigation mileage of each road condition type in the navigation data; Calculating the mileage energy consumption of each road condition type based on the navigation mileage of each road condition type, the temperature and the weather type; The comprehensive energy consumption of the hybrid vehicle is calculated based on the mileage energy consumption of each road condition type.

3. The driving mode adjustment method of a hybrid vehicle according to claim 2, characterized in that: The adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power includes: If the remaining power is greater than the comprehensive energy consumption, the driving mode of the hybrid vehicle is adjusted to motor drive; otherwise, the driving mode of the hybrid vehicle is adjusted based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type.

4. The driving mode adjustment method of a hybrid vehicle according to claim 2, characterized in that: The adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption, the remaining power and the mileage energy consumption of each road condition type includes: If the remaining power is less than the comprehensive energy consumption and greater than the mileage energy consumption of the congested road condition, adjusting the driving mode of the hybrid vehicle to motor drive when on the road section with the congested road condition; If the remaining power minus the mileage energy consumption of the congested road condition is greater than the mileage energy consumption of the low-speed road condition, adjusting the driving mode of the hybrid vehicle to motor drive on the road section with the low-speed road condition; If the road condition types are all high-speed road conditions, or the mileage energy consumption of the congested road conditions is greater than the remaining power, or the mileage energy consumption of the low-speed road conditions is greater than the remaining power, the driving mode of the hybrid vehicle is predicted based on the target algorithm model.

5. The driving mode adjustment method of a hybrid vehicle according to claim 4, characterized in that: Before predicting the driving mode of the hybrid vehicle based on the target algorithm model, the method includes: Acquire a data set of the target algorithm model, wherein the data set includes at least one of remaining battery power, temperature, weather, navigation mileage, road conditions, and driving mode; Dividing the data set into a training data set, a test data set and a validation data set; The target algorithm model is trained using the training data set, the parameters of the target algorithm model are adjusted using the validation data set, and the target algorithm model is tested using the test data set, until the prediction accuracy of the target algorithm model reaches a preset standard and the training is stopped.

6. The driving mode adjustment method of a hybrid vehicle according to claim 5, characterized in that: The target algorithm model is a recurrent neural network model.

7. A driving mode adjustment device for a hybrid vehicle, characterized in that: include: The acquisition module is used to obtain the remaining power, navigation data and weather data of the current hybrid vehicle; a calculation module, configured to calculate the comprehensive energy consumption of the hybrid vehicle based on the navigation data and the weather data; An adjustment module is used to adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining power.

8. A hybrid vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the driving mode adjustment method for a hybrid vehicle as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: The computer program or instruction is executed by a processor to implement the driving mode adjustment method of a hybrid vehicle as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the driving mode adjustment method of the hybrid vehicle as described in any one of claims 1 to 6 is implemented.

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