Electric quantity determination method and device and vehicle

Abstract

The embodiment of the invention discloses an electric quantity determination method and device and a vehicle. According to the embodiment of the invention, the vehicle information of the current vehicle and the real-time driving parameters of the current vehicle in the abnormal driving state are obtained, and the vehicle information and the real-time driving parameters are input into the power consumption prediction model, so that the power consumption of the current vehicle after entering the abnormal driving state is determined through the power consumption prediction model. And determining a real-time electric quantity value of the current vehicle in the abnormal driving state according to the initial electric quantity value and the power consumption. Wherein the initial electric quantity value is the electric quantity value when the current vehicle enters the abnormal driving state. Therefore, the power consumption prediction model is used for predicting the power consumption after the current vehicle enters the abnormal driving state, the real-time power value of the current vehicle in the abnormal driving state is determined according to the predicted power consumption, the problem of power distortion of the vehicle in the abnormal driving state can be avoided, and user experience is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and particularly to a method, device and vehicle for determining the power consumption. Background Art

[0002] In order to provide users with power consumption related services, such as low power alarm service, power consumption display service or route planning service, etc., existing electric vehicles usually need to determine their real-time power consumption value during driving.

[0003] Currently, electric vehicles usually determine the real-time power consumption value according to their own battery status. However, when an electric vehicle is in an abnormal driving state (such as uphill or downhill state), the battery status often changes due to interference, and the change in the battery status will cause a deviation between the real-time power consumption value determined by the electric vehicle and the true power consumption value, resulting in a problem of power consumption distortion and affecting the user experience. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, device and vehicle for determining the power consumption to avoid the problem of power consumption distortion when the vehicle is in an abnormal driving state and ensure the user experience.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining the power consumption, the method comprising:

[0006] Obtaining vehicle information of the current vehicle;

[0007] Obtaining real-time driving parameters of the current vehicle in an abnormal driving state;

[0008] Inputting the vehicle information and the real-time driving parameters into a power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model;

[0009] Determining the real-time power consumption value of the current vehicle in the abnormal driving state according to an initial power consumption value and the power consumption, wherein the initial power consumption value is the power consumption value of the current vehicle when entering the abnormal driving state.

[0010] In a second aspect, an embodiment of the present invention provides a device for determining the power consumption, the device comprising:

[0011] A first obtaining unit, configured to obtain vehicle information of the current vehicle;

[0012] A second obtaining unit, configured to obtain real-time driving parameters of the current vehicle in an abnormal driving state;

[0013] The first determination unit is configured to input the vehicle information and the real-time driving parameters into the power consumption prediction model, so as to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model;

[0014] The second determination unit is configured to determine the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption, where the initial power value is the power value of the current vehicle when entering the abnormal driving state.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described in any item of the first aspect is implemented.

[0016] In a fourth aspect, an embodiment of the present invention provides an electronic device, the device includes:

[0017] A memory for storing one or more computer program instructions;

[0018] A processor, and the one or more computer program instructions are executed by the processor to implement the method described in any item of the first aspect.

[0019] In a fifth aspect, an embodiment of the present invention provides a vehicle, the vehicle includes:

[0020] A vehicle body;

[0021] A sensor group arranged on the vehicle body for acquiring driving parameters;

[0022] A control device configured to execute the method described in any item of the first aspect

[0023] The embodiment of the present invention will acquire the vehicle information of the current vehicle and the real-time driving parameters of the current vehicle in the abnormal driving state, and input the vehicle information and the real-time driving parameters into the power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model, and then determine the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption. Wherein, the initial power value is the power value of the current vehicle when entering the abnormal driving state. Thus, by using the power consumption prediction model to predict the power consumption of the current vehicle after entering the abnormal driving state and determining the real-time power value of the current vehicle in the abnormal driving state according to the predicted power consumption, the embodiment of the present invention can avoid the problem of power distortion of the vehicle in the abnormal driving state and ensure the user experience. Description of the Drawings

[0024] Through the following description of the embodiments of the present invention with reference to the accompanying drawings, the above and other objects, features, and advantages of the present invention will become clearer. In the drawings:

[0025] Figure 1 It is a schematic diagram of an application system for the power determination method according to an embodiment of the present invention;

[0026] Figure 2 It is a schematic diagram of the hardware connection of an electric vehicle according to an embodiment of the present invention;

[0027] Figure 3 It is a flowchart of the power determination method according to an embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of the interface displayed by the display device according to an embodiment of the present invention;

[0029] Figure 5 It is a flowchart of the power consumption prediction model training method according to an embodiment of the present invention;

[0030] Figure 6 It is a schematic diagram of the power determination device according to an embodiment of the present invention;

[0031] Figure 7 It is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Embodiments

[0032] The following describes the present application based on embodiments, but the present application is not limited to these embodiments. In the following detailed description of the present application, some specific details are described in detail. Those skilled in the art can fully understand the present application without the description of these details. To avoid obscuring the essence of the present application, well-known methods, processes, flows, elements, and circuits are not described in detail.

[0033] In addition, those of ordinary skill in the art should understand that the accompanying drawings provided here are for illustrative purposes only, and the drawings are not necessarily drawn to scale.

[0034] Unless the context clearly requires otherwise, words such as "including" and "comprising" in the entire application document should be interpreted as having an inclusive meaning rather than an exclusive or exhaustive meaning; that is, it is the meaning of "including but not limited to".

[0035] In the description of the present application, it should be understood that terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0036] In the solutions described in this specification and the embodiments, if personal information processing is involved, it will be processed on the premise of having a legal basis (such as obtaining the consent of the personal information subject, or being necessary for performing a contract, etc.), and will only be processed within the specified or agreed scope. If a user refuses to process personal information other than the necessary information required for basic functions, it will not affect the user's use of the basic functions.

[0037] In the following description, an electric bicycle will be taken as an example for illustration. However, it should be understood that the solutions of the embodiments of the present invention can also be applied to other types of electric vehicles, such as electric cars, electric tricycles, or electric motorcycles, etc. In one implementable manner, the solutions of the embodiments of the present invention can also be applied to other devices that use electricity as the driving energy to achieve the moving function besides electric vehicles, such as electric robots, etc. Further, the solutions of the embodiments of the present invention can be applied to shared vehicles, can also be applied to relevant vehicles held by individual users, and can also be applied to public transportation vehicles, etc.

[0038] Figure 1 It is a schematic diagram of an application system for the power determination method of the embodiments of the present invention. As Figure 1 shown, the application system of the power determination method in the embodiments of the present invention may include an electric vehicle 11 and a server 12.

[0039] Among them, the electric vehicle 11 can be a vehicle that uses the electric energy provided by a battery as the driving energy to drive the wheels to achieve the moving function. In this embodiment, the electric vehicle 11 can determine its real-time power value in an abnormal driving state by interacting with the server 12. Among them, the abnormal driving state in this embodiment may specifically refer to the uphill and downhill states. It should be understood that in some embodiments, the abnormal driving state may also refer to other related driving states in which the electric vehicle has the same power distortion problem during driving, such as the emergency acceleration state or the emergency braking state, etc.

[0040] The server 12 can be a general data processing device for providing computing or application services. Optionally, the server 12 can be a single computer, can also be a cluster composed of multiple computers, or can also be a cloud server that elastically adjusts computing resources through cloud technology. In this embodiment, the server 12 can be the server of the operation manufacturer or operation platform to which the electric vehicle 11 belongs. A pre-trained power consumption prediction model can be deployed in the server 12, and the server 12 can provide real-time power value calculation services for the electric vehicle 11 through the power consumption prediction model.

[0041] Further, in order to avoid occupying excessive computing resources and communication links, the server 12 may provide the real-time power value calculation service for the electric vehicle 11 only when it is confirmed that the electric vehicle 11 is in an abnormal driving state. When it is confirmed that the electric vehicle 11 is not in an abnormal driving state, in this embodiment, the electric vehicle 11 may determine the real-time power value according to its own battery state parameters.

[0042] Specifically, when the electric vehicle 11 detects that it has entered an abnormal driving state, it may send a power determination request to the server 12. When the server 12 receives the power determination request, it may confirm that the electric vehicle 11 has entered an abnormal driving state. At this time, the server 12 may obtain the vehicle information of the electric vehicle 11 and the real-time driving parameters of the electric vehicle 11 in the abnormal driving state, and input the vehicle information and the real-time driving parameters into the power consumption prediction model to determine the power consumption of the electric vehicle 11 after entering the abnormal driving state through the power consumption prediction model, and then determine the real-time power value of the electric vehicle 11 in the abnormal driving state according to the initial power value (i.e., the power value when the electric vehicle 11 enters the abnormal driving state) and the power consumption, and then feedback the real-time power value to the electric vehicle 11. After receiving the real-time power value fed back by the server 12, the electric vehicle 11 may provide power-related services to the user according to the real-time power value, such as low power alarm service, power display service or route planning service, etc.

[0043] Thus, the embodiment of the present invention can predict the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model, and determine the real-time power value of the current vehicle in the abnormal driving state according to the predicted power consumption, so as to avoid the problem of power distortion of the vehicle in the abnormal driving state and ensure the user experience.

[0044] It should be understood that in order to enable the electric vehicle 11 to still determine its real-time power value in the abnormal driving state in the offline state, in an optional implementation manner, this embodiment may also deploy the trained power consumption prediction model in the control device of the electric vehicle 11. In this implementation manner, when the electric vehicle 11 detects that it is in an abnormal driving state, it may use the power consumption prediction model to predict its own power consumption after entering the abnormal driving state, and determine its real-time power value in the abnormal driving state according to the predicted power consumption.

[0045] Optionally, in order to implement the above power determination process, the electric vehicle 11 may include a communication component, a sensor group, and a control device (the communication component, the sensor group, and the control device are not shown in Figure 1(as shown in). The communication component can be used to implement data communication between the electric vehicle 11 and the server 12, so that the electric vehicle 11 can obtain the required data from the server 12 or the electric vehicle 11 can provide relevant data to the server 12.

[0046] The sensor group can be used to collect real-time driving parameters of the electric vehicle in an abnormal driving state. The real-time driving parameters can refer to the parameters that may affect the power consumption value of the electric vehicle in an abnormal driving state. Optionally, in this embodiment, the real-time driving parameters can at least include one or more combinations of the following: driving direction, start and end positions, driving distance, slope amplitude, driving speed. It should be understood that the specific real-time driving parameters can be set by relevant personnel according to actual needs in actual application. For example, the real-time driving parameters can also be set to include the acceleration and load weight of the electric vehicle according to actual needs. The present application does not limit the specific parameters included in the real-time driving parameters. Optionally, in order to obtain the above real-time driving parameters, the sensor group can include a variety of sensors, and each of the sensors can be used to obtain the corresponding real-time driving parameters.

[0047] The control device can be used to control the communication component to send a power determination request to the server 12 when it detects that the electric vehicle 11 enters an abnormal driving state, so that the server 12 can determine whether the current vehicle enters an abnormal driving state according to the power determination request. And, after sending the power determination request, the control device can also be used to control the sensor group to collect the real-time driving parameters of the electric vehicle 11 in the abnormal driving state, and control the communication component to send the collected real-time driving parameters to the server 12 in real time, so that the server 12 can provide a real-time power value calculation service for the electric vehicle 11 in the abnormal driving state according to the real-time driving parameters provided by the electric vehicle 11. Optionally, the control device can specifically be a hardware device for storing and executing corresponding programs, such as a single-chip microcomputer, etc. The control device can be integrated with the original control system of the electric vehicle, or can also be integrated into a separate power determination device.

[0048] It should be understood that when the power consumption prediction model is deployed in the control device of the electric vehicle in this embodiment, the control device can also determine the real-time power value of the electric vehicle 11 in the abnormal driving state by itself according to the real-time driving parameters after collecting the real-time driving parameters.

[0049] Figure 2 is a schematic diagram of the hardware connection of the electric vehicle according to an embodiment of the present invention. As Figure 2 shown, the electric vehicle can include a communication component 21, a sensor group 22, and a control device 23.

[0050] Among them, the communication component 21 is used to implement communication between the electric vehicle and the server. Optionally, the communication component 21 can adopt any one or combination of the following wireless communication networks to implement communication with the server: 5G mobile communication network technology (5th-Generation, 5G) system, Long Term Evolution (LTE) system, Global System for Mobile Communication (GSM), Bluetooth (Bluetooth, BT), Wireless Fidelity (Wi-Fi), Code Division Multiple Access (CDMA) network, Wideband Code Division Multiple Access (WCDMA) network, Long Range (Lora) technology or Zigbee protocol. This application does not limit the specific communication method adopted between the two.

[0051] The sensor group 22 can be used to collect real-time driving parameters of the electric vehicle in an abnormal driving state. Optionally, the real-time driving parameters can at least include one or more of the following combinations: driving direction, start and end positions, driving distance, slope amplitude, driving speed. Further, in order to obtain the above real-time driving parameters, the sensor group 32 can include a steering angle sensor, a position sensor, an acceleration sensor, a vehicle speed sensor, and a slope sensor, etc. Among them, the steering angle sensor can be used to determine the driving direction of the electric vehicle, that is, to determine whether the electric vehicle is in an uphill state or a downhill state. The position sensor can be used to determine the start and end positions of the electric vehicle, that is, to determine the position where the electric vehicle enters the abnormal driving state and the position at the current moment after entering the abnormal driving state. The vehicle speed sensor can be used to determine the driving speed and driving distance of the vehicle. It should be understood that the driving speed can refer to the average speed of the electric vehicle after entering the abnormal driving state, or it can refer to the current speed of the electric vehicle. The driving distance can refer to the distance traveled by the electric vehicle after entering the abnormal driving state. The slope sensor can be used to determine the pitch angle of the electric vehicle. It should be understood that the sensors given above for determining each real-time driving parameter are only for illustration. In actual application, the sensors for determining each real-time driving parameter can be specifically set by relevant personnel according to actual needs. This application does not limit the sensors used for determining each real-time driving parameter.

[0052] The control device 23 may be a hardware device for storing and executing corresponding programs. The control device 23 may be connected to each sensor in the communication component 21 and the sensor group 22 through a CAN (Controller Area Network) bus, a LIN (Local Interconnect Network) bus, a 485 bus, or the like. In this embodiment, the control device 23 may be configured to control each sensor in the sensor group 22 to collect real-time driving parameters of the electric vehicle in an abnormal driving state, and to control the communication component 21 to send a power determination request or the collected real-time driving parameters to the server. Optionally, the control device 23 may at least include a processor and a memory. The processor and the memory may be connected through a bus. The memory is adapted to store instructions or programs executable by the processor. The processor may be an independent microprocessor or a set of one or more microprocessors. Thus, the processor may execute the instructions stored in the memory to execute corresponding method flows to implement data processing and control of other devices.

[0053] It should be understood that Figure 2 the shown hardware structure and the connection relationships between the hardware devices are only for illustration, and the above hardware devices are not necessarily the hardware devices required to execute the power determination method in the embodiments of the present invention. It should be understood that in actual application, the hardware devices carried on the electric vehicle are not limited to Figure 2 the hardware devices shown in the figure. For example, a charging device, a power device, a vehicle display device, a Bluetooth beacon, and a controllable vehicle lock may also be carried on the electric vehicle. The present application does not limit the specific hardware devices carried on the electric vehicle.

[0054] Figure 3 is a flowchart of the power determination method according to an embodiment of the present invention. As Figure 3 shown, the power determination method may specifically include the following steps:

[0055] It should be understood that Figure 3 the execution subject of the shown power determination method may be a corresponding power determination device. Optionally, the power determination device may be the electric vehicle control device in the above embodiment or the server in the above embodiment.

[0056] S100. Obtain vehicle information of the current vehicle.

[0057] Specifically, when it is confirmed that the current vehicle enters an abnormal driving state, the device can obtain the vehicle information of the current vehicle. Among them, the vehicle information may refer to vehicle-related information that affects the power consumption value of the electric vehicle. Optionally, the vehicle information includes at least one or more combinations of the following: vehicle model, vehicle age, and historical driving mileage of the vehicle.

[0058] Optionally, in step S100, when the device is the control device of the current vehicle, the device can obtain the vehicle information of the current vehicle from the pre-stored data when detecting that the current vehicle enters an abnormal driving state. When the device is a server, in this embodiment, when the current vehicle detects that it enters an abnormal driving state, it can send a power determination request to the device. The device can confirm that the current vehicle enters an abnormal driving state and obtain the vehicle information of the current vehicle when receiving the power determination request.

[0059] Further, when the device is a server, the power determination request may include the vehicle identifier of the current vehicle. When obtaining the vehicle information, the device can parse the power determination request to obtain the vehicle identifier of the current vehicle, and obtain the vehicle information of the current vehicle according to the vehicle identifier. Optionally, the vehicle identifier may be a unique identifier for characterizing the current vehicle, such as a license plate number or a production number, etc., and the present application does not limit this.

[0060] Further, when the device is the control device of the current vehicle, in order to enable the device to determine whether the current vehicle enters or exits an abnormal driving state during the driving process of the current vehicle, the device can also control the slope sensor in the sensor group to continuously collect the pitch angle of the current vehicle during the driving process. When it is detected during the driving process that the pitch angle is greater than or equal to a preset angle, the device can confirm that the current vehicle enters an abnormal driving state. When it is detected during the driving process that the pitch angle is less than the preset angle threshold, the device can confirm that the current vehicle exits the abnormal driving state. Among them, the preset angle threshold can be specifically set by relevant personnel according to actual needs during actual application.

[0061] S200. Obtain the real-time driving parameters of the current vehicle in the abnormal driving state.

[0062] Specifically, when it is confirmed that the current vehicle enters an abnormal driving state, the device can also continuously obtain the real-time driving parameters of the current vehicle in the abnormal driving state. Among them, the real-time driving parameters. Optionally, the real-time driving parameters include at least one or more combinations of the following: driving direction, start and end positions, driving distance, slope amplitude, and driving speed.

[0063] Optionally, in step S200, when the device is the control device of the current vehicle, the real-time driving parameters can be collected by the device itself through each sensor in the sensor group. When the device is a server, in this embodiment, the current vehicle can collect its own real-time driving parameters in the abnormal driving state and upload the real-time driving parameters to the device. The device can obtain the real-time driving parameters of the current vehicle in the abnormal driving state by receiving the information uploaded by the vehicle.

[0064] S300. Input the vehicle information and the real-time driving parameters into the power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model.

[0065] Specifically, after obtaining the vehicle information and the real-time driving parameters of the current vehicle, the device can input the vehicle information and the real-time driving parameters into the power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model (that is, the power consumption value of the current vehicle during the process from entering the abnormal driving state to the current moment). Among them, the power consumption prediction model can be a pre-trained model, and the power consumption prediction model can be used to predict the power consumption of the vehicle after entering the abnormal driving state.

[0066] S400. Determine the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption.

[0067] Specifically, after predicting and determining the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model, the device can determine the real-time power value of the current vehicle in the abnormal driving state according to the predicted power consumption and the initial power value.

[0068] Among them, the initial power value can be the power value of the current vehicle when entering the abnormal driving state. Optionally, the initial power value can be determined by the device according to the battery state parameters of the current vehicle when entering the abnormal driving state. Among them, the battery state parameters can specifically refer to the voltage or discharge rate of the battery. Specifically, the voltage or discharge rate of the battery will gradually decrease as the battery power is consumed. Therefore, the device can determine the power value of the current vehicle when entering the abnormal driving state according to the battery voltage or discharge rate of the current vehicle when entering the abnormal driving state.

[0069] Optionally, in step S400, when determining the real-time power value, the device can calculate the difference between the initial power value and the power consumption and determine the difference as the real-time power value of the current vehicle in the abnormal driving state.

[0070] Optionally, after determining the real-time power value, this embodiment can also display the real-time power value to the user. Optionally, when the device is the control device of the current vehicle, the device can directly control the display device to display the real-time power value to the user. When the device is a server, the device can feedback the real-time power value to the current vehicle, and then the current vehicle controls the display device to display the real-time power value.

[0071] Figure 4 It is a schematic diagram of the interface displayed by the display device of the embodiment of the present invention. As Figure 4 shown, in order to enable the user to view the power value of the electric vehicle at any time, this embodiment can display the determined real-time power value in the display interface 41 of the display device. Optionally, the real-time power value can be displayed in text form or in the form of a progress pattern. This application does not limit the specific display method of the real-time power value. It should be understood that the relevant text and progress pattern displayed in the display interface 41 can change dynamically as the determined real-time power value changes.

[0072] Optionally, after determining the real-time power value, in order to enable the user to more intuitively understand the state of the current vehicle, this embodiment can also determine the remaining available driving distance of the current vehicle and display the remaining available driving distance to the user. As Figure 4 shown, this embodiment can display the remaining available driving distance in the display interface 41 of the display device. Further, the display method of the remaining available driving distance is similar to that of the real-time power value, and will not be elaborated here. Among them, the remaining available driving distance can be specifically determined by the device according to relevant parameters such as the real-time power value, average driving speed, and average power consumption of the current vehicle. It should be understood that the specific determination method of the remaining available driving distance can be set by relevant personnel according to actual needs in the actual application process. This application does not limit the specific determination method of the remaining available driving distance.

[0073] Optionally, after detecting that the current vehicle has exited the abnormal driving state, the device can also determine the real power consumption of the current vehicle during the entire abnormal driving process (that is, the process from entering the abnormal driving state to exiting the abnormal driving state), and optimize the power consumption prediction model according to the real power consumption and the predicted power consumption. Among them, the predicted power consumption can be the power value predicted by the power consumption prediction model for the current vehicle during the entire abnormal driving process.

[0074] Further, in order to determine the actual power consumption, the device can obtain the battery state parameters of the current vehicle when it exits the abnormal driving state, determine the actual power value of the current vehicle when it exits the abnormal driving state according to the battery state parameters, and then determine the actual power consumption of the current vehicle during the entire abnormal driving process according to the actual power value and the initial power value (i.e., the power value of the current vehicle when it enters the abnormal driving state).

[0075] Figure 5 The flowchart of the power consumption prediction model training method according to the embodiment of the present invention is as follows. Figure 5 As shown, the power consumption prediction model training method may specifically include the following steps:

[0076] It should be understood that the execution subject of the power consumption prediction model training method may specifically be the server in the above embodiment. Further, the trained power consumption prediction model may be deployed in the server or in the control device of the electric vehicle.

[0077] S510. Obtain the historical driving data of at least one vehicle.

[0078] Specifically, the server may obtain the historical driving data of at least one electric vehicle currently in operation.

[0079] S520. Determine the historical driving parameters and the consumed historical actual power consumption of each vehicle in the historical abnormal driving state according to the historical driving data.

[0080] Specifically, after obtaining the historical driving data, the server may determine the historical driving parameters and the consumed historical actual power consumption of each vehicle in the historical abnormal driving state according to the historical driving data. Among them, the historical driving parameters may be related parameters such as the driving direction, driving distance, slope amplitude, and driving speed during the entire historical abnormal driving process (i.e., the process from entering the historical abnormal driving state to exiting the historical abnormal driving state). The historical actual power consumption may be the actual power consumed by each vehicle during the entire historical abnormal driving process. Optionally, the historical actual power consumption may be determined by the server according to the battery state parameters of the vehicle when it enters the abnormal driving state and the battery state parameters when it exits the abnormal driving state.

[0081] S530. Use the historical driving parameters, the historical actual power consumption, and the vehicle information of each vehicle as training samples to train the model to be trained to obtain the power consumption prediction model.

[0082] Specifically, after obtaining the historical driving parameters and the historical actual power consumption of each vehicle in the historical abnormal driving state, the server may use the historical driving parameters, the historical actual power consumption, and the vehicle information of each vehicle as training samples to train a model to be trained to obtain the power consumption prediction model. Among them, the vehicle information may include vehicle model, vehicle usage years, vehicle historical driving mileage, etc.

[0083] Optionally, the model to be trained may be a linear regression model, a random forest model, a decision tree model, or a neural network model. Among them, the neural network model may include any one of the following neural networks or combinations of neural networks: Deep Convolutional Neural Networks (DCNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), Convolutional Neural Network (CNN), or Residual Network. It should be understood that the specific type of the model to be trained is not limited in this application. Further, when training the model to be trained, the training method adopted by the server may be a fully supervised training method or a semi-supervised training method, and the specific training method of the model to be trained is not limited in this application.

[0084] The embodiment of the present invention will obtain the vehicle information of the current vehicle and the real-time driving parameters of the current vehicle in the abnormal driving state, and input the vehicle information and the real-time driving parameters into the power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model, and then determine the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption. Among them, the initial power value is the power value of the current vehicle when entering the abnormal driving state. Thus, by using the power consumption prediction model to predict the power consumption of the current vehicle after entering the abnormal driving state, and determining the real-time power value of the current vehicle in the abnormal driving state according to the predicted power consumption, the embodiment of the present invention can avoid the problem of power distortion of the vehicle in the abnormal driving state and ensure the user experience.

[0085] Figure 6 It is a schematic diagram of the power determination device according to the embodiment of the present invention. As Figure 6 shown, the power determination device according to the embodiment of the present invention includes a first acquisition unit 61, a second acquisition unit 62, a first determination unit 63, and a second determination unit 64.

[0086] Specifically, the first acquisition unit 61 is used to acquire the vehicle information of the current vehicle;

[0087] The second acquisition unit 62 is configured to acquire the real-time driving parameters of the current vehicle in an abnormal driving state;

[0088] The first determination unit 63 is configured to input the vehicle information and the real-time driving parameters into a power consumption prediction model, so as to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model;

[0089] The second determination unit 64 is configured to determine the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption, where the initial power value is the power value of the current vehicle when entering the abnormal driving state.

[0090] The embodiments of the present invention will acquire the vehicle information of the current vehicle and the real-time driving parameters of the current vehicle in an abnormal driving state, and input the vehicle information and the real-time driving parameters into a power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model, and then determine the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption. Wherein, the initial power value is the power value of the current vehicle when entering the abnormal driving state. Thus, by using the power consumption prediction model to predict the power consumption of the current vehicle after entering the abnormal driving state, and determining the real-time power value of the current vehicle in the abnormal driving state according to the predicted power consumption, the embodiments of the present invention can avoid the problem of power distortion of the vehicle in the abnormal driving state and ensure the user experience.

[0091] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present invention. As Figure 7 shown, Figure 7 The electronic device shown may specifically be the server in the above embodiment, which includes a general computer hardware structure, and at least includes a processor 71 and a memory 72. The processor 71 and the memory 72 are connected through a bus 73. The memory 72 is adapted to store instructions or programs executable by the processor 71. The processor 71 may be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 71 executes the instructions stored in the memory 72, thereby executing the method flow of the embodiment of the present invention as described above to implement the processing of data and the control of other devices. The bus 73 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to a display controller 74, a display device, and an input / output (I / O) device 75. The input / output (I / O) device 75 may be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices well known in the art. Typically, the input / output (I / O) device 75 is connected to the system through an input / output (I / O) controller 76.

[0092] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, an apparatus (device), or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be implemented as a computer program product on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0093] The present application is described with reference to the flowcharts of methods, apparatuses (devices), and computer program products according to the embodiments of the present application. It should be understood that each process in the flowchart can be implemented by computer program instructions.

[0094] These computer program instructions can be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that implements the process Figure 1 specified functions in one or more of these processes.

[0095] These computer program instructions can also be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device for implementing the Figure 1 specified functions in one or more of these processes.

[0096] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, and the computer-readable program is used for a computer to execute the above-mentioned partial or all method embodiments.

[0097] That is, those skilled in the art can understand that all or part of the steps in implementing the above-mentioned method embodiments can be completed by specifying relevant hardware through a program. The program is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0098] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and variations can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for determining electric quantity, characterized in that The method includes: Obtaining vehicle information of the current vehicle; Obtaining real-time driving parameters of the current vehicle in an abnormal driving state; Inputting the vehicle information and the real-time driving parameters into a power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model; Determining the real-time power value of the current vehicle in the abnormal driving state according to an initial power value and the power consumption, where the initial power value is the power value of the current vehicle when entering the abnormal driving state.

2. The method according to claim 1, wherein After detecting that the current vehicle exits the abnormal driving state, the method further includes: Determining the actual power consumption of the current vehicle during the entire abnormal driving process; Optimizing the power consumption prediction model according to the actual power consumption and the predicted power consumption; Wherein, the predicted power consumption is the power value consumed by the current vehicle during the entire abnormal driving process predicted and determined by the power consumption prediction model.

3. The method according to claim 2, wherein Determining the actual power consumption of the current vehicle during the entire abnormal driving process includes: Obtaining battery state parameters of the current vehicle when exiting the abnormal driving state; Determining the actual power value of the current vehicle when exiting the abnormal driving state according to the battery state parameters; Determining the actual power consumption according to the actual power value and the initial power value.

4. The method according to claim 1, characterized in that After determining the real-time power value of the current vehicle in the abnormal driving state, the method further includes: Controlling a display device of the current vehicle to display the real-time power value.

5. The method according to claim 1, wherein After determining the real-time power value of the current vehicle in the abnormal driving state, the method further includes: Determining the remaining available driving distance of the current vehicle according to the real-time power value; Controlling a display device of the current vehicle to display the remaining available driving distance.

6. The method according to claim 1, characterized in that, The abnormal driving state is an uphill / downhill state; The method further includes: Responding to detecting that the pitch angle of the current vehicle is greater than or equal to a preset angle threshold through a slope sensor of the current vehicle, and confirming that the current vehicle enters the abnormal driving state; or Responding to detecting that the pitch angle of the current vehicle is less than the preset angle threshold through a slope sensor of the current vehicle, and confirming that the current vehicle exits the abnormal driving state.

7. The method according to claim 1, wherein Obtaining vehicle information of the current vehicle includes: Receiving a power determination request; Parsing the power determination request to obtain the vehicle identifier of the current vehicle; Obtaining the vehicle information of the current vehicle according to the vehicle identifier.

8. The method according to claim 1, characterized in that, Determining the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption includes: Determining the difference between the initial power value and the power consumption as the real-time power value of the current vehicle in the abnormal driving state.

9. The method according to claim 1, characterized in that, The power consumption prediction model is trained in the following manner: Obtaining historical driving data of at least one vehicle; Determining the historical driving parameters and the historical actual power consumption of each vehicle in the historical abnormal driving state according to the historical driving data; Use the historical driving parameters, the historical actual power consumption, and the vehicle information of each vehicle as training samples to train a model to be trained to obtain the power consumption prediction model.

10. The method according to any one of claims 1-9, characterized in that, The real-time driving parameters at least include a combination of one or more of the following: driving direction, start and end positions, driving distance, slope amplitude, driving speed.

11. The method according to any one of claims 1-9, characterized in that, The vehicle information at least includes a combination of one or more of the following: vehicle model, vehicle usage years, vehicle historical driving mileage.

12. An electric quantity determination device, characterized in that, The device includes: A first acquisition unit for acquiring the vehicle information of the current vehicle; A second acquisition unit for acquiring the real-time driving parameters of the current vehicle in an abnormal driving state; A first determination unit for inputting the vehicle information and the real-time driving parameters into the power consumption prediction model to determine the power consumption of the current vehicle after entering the abnormal driving state through the power consumption prediction model; A second determination unit for determining the real-time power value of the current vehicle in the abnormal driving state according to the initial power value and the power consumption, where the initial power value is the power value of the current vehicle when entering the abnormal driving state.

13. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The computer program instructions, when executed by a processor, implement the method according to any one of claims 1-11.

14. An electronic device, characterized in that, The device includes: A memory for storing one or more computer program instructions; A processor, where the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-11.

15. A vehicle, characterized in that, The vehicle includes: A vehicle body; A sensor group arranged on the vehicle body for acquiring driving parameters; A control device configured to execute the method according to any one of claims 1-11.

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