Vehicle remaining mileage determination method and device, vehicle and storage medium

Abstract

The invention discloses a vehicle remaining mileage determination method and device, a vehicle and a storage medium, and the method comprises the steps: determining a vehicle speed trajectory curve of the vehicle in a future travel based on the navigation information of the vehicle, predicting the energy consumption of the vehicle based on the vehicle speed trajectory curve, and obtaining the predicted energy consumption of the vehicle in the future travel, further, the remaining mileage of the vehicle is determined based on the predicted energy consumption, a future track speed curve of the vehicle can be calculated through navigation information fitting so as to predict an energy consumption value of the vehicle in a future use scene, then the remaining mileage of the vehicle under different vehicle use requirements and driving scenes can be determined, and the accuracy of remaining mileage prediction is ensured; and the user experience is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a vehicle remaining range determination method and device, a vehicle and a storage medium. BACKGROUND

[0002] The remaining range refers to the expected drivable range of the vehicle under the current vehicle state. Due to the limitations of battery technology, the driving range of electric vehicles is usually short, and the remaining range of the vehicle needs to be predicted to plan the vehicle trip and charging location in advance to reduce the anxiety of using electricity.

[0003] There are usually two types of traditional pure electric vehicle remaining range calculation methods. One is to map the battery state of charge (SOC) to the announced range under the standard operating conditions of the vehicle, and then interpolate the estimated SOC of the vehicle during driving to obtain the current remaining range of the vehicle. The other is to calculate the average power consumption of the vehicle based on the distance traveled and the power consumption in a recent period of time, and then calculate the current remaining range based on the average power consumption and the estimated remaining power (SOE) of the battery. However, both methods are relatively simple and do not consider real-world usage scenarios, resulting in low accuracy of the calculated remaining range and poor user experience. SUMMARY

[0004] The present application provides a vehicle remaining range determination method, device, vehicle and storage medium to solve the problem of low accuracy of the remaining range calculated by the traditional method, resulting in poor user experience.

[0005] A vehicle remaining range determination method is provided, comprising:

[0006] When it is determined that the vehicle has navigation information, determining a vehicle speed trajectory curve of the vehicle in a future trip based on the navigation information of the vehicle;

[0007] Predicting the energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain a predicted energy consumption of the vehicle in the future trip;

[0008] Determining the remaining range of the vehicle based on the predicted energy consumption.

[0009] Optionally, determining the remaining range of the vehicle based on the predicted energy consumption comprises:

[0010] Determining the actual energy consumption of the vehicle in real time during the vehicle's travel;

[0011] Correcting the predicted energy consumption based on the actual energy consumption to obtain a target energy consumption value;

[0012] Determining the remaining range of the vehicle based on the target energy consumption value and the remaining power of the vehicle.

[0013] Optionally, the energy consumption of the vehicle is predicted based on the vehicle speed trajectory curve, to obtain a predicted energy consumption of the vehicle in the future trip, comprising:

[0014] determining a current thermal management system power consumption of the vehicle;

[0015] predicting the predicted energy consumption of the vehicle in the future trip based on the vehicle speed trajectory curve and the thermal management system power consumption.

[0016] Optionally, the predicted energy consumption of the vehicle in the future trip is predicted based on the vehicle speed trajectory curve and the thermal management system power consumption, comprising:

[0017] determining a wheel end required torque of the vehicle in the future trip based on the vehicle speed trajectory curve;

[0018] performing torque conversion on the wheel end required torque to obtain a motor required torque of the vehicle in the future trip;

[0019] determining a motor efficiency loss based on the motor required torque, and determining a target electric power demand based on the motor efficiency loss;

[0020] determining a battery end power demand of the vehicle in the future trip according to the thermal management system power consumption, the target electric power demand and a low-voltage load power consumption of the vehicle;

[0021] predicting the predicted energy consumption of the vehicle in the future trip based on the battery end power demand of the vehicle in the future trip.

[0022] Optionally, the predicted energy consumption of the vehicle in the future trip is predicted based on the battery end power demand of the vehicle in the future trip, comprising:

[0023] performing time integration on the battery end power demand to obtain a battery end consumed electric quantity in the future trip;

[0024] determining a theoretical driving range of the vehicle in the future trip;

[0025] dividing the battery end consumed electric quantity by the theoretical driving range to obtain the predicted energy consumption of the vehicle in the future trip.

[0026] Optionally, the thermal management system power consumption is determined in the following manner:

[0027] predicting the current thermal management system power consumption of the vehicle based on historical driving data of the vehicle;

[0028] or,

[0029] determining the current thermal management system power consumption of the vehicle based on a current ambient temperature of the vehicle and a vehicle load.

[0030] Optionally, the vehicle speed trajectory curve of the vehicle within the future trip is determined based on the navigation information of the vehicle, comprising:

[0031] The driving style of the vehicle is determined;

[0032] The vehicle speed trajectory curve of the vehicle within the future trip is fitted based on the navigation information of the vehicle and the driving style.

[0033] Optionally, the vehicle speed trajectory curve of the vehicle within the future trip is fitted based on the navigation information of the vehicle and the driving style, comprising:

[0034] The route information and related traffic information of the vehicle within the future trip are determined based on the navigation information of the vehicle;

[0035] The vehicle speed trajectory curve of the vehicle within the future trip is obtained by predicting the vehicle speed at different time points based on the driving style, the route information and the related traffic information.

[0036] Optionally, the vehicle speed trajectory curve of the vehicle within the future trip is obtained by predicting the vehicle speed at different time points based on the driving style, the route information and the related traffic information, comprising:

[0037] A pre-trained vehicle speed trajectory prediction model is obtained;

[0038] The driving style, the route information and the related traffic information are input into the vehicle speed trajectory prediction model for vehicle speed trajectory prediction, so as to obtain the vehicle speed trajectory curve of the vehicle within the future trip.

[0039] Optionally, the driving style of the vehicle is determined, comprising:

[0040] The historical driving data of the vehicle is obtained, and the historical driving data comprises speed information and driver operation data at different time points;

[0041] The speed information and the driver operation data at different time points are input into a pre-trained driving style recognition model for driving style classification, so as to obtain the driving style of the vehicle.

[0042] Optionally, the method further comprises:

[0043] If it is determined that the vehicle does not have navigation information, the vehicle speed trajectory curve of the vehicle within the future trip is obtained by performing trip and vehicle speed trajectory prediction based on the historical driving data of the vehicle after it is determined that the historical driving data of the vehicle is saved.

[0044] Optionally, after it is determined that the vehicle does not have navigation information, the method further comprises:

[0045] If it is determined that the historical driving data of the vehicle is not saved, the target vehicle model, the location, the specific time and the environmental information of the vehicle are determined.

[0046] The target vehicle model, the location, the specific time and the environmental information are input into a pre-trained regression model to perform travel and energy consumption prediction, to obtain the predicted energy consumption of the vehicle in the future travel, and to determine the remaining mileage of the vehicle based on the predicted energy consumption.

[0047] A vehicle is provided, which comprises a vehicle remaining mileage determination device, the vehicle remaining mileage determination device comprising:

[0048] A first determination module is configured to determine a vehicle speed trajectory curve of the vehicle in the future travel based on the navigation information of the vehicle when it is determined that the vehicle has the navigation information.

[0049] A prediction module is configured to predict the energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain the predicted energy consumption of the vehicle in the future travel.

[0050] A second determination module is configured to determine the remaining mileage of the vehicle based on the predicted energy consumption.

[0051] A vehicle remaining mileage determination device is provided, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the vehicle remaining mileage determination method when executing the computer program.

[0052] A readable storage medium is provided, which stores a computer program, and the computer program implements the steps of the vehicle remaining mileage determination method when executed by a processor.

[0053] In one scheme of the vehicle remaining mileage determination method, device, vehicle and storage medium, when it is determined that the vehicle has the navigation information, the vehicle speed trajectory curve of the vehicle in the future travel is determined based on the navigation information of the vehicle, then the energy consumption of the vehicle is predicted based on the vehicle speed trajectory curve to obtain the predicted energy consumption of the vehicle in the future travel, and the remaining mileage of the vehicle is determined based on the predicted energy consumption. The future trajectory speed curve of the vehicle can be calculated by fitting the navigation information to predict the energy consumption value of the vehicle in the future use scenario, and the remaining mileage of the vehicle in different use requirements and driving scenarios can be determined to ensure the accuracy of the remaining mileage prediction, thereby improving the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0055] Figure 1is a structural schematic diagram of a vehicle in an embodiment of the present application;

[0056] Figure 2 is a flow schematic diagram of a vehicle remaining mileage determination method in an embodiment of the present application;

[0057] Figure 3 is Figure 2 is an implementation flow schematic diagram of step S10 in the method;

[0058] Figure 4 is Figure 2 is an implementation flow schematic diagram of step S20 in the method;

[0059] Figure 5 is Figure 2 is an implementation flow schematic diagram of step S30 in the method;

[0060] Figure 6 is Figure 1 is a structural schematic diagram of a vehicle remaining mileage determination device in an embodiment of the present application;

[0061] Figure 7 is another structural schematic diagram of a vehicle remaining mileage determination device in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0063] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should also be understood that the term "and / or" used in the specification and the appended claims of the present application refers to any combination of one or more of the associated listed items and all possible combinations thereof, and includes these combinations.

[0064] In addition, in the description and the appended claims of the present application, the terms "first", "second", "third", and the like are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0065] Reference within the specification of this document to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places within specified

[0066] It should be understood that the magnitude of the serial number of each step in the following embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the application.

[0067] In order to illustrate the technical solutions of the present application, the following will be described by specific embodiments.

[0068] It should be understood that in the traditional pure electric vehicle remaining range calculation method, one is to map the battery state of charge (SOC) and the announced range under the standard working condition of the vehicle, and then interpolate the estimated SOC of the vehicle during driving to obtain the current remaining range of the vehicle. This method will cause the calculated remaining range to become an abstract evaluation index, which is only related to the battery state of charge, completely deviates from the actual use scenario, and has low accuracy. Another is to calculate the average power consumption of the vehicle in the recent period based on the distance traveled and the power consumption in the recent period, and then calculate the current remaining range according to the average power consumption and the estimated battery remaining power (SOE). By introducing the average power consumption, this method avoids the problem that the remaining range completely deviates from the actual use scenario, but also causes the remaining range to be constantly changing due to the influence of the actual road spectrum and the driver's operation, and the mean value of the historical data cannot represent the future use scenario, resulting in a large deviation between the estimated remaining range and the actual remaining range, and insufficient accuracy.

[0069] The vehicle remaining range determination method provided by the embodiments of the present application can be applied to a vehicle as shown in the figure. Figure 1 The vehicle includes a vehicle remaining range determination device and a display device, wherein the vehicle remaining range determination device communicates with the display device through a bus or a network. The vehicle remaining range determination device includes a first determination module, a prediction module and a second determination module.

[0070] After the vehicle is started, the first determining module of the vehicle remaining mileage determining device needs to determine whether the vehicle has navigation information, when it is determined that the vehicle has navigation information, the navigation information of the vehicle is acquired, and the vehicle speed trajectory curve of the vehicle in the future journey is determined based on the navigation information of the vehicle, then the prediction module predicts the energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain the predicted energy consumption of the vehicle in the future journey, and then the second determining module determines the remaining mileage of the vehicle based on the predicted energy consumption, so as to subsequently display the remaining mileage to the user through the display device. In the embodiment, the future trajectory speed curve of the vehicle can be calculated through the navigation information to predict the energy consumption value of the vehicle in the future use scenario, and then the remaining mileage of the vehicle under different use requirements and driving scenarios can be determined. It ensures the accuracy of the remaining mileage prediction, enables the user to have a more accurate and clear understanding of the endurance of the vehicle, reduces the user's concern about the insufficient power of the vehicle, alleviates the user's mileage anxiety, improves the user's satisfaction and reliability, and thus improves the user experience.

[0071] In the embodiment, the vehicle can be a pure electric vehicle or a hybrid vehicle. The display device can be any device for information display installed on the vehicle, such as a vehicle machine or a related instrument. The vehicle remaining mileage determining device can be a vehicle control unit on the vehicle, such as a vehicle controller, or a combination of other controllers; the vehicle remaining mileage determining device can also be a server in the cloud.

[0072] In an embodiment, as shown in Figure 2 , a vehicle remaining mileage determining method is provided, which is applied to the vehicle remaining mileage determining device in Figure 1 for example, and includes the following steps:

[0073] S10: When it is determined that the vehicle has navigation information, the vehicle speed trajectory curve of the vehicle in the future journey is determined based on the navigation information of the vehicle.

[0074] After the vehicle is started, the vehicle remaining mileage determining device determines whether the vehicle has navigation information, when it is determined that the vehicle has navigation information, the navigation information on the vehicle is acquired, and the vehicle speed trajectory curve of the vehicle in the future journey is determined based on the navigation information of the vehicle. The vehicle speed trajectory curve is also called a vehicle speed-time curve, which is a curve representing the relationship between vehicle speed and time.

[0075] In the embodiment, the navigation information can generally include the starting point and the ending point of the future journey (i.e. the starting point and the ending point of the planned route), and the route information (such as road conditions such as slopes and curves) and traffic conditions (such as traffic lights, speed limits, and traffic restrictions) of the planned route. According to the current vehicle speed and the navigation information, the vehicle speed at different times in the future journey can be predicted to obtain the vehicle speed trajectory curve of the vehicle in the future journey.

[0076] S20: predicting the energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain the predicted energy consumption of the vehicle in the future journey.

[0077] The energy consumption (electricity consumption) of the vehicle is different at different speeds. After obtaining the vehicle speed trajectory curve in the future journey of the vehicle, the vehicle remaining range determination device predicts the energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain the predicted energy consumption of the vehicle in the future journey.

[0078] For example, the vehicle can be tested in advance to obtain vehicle energy consumption data under different working conditions (including different speeds and loads). After obtaining the vehicle speed trajectory curve, the speed at different times can be known according to the vehicle speed trajectory curve, and then the current load of the vehicle and the speed at different times can be used to query the predicted energy consumption of the vehicle in the future journey from the vehicle energy consumption data.

[0079] S30: determining the remaining range of the vehicle based on the predicted energy consumption.

[0080] After predicting the predicted energy consumption of the vehicle in the future journey, the vehicle remaining range determination device determines the remaining range of the vehicle based on the predicted energy consumption. For example, the remaining power of the current vehicle can be obtained through the battery management system (Battery Management System, BMS), and then the remaining power of the current vehicle is divided by the predicted energy consumption to obtain the remaining range of the vehicle. Then, the calculated remaining range of the vehicle can be displayed to the user through the display device.

[0081] In this embodiment, the vehicle speed trajectory curve in the future journey of the vehicle is determined based on the navigation information of the vehicle, and then the energy consumption of the vehicle is predicted based on the vehicle speed trajectory curve to obtain the predicted energy consumption of the vehicle in the future journey, and then the remaining range of the vehicle is determined based on the predicted energy consumption, so that the remaining range is displayed to the user through the display device. The future trajectory speed curve of the vehicle can be calculated by fitting the navigation information to predict the energy consumption value of the vehicle in the future use scenario, and then the remaining range of the vehicle under different use requirements and driving scenarios can be determined. It ensures the accuracy of the remaining range prediction, so that the user can have a more accurate and clear understanding of the endurance of the vehicle, reduces the user's concern about the lack of power of the vehicle, and alleviates the user's range anxiety, which can help the driver to better plan the journey and avoid the embarrassing situation of not being able to reach the destination during the journey, improve the user's satisfaction and reliability, thereby improving the user experience and increasing the market competitiveness of the pure electric vehicle.

[0082] In addition, the prediction accuracy of the remaining range prediction can also improve the practicability and convenience of the electric vehicle and improve the user's driving experience; it can also avoid deep discharge and overcharge of the battery, which helps to prolong the service life of the battery and reduce the maintenance cost of the battery.

[0083] In an embodiment, as shown in FIG. 1, the step S10, i.e. determining the vehicle speed trajectory curve of the vehicle within the future journey based on the navigation information of the vehicle, specifically comprises the following steps: Figure 3

[0084] S11: determining the driving style of the vehicle at present.

[0085] After the vehicle is started, the vehicle remaining mileage determination device needs to determine the driving style of the current driver of the vehicle in the process of performing the remaining mileage determination task. For example, the vehicle remaining mileage determination device can control the display device of the vehicle to show the user (driver) with multiple driving style options, so that the user can select the appropriate driving style according to his own driving habits; the vehicle remaining mileage determination device takes the driving style selected by the user as the driving style of the vehicle at present, which is simple and convenient, and has high accuracy.

[0086] S12: fitting the vehicle speed trajectory curve of the vehicle within the future journey based on the navigation information of the vehicle and the driving style.

[0087] It should be understood that, due to the differences in driving habits, driving skills and driving attitudes of different drivers, different drivers have different driving styles, such as safe driving style, aggressive driving style, slow driving style and experienced driving style. Drivers with different driving styles have different control over the vehicle speed, and the driving style deeply affects the change of the vehicle speed trajectory. Therefore, after determining the driving style of the vehicle at present, the vehicle remaining mileage determination device fits the vehicle speed trajectory curve of the vehicle within the future journey based on the navigation information of the vehicle and the driving style.

[0088] For example, the navigation information of the vehicle includes the route information of the future journey, such as the starting point and the ending point of the future journey. The vehicle remaining mileage determination device determines the driving route of the vehicle according to the route information of the future journey, then performs vehicle speed trajectory prediction based on the current vehicle speed to obtain the vehicle speed trajectory curve, then determines the correction coefficient corresponding to the current driving style, fits the correction coefficient with the vehicle speed trajectory curve, and obtains the vehicle speed trajectory curve of the vehicle within the future journey under the current driving style. The correction coefficient is a pre-calibrated parameter, and the influence of different driving styles on the vehicle speed trajectory is different because the correction coefficients corresponding to different driving styles are different.

[0089] ​In this embodiment, the step of determining the vehicle speed trajectory curve in the future journey based on the navigation information of the vehicle is refined by determining the current driving style of the vehicle, and then fitting the vehicle speed trajectory curve in the future journey based on the navigation information of the vehicle and the driving style. When the vehicle speed trajectory is predicted, the vehicle speed trajectory is corrected based on the driving style of the current driver of the vehicle, which further improves the accuracy of the vehicle speed trajectory curve, thereby improving the accuracy of the subsequent vehicle energy consumption prediction, and further improving the accuracy of the remaining range prediction.

[0090] In an embodiment, the step S11 of determining the current driving style of the vehicle specifically includes the following steps:

[0091] S111: Obtain the historical driving data of the vehicle, and the historical driving data includes speed information and driver operation data at different time points.

[0092] When determining the driving style of the current driver of the vehicle, the vehicle remaining range determination device can also obtain the historical driving data of the vehicle. The historical driving data of the vehicle includes speed information of the vehicle at different time points and driver operation data at different time points. The speed information includes the speed and acceleration of the vehicle, and the driver operation data includes the opening degree of the accelerator pedal, the depth of the brake pedal, etc.

[0093] S112: Input the speed information and the driver operation data at different time points into the pre-trained driving style recognition model to classify the driving style, and obtain the current driving style of the vehicle.

[0094] Then, the vehicle remaining range determination device needs to obtain the pre-trained driving style recognition model, and input the speed information and the driver operation data at different time points into the pre-trained driving style recognition model to classify the driving style, and obtain the current driving style of the vehicle.

[0095] The driving style recognition model is a classification model trained by deep learning based on the historical driving data of the same vehicle model. The driving style recognition model is trained by taking the historical driving data of the same vehicle model as the training sample, which further improves the accuracy of the driving style recognition. In other embodiments, the driving style recognition model can also be a classification model trained based on the driving data of different vehicle models on the market.

[0096] In this embodiment, the vehicle remaining mileage determination device obtains historical driving data of the vehicle, the historical driving data including speed information and driver operation data at different time points, and then inputs the speed information and the driver operation data at different time points into a driving style recognition model trained in advance to classify the driving style and obtain the current driving style of the vehicle. The driving style is recognized based on the historical driving data of the vehicle. On the one hand, the driving style can be more accurately determined by using big data analysis and recognition technology. On the other hand, the driving style confirmation with the user is not required, the risk of deviation of the vehicle speed trajectory curve caused by inaccurate judgment of the user style is reduced, the interaction cost with the user is reduced, the intelligence of the vehicle is improved, and the user experience is improved.

[0097] In an embodiment, in step S12, the vehicle speed trajectory curve of the vehicle in the future journey is fitted based on the navigation information of the vehicle and the driving style, specifically including the following steps:

[0098] S121: Based on the navigation information of the vehicle, the route information and related traffic information of the vehicle in the future journey are determined.

[0099] After determining the current driving style of the vehicle, the vehicle remaining mileage determination device determines the route information and related traffic information of the vehicle in the future journey based on the navigation information of the vehicle. The route information includes the starting point and the ending point of the planned route (i.e. the future journey), and the road conditions such as turns and slopes. The related traffic information includes speed limit, traffic conditions (traffic lights, traffic restrictions, congestion conditions) and other information.

[0100] S122: Based on the driving style, the route information and the related traffic information, the speed of the vehicle at different time points is predicted to obtain the vehicle speed trajectory curve in the future journey.

[0101] After determining the route information and related traffic information of the vehicle in the future journey, the vehicle remaining mileage determination device predicts the speed of the vehicle at different time points based on the driving style, the route information and the related traffic information to obtain the vehicle speed trajectory curve in the future journey.

[0102] For example, the navigation information of the vehicle includes the route information of the future journey (such as the starting point and the ending point of the planned route, and the road conditions such as turns and slopes), and the related traffic information such as speed limit and traffic conditions. The vehicle remaining mileage determination device performs speed trajectory prediction to obtain the speed trajectory curve according to the route information of the future journey and the related traffic information such as speed limit and traffic conditions, and then determines the correction coefficient corresponding to the current driving style, fits the correction coefficient with the speed trajectory curve to obtain the vehicle speed trajectory curve in the future journey under the current driving style. The correction coefficient is a pre-calibrated parameter.

[0103] In an embodiment, to improve the accuracy of the correction coefficient, real vehicle tests of different driving styles are needed for the same vehicle model, and after obtaining the real vehicle test data, big data analysis is performed to obtain correction coefficients corresponding to different driving styles.

[0104] In this embodiment, the vehicle remaining mileage determination device determines the route information and related traffic information of the vehicle in the future journey based on the navigation information of the vehicle, and predicts the vehicle speed at different time points based on the driving style, route information and related traffic information, to obtain the vehicle speed trajectory curve in the future journey, further refining the step of fitting the vehicle speed trajectory curve in the future journey. When performing speed trajectory prediction, the influence of driving style, route information and related traffic information on speed is considered, further improving the accuracy of the speed trajectory curve.

[0105] In an embodiment, in step S122, the speed information of the vehicle at different time points is predicted based on the driving style, route information and related traffic information to obtain the vehicle speed trajectory curve in the future journey, which includes the following steps:

[0106] S1221: Obtain the pre-trained vehicle speed trajectory prediction model.

[0107] After determining the route information and related traffic information of the vehicle in the future journey based on the navigation information of the vehicle, the vehicle remaining mileage determination device needs to first obtain the pre-trained vehicle speed trajectory prediction model.

[0108] The vehicle speed trajectory prediction model is a neural network model pre-trained based on historical driving big data of multiple vehicles of the same model; the training samples of the neural network model are the driving style, route information and related traffic information in the historical driving big data of the same vehicle model. Using the historical driving big data of the same vehicle model for model training can improve the accuracy of the vehicle speed trajectory prediction model, and further improve the accuracy of the predicted vehicle speed trajectory curve. In other embodiments, the vehicle speed trajectory prediction model can also be a neural network model trained based on driving data of different vehicle models on the market.

[0109] S1222: Input the driving style, route information and related traffic information into the vehicle speed trajectory prediction model for vehicle speed trajectory prediction to obtain the vehicle speed trajectory curve in the future journey.

[0110] Then, the vehicle remaining mileage determination device will input the driving style, route information and related traffic information into the vehicle speed trajectory prediction model for vehicle speed trajectory prediction to obtain the vehicle speed trajectory curve in the future journey.

[0111] In this embodiment, the vehicle speed trajectory prediction model pre-trained is obtained, and then the driving style, route information and related traffic information are input into the vehicle speed trajectory prediction model to perform vehicle speed trajectory prediction, so as to obtain the vehicle speed trajectory curve of the vehicle in the future journey, and the obtaining method of the vehicle speed trajectory curve is determined. Based on the driving style, route information and related traffic information, the neural model of deep learning is used to perform vehicle speed trajectory prediction, so as to ensure the accuracy of the vehicle speed trajectory curve, reduce the data fitting time, and improve the output speed of the vehicle speed trajectory curve.

[0112] In an embodiment, as shown in FIG. 2, in step S20, the energy consumption of the vehicle is predicted based on the vehicle speed trajectory curve, and the predicted energy consumption of the vehicle in the future journey is obtained, which includes the following steps: Figure 4

[0113] S21: Determine the current thermal management system power consumption of the vehicle.

[0114] It should be understood that the energy consumption of the vehicle is not only affected by the vehicle speed, but also affected by the working of the thermal management system (i.e. the battery end power consumption of the electric vehicle). After determining the vehicle speed trajectory curve of the vehicle in the future journey, the vehicle remaining range determination device needs to determine the current thermal management system power consumption of the vehicle. Among them, the energy consumption of the air conditioner in the thermal management system is the largest, so the thermal management system power consumption in this embodiment can only include the air conditioner power consumption, which directly replaces the thermal management system power consumption, and the calculation amount is small. In other embodiments, in order to ensure the accuracy of the thermal management system power consumption, the thermal management system power consumption also includes the power consumption of other devices in the thermal management system, such as battery thermal management power consumption, motor electrical control system thermal management power consumption, etc.

[0115] Specifically, the determination method of the thermal management system power consumption can be that the vehicle remaining range determination device determines the current thermal management system power consumption of the vehicle based on the current environmental temperature of the vehicle and the vehicle load. The current environmental temperature of the vehicle and the vehicle load (including the cold load and hot load of the thermal management system such as the passenger compartment, the battery and the motor) are used as the traditional thermal management model to calculate the power consumption, so as to obtain the thermal management system power consumption. This method directly uses the original configuration of the vehicle to estimate, which is more simple and convenient.

[0116] ​In an embodiment, the manner of determining the thermal management system power consumption can also be that the vehicle remaining mileage determining device predicts the current thermal management system power consumption of the vehicle based on historical driving data of the vehicle. Specifically, the vehicle remaining mileage determining device acquires a pre-trained power consumption prediction model, which is a neural network model trained based on historical driving data of vehicles of the same model, to improve the accuracy of the power consumption prediction model. Then, the vehicle remaining mileage determining device determines input parameter data in a recent time period from the historical driving data of the vehicle, the input parameter data including recorded time (including date, day of the week, and specific time point), states of devices in the thermal management system (such as air conditioner switch state, etc.), temperature (such as system set temperature, ambient temperature), air volume, and related system power consumption (including vehicle heater power consumption and electronic stability system power consumption). Finally, the vehicle remaining mileage determining device inputs the recorded time, states of devices in the thermal management system, temperature, air volume, and related system power consumption determined from the historical driving data into the power consumption prediction model for power consumption prediction to obtain the thermal management system power consumption. The thermal management system power consumption is predicted based on the historical driving data of the vehicle, which is more accurate.

[0117] S22: predicting the predicted energy consumption of the vehicle in the future journey based on the vehicle speed trajectory curve and the thermal management system power consumption.

[0118] After determining the thermal management system power consumption of the vehicle, the vehicle remaining mileage determining device predicts the predicted energy consumption of the vehicle in the future journey based on the vehicle speed trajectory curve and the thermal management system power consumption.

[0119] For example, the vehicle remaining mileage determining device can calculate the current motor demand torque based on the vehicle speed trajectory curve, and then determine the current motor demand torque based on the motor demand torque, and add the current motor demand torque and the current thermal management system power consumption to obtain the battery end power demand of the whole vehicle, that is, the predicted energy consumption of the vehicle in the future journey.

[0120] In this embodiment, the vehicle remaining mileage determining device refines the manner of obtaining the predicted energy consumption of the vehicle in the future journey by determining the current thermal management system power consumption of the vehicle and predicting the predicted energy consumption of the vehicle in the future journey based on the vehicle speed trajectory curve and the thermal management system power consumption. When determining the predicted energy consumption of the vehicle in the future journey, the influence of the thermal management system on the energy consumption of the vehicle is considered, the accuracy of the predicted energy consumption is improved, and a more accurate data basis is provided for subsequent calculation of the remaining mileage.

[0121] In an embodiment, in step S22, that is, predicting the predicted energy consumption of the vehicle in the future journey based on the vehicle speed trajectory curve and the thermal management system power consumption, the following steps are specifically included:

[0122] S221: Determine the battery-end consumed power of the vehicle in the future trip based on the vehicle speed trajectory curve and the thermal management system power consumption.

[0123] After determining the vehicle speed trajectory curve of the vehicle in the future trip, the vehicle remaining range determining device needs to determine the battery-end consumed power of the vehicle in the future trip based on the vehicle speed trajectory curve and the thermal management system power consumption. For example, the vehicle remaining range determining device can calculate the current motor demand torque based on the vehicle speed trajectory curve, and then determine the current motor demand torque based on the motor demand torque. The current motor demand torque is added to the current thermal management system power consumption to obtain the battery-end power demand of the vehicle, and then the battery-end power demand is time integrated (the time can be a preset time period or a unit time), that is, the battery-end consumed power of the vehicle in the future trip is obtained.

[0124] In other embodiments, the vehicle remaining range determining device pre-trains an electric quantity prediction model based on vehicle big data, and then inputs the vehicle speed trajectory curve and the thermal management system power consumption to call the electric quantity prediction model to predict the battery consumption, thereby obtaining the battery-end consumed power of the vehicle in the future trip.

[0125] S222: Determine the theoretical driving range of the vehicle in the future trip.

[0126] At the same time, the vehicle remaining range determining device also needs to determine the theoretical driving range of the vehicle in the future trip. The theoretical driving range is the driving range of the vehicle after driving at the current actual vehicle speed for a period of time. For example, the vehicle remaining range determining device can first determine the current actual vehicle speed of the vehicle, and then time integrate (the integration time needs to be consistent with the integration time of the battery-end power demand) the actual vehicle speed to obtain the theoretical driving range.

[0127] In other embodiments, the vehicle remaining range determining device can also determine the theoretical driving range by other means. For example, in the future trip, the vehicle remaining range determining device can obtain the current actual wheel-end torque of the vehicle, and input the current actual wheel-end torque of the vehicle to calculate the vehicle speed through the vehicle longitudinal dynamics model to obtain the range speed corresponding to the current actual wheel-end torque of the vehicle, which is denoted as the actual vehicle speed. Then, the actual vehicle speed is time integrated to obtain the theoretical driving range of the vehicle. Through the current actual wheel-end torque, the vehicle dynamics can be combined to predict a more accurate actual vehicle speed, and then the actual vehicle speed is integrated to obtain a more accurate theoretical driving range.

[0128] S223: Divide the battery-end consumed power by the theoretical driving range to obtain the predicted energy consumption of the vehicle in the future trip.

[0129] After obtaining the battery-end consumption power and the theoretical driving range of the vehicle in the future trip, the vehicle remaining range determining device directly divides the battery-end consumption power by the theoretical driving range to obtain the predicted energy consumption of the vehicle in the future trip. The vehicle remaining range determining device determines the actual driving range (theoretical driving range) of the vehicle in the process by the current actual vehicle speed (or actual wheel-end torque), and can correct the vehicle energy consumption estimated according to the vehicle speed trajectory curve and the thermal management system power consumption based on the actual driving range to obtain a more accurate predicted energy consumption.

[0130] In the embodiment, the battery-end consumption power of the vehicle in the future trip is determined based on the vehicle speed trajectory curve and the thermal management system power consumption, the theoretical driving range of the vehicle in the future trip is determined, and then the battery-end consumption power is divided by the theoretical driving range to obtain the predicted energy consumption of the vehicle in the future trip, which clearly shows the implementation process of predicting the predicted energy consumption of the vehicle in the future trip based on the vehicle speed trajectory curve and the thermal management system power consumption. By the vehicle speed trajectory curve and the thermal management system power consumption, the battery-end consumption power can be predicted more accurately, and then the energy consumption can be corrected based on the actual driving range to calculate a more accurate predicted energy consumption.

[0131] In an embodiment, in step S221, the battery-end consumption power of the vehicle in the future trip is determined based on the vehicle speed trajectory curve and the thermal management system power consumption, which specifically includes the following steps:

[0132] S2211: Determine the battery-end power demand of the vehicle in the future trip based on the vehicle speed trajectory curve and the thermal management system power consumption.

[0133] The vehicle remaining range determining device needs to determine the battery-end power demand of the vehicle in the future trip based on the vehicle speed trajectory curve. The battery-end power demand includes the electric power demand of the motor driving the vehicle to run and the thermal management system power consumption for maintaining the operation of the thermal management system.

[0134] For example, the vehicle remaining range determining device determines the wheel-end demand torque in the future trip based on the vehicle speed trajectory curve in the future trip of the vehicle, and then converts the wheel-end demand torque to the motor demand torque to obtain the motor demand torque of the vehicle. Then, the vehicle remaining range determining device calculates the target electric power demand according to the motor demand torque. Finally, the vehicle remaining range determining device adds the thermal management system power consumption and the target electric power demand to obtain the battery-end power demand of the vehicle in the future trip. With the vehicle speed trajectory curve as the input, a series of torque conversion and efficiency demand calculation are performed, and then the thermal management system power consumption is added, so that the accurate battery-end power demand can be obtained, which provides an accurate basis for subsequent predicted energy consumption and remaining range calculation.

[0135] S2212: Time-integrate the battery terminal power demand to obtain the battery terminal consumption power in the future trip.

[0136] After the battery terminal power demand is calculated, the vehicle remaining mileage determination device time-integrates the battery terminal power demand based on the time length required for future formation to obtain the battery terminal consumption power in the future trip.

[0137] In this embodiment, the vehicle remaining mileage determination device determines the battery terminal power demand of the vehicle in the future trip based on the vehicle speed trajectory curve and the thermal management system power consumption, and then time-integrates the battery terminal power demand to obtain the battery terminal consumption power in the future trip, which refines the process of battery terminal consumption power. The vehicle speed trajectory curve and the thermal management system power consumption can be used to estimate a more accurate battery terminal power demand, and the battery terminal consumption power can be obtained by directly integrating it, which is accurate and intuitive.

[0138] In an embodiment, in step S2211, the battery terminal power demand of the vehicle in the future trip is determined based on the vehicle speed trajectory curve and the thermal management system power consumption, specifically including the following steps:

[0139] In an embodiment, as shown in Figure 5 In step S30, the remaining mileage of the vehicle is determined based on the predicted energy consumption, specifically including the following steps:

[0140] S31: Real-time determination of the actual energy consumption of the vehicle during vehicle travel.

[0141] After determining the predicted energy consumption of the vehicle in the future trip, the vehicle remaining mileage determination device needs to obtain real-time running information of the vehicle during the actual travel of the vehicle in the future trip, and then determine the actual energy consumption (i.e. the cumulative energy consumption of the vehicle from the start of the future trip to the current time) of the vehicle according to the real-time running information.

[0142] S32: Correct the predicted energy consumption based on the actual energy consumption to obtain a target energy consumption value.

[0143] After determining the actual energy consumption of the vehicle, the vehicle remaining mileage determination device needs to correct the predicted energy consumption based on the actual energy consumption to obtain a target energy consumption value, in order to balance the accuracy and mutation frequency of the calculated vehicle energy consumption.

[0144] Specifically, a preset energy consumption threshold calibrated in advance according to real vehicle test data can be acquired, and then a difference between the predicted energy consumption and the current actual energy consumption of the vehicle is determined. If the difference is less than or equal to the preset energy consumption threshold, it indicates that the energy consumption error is small, and the predicted energy consumption is directly taken as the target energy consumption value. If the difference is greater than the preset energy consumption threshold, it indicates that the energy consumption error is too large, and the predicted energy consumption of the vehicle needs to be determined again based on the current position according to the foregoing content, and the re-determined predicted energy consumption is taken as the target energy consumption value, thereby improving the accuracy of the energy consumption calculation of the vehicle.

[0145] S33: determining the remaining mileage of the vehicle based on the target energy consumption value and the remaining power of the vehicle.

[0146] After the target energy consumption value is obtained by correcting the predicted energy consumption based on the actual energy consumption, the vehicle remaining mileage determination device determines the remaining mileage of the vehicle based on the target energy consumption value and the remaining power of the vehicle. That is, the current remaining power of the vehicle is acquired through the battery management system, and then the remaining power is divided by the target energy consumption value, and the remaining mileage of the vehicle is obtained.

[0147] In this embodiment, the actual energy consumption of the vehicle is determined in real time during the travel of the vehicle, and then the predicted energy consumption is corrected based on the actual energy consumption to obtain the target energy consumption value of the vehicle, and then the remaining mileage of the vehicle is determined based on the target energy consumption value and the remaining power of the vehicle, thereby clearly defining the specific steps of determining the remaining mileage of the vehicle based on the predicted energy consumption. By correcting the predicted energy consumption based on the actual energy consumption, the accuracy of the energy consumption calculation can be further improved, and the accuracy of the remaining mileage can be further improved.

[0148] In an embodiment, the method further specifically includes the following steps:

[0149] S01: if it is determined that the vehicle does not have navigation information, after it is determined that the historical driving data of the vehicle is saved, the travel and speed trajectory prediction is performed based on the historical driving data to obtain the speed trajectory curve of the vehicle in the future travel.

[0150] After the vehicle is started, before the speed trajectory curve of the vehicle in the future travel is determined, the vehicle remaining mileage determination device needs to first determine whether the vehicle has navigation information, so as to perform different speed trajectory curve determination strategies according to the determination result.

[0151] If it is determined that the vehicle has navigation information, the vehicle remaining mileage determination device determines the speed trajectory curve of the vehicle in the future travel based on the navigation information of the vehicle, so as to subsequently perform energy consumption prediction based on the speed trajectory curve of the vehicle in the future travel, determine the predicted energy consumption of the vehicle in the future travel, and further determine the remaining mileage of the vehicle.

[0152] If it is determined that the vehicle does not have navigation information, the vehicle remaining mileage determining device determines whether historical driving data of the vehicle is saved, and after determining that the historical driving data of the vehicle is saved, the vehicle remaining mileage determining device performs a trip and vehicle speed trajectory prediction based on the historical driving data to obtain a vehicle speed trajectory curve of the vehicle in a future trip.

[0153] Specifically, the vehicle remaining mileage determining device first determines a current location of the vehicle, a current time (which can include a date, a day of the week, and a time of day), and then performs a regression analysis on the historical driving data to determine a possible trip that can be planned at the current time and the current location. Then, the vehicle remaining mileage determining device determines navigation information of the possible trip (i.e., a future trip), and then fits and corrects a vehicle speed trajectory curve of the future trip according to the navigation information of the future trip and an actual vehicle speed trajectory curve of the trip in the historical driving data. The actual vehicle speed trajectory curve of the trip in the historical driving data can correct the vehicle speed trajectory curve predicted from the navigation information, further improving the accuracy of the vehicle speed trajectory curve. Subsequently, the vehicle remaining mileage determining device performs energy consumption prediction based on the predicted and more accurate vehicle speed trajectory curve, and then determines the vehicle remaining mileage based on the predicted energy consumption.

[0154] For example, the current location of the vehicle is a company, and the current time is 7:00 pm on Friday. After the vehicle remaining mileage determining device performs a regression analysis on the historical driving data, it determines that the possible trip that can be planned at the current time and the current location is a home trip from the company to the home, and automatically generates navigation information between the company and the home addresses. Then, the vehicle remaining mileage determining device predicts a vehicle speed trajectory curve based on the navigation information, and the specific prediction steps are as described above and will not be repeated here. Then, the vehicle remaining mileage determining device further determines vehicle speed trajectory curves of all home trips at around 7:00 pm on Friday in the historical driving data, denoted as historical vehicle speed trajectory curves, and then corrects the predicted vehicle speed trajectory curve in terms of time and vehicle speed based on the historical vehicle speed trajectory curves, thereby obtaining a corrected vehicle speed trajectory curve for subsequent energy consumption prediction and remaining mileage calculation.

[0155] In other embodiments, after performing a regression analysis on the historical driving data to determine a possible trip that can be planned at the current time and the current location, the vehicle speed trajectory curve can also be directly determined according to the navigation information, and the specific steps are as described above and will not be repeated here.

[0156] In this embodiment, if it is determined that the vehicle does not have navigation information, after it is determined that the historical driving data of the vehicle is not saved, the trip and speed trajectory prediction is performed based on the historical driving data to obtain the speed trajectory curve of the vehicle in the future trip. In this process, different ways of determining the speed trajectory curve are determined. When the vehicle does not have navigation information, the speed trajectory prediction is performed based on the historical driving data, so that a more accurate speed trajectory curve based on big data analysis is obtained, which provides a more accurate data basis for subsequent determination of the remaining mileage. In addition, when the user does not perform navigation, the historical driving data of the vehicle is automatically used to plan the trip for the user, which can increase the intelligence of the vehicle and improve the user experience.

[0157] In an embodiment, after step S04, i.e., after it is determined that the vehicle does not have navigation information, the method further comprises the following steps:

[0158] S40: If it is determined that the historical driving data of the vehicle is not saved, the target vehicle model, the location, the specific time and the environmental information of the vehicle are determined.

[0159] After it is determined that the vehicle does not have navigation information, if the vehicle remaining mileage determination device determines that the historical driving data of the vehicle is not saved (neither on the vehicle nor on the cloud server), the vehicle remaining mileage determination device determines the target vehicle model, the location, the specific time (which can include the date, the day of the week and the time) and the environmental information of the vehicle. The environmental information can include the current environmental temperature, the weather condition, etc.

[0160] S50: The target vehicle model, the location, the specific time and the environmental information are input into the pre-trained regression model to perform trip and energy consumption prediction, to obtain the predicted energy consumption of the vehicle in the future trip, and to determine the remaining mileage of the vehicle based on the predicted energy consumption.

[0161] Then, the vehicle remaining mileage determination device obtains a pre-trained regression model, which is a data recall model trained based on the historical driving data of other vehicles of the same vehicle model as the vehicle. Through the regression model, the vehicle trip and the corresponding vehicle energy consumption that match the model input can be recalled from the historical driving data of other vehicles of the same vehicle model as the vehicle.

[0162] After obtaining the pre-trained regression model, the vehicle remaining mileage determining device inputs the target vehicle model, the current location, the specific time and the environmental information into the pre-trained regression model to perform trip and energy consumption prediction, and obtains at least one historical trip matched with the target vehicle model, the current location, the specific time and the environmental information, and the actual energy consumption corresponding to the historical trip. Then, the vehicle remaining mileage determining device records the actual energy consumption corresponding to the historical trip as the predicted energy consumption of the vehicle in the future trip, and obtains at least one predicted energy consumption in the future trip. Finally, the vehicle remaining mileage determining device determines the remaining mileage of the vehicle according to the at least one predicted energy consumption in the future trip.

[0163] After obtaining the at least one predicted energy consumption in the future trip, if there is only one predicted energy consumption, i.e., there is only one future trip (historical trip), the vehicle remaining mileage determining device determines the remaining mileage of the vehicle in the future trip based on the only predicted energy consumption and the current remaining power of the vehicle. If there are multiple predicted energy consumptions, i.e., there are multiple future trips (historical trips), the vehicle remaining mileage determining device can determine the predicted energy consumption with the maximum energy consumption value, and determine the remaining mileage of the vehicle in the corresponding future trip based on the predicted energy consumption with the maximum energy consumption value and the current remaining power of the vehicle.

[0164] In other embodiments, if there are multiple predicted energy consumptions, the vehicle remaining mileage determining device can further feed back all the future trips (historical trips) to the user for the user to select a suitable trip, and determine the remaining mileage of the vehicle in the future trip selected by the user based on the predicted energy consumption corresponding to the future trip selected by the user and the current remaining power of the vehicle, thereby further improving the user experience.

[0165] In this embodiment, after determining that the vehicle does not have navigation information, if it is determined that the vehicle does not save historical driving data, the target vehicle model, the current location, the specific time and the environmental information of the vehicle are determined, the target vehicle model, the current location, the specific time and the environmental information are input into the pre-trained regression model to perform trip and energy consumption prediction, the predicted energy consumption of the vehicle in the future trip is obtained, and the remaining mileage of the vehicle is determined based on the predicted energy consumption. Through this process, the remaining mileage determination method under the condition that there is neither navigation information nor historical driving data of the vehicle is further clarified, and the remaining mileage prediction can be performed in different use scenarios. At the same time, based on the current location, time and environmental information of the vehicle, the power consumption of the future trip of the vehicle can be obtained more accurately by taking the big data of the same vehicle model as a reference, and the remaining mileage of the vehicle can be calculated more accurately, which can help the driver to better plan the trip and avoid the embarrassing situation that the destination cannot be reached during the trip. This will improve the practicability and convenience of the vehicle and improve the user experience of using the vehicle.

[0166] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0167] In an embodiment, a vehicle remaining mileage determination apparatus is provided, which corresponds to the vehicle remaining mileage determination method in the above embodiments. As shown in the figure, the vehicle remaining mileage determination apparatus comprises a first determination module 601, a prediction module 602 and a second determination module 603. The functions of each module are described in detail as follows: Figure 6

[0168] The first determination module 601 is configured to determine a vehicle speed trajectory curve of the vehicle in a future journey based on navigation information of the vehicle when it is determined that the vehicle has the navigation information.

[0169] The prediction module 602 is configured to predict energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain predicted energy consumption of the vehicle in the future journey.

[0170] The second determination module 603 is configured to determine the remaining mileage of the vehicle based on the predicted energy consumption.

[0171] Optionally, the second determination module 603 is specifically configured to:

[0172] determine actual energy consumption of the vehicle in real time during the journey of the vehicle;

[0173] correct the predicted energy consumption based on the actual energy consumption to obtain a target energy consumption value;

[0174] determine the remaining mileage of the vehicle based on the target energy consumption value and the remaining power of the vehicle.

[0175] Optionally, the prediction module 602 is specifically configured to:

[0176] determine current power consumption of a thermal management system of the vehicle;

[0177] predict the predicted energy consumption of the vehicle in the future journey based on the vehicle speed trajectory curve and the power consumption of the thermal management system.

[0178] Optionally, the prediction module 602 is specifically further configured to:

[0179] determine battery end consumption power of the vehicle in the future journey based on the vehicle speed trajectory curve and the power consumption of the thermal management system;

[0180] determine theoretical driving mileage of the vehicle in the future journey;

[0181] divide the battery end consumption power by the theoretical driving mileage to obtain the predicted energy consumption of the vehicle in the future journey.

[0182] ​Optionally, the prediction module 602 is specifically further configured to:

[0183] determine the battery end power demand of the vehicle in the future trip based on the vehicle speed trajectory curve and the thermal management system power consumption;

[0184] time-integrate the battery end power demand to obtain the battery end power consumption in the future trip.

[0185] Optionally, the prediction module 602 is specifically further configured to:

[0186] determine the wheel end demand torque of the vehicle in the future trip based on the vehicle speed trajectory curve;

[0187] convert the wheel end demand torque to obtain the motor demand torque of the vehicle in the future trip;

[0188] determine the motor efficiency loss based on the motor demand torque, and determine the target electric power demand based on the motor efficiency loss;

[0189] sum the thermal management system power consumption, the target electric power demand and the low-voltage load power consumption of the vehicle to obtain the battery end power demand of the vehicle in the future trip.

[0190] Optionally, the prediction module 602 is specifically further configured to determine the thermal management system power consumption in the following manner:

[0191] predict the current thermal management system power consumption of the vehicle based on the historical driving data of the vehicle;

[0192] or,

[0193] determine the current thermal management system power consumption of the vehicle based on the current ambient temperature of the vehicle and the vehicle load.

[0194] Optionally, the first determination module 601 is specifically configured to:

[0195] determine the current driving style of the vehicle;

[0196] fit the vehicle speed trajectory curve of the vehicle in the future trip based on the navigation information of the vehicle and the driving style.

[0197] Optionally, the first determination module 601 is specifically further configured to:

[0198] determine the route information and the related traffic information of the vehicle in the future trip based on the navigation information of the vehicle;

[0199] predict the vehicle speed of the vehicle at different time points based on the driving style, the route information and the related traffic information, to obtain the vehicle speed trajectory curve of the vehicle in the future trip.

[0200] Optionally, the first determination module 601 is specifically further configured to:

[0201] obtain a pre-trained vehicle speed trajectory prediction model;

[0202] input the driving style, route information and related traffic information into the vehicle speed trajectory prediction model to perform vehicle speed trajectory prediction, and obtain a vehicle speed trajectory curve of the vehicle in the future trip.

[0203] Optionally, the first determination module 601 is specifically configured to:

[0204] obtain historical driving data of the vehicle, the historical driving data including speed information and driver operation data at different time points;

[0205] input the speed information and the driver operation data at different time points into a pre-trained driving style recognition model to perform driving style classification, and obtain a current driving style of the vehicle.

[0206] Optionally, before determining the vehicle speed trajectory curve of the vehicle in the future trip based on the navigation information of the vehicle, the first determination module 601 is further configured to:

[0207] if it is determined that the vehicle does not have the navigation information, after determining that the historical driving data of the vehicle is saved, performing trip and vehicle speed trajectory prediction based on the historical driving data to obtain the vehicle speed trajectory curve of the vehicle in the future trip.

[0208] Optionally, after determining that the vehicle does not have the navigation information, the first determination module 601 is specifically configured to:

[0209] if it is determined that the historical driving data of the vehicle is not saved, determining a target vehicle model, a location, a specific time and environmental information of the vehicle;

[0210] input the target vehicle model, the location, the specific time and the environmental information into a pre-trained regression model to perform trip and energy consumption prediction, obtain predicted energy consumption of the vehicle in the future trip, and determine the remaining range of the vehicle based on the predicted energy consumption.

[0211] The specific limitations of the vehicle remaining range determination apparatus can be referred to the limitations of the vehicle remaining range determination method in the foregoing, which will not be repeated here. Each module in the vehicle remaining range determination apparatus described above can be realized by software, hardware and combinations thereof in whole or in part. Each module described above can be embedded in or independent of the processor in the vehicle remaining range determination apparatus in hardware form, or can be stored in the memory in the vehicle remaining range determination apparatus in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0212] In one embodiment, a vehicle remaining range determination apparatus is provided, which can be a server or an on-board control unit. The vehicle remaining range determination apparatus comprises a processor, a memory and a database connected through a system bus. The processor of the vehicle remaining range determination apparatus is configured to provide computing and control capabilities. The memory of the vehicle remaining range determination apparatus comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store data used by the vehicle remaining range determination method. The computer program, when executed by the processor, implements a vehicle remaining range determination method.

[0213] In one embodiment, as shown in FIG. 1, a vehicle remaining range determination apparatus is provided, which comprises a memory, a processor and a computer program stored in the memory and executable in the processor, and the processor implements the following steps when executing the computer program: Figure 7

[0214] When it is determined that the vehicle has navigation information, a vehicle speed trajectory curve of the vehicle within a future trip is determined based on the navigation information of the vehicle;

[0215] The energy consumption of the vehicle is predicted based on the vehicle speed trajectory curve, to obtain a predicted energy consumption of the vehicle within the future trip;

[0216] The remaining range of the vehicle is determined based on the predicted energy consumption.

[0217] In one embodiment, a readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, implements the following steps:

[0218] When it is determined that the vehicle has navigation information, a vehicle speed trajectory curve of the vehicle within a future trip is determined based on the navigation information of the vehicle;

[0219] The energy consumption of the vehicle is predicted based on the vehicle speed trajectory curve, to obtain a predicted energy consumption of the vehicle within the future trip;

[0220] The remaining range of the vehicle is determined based on the predicted energy consumption.

[0221] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When executed, the computer program can include the processes of the above-mentioned embodiments. Any reference to a memory, a storage, a database or other medium in the embodiments provided by the present application can include a non-volatile and / or volatile memory.​

[0222] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is taken as an example, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0223] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the foregoing embodiments of the present application have been described in detail, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of determining a remaining distance of a vehicle, characterized by, The method comprises: determining a vehicle speed trajectory curve of the vehicle within a future journey based on navigation information of the vehicle when it is determined that the vehicle has the navigation information; predicting energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain predicted energy consumption of the vehicle within the future journey; determining the remaining range of the vehicle based on the predicted energy consumption.

2. The vehicle remaining distance determination method according to claim 1, characterized by, The determining the remaining range of the vehicle based on the predicted energy consumption comprises: determining actual energy consumption of the vehicle in real time during travel of the vehicle; correcting the predicted energy consumption based on the actual energy consumption to obtain a target energy consumption value; determining the remaining range of the vehicle based on the target energy consumption value and the remaining electric quantity of the vehicle.

3. The vehicle remaining distance determination method according to claim 1, characterized by, The predicting energy consumption of the vehicle based on the vehicle speed trajectory curve to obtain predicted energy consumption of the vehicle within the future journey comprises: determining current thermal management system power consumption of the vehicle; predicting the predicted energy consumption of the vehicle within the future journey based on the vehicle speed trajectory curve and the thermal management system power consumption.

4. The vehicle remaining distance determination method according to claim 3, characterized by, The predicting the predicted energy consumption of the vehicle within the future journey based on the vehicle speed trajectory curve and the thermal management system power consumption comprises: determining wheel end demand torque of the vehicle within the future journey based on the vehicle speed trajectory curve; performing torque conversion on the wheel end demand torque to obtain motor demand torque of the vehicle within the future journey, and determining a target electric power demand based on the motor demand torque; determining battery end power demand of the vehicle within the future journey according to the thermal management system power consumption, the target electric power demand and low-voltage load power consumption of the vehicle; predicting the predicted energy consumption of the vehicle within the future journey based on the battery end power demand of the vehicle within the future journey.

5. The vehicle remaining distance determination method according to claim 3, characterized by, The thermal management system power consumption is determined by: predicting the current thermal management system power consumption of the vehicle based on historical driving data of the vehicle; or determining the current thermal management system power consumption of the vehicle based on the current ambient temperature and vehicle load of the vehicle.

6. The vehicle remaining distance determination method according to claim 1, characterized by, The determining the vehicle speed trajectory curve of the vehicle within the future journey based on the navigation information of the vehicle comprises: determining a current driving style of the vehicle; fitting the vehicle speed trajectory curve of the vehicle within the future journey based on the navigation information of the vehicle and the driving style.

7. The vehicle remaining distance determination method according to claim 6, characterized by, The fitting the vehicle speed trajectory curve of the vehicle within the future journey based on the navigation information of the vehicle and the driving style comprises: determining route information and related traffic information of the vehicle within the future journey based on the navigation information of the vehicle; predicting the vehicle speed of the vehicle at different time points based on the driving style, the route information and the related traffic information to obtain the vehicle speed trajectory curve of the vehicle within the future journey.

8. The vehicle remaining distance determination method according to claim 6, characterized by, The determining the current driving style of the vehicle comprises: obtaining historical driving data of the vehicle, the historical driving data comprising speed information and driver operation data at different time points; The speed information and the driver operation data at different time points are input into a pre-trained driving style recognition model to perform driving style classification, so as to obtain the current driving style of the vehicle.

9. The method of claim 1-8, wherein, The method further comprises: If it is determined that the vehicle does not have the navigation information, after determining that the historical driving data of the vehicle is saved, the trip and speed trajectory are predicted based on the historical driving data, so as to obtain the speed trajectory curve of the vehicle in the future trip.

10. The vehicle remaining distance determination method according to claim 9, characterized by, After determining that the vehicle does not have the navigation information, the method further comprises: If it is determined that the historical driving data of the vehicle is not saved, the target vehicle model, the location, the specific time and the environmental information of the vehicle are determined; The target vehicle model, the location, the specific time and the environmental information are input into a pre-trained regression model to perform trip and energy consumption prediction, so as to obtain the predicted energy consumption of the vehicle in the future trip, and determine the remaining mileage of the vehicle based on the predicted energy consumption.

11. A vehicle comprising a vehicle range remaining determination device, characterized by comprising: The vehicle remaining mileage determination device comprises: A first determination module configured to determine the speed trajectory curve of the vehicle in the future trip based on the navigation information of the vehicle when it is determined that the vehicle has the navigation information; A prediction module configured to predict the energy consumption of the vehicle based on the speed trajectory curve, so as to obtain the predicted energy consumption of the vehicle in the future trip; A second determination module configured to determine the remaining mileage of the vehicle based on the predicted energy consumption.

12. A vehicle remaining distance determination apparatus comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the vehicle remaining mileage determination method according to any one of claims 1 to 10.

13. A readable storage medium, the readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the vehicle remaining mileage determination method according to any one of claims 1 to 10.

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