Method and apparatus for adjusting propulsion mode of hybrid electric vehicle, hybrid electric vehicle, medium, and program product

Abstract

The present application relates to a method and apparatus for adjusting a propulsion mode of a hybrid electric vehicle, a hybrid electric vehicle, and a medium. The method comprises: acquiring a state of charge, navigation data, and weather data of a current hybrid electric vehicle; calculating comprehensive energy consumption of the hybrid electric vehicle on the basis of the navigation data and the weather data; and adjusting a propulsion mode of the hybrid electric vehicle on the basis of the comprehensive energy consumption and the state of charge.

Description

Hybrid vehicle drive mode adjustment methods, devices, hybrid vehicles, media and program products

[0001] This application claims priority to Chinese Patent Application No. 202510005047.5, filed on January 2, 2025, entitled "Method, Apparatus, Hybrid Vehicle and Medium for Adjusting Drive Mode of Hybrid Vehicle", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of hybrid vehicle technology, and in particular to a method, apparatus, hybrid vehicle, medium, and program product for adjusting the drive mode of a hybrid vehicle. Background Technology

[0003] Currently, plug-in hybrid electric vehicles (PHEVs) are widely used due to their flexible drive mechanism. PHEVs typically have two power sources: an internal combustion engine and an electric motor. Summary of the Invention

[0004] This application provides a method, apparatus, hybrid vehicle, medium, and program product for adjusting the drive mode of a hybrid vehicle.

[0005] According to a first aspect of the embodiments of this application, a method for adjusting the driving mode of a hybrid vehicle is provided, comprising the following steps: obtaining the remaining battery 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 battery power.

[0006] In some embodiments of this application, weather data includes temperature and weather type, navigation data includes road condition type and navigation mileage, and road condition type includes: highway road condition, low-speed road condition and congested road condition. Before adjusting the driving mode of the hybrid vehicle based on the comprehensive energy consumption and remaining battery power, the method further 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.

[0007] In some embodiments of this application, adjusting the driving mode of a hybrid vehicle based on comprehensive energy consumption and remaining battery power includes: adjusting the driving mode of the hybrid vehicle to electric motor drive in response to the remaining battery power being greater than the comprehensive energy consumption; and adjusting the driving mode of the hybrid vehicle based on comprehensive energy consumption, remaining battery power, and mileage energy consumption for each road condition type in response to the remaining battery power being less than or equal to the comprehensive energy consumption.

[0008] In some embodiments of this application, adjusting the driving mode of a hybrid vehicle based on comprehensive energy consumption, remaining battery power, and mileage energy consumption for each road condition type includes: adjusting the driving mode of the hybrid vehicle to electric motor drive when the hybrid vehicle travels to a congested road segment if the remaining battery power is less than the comprehensive energy consumption but greater than the mileage energy consumption for congested road conditions; adjusting the driving mode of the hybrid vehicle to electric motor drive when the hybrid vehicle travels to a low-speed road segment if the remaining battery power minus the mileage energy consumption for congested road conditions is greater than the mileage energy consumption for low-speed road conditions; and adjusting the driving mode of the hybrid vehicle based on a target algorithm model if all road conditions are high-speed road conditions, or the mileage energy consumption for congested road conditions is greater than the remaining battery power, or the mileage energy consumption for low-speed road conditions is greater than the remaining battery power.

[0009] In some embodiments of this application, before adjusting the driving mode of the hybrid vehicle based on the target algorithm model, the method further includes: acquiring a dataset of the target algorithm model, wherein the dataset includes at least one of battery remaining power, temperature, weather, navigation mileage, road conditions, and driving mode; dividing the dataset into a training dataset, a test dataset, and a validation dataset; training the target algorithm model using the training dataset, adjusting the parameters of the target algorithm model using the validation dataset, and testing the target algorithm model using the test dataset; and stopping training in response to the prediction accuracy of the target algorithm model reaching a preset standard.

[0010] In some embodiments of this application, the target algorithm model is a recurrent neural network model.

[0011] According to a second aspect of the embodiments of this application, a hybrid vehicle drive mode adjustment device is provided, comprising: an acquisition module for acquiring the remaining battery power, navigation data, and weather data of the current hybrid vehicle; a calculation module for calculating the comprehensive energy consumption of the hybrid vehicle based on the navigation data and weather data; and an adjustment module for adjusting the drive mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining battery power.

[0012] In some embodiments of this application, weather data includes temperature and weather type, and navigation data includes road condition type and navigation mileage. Road condition types include: highway road conditions, low-speed road conditions, and congested road conditions. The device further includes: an identification module, 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 remaining battery power; calculate the mileage energy consumption of each road condition type based on the navigation mileage, temperature, and weather type; and calculate the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption of each road condition type.

[0013] In some embodiments of this application, the adjustment module is further configured to: adjust the driving mode of the hybrid vehicle to electric motor drive in response to the remaining battery power being greater than the comprehensive energy consumption; and adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type in response to the remaining battery power being less than or equal to the comprehensive energy consumption.

[0014] In some embodiments of this application, the adjustment module is further configured to: adjust the driving mode of the hybrid vehicle to electric motor drive when the hybrid vehicle travels to a congested road segment if the remaining battery power is less than the overall energy consumption but greater than the mileage energy consumption in congested road conditions; adjust the driving mode of the hybrid vehicle to electric motor drive when the hybrid vehicle travels to a low-speed road segment if the remaining battery power minus the mileage energy consumption in congested road conditions is greater than the mileage energy consumption in low-speed road conditions; and adjust the driving mode of the hybrid vehicle based on the target algorithm model if all road conditions are high-speed road conditions, or the mileage energy consumption in congested road conditions is greater than the remaining battery power, or the mileage energy consumption in low-speed road conditions is greater than the remaining battery power.

[0015] In some embodiments of this application, the apparatus further includes: a training module, configured to acquire a dataset of the target algorithm model before adjusting the driving mode of the hybrid vehicle based on the target algorithm model, wherein the dataset includes at least one of battery remaining power, temperature, weather, navigation mileage, road conditions, and driving mode; divide the dataset into a training dataset, a test dataset, and a validation dataset; train the target algorithm model using the training dataset, adjust the parameters of the target algorithm model using the validation dataset, and test the target algorithm model using the test dataset; and stop training in response to the prediction accuracy of the target algorithm model reaching a preset standard.

[0016] In some embodiments of this application, the target algorithm model is a recurrent neural network model.

[0017] According to a third aspect of the embodiments of this application, a hybrid vehicle is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the hybrid vehicle drive mode adjustment method as described above when executing the program.

[0018] According to a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program or instructions are stored, wherein when the computer program or instructions are executed by a processor of a computer device, the computer device performs the hybrid vehicle drive mode adjustment method as described above.

[0019] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program or instructions, wherein when the computer program or instructions are executed by a processor of a computer device, the computer device performs the hybrid vehicle drive mode adjustment method as described above.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0022] Figure 1 is a flowchart of a method for adjusting the drive mode of a hybrid vehicle according to an embodiment of this application;

[0023] Figure 2 is a schematic diagram of the sequence-to-sequence transformation and generation process of a recurrent neural network (RNN) model according to an embodiment of this application;

[0024] Figure 3 is a system control schematic diagram of a hybrid vehicle drive mode adjustment method according to an embodiment of this application;

[0025] Figure 4 is a detailed flowchart of the method for adjusting the drive mode of a hybrid vehicle according to an embodiment of this application;

[0026] Figure 5 is a schematic diagram of a drive mode adjustment device for a hybrid vehicle according to an embodiment of this application;

[0027] Figure 6 is a structural schematic diagram of a hybrid vehicle according to an embodiment of this application. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0029] In related technologies, the batteries in hybrid electric vehicles (HEVs) are typically much smaller than those in pure electric vehicles, about 20% the size. With the continuous development of HEV technology, optimizing energy consumption under different driving conditions within a limited battery capacity has become a key issue for improving energy efficiency. HEVs usually rely on simple rules to switch between electric and internal combustion engine drive. For example, pure electric drive is used below 60 km / h, and internal combustion engine drive is used above 60 km / h, or the user manually switches between the two. These rules do not consider multiple factors affecting energy consumption and lack flexibility.

[0030] In view of the problems existing in the related technologies, this application provides a method, device, hybrid vehicle, medium and program product for adjusting the driving mode of a hybrid vehicle, so as to solve the problems of simple driving mode switching logic and lack of flexibility in the related technologies.

[0031] The following description, with reference to the accompanying drawings, describes a method, apparatus, hybrid vehicle, medium, and program product for adjusting the drive mode of a hybrid vehicle according to embodiments of this application.

[0032] This application provides a method for adjusting the drive mode of a hybrid vehicle. In this method, the overall energy consumption of the hybrid vehicle can be calculated based on the vehicle's current navigation data and weather data. The drive mode is then adjusted based on the remaining battery power and the overall energy consumption, fully utilizing data from the vehicle's driving process to automatically adjust the drive mode. This method offers high flexibility, eliminating the need for manual switching of the vehicle's drive mode by the user, and can reduce energy consumption and increase driving range. Therefore, it solves the problems of simple and inflexible drive mode switching logic in related technologies.

[0033] Specifically, Figure 1 is a schematic flowchart of a method for adjusting the drive mode of a hybrid vehicle according to an embodiment of this application. This method can be executed by a controller in the hybrid vehicle. The specific controller can be adjusted according to the actual configuration of the vehicle.

[0034] As shown in Figure 1, the method for adjusting the drive mode of this hybrid vehicle includes the following steps:

[0035] In step S101, the remaining battery power, navigation data, and weather data of the current hybrid vehicle are obtained.

[0036] In some embodiments, weather data includes temperature and weather type, such as rain, snow, or sunny; navigation data includes road condition type and navigation distance, such as congested road conditions, low-speed road conditions, or high-speed road conditions. The hybrid vehicle in this application refers to a hybrid electric vehicle, that is, a vehicle that can be driven by an internal combustion engine and / or an electric motor.

[0037] In step S102, the overall energy consumption of the hybrid vehicle is calculated based on navigation data and weather data.

[0038] It is understood that the embodiments of this application can calculate the comprehensive energy consumption of the hybrid vehicle based on navigation data and weather data, wherein the comprehensive energy consumption is the sum of the energy consumption required for the hybrid vehicle to drive under each road condition in the navigation mileage.

[0039] In this embodiment of the application, before calculating the overall energy consumption of the hybrid vehicle based on the remaining battery power, navigation data, and weather data, the method further includes: identifying the navigation mileage for each road condition type in the navigation data; calculating the mileage energy consumption for each road condition type based on the navigation mileage, temperature, and weather type for each road condition type; and calculating the overall energy consumption of the hybrid vehicle based on the mileage energy consumption for each road condition type.

[0040] It is understood that the embodiments of this application can identify the navigation mileage, temperature, and weather type for each road condition in the navigation data and calculate the mileage energy consumption for each road condition, and then calculate the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption for each road condition. It should be noted that the mileage energy consumption for each road condition in this application refers to the energy consumption expected to be required for the vehicle to travel the navigation mileage corresponding to each road condition.

[0041] In some embodiments, high-speed driving mileage energy consumption N1 = high-speed mileage K1 * energy consumption per 100 km under ideal conditions / 100 km * temperature weight T1 * weather weight C1;

[0042] Low-speed mileage energy consumption N2 = low-speed mileage K2 * energy consumption per 100 km under ideal conditions / 100 km * temperature weight T2 * weather weight C2;

[0043] Energy consumption per congested distance N3 = Congested distance K3 * Energy consumption per 100 km under ideal conditions / 100 km * Temperature weight T3 * Weather weight C3;

[0044] Total energy consumption = N = N1 + N2 + N3.

[0045] In step S103, the driving mode of the hybrid vehicle is adjusted based on the overall energy consumption and the remaining battery power.

[0046] It is understood that the embodiments of this application can adjust the driving mode of the hybrid vehicle based on the overall energy consumption and remaining power, making full use of the data during the vehicle's driving process to automatically adjust the driving mode without requiring manual switching by the user, and can reduce the vehicle's energy consumption and increase driving range.

[0047] In this embodiment, adjusting the driving mode of a hybrid vehicle based on overall energy consumption and remaining battery power includes: if the remaining battery power is greater than the overall energy consumption, adjusting the driving mode of the hybrid vehicle to electric motor drive; otherwise, adjusting the driving mode of the hybrid vehicle based on overall energy consumption, remaining battery power, and mileage energy consumption for each road condition type. That is, in response to the remaining battery power being greater than the overall energy consumption, the driving mode of the hybrid vehicle is adjusted to electric motor drive; in response to the remaining battery power being less than or equal to the overall energy consumption, the driving mode of the hybrid vehicle is adjusted based on the overall energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type.

[0048] In some embodiments, the driving mode is adjusted by comparing the relationship between the overall energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type. The specific adjustment method is as follows.

[0049] In some embodiments, road condition types include: highway conditions, low-speed conditions, and congested conditions. The driving mode of the hybrid vehicle is adjusted based on the overall energy consumption, remaining battery power, and the mileage energy consumption for each road condition type. This includes: if the remaining battery power is less than the overall energy consumption but greater than the mileage energy consumption for congested conditions, then the driving mode of the hybrid vehicle is adjusted to electric motor drive in congested road sections; if the remaining battery power minus the mileage energy consumption for congested conditions is greater than the mileage energy consumption for low-speed conditions, then the driving mode of the hybrid vehicle is adjusted to electric motor drive in low-speed road sections; if all road condition types are highway conditions, or the mileage energy consumption for congested conditions is greater than the remaining battery power, or the mileage energy consumption for low-speed conditions is greater than the remaining battery power, then the driving mode of the hybrid vehicle is predicted based on the target algorithm model.

[0050] In other words, in response to the remaining battery power being less than the overall energy consumption and greater than the energy consumption per mile in congested traffic conditions, the hybrid vehicle's driving mode is adjusted to electric motor drive when it travels to a congested section of road; in response to the remaining battery power minus the energy consumption per mile in congested traffic conditions being greater than the energy consumption per mile in low-speed traffic conditions, the hybrid vehicle's driving mode is adjusted to electric motor drive when it travels to a low-speed section of road; in response to all road conditions being highway conditions, or the energy consumption per mile in congested traffic conditions being greater than the remaining battery power, or the energy consumption per mile in low-speed traffic conditions being greater than the remaining battery power, the driving mode of the hybrid vehicle is adjusted based on the target algorithm model.

[0051] In some embodiments, the target algorithm model can be an RNN, but is not limited to this model. For example, it can also be a Long Short-Term Memory (LSTM) artificial neural network model or a Gated Recurrent Unit (GRU) model.

[0052] Specifically, when the remaining battery power is greater than the total energy consumption, it indicates that the battery has enough power to support the entire journey. Therefore, the hybrid vehicle's drive mode is adjusted to electric motor drive, thereby reducing operating costs and vehicle energy consumption.

[0053] When the remaining battery power is less than the overall energy consumption but greater than the energy consumption per mile in congested traffic, the hybrid vehicle's drive mode is switched to electric motor drive when in congested areas (i.e., when the vehicle is traveling in a congested area). This is because the engine is less efficient at low speeds or when stationary, while electric motor drive is more efficient.

[0054] If the remaining battery power minus the energy consumption under congested road conditions is greater than the energy consumption under low-speed road conditions, the hybrid vehicle's drive mode will also be adjusted to the drive motor under low-speed road conditions (i.e. when the vehicle is driving in low-speed road conditions) in order to make full use of the advantages of electric mode without affecting the range.

[0055] When all road conditions are highways, or the energy consumption per mile is greater than the remaining battery power in congested sections, or the energy consumption per mile is greater than the remaining battery power in low-speed conditions, the driving mode of the hybrid vehicle is predicted or adjusted based on the target algorithm model, including but not limited to internal combustion engine main drive + electric motor auxiliary drive, internal combustion engine auxiliary drive + electric motor main drive, or pure internal combustion engine drive.

[0056] In some embodiments, before adjusting the driving mode of the hybrid vehicle based on the target algorithm model, the method further includes: acquiring a dataset of the target algorithm model, wherein the dataset includes at least one of remaining battery power, temperature, weather, navigation range, road conditions, and driving mode; dividing the dataset into a training dataset, a test dataset, and a validation dataset; training the target algorithm model using the training dataset, adjusting the parameters of the target algorithm model using the validation dataset, and testing the target algorithm model using the test dataset, until the prediction accuracy of the target algorithm model reaches a preset standard and training is stopped. That is, the model is continuously trained and the prediction accuracy of the model is evaluated; training is stopped in response to the prediction accuracy of the target algorithm model reaching a preset standard.

[0057] It is understood that the embodiments of this application can obtain the dataset of the target algorithm, divide the dataset into training dataset, test dataset and validation dataset, so as to train the target algorithm model.

[0058] In some embodiments, the specific training process of the target algorithm model is as follows:

[0059] Step 1: Dataset Preparation: 1) Collect data from user driving logs (as shown in Table 1) on the top 100 vehicles with low energy consumption under low-speed, high-speed, and combined low-speed and high-speed conditions. Then, use time-series data on vehicles with relatively low energy consumption (as shown in Table 2), including dimensions such as vehicle VIN code, time, remaining battery power, temperature, weather, navigation mileage, road conditions, and drive mode; 2) Data preprocessing, such as cleaning, anomaly handling, and data labeling; 3) Divide the dataset into 70% training dataset, 20% test dataset, and 10% validation dataset; 4) Convert the multi-dimensional time-series data in Table 2 into RNN format through time series transformation, normalization, and sequence imputation to ensure that the data can be effectively understood and processed. Table 2 contains data on vehicles with model T1-TX (two-wheel drive), originating from Town A, and destined for the Town A market.

[0060] Table 1

[0061] Table 2

[0062] Step 2: Model Training: Input the prepared training data into the RNN model for data training and learning, capture long-term dependency features in the time series data, and use test and validation set data to tune the model parameters. By adjusting the parameters to optimize the model during training, the model accuracy can be improved. The sequence-to-sequence transformation and generation process of the RNN model is shown in Figure 2.

[0063] Step 3: Test and evaluate the model: Test the model using a test set and calculate the accuracy of the model's predictions to evaluate the model's performance and select the best model parameters.

[0064] It should be noted that steps 2-3 can be performed together. That is, after each parameter tuning, the prediction accuracy can be calculated once; if the accuracy is not up to standard, the process of training, parameter tuning, and accuracy calculation is repeated; until the accuracy is up to standard, the training process is stopped, and the model parameters at this time are considered the optimal model parameters.

[0065] Step 4: Model Deployment and Monitoring: Deploy the trained model to the cloud server, continuously monitor the model's performance and prediction results, and fine-tune it based on the actual results.

[0066] In summary, the hybrid vehicle drive mode adjustment method of this application embodiment combines a navigation service system, a battery service system, an intelligent control system, and a drive controller (the specific system control is shown in Figure 3), making full use of different driving conditions to select a low-energy-consumption drive mode. Specifically, the method of this embodiment can collect navigation mileage, road conditions, weather, and remaining battery power in real time, and predict and control the drive mode of the plug-in hybrid system through an algorithm model to achieve the effect of reducing energy consumption and increasing driving range.

[0067] 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 level of other energy-consuming components such as air conditioning, headlights, and windshield 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 the driving range, and decides whether to use the electric motor or the internal combustion engine for primary driving. For example, if the driving range is 30km and the remaining power corresponds to a driving range of 30km, then the electric motor will drive the primary driving range, with the internal combustion engine providing auxiliary driving.

[0070] The navigation service system obtains the user's navigation mileage and traffic data in real time. In low-speed or congested traffic conditions, electric motor drive is more suitable, but the remaining battery power should also be considered. In high-speed driving, internal combustion engine drive is more suitable, but if there is enough remaining battery power, electric motor can also assist in driving to reduce fuel consumption.

[0071] The intelligent control system, when the user presses the accelerator pedal, 1) calculates the comprehensive energy consumption based on navigation mileage, road conditions, and weather; 2.1) when the remaining battery power is greater than the comprehensive energy consumption, the system uses the motor to drive at 100% and outputs a command; 2.2) when the remaining battery power is less than the comprehensive energy consumption, in response to the remaining battery power being greater than the energy consumption of congested mileage, the system uses the motor to drive at 100% in congested road sections and outputs a command; 2.3) in response to the remaining battery power after subtracting the energy consumption of congested mileage (i.e., remaining battery power - energy consumption of congested mileage) being greater than the energy consumption of low-speed (vehicle speed below 60km / h) mileage, the system uses the motor to drive at 100% in low-speed road sections and outputs a command; 2.4) when the remaining battery power (remaining battery power - energy consumption of congested mileage - energy consumption of low-speed mileage) or the battery power is insufficient (e.g., the remaining battery power cannot be less than the energy consumption of congested road conditions or less than the energy consumption of low-speed road conditions), the system uses a model predictive driving mode. The model takes the aforementioned multidimensional data as input, predicts the driving mode through the model, such as internal combustion engine drive 100%, internal combustion engine main drive a% + electric motor auxiliary drive b%, internal combustion engine auxiliary drive a% + electric motor main drive b%, and outputs commands. The calculation process of the intelligent control system is shown in Figure 4.

[0072] The drive controller is used to drive the internal combustion engine and the electric motor according to the input instructions, as well as the power of each drive source.

[0073] In some embodiments, the implementation flow of the hybrid vehicle drive mode adjustment method according to this application is as follows:

[0074] Step 1: After the user gets into the car and starts the vehicle, the vehicle's infotainment system will automatically start the battery service system, weather service system, navigation service system, and intelligent control system.

[0075] Step 2: The navigation service system acquires real-time traffic data and calculates the highway mileage, low-speed mileage, and congested mileage to the navigation destination, and outputs the data to the intelligent control system.

[0076] Step 3: The weather service system acquires real-time temperature and weather data for each mileage segment, such as rain, snow, and low visibility, and outputs the data 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: Energy consumption for high-speed driving mileage N1 = High-speed mileage K1 * Energy consumption per 100km under ideal conditions / 100km * Temperature weight T1 * Weather weight C1; Energy consumption for low-speed mileage N2 = Low-speed mileage K2 * Energy consumption per 100km under ideal conditions / 100km * Temperature weight T2 * Weather weight C2; Energy consumption for congested mileage N3 = Congested mileage K3 * Energy consumption per 100km under ideal conditions / 100km * Temperature weight T3 * Weather weight C3.

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

[0080] Step 7: Obtain the energy consumption N3 for congested mileage. When the remaining battery power D1 is greater than the energy consumption N3 for congested mileage, use the motor to drive at 100% when the vehicle travels to a congested section and output the command to execute step 10; otherwise, execute step 8.

[0081] Step 8: Obtain the low-speed range energy consumption N2, calculate the remaining battery power D2 = D1 - N3. If the remaining battery power D2 is greater than the low-speed range energy consumption N2, use the motor to drive at 100% when the vehicle travels on a low-speed road section, and output the command to execute step 10; otherwise, execute step 9.

[0082] Step 9: When driving at high speed or in congested traffic, if the energy consumption N3 is greater than the remaining battery charge D1, or when the energy consumption N2 is greater than the remaining battery charge D2, input multidimensional data into the model, predict the driving mode (internal combustion engine main drive a% + electric motor auxiliary drive b%, internal combustion engine drive a% or internal combustion engine auxiliary drive a% + electric motor main drive b%) through the algorithm model, and output instructions.

[0083] Step 10: The drive controller controls the vehicle's drive mode according to the received instructions, or controls the drive mode and ratio output by the system according to the algorithm, and controls the internal combustion engine and electric motor drive and power.

[0084] The hybrid vehicle drive mode adjustment method proposed in this application can calculate the comprehensive energy consumption of the hybrid vehicle based on the vehicle's current navigation data and weather data, and adjust the hybrid vehicle's drive mode according to the remaining battery power and comprehensive energy consumption. This method fully utilizes data from the vehicle's driving process to automatically adjust the drive mode, offering high flexibility and eliminating the need for manual switching by the user. Furthermore, it reduces vehicle energy consumption and increases driving range. Therefore, it solves the technical problems of related technologies, such as the simple logic and lack of flexibility in switching hybrid vehicle drive modes.

[0085] Next, referring to the accompanying drawings, a hybrid vehicle drive mode adjustment device according to an embodiment of this application is described.

[0086] Figure 5 is a block diagram of a hybrid vehicle drive mode adjustment device according to an embodiment of this application.

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

[0088] The acquisition module 100 is used to acquire the remaining battery 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; and the adjustment module 300 is used to adjust the driving mode of the hybrid vehicle based on the comprehensive energy consumption and remaining battery power.

[0089] In some embodiments, weather data includes temperature and weather type, and navigation data includes road condition type and navigation distance. Road condition type includes: highway road condition, low-speed road condition and congested road condition.

[0090] In some embodiments of this application, the apparatus 10 further includes an identification module.

[0091] The identification module is used to identify the navigation mileage for 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 battery power; calculate the mileage energy consumption for each road condition type based on the navigation mileage, temperature and weather type; and calculate the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption for each road condition type.

[0092] In some embodiments, the adjustment module 300 is further configured to: if the remaining battery power is greater than the overall energy consumption, adjust the driving mode of the hybrid vehicle to electric motor drive; otherwise, adjust the driving mode of the hybrid vehicle based on the overall energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type. That is, in response to the remaining battery power being greater than the overall energy consumption, the driving mode of the hybrid vehicle is adjusted to electric motor drive; in response to the remaining battery power being less than or equal to the overall energy consumption, the driving mode of the hybrid vehicle is adjusted based on the overall energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type.

[0093] In some embodiments, the adjustment module 300 is further configured to: if the remaining battery power is less than the overall energy consumption but greater than the mileage energy consumption in congested road conditions, then adjust the driving mode of the hybrid vehicle to electric motor drive in congested road sections; if the remaining battery power minus the mileage energy consumption in congested road conditions is greater than the mileage energy consumption in low-speed road conditions, then adjust the driving mode of the hybrid vehicle to electric motor drive in low-speed road sections; if all road conditions are highway conditions, or the mileage energy consumption in congested road conditions is greater than the remaining battery power, or the mileage energy consumption in low-speed road conditions is greater than the remaining battery power, then adjust the driving mode of the hybrid vehicle based on the target algorithm model. In other words, in response to the remaining battery power being less than the overall energy consumption and greater than the energy consumption per mile in congested traffic conditions, the hybrid vehicle's driving mode is adjusted to electric motor drive when it travels to a congested section of road; in response to the remaining battery power minus the energy consumption per mile in congested traffic conditions being greater than the energy consumption per mile in low-speed traffic conditions, the hybrid vehicle's driving mode is adjusted to electric motor drive when it travels to a low-speed section of road; in response to all road conditions being highway conditions, or the energy consumption per mile in congested traffic conditions being greater than the remaining battery power, or the energy consumption per mile in low-speed traffic conditions being greater than the remaining battery power, the driving mode of the hybrid vehicle is adjusted based on the target algorithm model.

[0094] In this embodiment of the application, the apparatus 10 further includes a training module.

[0095] The training module is used to acquire the target algorithm model's dataset before adjusting the hybrid vehicle's drive mode based on the target algorithm model. The dataset includes at least one of the following: remaining battery power, temperature, weather, navigation range, road conditions, and drive mode. The dataset is divided into a training dataset, a test dataset, and a validation dataset. The target algorithm model is trained using the training dataset, its parameters are adjusted using the validation dataset, and its target algorithm model is tested using the test dataset until the prediction accuracy of the target algorithm model reaches a preset standard, at which point training stops.

[0096] In this embodiment, the target algorithm model is a recurrent neural network model. That is, the above-described model training, verification, and testing process is continuously executed; training stops when the prediction accuracy of the target algorithm model reaches a preset standard.

[0097] It should be noted that the foregoing explanation of the method for adjusting the drive mode of a hybrid vehicle also applies to the drive mode adjustment device of the hybrid vehicle in this embodiment, and will not be repeated here.

[0098] The hybrid vehicle drive mode adjustment device proposed in the embodiments of this application can calculate the comprehensive energy consumption of the hybrid vehicle based on the current navigation data and weather data of the hybrid vehicle, and adjust the hybrid vehicle drive mode based on the remaining power and comprehensive energy consumption. It makes full use of the data during the vehicle's driving process to automatically adjust the drive mode, which is highly flexible and does not require manual switching by the user. It can also reduce the vehicle's energy consumption and increase driving range.

[0099] Figure 6 is a structural schematic diagram of a hybrid vehicle provided in an embodiment of this application. The hybrid vehicle may include:

[0100] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0101] When the processor 602 executes the program, it performs the hybrid vehicle drive mode adjustment method provided in the above embodiments.

[0102] Furthermore, hybrid vehicles also include:

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

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

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

[0106] If the memory 601, processor 602, and communication interface 603 are implemented independently, they can be interconnected via a bus to 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, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in Figure 6, but this does not indicate that there is only one bus or one type of bus.

[0107] In some embodiments, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

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

[0109] This application also provides a computer-readable storage medium storing a computer program or instructions thereon, which, when executed by a processor of a computer device, causes the computer device to perform the hybrid vehicle drive mode adjustment method as described above.

[0110] This application also provides a computer program product, including a computer program or instructions, which, when executed by a processor of a computer device, cause the computer device to perform the hybrid vehicle drive mode adjustment method as described above.

[0111] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

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

[0113] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0114] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0115] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

Claims

1. A method for adjusting the drive mode of a hybrid vehicle, comprising the following steps: Obtain the remaining battery power, navigation data, and weather data of the current hybrid vehicle; The overall energy consumption of the hybrid vehicle is calculated based on the navigation data and the weather data. The driving mode of the hybrid vehicle is adjusted based on the overall energy consumption and the remaining battery power.

2. The method according to claim 1, wherein, The weather data includes temperature and weather type; the navigation data includes road condition type and navigation mileage; the road condition type includes: highway conditions, low-speed conditions, and congested conditions; before adjusting the drive mode of the hybrid vehicle based on the comprehensive energy consumption and the remaining battery power, the method further includes: Identify the navigation mileage for each road condition type in the navigation data; Calculate the mileage energy consumption for each road condition type based on the navigation mileage for each road condition type, the temperature, and the weather type; The overall energy consumption of the hybrid vehicle is calculated based on the mileage energy consumption for each road condition type.

3. The method according to claim 2, wherein, The adjustment of the hybrid vehicle's drive mode based on the combined energy consumption and the remaining battery power includes: In response to the remaining battery power being greater than the total energy consumption, the driving mode of the hybrid vehicle is adjusted to electric motor drive; In response to the remaining battery power being less than or equal to the overall energy consumption, the driving mode of the hybrid vehicle is adjusted based on the overall energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type.

4. The method according to claim 3, characterized in that, The adjustment of the hybrid vehicle's drive mode based on the overall energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type includes: In response to the remaining battery power being less than the overall energy consumption and greater than the mileage energy consumption in the congested road conditions, the driving mode of the hybrid vehicle is adjusted to electric motor drive when the hybrid vehicle travels to the congested road section. In response to the fact that the remaining power minus the energy consumption under congested conditions is greater than the energy consumption under low-speed conditions, the hybrid vehicle's driving mode is adjusted to electric motor drive when the hybrid vehicle travels to the section of road with low-speed conditions. In response to the fact that all the road conditions are highway conditions, or the energy consumption per mile of the congested road conditions is greater than the remaining battery power, or the energy consumption per mile of the low-speed road conditions is greater than the remaining battery power, the driving mode of the hybrid vehicle is adjusted based on the target algorithm model.

5. The method according to claim 4, wherein, Before adjusting the drive mode of the hybrid vehicle based on the target algorithm model, the method further includes: Obtain the dataset of the target algorithm model, wherein the dataset includes at least one of the following: remaining battery power, temperature, weather, navigation mileage, road conditions, and driving mode; The dataset is divided into a training dataset, a test dataset, and a validation dataset. The target algorithm model is trained using the training dataset, the parameters of the target algorithm model are adjusted using the validation dataset, and the target algorithm model is tested using the test dataset. Training stops when the prediction accuracy of the target algorithm model reaches a preset standard.

6. The method according to any one of claims 4-5, wherein, The target algorithm model is a recurrent neural network model.

7. A drive mode adjustment device for a hybrid vehicle, comprising: The acquisition module is used to acquire the remaining battery power, navigation data, and weather data of the current hybrid vehicle; A calculation module is used to calculate the overall 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 overall energy consumption and the remaining battery power.

8. The apparatus according to claim 7, wherein, The weather data includes temperature and weather type, and the navigation data includes road condition type and navigation distance. The road condition type includes: highway road conditions, low-speed road conditions, and congested road conditions. The device further includes: an identification module, configured to identify the navigation mileage for 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 battery power; calculate the mileage energy consumption for each road condition type based on the navigation mileage for each road condition type, the temperature, and the weather type; and calculate the comprehensive energy consumption of the hybrid vehicle based on the mileage energy consumption for each road condition type.

9. The apparatus according to claim 8, wherein, The adjustment module is further used for: In response to the remaining battery power being greater than the total energy consumption, the driving mode of the hybrid vehicle is adjusted to electric motor drive; In response to the remaining battery power being less than or equal to the overall energy consumption, the driving mode of the hybrid vehicle is adjusted based on the overall energy consumption, the remaining battery power, and the mileage energy consumption for each road condition type.

10. The apparatus according to claim 9, wherein, The adjustment module is further used for: In response to the remaining battery power being less than the overall energy consumption and greater than the mileage energy consumption in the congested road conditions, the driving mode of the hybrid vehicle is adjusted to electric motor drive when the hybrid vehicle travels to the congested road section. In response to the fact that the remaining power minus the energy consumption under congested conditions is greater than the energy consumption under low-speed conditions, the hybrid vehicle's driving mode is adjusted to electric motor drive when the hybrid vehicle travels to the section of road with low-speed conditions. In response to the fact that all the road conditions are highway conditions, or the energy consumption per mile of the congested road conditions is greater than the remaining battery power, or the energy consumption per mile of the low-speed road conditions is greater than the remaining battery power, the driving mode of the hybrid vehicle is adjusted based on the target algorithm model.

11. The apparatus according to claim 10, wherein, The device further includes: a training module, configured to acquire a dataset of the target algorithm model before adjusting the driving mode of the hybrid vehicle based on the target algorithm model, wherein the dataset includes at least one of battery remaining power, temperature, weather, navigation range, road conditions, and driving mode; divide the dataset into a training dataset, a test dataset, and a validation dataset; train the target algorithm model using the training dataset, adjust the parameters of the target algorithm model using the validation dataset, and test the target algorithm model using the test dataset; and stop training in response to the prediction accuracy of the target algorithm model reaching a preset standard.

12. The apparatus according to any one of claims 9-10, wherein, The target algorithm model is a recurrent neural network model.

13. A hybrid vehicle, comprising: The device includes 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 the drive mode adjustment method for a hybrid vehicle as described in any one of claims 1-6.

14. A computer-readable storage medium having a computer program or instructions stored thereon, wherein, When the computer program or instructions are executed by the processor of the computer device, the computer device performs the drive mode adjustment method for a hybrid vehicle as described in any one of claims 1-6.

15. A computer program product comprising a computer program or instructions, wherein, When the computer program or instructions are executed by the processor of the computer device, the computer device performs the drive mode adjustment method for a hybrid vehicle as described in any one of claims 1-6.

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