A vehicle energy consumption prediction method, system and device

Abstract

The application relates to the field of automobile technology and discloses a vehicle energy consumption prediction method, system and device.The method comprises the following steps: acquiring route information, vehicle position information and average energy consumption information of a vehicle; according to the acquired information and an initial energy consumption prediction model, the energy consumption of the vehicle on an energy consumption calibration section is predicted and recorded to obtain first energy consumption information and actual energy consumption information; if the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, the parameters of the initial energy consumption prediction model are iteratively updated according to the difference until a target energy consumption prediction model meeting a preset condition is obtained; finally, the energy consumption of a to-be-traveled section is predicted according to the target energy consumption prediction model to obtain second energy consumption information, and energy consumption prompt information is generated according to the second energy consumption information. Through application of the technical scheme of the application, the energy consumption of a pure electric vehicle can be predicted and updated, and the problem that the difference between the predicted energy consumption and the actual energy consumption is large when the energy consumption of the vehicle is predicted can be avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobiles, in particular to a vehicle energy consumption prediction method, system and device. BACKGROUND

[0002] An electric vehicle, in particular a pure electric vehicle, is powered by electricity, and the endurance mileage of the pure electric vehicle is shorter than that of a vehicle using fossil energy due to the limited storage capacity of the power battery. In order to avoid the vehicle breaking down, the user will continuously pay attention to the endurance mileage of the vehicle during driving. The endurance mileage of the vehicle is calculated based on the remaining energy status and the energy consumption status of the vehicle, so the prediction of the endurance mileage of the vehicle can also be regarded as the prediction of the energy consumption of the vehicle.

[0003] In order to facilitate the user to obtain the energy consumption of the vehicle, a corresponding functional module is arranged in the vehicle body of most electric vehicles to make a prediction, for example, the average energy consumption and the driving distance of the last time period are used to predict the energy consumption of the vehicle, or the energy consumption of the vehicle on a route in front is predicted by a navigation system.

[0004] However, there are many factors affecting the energy consumption of the vehicle, such as driving behavior habits, road influence, weather influence, vehicle influence, etc., and some factors such as driving behavior habits may not be effectively analyzed by the vehicle, thereby affecting the result of the energy consumption prediction. Therefore, only by using the historical energy consumption or the road condition to predict the energy consumption, there is a large difference between the predicted energy consumption and the actual energy consumption, thereby leading to the inaccuracy of the predicted endurance mileage obtained by the user, and affecting the driving experience of the user. SUMMARY

[0005] The present application provides a vehicle energy consumption prediction method, system and device to solve the problem of large difference between the predicted energy consumption and the actual energy consumption when predicting the energy consumption of the vehicle.

[0006] According to a first aspect of the embodiments of the present application, a vehicle energy consumption prediction method is provided, comprising:

[0007] obtaining route information, vehicle position information and average energy consumption information of the vehicle; the route information comprises a to-be-traveled road section and a plurality of energy consumption calibration road sections; predicting energy consumption of the vehicle on the energy consumption calibration road sections according to an initial energy consumption prediction model and the average energy consumption information to obtain first energy consumption information; determining actual energy consumption information of the vehicle when traveling on the energy consumption calibration road sections according to the energy consumption calibration road sections and the vehicle position information; if a difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively updating parameters of the initial energy consumption prediction model according to the difference until a target energy consumption prediction model meeting a preset condition is obtained; predicting energy consumption of the to-be-traveled road section in the route information according to the target energy consumption prediction model to obtain second energy consumption information; and generating energy consumption prompt information according to the second energy consumption information.

[0008] In an optional manner, the initial energy consumption prediction model comprises at least one of a weather sub-model, a road condition sub-model, a vehicle weight sub-model, a slope sub-model and a driving habit sub-model, and before the energy consumption of the vehicle on the energy consumption calibration road sections is predicted according to the initial energy consumption prediction model and the average energy consumption information, the method further comprises at least one of the following:

[0009] determining weather information according to the vehicle position information and inputting the weather information into the weather sub-model; determining road condition information according to the route information and inputting the road condition information into the road condition sub-model; determining vehicle weight information according to identification information of the vehicle and inputting the vehicle weight information into the vehicle weight sub-model; determining slope information according to a slope signal collected by a sensor of the vehicle and inputting the slope information into the slope sub-model; and determining a driving habit type of the driver according to a change rate of an accelerator pedal opening degree and a change rate of acceleration of the vehicle and inputting the driving habit type into the driving habit sub-model.

[0010] In an optional manner, the determination of the driving habit type of the driver according to the change rate of the accelerator pedal opening degree and the change rate of acceleration of the vehicle comprises: if the change rate of the accelerator pedal opening degree is greater than a preset opening degree change rate, or if the change rate of acceleration of the vehicle is greater than a preset acceleration change rate within a preset time length, determining that the driving habit type of the driver is a first driving habit type; or if the change rate of the accelerator pedal opening degree is less than a preset opening degree change rate, or if the change rate of acceleration of the vehicle is less than a preset acceleration change rate within a preset time length, determining that the driving habit type of the driver is a second driving habit type.

[0011] In an optional manner, the prediction of the energy consumption of the vehicle on the energy consumption calibration road sections according to the initial energy consumption prediction model and the average energy consumption information to obtain the first energy consumption information comprises:

[0012] According to the weather information and the weather sub-model, a first additional energy consumption corresponding to the weather information is determined; according to the road condition information and the road condition sub-model, a second additional energy consumption corresponding to the road condition information is determined; according to the vehicle weight information and the vehicle weight sub-model, a third additional energy consumption corresponding to the vehicle weight information is determined; according to the slope information and the slope sub-model, a fourth additional energy consumption corresponding to the slope information is determined; according to the driving habit type and the driving habit sub-model, a fifth additional energy consumption corresponding to the driving habit type is determined; and according to the average energy consumption information and at least one of the first additional energy consumption, the second additional energy consumption, the third additional energy consumption, the fourth additional energy consumption and the fifth additional energy consumption, the first energy consumption information is determined.

[0013] In an optional manner, the parameters of the initial energy consumption prediction model include at least one of a first energy consumption coefficient corresponding to the weather sub-model, a second energy consumption coefficient corresponding to the road condition sub-model, a third energy consumption coefficient corresponding to the vehicle weight sub-model, a fourth energy consumption coefficient corresponding to the slope sub-model, and a fifth energy consumption coefficient corresponding to the driving habit sub-model, and the updating of the parameters of the initial energy consumption prediction model according to the difference value includes at least one of the following:

[0014] According to the difference value and the weather sub-model, the first energy consumption coefficient is updated; according to the difference value and the road condition sub-model, the second energy consumption coefficient is updated; according to the difference value and the vehicle weight sub-model, the third energy consumption coefficient is updated; according to the difference value and the slope sub-model, the fourth energy consumption coefficient is updated; and according to the difference value and the driving habit sub-model, the fifth energy consumption coefficient is updated.

[0015] In an optional manner, the slope information includes at least one of a slope angle and a slope length, and if the slope angle indicates uphill, the fourth additional energy consumption corresponding to the slope information is positive; if the slope angle indicates downhill, the fourth additional energy consumption corresponding to the slope information is negative.

[0016] In an optional manner, before the prediction of the energy consumption of the vehicle on the energy consumption calibration road section according to the initial energy consumption prediction model and the average energy consumption information to obtain the first energy consumption information, the method further includes: sending an energy consumption prediction request to a server, the energy consumption prediction request being used to request the server to predict the energy consumption of the vehicle.

[0017] According to a second aspect of the embodiments of the present application, a vehicle energy consumption prediction system is provided, which includes:

[0018] The information collection unit is configured to acquire route information, vehicle position information and average energy consumption information of the vehicle, and determine actual energy consumption information of the vehicle when driving in the energy consumption calibration section according to the energy consumption calibration section and the vehicle position information;

[0019] The energy consumption calculation unit is configured to predict energy consumption of the vehicle in the energy consumption calibration section according to an initial energy consumption prediction model to obtain first energy consumption information, and predict energy consumption of a to-be-driven section in the route information according to a target energy consumption prediction model to obtain second energy consumption information.

[0020] The energy consumption calibration unit is configured to, if a difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively update parameters of the initial energy consumption prediction model according to the difference until the target energy consumption prediction model satisfying a preset condition is obtained.

[0021] The energy consumption prompting unit is configured to generate energy consumption prompting information according to the second energy consumption information.

[0022] According to a third aspect of the embodiments of the present application, a vehicle energy consumption prediction device is provided, which comprises a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus; the memory is configured to store at least one executable instruction, and the executable instruction causes the processor to perform operations of the vehicle energy consumption prediction method according to any one of the preceding aspects.

[0023] According to a fourth aspect of the embodiments of the present application, a computer readable storage medium is provided, and the storage medium stores at least one executable instruction, and the executable instruction causes a vehicle energy consumption prediction system / device to perform operations of the vehicle energy consumption prediction method according to any one of the preceding aspects when the vehicle energy consumption prediction system / device runs.

[0024] The embodiment of the present application provides a vehicle energy consumption prediction method, system and device, wherein the method comprises the following steps: firstly, acquiring route information, vehicle position information and average energy consumption information of a vehicle; according to the acquired information, predicting energy consumption of the vehicle in an energy consumption calibration section according to an initial energy consumption prediction model and the average energy consumption information, to obtain first energy consumption information; then, according to the energy consumption calibration section and the vehicle position information, determining actual energy consumption information of the vehicle when the vehicle travels in the energy consumption calibration section; if a difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively updating parameters of the initial energy consumption prediction model according to the difference until a target energy consumption prediction model meeting a preset condition is obtained; finally, predicting energy consumption of a to-be-traveled section according to the target energy consumption prediction model, to obtain second energy consumption information, and generating energy consumption prompt information according to the second energy consumption information. By applying the technical scheme of the present application, the energy consumption of a pure electric vehicle can be predicted and updated, and the problem that the difference between the predicted energy consumption and the actual energy consumption is large when the energy consumption of the vehicle is predicted can be avoided.

[0025] The above description is only a summary of the technical scheme of the embodiment of the present application, in order to more clearly understand the technical means of the embodiment of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, characteristics and advantages of the embodiment of the present application more obvious and easy to understand, the specific embodiment of the present application is described below. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical scheme of the present application, the drawings needed in the embodiments will be briefly introduced below. Obviously, other drawings can also be obtained by those skilled in the art without creative labor.

[0027] Figure 1 The flowchart of the vehicle energy consumption prediction method in the embodiment of the present application is shown in the figure.

[0028] Figure 2 The flowchart of another vehicle energy consumption prediction method in the embodiment of the present application is shown in the figure.

[0029] Figure 3 The flowchart of another vehicle energy consumption prediction method in the embodiment of the present application is shown in the figure.

[0030] Figure 4 The flowchart of another vehicle energy consumption prediction method in the embodiment of the present application is shown in the figure.

[0031] Figure 5 The timing diagram of the vehicle energy consumption prediction method in the embodiment of the present application is shown in the figure.

[0032] Figure 6 The structural schematic diagram of the vehicle energy consumption prediction system provided by the embodiment of the present application is shown in the figure.

[0033] Figure 7 A structural schematic diagram of a vehicle energy consumption prediction device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0034] The embodiments will be described in detail below with reference to examples thereof as illustrated in the accompanying drawings. In the following description, same numbers in different drawings represent same or similar elements unless otherwise represented. The implementations described in the following embodiments are not meant to represent all implementations consistent with the present application. They are merely examples of systems and methods consistent with some aspects of the present application as detailed in the claims.

[0035] Since the power source of the pure electric vehicle is all the power in the power battery, the storage capacity in the power battery is closely related to the endurance mileage of the pure electric vehicle. Limited by the storage capacity of the power battery and the characteristics of the battery charging, compared with the vehicles using fossil energy, such as fuel vehicles, the endurance mileage is shorter and the storage capacity is not easy to supplement, so the user will pay special attention to the endurance mileage of the vehicle when driving the pure electric vehicle, so as to avoid insufficient storage capacity of the vehicle and unable to reach the destination.

[0036] When calculating the endurance mileage, the energy consumption of the vehicle is predicted, and then the endurance mileage is calculated by the remaining energy and the energy consumption. Therefore, when calculating the endurance mileage, the energy consumption of the vehicle needs to be predicted. However, since the energy consumption of the electric vehicle is affected by many factors, such as weather, road conditions or driving behavior habits, etc., when the energy consumption is predicted, the change of the above factors will affect the accuracy of the energy consumption prediction, so that the endurance mileage presented to the user is not accurate, which affects the driving experience of the user.

[0037] In order to solve the above problems, a vehicle energy consumption prediction method, system and device are disclosed in the present application. By analyzing the position information and route information of the vehicle, the predicted energy consumption is obtained, and the predicted energy consumption is updated by real-time energy consumption, so as to make the vehicle more accurately predict the subsequent energy consumption of the route in driving, and improve the accuracy of the prediction result of the endurance mileage of the vehicle.

[0038] The file generation method disclosed in the embodiment of the present application will be described below.

[0039] Figure 1 A flowchart of a vehicle energy consumption prediction method in an embodiment of the present application is shown. As shown in Figure 1 The prediction method comprises:

[0040] S110: obtaining route information, vehicle position information and average energy consumption information of the vehicle.

[0041] The navigation system on board the vehicle can obtain the navigation route set by the user and the current position information of the vehicle after the user activates the vehicle. For example, when the user gets into the driving seat and activates the vehicle, the user needs to determine the destination, and the vehicle on-board system can give a navigation route based on the current position of the vehicle and the destination position, and the route information and the position information of the vehicle can be obtained through the navigation route. In some embodiments, the position information of the vehicle can also be directly obtained by the navigation system through the positioning position of the vehicle. The route information includes a to-be-traveled road section and a plurality of energy consumption calibration road sections, the lengths of the plurality of energy consumption calibration road sections are the same, the length of the to-be-traveled road section is greater than that of the energy consumption calibration road section, and the starting point of the first energy consumption calibration road section is the starting position in the route information, and the ending point of the to-be-traveled road section is the destination position in the route information.

[0042] The average energy consumption information of the vehicle can be obtained through the records in the past driving process of the vehicle, for example, the energy consumption of the vehicle in any route or road section can be recorded, and the average energy consumption information of the vehicle can be obtained through calculation with the corresponding driving mileage.

[0043] For example, when recording the average energy consumption information, the recording of the average energy consumption information needs to be performed when the vehicle is in a drivable state. In some embodiments, when the vehicle is in a drivable state and the vehicle speed reaches a certain threshold, the recording of the average energy consumption is performed, and when the vehicle speed is less than the threshold and lasts for a period of time, the recording and updating of the average energy consumption information is stopped. However, this case ignores the power consumption of the electronic devices in the vehicle when the vehicle is in a drivable state for a long time, and therefore in another part of the embodiments, the energy consumption can be recorded when the vehicle is in a drivable state, so as to more reflect the average energy consumption condition in the historical driving time or the historical driving distance.

[0044] It should be understood that only when the driving time or the driving distance of the vehicle reaches a certain threshold, the recorded energy consumption information has a certain accuracy, and when the driving time or the driving distance of the vehicle meets the condition during the recording process, the recorded energy consumption information can be stored as the average energy consumption information. For example, the threshold of the driving time can be set to 1 hour, and the threshold of the driving distance can be set to 10 km, that is, when the driving time of the vehicle is greater than or equal to 1 hour, or the driving distance of the vehicle is greater than or equal to 10 km, the recorded energy consumption information can be used as the average energy consumption information. The above threshold size is only exemplary, and the threshold of the driving time and the threshold of the driving distance can be set by the user, and the specific size of the threshold is not limited in the present application.

[0045] In some embodiments, the average energy consumption information further includes driving information, that is, the road conditions and weather information of the vehicle during the driving process, so that the historical driving record of the vehicle is more specific, and the energy consumption prediction through the average energy consumption information in the subsequent steps is facilitated.

[0046] It should be noted that after the user determines the destination, multiple navigation routes can be generated, and in the subsequent energy consumption prediction process, different navigation routes can be processed. For example, energy consumption prediction is performed on all generated navigation routes; for another example, the closest navigation route is selected for subsequent energy consumption prediction by judging the distance difference between different navigation routes; for another example, any navigation route is selected for subsequent energy consumption prediction processing by the user's instruction.

[0047] S120: According to the initial energy consumption prediction model and the average energy consumption information, the energy consumption of the vehicle in the energy consumption calibration section is predicted to obtain the first energy consumption information.

[0048] After obtaining the average energy consumption information, the average energy consumption information can be selected by the initial energy consumption prediction model to obtain the predicted energy consumption of the vehicle in the energy consumption calibration section, i.e., the first energy consumption information. For example, the energy consumption of the section with the same length as the energy consumption calibration section and the closest driving information in the average energy consumption information can be selected as the first energy consumption information. It should be noted that the obtained first energy consumption information is the energy consumption prediction information of the vehicle driving in an energy consumption calibration section.

[0049] S130: According to the energy consumption calibration section and the vehicle position information, the actual energy consumption information of the vehicle driving in the energy consumption calibration section is determined.

[0050] The actual energy consumption information can be obtained by the power consumption of the vehicle passing through the energy consumption calibration section and the length of the energy consumption calibration section when the vehicle is in the driving state. For example, the power consumption of the vehicle in the driving process includes the high-voltage module power consumption, the low-voltage module power consumption, and the heat loss power consumption. However, since the heat loss power consumption is small, in some embodiments, the energy consumption caused by the heat loss is considered as 0 or ignored. When calculating the power consumption, since the high-voltage power battery and the storage battery providing low voltage are separately arranged in some vehicles, the high-voltage module power consumption and the low-voltage module power consumption of the vehicle can be calculated respectively to obtain the actual power consumption of the vehicle as a whole. The voltage and current of the output end of the power battery and the storage battery can be used to calculate the energy consumption. In some embodiments, since the low-voltage power supply can also be provided by the power battery, the actual power consumption of the vehicle can also be calculated by directly measuring the current and voltage of the bus of the power battery.

[0051] It should be understood that in the present embodiment, the actual energy consumption information refers to the average energy consumption information of the vehicle in the energy consumption calibration section that has been driven, which can be calculated by the consumption power or consumption percentage of the battery and the length of the energy consumption calibration section.

[0052] S140: If the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively update the parameters of the initial energy consumption prediction model according to the difference until a target energy consumption prediction model that meets the preset condition is obtained.

[0053] The parameters of the initial energy consumption prediction model refer to the proportion of different influencing factors in the prediction result of the energy consumption prediction model, and the preset condition refers to the difference between the first energy consumption information and the actual energy consumption information being less than or equal to the preset threshold.

[0054] After obtaining the first energy consumption information and the actual energy consumption information of a certain energy calibration section, the first energy consumption information and the actual energy consumption information are compared to obtain the difference between the two, and the difference is compared with the preset threshold. If the difference is greater than the preset threshold, it means that there is a large difference between the predicted energy consumption and the actual energy consumption, and the energy consumption prediction value of the subsequent energy calibration section and even the to-be-traveled section needs to be adjusted. Therefore, the parameters of the initial energy consumption prediction model need to be adjusted according to the difference, so that the first energy consumption information predicted subsequently is closer to the actual energy consumption information, and the difference between the two is reduced, until the difference between the first energy consumption information and the actual energy consumption information is less than or equal to the preset threshold.

[0055] When the first energy consumption information obtained by the initial energy consumption prediction model meets the preset condition, the initial energy consumption prediction model is considered as the target energy consumption prediction model, and the target energy consumption prediction model is used to predict the energy consumption of the subsequent to-be-traveled section of the energy calibration section.

[0056] S150: Predict the energy consumption of the to-be-traveled section according to the target energy consumption prediction model to obtain second energy consumption information.

[0057] After obtaining the target energy consumption prediction model, the energy consumption of the subsequent section in the route information, i.e., the to-be-traveled section, needs to be predicted to obtain the second energy consumption information of the vehicle. The second energy consumption information is used to represent the energy consumption required for the vehicle to travel from the current position to the destination.

[0058] S160: Generate energy consumption prompt information according to the second energy consumption information.

[0059] After obtaining the second energy consumption information, the second energy consumption information is compared with the current remaining power of the vehicle, and according to the comparison result, the corresponding energy consumption prompt information is generated. For example, when the energy consumption in the second energy consumption information is less than the current remaining power of the vehicle, information is generated to prompt the user about the remaining power of the vehicle after this trip; if the energy consumption in the second energy consumption information is greater than or equal to the current remaining power of the vehicle, the user is reminded that the power of the vehicle is insufficient to complete the current route, and according to the route information, the user is provided with charging stations along the way that the vehicle can reach, so as to facilitate the user to charge later.

[0060] In some embodiments, in order to avoid the second energy consumption information being too close to the current remaining power of the vehicle and causing the vehicle to be powered off, the content of the generated energy consumption prompt information can also be determined by the proportion of the energy consumption in the second energy consumption information to the current remaining power of the vehicle. For example, if the predicted second energy consumption information accounts for more than 90% of the current remaining power, the user is provided with charging stations that the vehicle can reach along the route according to the route information, and is reminded to charge; if the proportion of the predicted second energy consumption information to the current remaining power is less than or equal to 90%, the user is prompted about the remaining power of the vehicle after completing this trip.

[0061] It should be noted that in some embodiments of the present application, after obtaining the second energy consumption information, the current actual energy consumption information of the vehicle is also continuously collected, and the difference is obtained by comparing the corresponding part of the second energy consumption information with the actual energy consumption information. If the difference is less than or equal to the preset threshold, it is considered that the second energy consumption information is still relatively accurate, and if the difference is greater than the preset threshold, the result of the second energy consumption information does not match the actual energy consumption, and the parameters of the target energy consumption prediction model need to be adjusted until the difference between the obtained energy consumption information and the actual energy consumption information is less than or equal to the preset threshold.

[0062] Through the above technical solution, the parameters of the energy consumption prediction model are adjusted by comparing the predicted energy consumption information in real time, so that the predicted energy consumption of the vehicle in the navigation route is closer to the actual energy consumption, the accuracy of energy consumption prediction is improved, the anxiety of the user is reduced, and the use experience of the user is improved.

[0063] Figure 2 A flowchart of another vehicle energy consumption prediction method in an embodiment of the present application is shown. In order to make the initial energy consumption prediction model more accurately predict the energy consumption of the vehicle, a sub-model for quantifying factors affecting the size of energy consumption is set in the model to make the energy consumption prediction model more accurately and inversely predict, as shown in Figure 2 The prediction method includes:

[0064] S210: Obtain route information, vehicle position information and average energy consumption information of the vehicle.

[0065] S220: According to the initial energy consumption prediction model and the average energy consumption information, the energy consumption of the vehicle in the energy consumption calibration section is predicted to obtain the first energy consumption information.

[0066] In order to quantify the factors affecting the energy consumption of the vehicle, at least one of the weather sub-model, the road condition sub-model, the vehicle weight sub-model, the slope sub-model and the driving habit sub-model is included in the initial energy consumption prediction model, so that various information is obtained by sensors and other devices during the energy consumption prediction process to facilitate calculation and adjustment. For example, during the energy consumption prediction process, the method includes at least one of the following steps:

[0067] S221: Determine the weather information according to the vehicle position information and input the weather information into the weather sub-model.

[0068] The weather information includes the weather and the temperature. For example, according to the vehicle position information, the city or the region where the vehicle is currently located can be obtained, and then the current temperature and the weather condition of the city or the region can be obtained through the built-in network connection unit of the vehicle.

[0069] In some embodiments, the weather condition of the outside world can also be obtained by loading a camera and a temperature sensor on the vehicle body. After the weather information is obtained, the weather information can be input into the weather sub-model to take the weather factor as a parameter affecting the energy consumption information.

[0070] S222: Determine the road condition information according to the route information and input the road condition information into the road condition sub-model.

[0071] The road condition information is obtained according to the navigation information to obtain the congestion condition in the route, and then the overall road condition information in the route is obtained. After the vehicle obtains the road condition information, the vehicle inputs the road condition information into the road condition sub-model. For example, the vehicle can directly obtain the congestion condition in the route through the navigation unit of the vehicle.

[0072] It should be understood that the road condition information is constantly changing over time, so the road condition information obtained during the driving of the vehicle will change. When the road condition information changes greatly, for example, the congestion state is aggravated, the energy consumption prediction model needs to update the predicted energy consumption, thereby increasing the accuracy of the energy consumption prediction.

[0073] S223: Determine the vehicle weight information according to the identification information of the vehicle and input the vehicle weight information into the vehicle weight sub-model.

[0074] The identification information is information used to reflect the real parameters of the vehicle, such as the vehicle weight and the torque. The identification information is related to the model of the vehicle. When the number of passengers in the vehicle or the amount of cargo is small, the vehicle weight information can be obtained through the identification information in the identification information of the vehicle. After the vehicle weight information is determined, the vehicle weight information is input into the vehicle weight sub-model.

[0075] In some embodiments, pressure sensors can be arranged at positions such as the cabin and the trunk to obtain the weight of the passengers and the amount of cargo in the vehicle. In combination with the vehicle weight in the identification information, more accurate weight data of the vehicle can be obtained.

[0076] S224: Determine the slope information according to the slope signal collected by the sensor of the vehicle and input the slope information into the slope sub-model.

[0077] The slope information can include at least one of a slope length and a slope angle. For example, an angle sensor can be arranged on a chassis of the vehicle to obtain an angle of the vehicle passing through a slope during driving of the vehicle. A positive or negative value of the angle can indicate whether the vehicle is in an uphill state or a downhill state. For example, when the angle is positive, the vehicle is in the uphill state, and when the angle is negative, the vehicle is in the downhill state. The slope length can be determined according to a speed of the vehicle and a time range of the angle change.

[0078] In some embodiments, the slope information can be determined according to route information in the navigation information when the slope information is obtained. If the route information includes a slope, the length and the angle of the slope can be determined. The obtained slope information needs to be input into the slope sub-model to more accurately describe the energy consumption prediction.

[0079] S225: Determine the driving habit type of the driver according to the change rate of the accelerator pedal opening degree and the change rate of the acceleration of the vehicle, and input the driving habit type into the driving habit sub-model.

[0080] The change rate of the accelerator pedal opening degree is a change of the accelerator pedal opening degree in a unit of time, and the change rate of the acceleration is a change of the acceleration of the vehicle in a unit of time. According to any one of the two change rates, the driving habit type of the driver can be determined. After the driving habit type is determined, the driving habit type is input into the driving habit sub-model.

[0081] For example, after the change rate of the accelerator pedal opening degree or the change rate of the acceleration of the vehicle is obtained, if the change rate of the accelerator pedal opening degree is greater than a preset opening degree change rate, or if the change rate of the acceleration of the vehicle in a preset time period is greater than a preset acceleration change rate, the driving habit type of the driver is determined as a first driving habit type.

[0082] If the change rate of the accelerator pedal opening degree is less than the preset opening degree change rate, or if the change rate of the acceleration of the vehicle in the preset time period is less than the preset acceleration change rate, the driving habit type of the driver is determined as a second driving habit type. The additional energy consumption corresponding to the first driving habit type is higher than the additional energy consumption corresponding to the second driving habit type.

[0083] In some embodiments, a third driving habit type can also be designed, the additional energy consumption corresponding to the third driving habit type being lower than the additional energy consumption corresponding to the first driving habit type and higher than the additional energy consumption corresponding to the second driving habit type. Specifically, the preset opening rate and the preset acceleration rate are both a numerical range. When the change rate of the accelerator pedal opening is greater than the preset opening rate range or the acceleration rate of the vehicle within the preset time period is greater than the preset acceleration rate range, it is determined that the driving habit type of the driver is the first driving habit type. When the change rate of the accelerator pedal opening is less than the preset opening rate range or the acceleration rate of the vehicle within the preset time period is less than the preset acceleration rate range, it is determined that the driving habit type of the driver is the second driving habit type. When the change rate of the accelerator pedal opening is within the preset opening rate range or the acceleration rate of the vehicle within the preset time period is within the preset acceleration rate range, it is determined that the driving habit type of the driver is the third driving habit type.

[0084] It should be noted that when judging the driving habit type, the change rate of the accelerator pedal opening and the acceleration rate are both collected, and the acceleration rate of the vehicle is used as the main basis for judging the driving habit type.

[0085] S230: According to the energy consumption calibration section and the vehicle position information, the actual energy consumption information of the vehicle when driving on the energy consumption calibration section is determined.

[0086] Through the information obtained in the above steps S221 to S225, the factors that can affect the energy consumption of the vehicle are determined and obtained, and then the parameters in the initial energy consumption prediction model are set through these factors, so as to quantify the influence of different factors, facilitating subsequent model updating.

[0087] S240: If the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, the parameters of the initial energy consumption prediction model are iteratively updated according to the difference until a target energy consumption prediction model satisfying a preset condition is obtained.

[0088] The parameters of at least one sub-model in the initial energy consumption prediction model are adjusted through the difference between the actual energy consumption information and the first energy consumption information, so as to update the initial energy consumption prediction model until a target energy consumption prediction model capable of more accurately predicting energy consumption is obtained.

[0089] S250: According to the target energy consumption prediction model, the energy consumption of the to-be-traveled section in the route information is predicted to obtain second energy consumption information.

[0090] S260: According to the second energy consumption information, energy consumption prompt information is generated.

[0091] The execution process of steps S210 to S220 in the method is the same as the execution process of steps S110 to S120 described above, and therefore steps S210 to S220 will not be described in detail in the present application; and the execution process of steps S250 to S260 in the method is the same as the execution process of steps S150 to S160 described above, and therefore steps S250 to S260 will not be described in detail in the present application.

[0092] By the above technical solution, the energy consumption influence caused by weather, road conditions, vehicle weight, slope and driving habit type is quantified into the model, so that the energy consumption prediction model can more accurately predict the energy consumption of the vehicle.

[0093] Figure 3 A flowchart of another vehicle energy consumption prediction method in an embodiment of the present application is shown. The external environmental influence or user influence is quantified by a sub-model in the energy consumption prediction model, so that the energy consumption prediction is more accurate, as shown in Figure 3 The prediction method further includes:

[0094] S310: Obtain route information, vehicle position information and average energy consumption information of the vehicle.

[0095] S321: Determine a first additional energy consumption corresponding to the weather information according to the weather information and a weather sub-model.

[0096] According to the weather condition obtained in the above method S221, the first additional energy consumption corresponding to the weather information can be determined. Specifically, in the case of the same other influencing factors, when the weather is sunny or the temperature is high, the value of the first additional energy consumption will decrease to a certain extent, and thus the proportion of the first additional energy consumption in the first energy consumption information decreases; and when the weather is rainy or the temperature is low, the value of the first additional energy consumption will increase to a certain extent, and thus the proportion of the first additional energy consumption in the first energy consumption information increases.

[0097] In some embodiments, the weather sub-model corresponds to a first energy consumption coefficient, and the proportion of the first additional energy consumption in the first energy consumption information can be increased or decreased by directly adjusting the first energy consumption coefficient in the weather sub-model, so as to calculate the influence of the weather factor on the energy consumption.

[0098] S322: Determine a second additional energy consumption corresponding to the road condition information according to the road condition information and a road condition sub-model.

[0099] According to the road condition information obtained in the above method S222, the second additional energy consumption corresponding to the road condition information can be calculated by the road condition sub-model. For example, since the vehicle needs to frequently accelerate and decelerate when stuck in traffic and the vehicle is in a drivable state for a longer time, in the case of the same other influencing factors, the more congested the road condition information shows, the greater the value of the second additional energy consumption, and the higher the proportion of the second additional energy consumption in the first energy consumption information.

[0100] In some embodiments, the road condition sub-model corresponds to a second energy consumption coefficient, and the size of the second additional energy consumption can be adjusted by adjusting the second energy consumption coefficient, so that the influence of the road condition can be calculated.

[0101] S323: According to the vehicle weight information and the vehicle weight sub-model, a third additional energy consumption corresponding to the vehicle weight information is determined.

[0102] According to the vehicle weight information obtained in the above method S223 and the vehicle weight sub-model, the influence of the vehicle weight information on the predicted energy consumption can be calculated. In the case that other influencing factors are the same, the heavier the vehicle, the higher the energy consumption, and the higher the proportion in the first energy consumption information. Meanwhile, in some embodiments, the third energy consumption coefficient corresponding to the vehicle weight sub-model is used to reflect the proportion of the influence of the vehicle weight on the energy consumption.

[0103] S324: According to the slope information and the slope sub-model, a fourth additional energy consumption corresponding to the slope information is determined.

[0104] The slope information includes at least one of the slope angle and the length of the slope, as described in the above S224 step. For example, the up and down states of the vehicle can be indicated by the positive and negative of the slope angle. If the slope angle is positive, the vehicle is in an uphill state, and if the slope angle is negative, the vehicle is in a downhill state.

[0105] Since the thrust required by the vehicle to go uphill is higher than that on flat ground, and the thrust required by the vehicle to go downhill is lower than that on flat ground, the fourth additional energy consumption is positive when the vehicle goes uphill, and the fourth additional energy consumption is negative when the vehicle goes downhill. That is, if the slope angle indicates uphill, the fourth additional energy consumption corresponding to the slope information is positive; if the slope angle indicates downhill, the fourth additional energy consumption corresponding to the slope information is negative.

[0106] In some embodiments, the slope sub-model corresponds to a fourth energy consumption coefficient, which is used to evaluate the overall influence of the slope information on the first energy consumption information, i.e., the proportion of the slope information in the first energy consumption information.

[0107] S325: According to the driving habit type and the driving habit sub-model, a fifth additional energy consumption corresponding to the driving habit type is determined.

[0108] After obtaining the driving habit type of the user through the above S225 step, the fifth additional energy consumption corresponding to the driving habit can be determined according to the different driving habit types of the user, so as to obtain the influence of the driving habit type on the first energy consumption information.

[0109] In some embodiments, the driving habit sub-model also corresponds to a fifth energy consumption coefficient, and the influence of the driving habit type on the energy consumption of the vehicle can be reflected through the fifth energy consumption coefficient.

[0110] S326: Determine the first energy consumption information according to the average energy consumption information and at least one of the first additional energy consumption, the second additional energy consumption, the third additional energy consumption, the fourth additional energy consumption and the fifth additional energy consumption.

[0111] After the average energy consumption information, the first additional energy consumption, the second additional energy consumption, the third additional energy consumption, the fourth additional energy consumption and the fifth additional energy consumption are obtained through the above steps, the above additional energy consumptions are correspondingly calculated with the energy consumption values in the average energy consumption information, and the first energy consumption information is determined.

[0112] S330: Determine the actual energy consumption information of the vehicle when driving on the energy consumption calibration road section according to the energy consumption calibration road section and the vehicle position information.

[0113] S340: If the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively update the parameters of the initial energy consumption prediction model according to the difference until a target energy consumption prediction model satisfying a preset condition is obtained.

[0114] For example, the initial energy consumption prediction model includes at least one of a weather sub-model, a road condition sub-model, a vehicle weight sub-model, a slope sub-model and a driving habit sub-model, and the parameters of the initial energy consumption prediction model include at least one of a first energy consumption coefficient, a second energy consumption coefficient, a third energy consumption coefficient, a fourth energy consumption coefficient and a fifth energy consumption coefficient. When the parameters of the initial energy consumption prediction model are iteratively updated, the above energy consumption coefficients are mainly adjusted and updated, and therefore updating the parameters of the initial energy consumption prediction model is to adjust the proportions of the energy consumptions influenced by different factors in the first energy consumption information.

[0115] For example, updating the parameters of the initial energy consumption prediction model can include at least one of the following steps: updating the first energy consumption coefficient according to the difference and the weather sub-model; updating the second energy consumption coefficient according to the difference and the road condition sub-model; updating the third energy consumption coefficient according to the difference and the vehicle weight sub-model; updating the fourth energy consumption coefficient according to the difference and the slope sub-model; and updating the fifth energy consumption coefficient according to the difference and the driving habit sub-model.

[0116] S350: Predict the energy consumption of the to-be-driven road section in the route information according to the target energy consumption prediction model to obtain second energy consumption information.

[0117] S360: Generate energy consumption prompt information according to the second energy consumption information.

[0118] The execution process of steps S310 to S320 in the method is the same as the execution process of steps S110 to S120 described above, so steps S310 to S320 will not be described in detail in this application; and the execution process of steps S350 to S360 in the method is the same as the execution process of steps S150 to S160 described above, so steps S350 to S360 will not be described in detail in this application.

[0119] By the above technical solution, the additional energy consumption caused by different factors affecting energy consumption is added to the predicted energy consumption information, and the initial energy consumption prediction model is adjusted by comparing the proportion or coefficient of different factors, so that the energy consumption prediction result is more accurate.

[0120] Figure 4 A flowchart of another vehicle energy consumption prediction method in an embodiment of the application is shown; Figure 5 A timing diagram of a vehicle energy consumption prediction method in an embodiment of the application is shown. Since the vehicle end has the problems of large data volume and slow processing speed in energy consumption prediction, and a large number of parameters need to be imported into the vehicle end for separate prediction by the vehicle end, the efficiency of energy consumption prediction is low, so as shown in Figure 4 and Figure 5 The prediction method in the application can further include:

[0121] S410: Obtain route information and vehicle position information of the vehicle.

[0122] S420: Send an energy consumption prediction request to the server.

[0123] The energy consumption prediction request is used to request the server to predict the energy consumption of the vehicle. Specifically, when the energy consumption prediction request is sent to the request server, the route information and the vehicle position information of the vehicle are also sent to the server end, so that subsequent processes are calculated and processed by the server end, thereby reducing the operation pressure of the vehicle end and enabling faster prediction of energy consumption. For example, when obtaining slope information, the slope information can be obtained through navigation information, and the current driving state of the vehicle is calculated and obtained through cloud computing, i.e., uphill or downhill, so as to obtain the influence of the slope information on the energy consumption of the vehicle.

[0124] It should be understood that in order to enable the server end to predict the energy consumption of the vehicle, the initial energy consumption prediction model also needs to be set in the server end, so as to achieve the purpose of quickly iterating and updating to obtain the target energy consumption prediction model.

[0125] S430: According to the initial energy consumption prediction model and the average energy consumption information, the energy consumption of the vehicle on the energy consumption calibration section is predicted to obtain first energy consumption information.

[0126] S440: determining actual energy consumption information of the vehicle when driving in the energy consumption calibration section according to the energy consumption calibration section and the vehicle position information.

[0127] S450: if the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively updating parameters of the initial energy consumption prediction model according to the difference until a target energy consumption prediction model meeting a preset condition is obtained.

[0128] S460: predicting energy consumption of a to-be-driven section in the route information according to the target energy consumption prediction model to obtain second energy consumption information.

[0129] S470: generating energy consumption prompt information according to the second energy consumption information.

[0130] The execution processes of S410 and S430 to S470 in the method are the same as the execution processes of S110 to S160 described above, and only the data calculation in S430 to S470 is transferred to the server side, so S410 and S430 to S470 will not be described in detail herein.

[0131] Through the above technical solution, the information to be processed and the energy consumption prediction request are sent to the server side, so that energy consumption prediction and model updating are realized through the server side, the consumption of the vehicle side is reduced, and the operation efficiency is improved.

[0132] Based on the above vehicle energy consumption prediction method, the present application also provides a vehicle energy consumption prediction system 600, as shown in Figure 6 The system comprises:

[0133] An information collection unit 610 is configured to obtain route information, vehicle position information and average energy consumption information of the vehicle, and determine actual energy consumption information of the vehicle when driving in an energy consumption calibration section according to the energy consumption calibration section and the vehicle position information.

[0134] An energy consumption calculation unit 620 is configured to predict energy consumption of the vehicle in the energy consumption calibration section according to an initial energy consumption prediction model to obtain first energy consumption information, and predict energy consumption of a to-be-driven section in the route information according to a target energy consumption prediction model to obtain second energy consumption information.

[0135] An energy consumption calibration unit 630 is configured to, if the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively update parameters of the initial energy consumption prediction model according to the difference until a target energy consumption prediction model meeting a preset condition is obtained.

[0136] An energy consumption prompt unit 640 is configured to generate energy consumption prompt information according to the second energy consumption information.

[0137] In an optional mode, the initial energy consumption prediction model comprises at least one of a weather sub-model, a road condition sub-model, a vehicle weight sub-model, a slope sub-model, and a driving habit sub-model, and the energy consumption calculation unit 620 is further configured to perform at least one of the following: determining weather information according to vehicle location information and inputting the weather information into the weather sub-model; determining road condition information according to route information and inputting the road condition information into the road condition sub-model; determining vehicle weight information according to identification information of the vehicle and inputting the vehicle weight information into the vehicle weight sub-model; determining slope information according to a slope signal collected by a sensor of the vehicle and inputting the slope information into the slope sub-model; and determining a driving habit type of the driver according to a change rate of the accelerator pedal opening degree and a change rate of the acceleration of the vehicle and inputting the driving habit type into the driving habit sub-model.

[0138] In an optional mode, the energy consumption calculation unit 620 is further configured to: if the change rate of the accelerator pedal opening degree is greater than a preset opening degree change rate, or if the change rate of the acceleration of the vehicle is greater than a preset acceleration change rate within a preset time period, determine that the driving habit type of the driver is a first driving habit type; if the change rate of the accelerator pedal opening degree is less than the preset opening degree change rate, or if the change rate of the acceleration of the vehicle is less than the preset acceleration change rate within the preset time period, determine that the driving habit type of the driver is a second driving habit type; and wherein the additional energy consumption corresponding to the first driving habit type is higher than the additional energy consumption corresponding to the second driving habit type.

[0139] In an optional mode, the energy consumption calculation unit 620 is further configured to: determine a first additional energy consumption corresponding to the weather information according to the weather information and the weather sub-model; determine a second additional energy consumption corresponding to the road condition information according to the road condition information and the road condition sub-model; determine a third additional energy consumption corresponding to the vehicle weight information according to the vehicle weight information and the vehicle weight sub-model; determine a fourth additional energy consumption corresponding to the slope information according to the slope information and the slope sub-model; determine a fifth additional energy consumption corresponding to the driving habit type according to the driving habit type and the driving habit sub-model; and determine the first energy consumption information according to the average energy consumption information and at least one of the first additional energy consumption, the second additional energy consumption, the third additional energy consumption, the fourth additional energy consumption, and the fifth additional energy consumption.

[0140] In an optional mode, the parameters of the initial energy consumption prediction model comprise at least one of a first energy consumption coefficient corresponding to the weather sub-model, a second energy consumption coefficient corresponding to the road condition sub-model, a third energy consumption coefficient corresponding to the vehicle weight sub-model, a fourth energy consumption coefficient corresponding to the slope sub-model, and a fifth energy consumption coefficient corresponding to the driving habit sub-model, and the energy consumption calibration unit 630 is further configured to perform at least one of the following: updating the first energy consumption coefficient according to the difference value and the weather sub-model; updating the second energy consumption coefficient according to the difference value and the road condition sub-model; updating the third energy consumption coefficient according to the difference value and the vehicle weight sub-model; updating the fourth energy consumption coefficient according to the difference value and the slope sub-model; and updating the fifth energy consumption coefficient according to the difference value and the driving habit sub-model.

[0141] In an optional mode, the slope information includes at least one of a slope angle and a ramp length, the fourth additional energy consumption corresponding to the slope information is positive if the slope angle indicates uphill, and the fourth additional energy consumption corresponding to the slope information is negative if the slope angle indicates downhill.

[0142] In an optional mode, the information collection unit 610 is further configured to send an energy consumption prediction request to the server, the energy consumption prediction request being used to request the server to predict the energy consumption of the vehicle.

[0143] Figure 7 A structure diagram of a vehicle energy consumption prediction device is shown, and the embodiments of the present application do not limit the specific implementation of the electronic device.

[0144] Based on the vehicle energy consumption prediction method, the present application further provides a vehicle energy consumption prediction device, as shown in the figure, the electronic device can include a processor 702, a communications interface 704, a memory 706, and a communications bus 708. Figure 7

[0145] The processor 702, the communications interface 704, and the memory 706 can communicate with each other through the communications bus 708. The communications interface 704 is used to communicate with network elements such as clients or other servers. The processor 702 is used to execute the program 710, and can execute the related steps in the above vehicle energy consumption prediction method embodiments.

[0146] Specifically, the program 710 can include program codes, and the program codes include computer executable instructions.

[0147] The processor 702 can be a central processing unit CPU, or an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the electronic device can be the same type of processor, such as one or more CPUs; or can be different types of processors, such as one or more CPUs and one or more ASICs.

[0148] The memory 706 is used to store the program 710. The memory 706 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.

[0149] ​The program 710 can be specifically invoked by the processor 702 to enable the electronic device to perform the following operations:

[0150] Obtain route information, vehicle position information and average energy consumption information of the vehicle.

[0151] According to the initial energy consumption prediction model and the average energy consumption information, the energy consumption of the vehicle on the energy consumption calibration section is predicted to obtain first energy consumption information.

[0152] According to the energy consumption calibration section and the vehicle position information, the actual energy consumption information of the vehicle when driving on the energy consumption calibration section is determined.

[0153] If the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, the parameters of the initial energy consumption prediction model are iteratively updated according to the difference until a target energy consumption prediction model that meets a preset condition is obtained.

[0154] According to the target energy consumption prediction model, the energy consumption of the to-be-traveled section in the route information is predicted to obtain second energy consumption information.

[0155] According to the second energy consumption information, energy consumption prompt information is generated.

[0156] In an optional manner, the initial energy consumption prediction model includes at least one of a weather sub-model, a road condition sub-model, a vehicle weight sub-model, a slope sub-model and a driving habit sub-model, and before the energy consumption of the vehicle on the energy consumption calibration section is predicted according to the initial energy consumption prediction model and the average energy consumption information, the method further includes at least one of the following:

[0157] According to the vehicle position information, weather information is determined and input into the weather sub-model;

[0158] According to the route information, road condition information is determined and input into the road condition sub-model;

[0159] According to the identification information of the vehicle, vehicle weight information is determined and input into the vehicle weight sub-model;

[0160] According to the slope signal collected by the sensor of the vehicle, slope information is determined and input into the slope sub-model;

[0161] According to the change rate of the accelerator pedal opening degree and the acceleration change rate of the vehicle, the driving habit type of the driver is determined and input into the driving habit sub-model.

[0162] In an optional manner, according to the change rate of the accelerator pedal opening degree and the acceleration change rate of the vehicle, the driving habit type of the driver is determined, including:

[0163] If the change rate of the accelerator pedal opening degree is greater than a preset opening degree change rate, or if the acceleration change rate of the vehicle within a preset time period is greater than a preset acceleration change rate, the driving habit type of the driver is determined as a first driving habit type; if the change rate of the accelerator pedal opening degree is less than the preset opening degree change rate, or if the acceleration change rate of the vehicle within the preset time period is less than the preset acceleration change rate, the driving habit type of the driver is determined as a second driving habit type; wherein the additional energy consumption corresponding to the first driving habit type is higher than the additional energy consumption corresponding to the second driving habit type.

[0164] In an optional manner, the energy consumption of the vehicle on the energy consumption calibration section is predicted according to the initial energy consumption prediction model and the average energy consumption information to obtain first energy consumption information, including:

[0165] According to the weather information and the weather sub-model, a first additional energy consumption corresponding to the weather information is determined;

[0166] According to the road condition information and the road condition sub-model, a second additional energy consumption corresponding to the road condition information is determined;

[0167] According to the vehicle weight information and the vehicle weight sub-model, a third additional energy consumption corresponding to the vehicle weight information is determined;

[0168] According to the slope information and the slope sub-model, a fourth additional energy consumption corresponding to the slope information is determined;

[0169] According to the driving habit type and the driving habit sub-model, a fifth additional energy consumption corresponding to the driving habit type is determined;

[0170] According to the average energy consumption information, the first additional energy consumption, the second additional energy consumption, the third additional energy consumption, the fourth additional energy consumption and the fifth additional energy consumption, the first energy consumption information is determined.

[0171] In an optional manner, the weather sub-model corresponds to a first energy consumption coefficient, the road condition sub-model corresponds to a second energy consumption coefficient, the vehicle weight sub-model corresponds to a third energy consumption coefficient, the slope sub-model corresponds to a fourth energy consumption coefficient, and the driving habit sub-model corresponds to a fifth energy consumption coefficient; the parameters of the initial energy consumption prediction model include at least one of the first energy consumption coefficient, the second energy consumption coefficient, the third energy consumption coefficient, the fourth energy consumption coefficient and the fifth energy consumption coefficient.

[0172] In an optional manner, the slope information includes at least one of a slope angle and a slope length, if the slope angle indicates uphill, the fourth additional energy consumption corresponding to the slope information is positive; if the slope angle indicates downhill, the fourth additional energy consumption corresponding to the slope information is negative.

[0173] In an optional manner, before predicting the energy consumption of the vehicle on the energy consumption calibration section according to the initial energy consumption prediction model and the average energy consumption information to obtain the first energy consumption information, the method further comprises: sending an energy consumption prediction request to the server, the energy consumption prediction request being used to request the server to predict the energy consumption of the vehicle.

[0174] The embodiment of the present application further provides a computer readable storage medium, and at least one executable instruction is stored in the storage medium, and when the executable instruction runs on the vehicle energy consumption prediction system / device, the vehicle energy consumption prediction system / device executes the operation of the vehicle energy consumption prediction method in any one of the foregoing.

[0175] The embodiment of the present application provides a vehicle energy consumption prediction method, system and device, wherein the method comprises the following steps: first, obtaining route information, vehicle position information and average energy consumption information of the vehicle; according to the obtained information, predicting the energy consumption of the vehicle on an energy consumption calibration section according to an initial energy consumption prediction model and the average energy consumption information to obtain first energy consumption information; then, according to the energy consumption calibration section and the vehicle position information, determining actual energy consumption information of the vehicle when the vehicle drives on the energy consumption calibration section; if the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, iteratively updating the parameters of the initial energy consumption prediction model according to the difference until a target energy consumption prediction model meeting a preset condition is obtained; finally, predicting the energy consumption of a to-be-traveled section according to the target energy consumption prediction model to obtain second energy consumption information, and generating energy consumption prompt information according to the second energy consumption information. By applying the technical solution of the present application, the energy consumption of the pure electric vehicle can be predicted and updated, and the problem that the difference between the predicted energy consumption and the actual energy consumption is large when the energy consumption of the vehicle is predicted can be avoided.

[0176] In the description provided herein, a large number of specific details are explained. However, it can be understood that the embodiments of the present application can be practiced without these specific details. Similarly, in order to simplify the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. Among them, the claims following the detailed description are thus expressly incorporated into the detailed description, wherein each claim itself is a separate embodiment of the present application.

[0177] Those skilled in the art can understand that the modules in the device in the embodiments can be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and furthermore can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive.

[0178] It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word 'comprising' does not exclude the presence of elements or steps other than those listed in a claim. The word 'a' or 'an' preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a system claim enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words 'first','second', and 'third', etc. do not imply any ordering. These words are used to distinguish a certain element from another one. The steps of the methods described herein can be carried out in any order unless otherwise specified or clear from the context.

Claims

1. A method for predicting vehicle energy consumption, characterized in that, include: Obtain vehicle route information, vehicle location information, and average energy consumption information; the route information includes the road segment to be driven and multiple energy consumption calibration road segments, wherein the multiple energy consumption calibration road segments are of the same length, the length of the road segment to be driven is greater than that of the energy consumption calibration road segments, and the starting point of the first energy consumption calibration road segment is the starting position in the route information, and the ending point of the road segment to be driven is the destination position in the route information. The energy consumption of the vehicle in the energy consumption calibration road section is predicted based on the initial energy consumption prediction model and the average energy consumption information. The energy consumption of the road section with the same length as the energy consumption calibration road section and similar driving information is selected as the first energy consumption information. Based on the energy consumption calibration road section and the vehicle location information, determine the actual energy consumption information of the vehicle when driving in the energy consumption calibration road section; If the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, the parameters of the initial energy consumption prediction model are iteratively updated according to the difference until a target energy consumption prediction model that meets the preset conditions is obtained. The energy consumption of the road segment to be driven is predicted based on the target energy consumption prediction model to obtain the second energy consumption information; Based on the second energy consumption information, an energy consumption reminder message is generated; The initial energy consumption prediction model includes a weather sub-model, a road condition sub-model, a vehicle weight sub-model, a slope sub-model, and a driving habit sub-model. Before predicting the energy consumption of the vehicle on the energy consumption calibration road section based on the initial energy consumption prediction model and the average energy consumption information, the method further includes: Based on the vehicle location information, weather information is determined and input into the weather sub-model; Based on the route information, determine the road condition information and input it into the road condition sub-model; Based on the vehicle's identification information, determine the vehicle weight information and input it into the vehicle weight sub-model; Based on the slope signals collected by the vehicle's sensors, the slope information is determined and input into the slope sub-model; Based on the rate of change of accelerator pedal opening and the rate of change of vehicle acceleration, the driver's driving habit type is determined and input into the driving habit sub-model.

2. The vehicle energy consumption prediction method according to claim 1, characterized in that, The step of determining the driver's driving habit type based on the rate of change of accelerator pedal opening and the rate of change of vehicle acceleration includes: If the rate of change of the accelerator pedal opening is greater than the preset rate of change of opening, or if the rate of change of the vehicle's acceleration within a preset time period is greater than the preset rate of change of acceleration, then the driver's driving habit type is determined to be the first driving habit type. If the rate of change of the accelerator pedal opening is less than the preset rate of change of opening, or if the rate of change of the vehicle's acceleration within a preset time period is less than the preset rate of change of acceleration, then the driver's driving habit type is determined to be the second driving habit type.

3. The vehicle energy consumption prediction method according to claim 1, characterized in that, Based on the initial energy consumption prediction model and the average energy consumption information, the energy consumption of the vehicle on the energy consumption calibration road section is predicted to obtain first energy consumption information, including: Based on the weather information and the weather sub-model, determine the first additional energy consumption corresponding to the weather information; Based on the road condition information and the road condition sub-model, determine the second additional energy consumption corresponding to the road condition information; Based on the vehicle weight information and the vehicle weight sub-model, determine the third additional energy consumption corresponding to the vehicle weight information; Based on the slope information and the slope sub-model, determine the fourth additional energy consumption corresponding to the slope information; Based on the driving habit type and the driving habit sub-model, determine the fifth additional energy consumption corresponding to the driving habit type; The first energy consumption information is determined based on the average energy consumption information and at least one of the first additional energy consumption, the second additional energy consumption, the third additional energy consumption, the fourth additional energy consumption, and the fifth additional energy consumption.

4. The vehicle energy consumption prediction method according to claim 3, characterized in that, The parameters of the initial energy consumption prediction model include at least one of the following: a first energy consumption coefficient corresponding to the weather sub-model, a second energy consumption coefficient corresponding to the road condition sub-model, a third energy consumption coefficient corresponding to the vehicle weight sub-model, a fourth energy consumption coefficient corresponding to the slope sub-model, and a fifth energy consumption coefficient corresponding to the driving habit sub-model. The iterative update of the parameters of the initial energy consumption prediction model based on the difference includes at least one of the following: Update the first energy consumption coefficient based on the difference and the weather sub-model; Update the second energy consumption coefficient based on the difference and the road condition sub-model; The third energy consumption coefficient is updated based on the difference and the vehicle weight sub-model; The fourth energy consumption coefficient is updated based on the difference and the slope sub-model; The fifth energy consumption coefficient is updated based on the difference and the driving habit sub-model.

5. The vehicle energy consumption prediction method according to claim 3, characterized in that, The slope information includes at least one of slope angle and slope length. If the slope angle indicates an uphill slope, the fourth additional energy consumption corresponding to the slope information is a positive value; if the slope angle indicates a downhill slope, the fourth additional energy consumption corresponding to the slope information is a negative value.

6. The vehicle energy consumption prediction method according to claim 1, characterized in that, Before predicting the energy consumption of the vehicle on the energy consumption calibration road segment based on the initial energy consumption prediction model and the average energy consumption information to obtain the first energy consumption information, the method further includes: Send an energy consumption prediction request to the server, the energy consumption prediction request being used to request the server to predict the energy consumption of the vehicle.

7. A vehicle energy consumption prediction system, characterized in that, include: An information acquisition unit is used to acquire the vehicle's route information, vehicle location information, and average energy consumption information, wherein multiple energy consumption calibration road segments are of the same length, the length of the road segment to be traveled is greater than that of the energy consumption calibration road segments, and the starting point of the first energy consumption calibration road segment is the starting position in the route information, and the ending point of the road segment to be traveled is the destination position in the route information; and, based on the energy consumption calibration road segments and the vehicle location information, to determine the actual energy consumption information of the vehicle when traveling in the energy consumption calibration road segments; The energy consumption calculation unit is used to predict the energy consumption of the vehicle on the energy consumption calibration road segment according to an initial energy consumption prediction model. It selects the energy consumption of a road segment with the same length as the energy consumption calibration road segment and similar driving information from the average energy consumption information as the first energy consumption information. The initial energy consumption prediction model includes a weather sub-model, a road condition sub-model, a vehicle weight sub-model, a slope sub-model, and a driving habit sub-model. It is also used to determine weather information based on the vehicle location information and input it into the weather sub-model; and to determine road condition information based on the route information and input it into the road condition sub-model. Based on the vehicle's identification information, the vehicle weight information is determined and input into the vehicle weight sub-model; based on the slope signals collected by the vehicle's sensors, the slope information is determined and input into the slope sub-model; based on the rate of change of accelerator pedal opening and the rate of change of vehicle acceleration, the driver's driving habit type is determined and input into the driving habit sub-model; and it is also used to predict the energy consumption of the road segment to be traveled in the route information according to the target energy consumption prediction model to obtain second energy consumption information; An energy consumption calibration unit is used to iteratively update the parameters of the initial energy consumption prediction model based on the difference if the difference between the first energy consumption information and the actual energy consumption information is greater than a preset threshold, until the target energy consumption prediction model that meets the preset conditions is obtained. An energy consumption reminder unit is used to generate energy consumption reminder information based on the second energy consumption information.

8. A vehicle energy consumption prediction device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of a vehicle energy consumption prediction method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the vehicle energy consumption prediction system / device, causes the vehicle energy consumption prediction system / device to perform the operation of the vehicle energy consumption prediction method as described in any one of claims 1-6.

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