Vehicle range prediction method, device, electronic device and storage medium

Abstract

The present disclosure proposes a method, device, electronic device and storage medium for predicting vehicle cruising range, which relates to the field of autonomous driving technology. The method includes: obtaining parameter data of the vehicle during current driving; determining the predicted energy consumption of each energy-consuming component according to the preset energy consumption prediction strategy of multiple energy-consuming components in the vehicle and the parameter data; generating the predicted energy consumption of the entire vehicle according to the predicted energy consumption of each energy-consuming component; and determining the corresponding cruising range according to the predicted energy consumption of the entire vehicle based on a preset mapping relationship. In this way, the vehicle can be divided into different energy-consuming components, and the corresponding energy consumption prediction strategy can be applied according to the working condition of each energy-consuming component, thereby making the predicted energy consumption value of each energy-consuming component more consistent with the actual energy consumption value, thereby making the predicted energy consumption of the entire vehicle more accurate and reliable, and thereby making the predicted cruising range more credible.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and in particular to a method, device, electronic device, and storage medium for predicting vehicle range. Background Art

[0002] During the vehicle development process, vehicle energy consumption is an important indicator affecting vehicle development. In order to ensure that the energy consumption after development is completed reaches the preset energy consumption target, it is necessary to continuously conduct working condition tests on the entire vehicle.

[0003] Current vehicles typically only predict the energy consumption of a vehicle for a particular trip based on traffic conditions and battery power. Because these factors are very limited, the actual vehicle energy consumption can differ significantly, leading to inaccurate range predictions. Summary of the Invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the purpose of the present disclosure is to provide a method, device, electronic device and storage medium for predicting vehicle range.

[0006] The method for predicting vehicle range proposed in the first embodiment of the present disclosure includes:

[0007] Get the parameter data of the vehicle currently driving;

[0008] Determining the predicted energy consumption of each energy-consuming component according to preset energy consumption prediction strategies of multiple energy-consuming components in the vehicle and the parameter data;

[0009] generating a predicted energy consumption of the entire vehicle according to the predicted energy consumption of each of the energy-consuming components;

[0010] Based on the preset mapping relationship, the corresponding cruising range is determined according to the predicted energy consumption of the entire vehicle.

[0011] A vehicle range prediction device according to a second embodiment of the present disclosure includes:

[0012] The acquisition module is used to obtain the parameter data of the vehicle during its current driving;

[0013] a first determining module, configured to determine the predicted energy consumption of each energy-consuming component in the vehicle based on a preset energy consumption prediction strategy for the plurality of energy-consuming components and the parameter data;

[0014] A generating module, configured to generate a predicted energy consumption of the entire vehicle according to the predicted energy consumption of each of the energy-consuming components;

[0015] The second determination module is used to determine the corresponding cruising range according to the predicted energy consumption of the vehicle based on a preset mapping relationship.

[0016] The third aspect embodiment of the present disclosure proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the vehicle range prediction method proposed in the first aspect embodiment of the present disclosure is implemented.

[0017] The fourth embodiment of the present disclosure proposes a non-temporary computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for predicting the vehicle cruising range proposed in the first embodiment of the present disclosure is implemented.

[0018] The fifth embodiment of the present disclosure provides a vehicle on which the electronic device provided in the third embodiment of the present disclosure is provided.

[0019] In the disclosed embodiment, the parameter data of the vehicle during its current driving is first obtained. Then, based on the preset energy consumption prediction strategies and parameter data of multiple energy-consuming components in the vehicle, the predicted energy consumption of each energy-consuming component is determined. Then, based on the predicted energy consumption of each energy-consuming component, the predicted energy consumption of the entire vehicle is generated. Then, based on the preset mapping relationship, the corresponding cruising range is determined based on the predicted energy consumption of the entire vehicle. In this way, the vehicle can be divided into different energy-consuming components, and the corresponding energy consumption prediction strategies can be applied according to the operating conditions of each energy-consuming component. This makes the predicted energy consumption value of each energy-consuming component more consistent with the actual energy consumption value, thereby making the predicted energy consumption of the entire vehicle more accurate and reliable, and thus making the predicted cruising range more reliable.

[0020] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0022] Figure 1 This is a flow chart of a method for predicting vehicle cruising range proposed in one embodiment of the present disclosure;

[0023] Figure 2 is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure;

[0024] Figure 3 is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure;

[0025] Figure 4is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure;

[0026] Figure 5 is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure;

[0027] Figure 6 is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure;

[0028] Figure 7 is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure;

[0029] Figure 8 is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure;

[0030] Figure 9 1 is a schematic structural diagram of a vehicle range prediction device according to an embodiment of the present disclosure;

[0031] Figure 10 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0032] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present disclosure and are not to be construed as limiting the present disclosure. On the contrary, the embodiments of the present disclosure include all variations, modifications, and equivalents that fall within the spirit and scope of the appended claims.

[0033] The present disclosure provides a method for predicting vehicle range. This method can be performed by a vehicle range prediction device provided herein. This device can be implemented using software and / or hardware, or can be performed by an electronic device provided herein. The vehicle range prediction device or electronic device can be configured in a vehicle. Below, the vehicle range prediction method provided herein is performed using the vehicle range prediction device provided herein, without limiting the present disclosure, and is hereinafter referred to as the "device."

[0034] Figure 1 1 is a flow chart of a method for predicting vehicle cruising range proposed in one embodiment of the present disclosure.

[0035] like Figure 1 As shown, the method for predicting the vehicle's cruising range includes:

[0036] S101: Acquire parameter data of the vehicle currently traveling.

[0037] In the present disclosure, the vehicle may be a pure electric vehicle or a hybrid electric vehicle, which is not limited here.

[0038] The parameter data may be driving condition parameters, vehicle parameters, and user habit parameters acquired while the vehicle is currently traveling, and are not limited here.

[0039] Specifically, the driving condition parameters may include the vehicle's current driving scene data, such as traffic condition data, road data, pedestrian data, wind resistance, tire rolling resistance, obstacle data, ambient temperature data, ambient humidity data, ambient weather data, daily meteorological data, driving time period, etc., which are not limited here.

[0040] Among them, vehicle parameters may include the current speed of the vehicle, image data captured by the camera, data obtained by various sensors (such as the mass measured by the weight sensor), battery health status, battery power, battery discharge power, battery cell internal resistance, battery current, battery voltage, battery temperature, and actual usage parameters of various cabin equipment (such as audio volume, screen brightness), air-conditioning temperature, etc., which are not limited here.

[0041] It should be noted that users typically have their own personal driving habits, such as driving speed, air conditioning temperature setting range, and cabin equipment settings (audio volume setting range, screen brightness setting range), etc. It is understandable that the use of the vehicle and its equipment will significantly affect the vehicle's overall energy consumption, and thus the vehicle's range.

[0042] Furthermore, users' vehicle usage varies at different times and in different driving scenarios. For example, their vehicle usage differs between weekends and weekdays, between commuting and rural areas, and between daytime and nighttime. There are many other uses that we won't elaborate on here. In other words, users' vehicle usage varies at different times and in different driving scenarios.

[0043] Optionally, in the present disclosure, the device can determine the user habit parameters corresponding to the current driving time based on the user's usage records of various in-vehicle devices and driving speed records before the current driving time, which is not limited here.

[0044] In some cases, the number of people in a vehicle can significantly impact vehicle usage. For example, the air conditioning temperature settings will differ between a single person in the driver's seat and a fully occupied vehicle. The usage of various in-cabin devices (such as gaming devices, music players, video players, navigation systems, and in-car virtual reality devices) will also differ.

[0045] Therefore, the seat distribution of each person and the corresponding cockpit equipment usage habit data can be pre-associated and stored in the user habit database, or each driving time period and the corresponding cockpit equipment usage habit data can be pre-associated and stored in the user habit database, which is not limited here.

[0046] As a possible implementation method, the device can obtain user habit data corresponding to the current vehicle occupant seat distribution and driving time period from a pre-established user habit database, which is not limited here.

[0047] S102: Determine the predicted energy consumption of each energy-consuming component according to preset energy consumption prediction strategies and parameter data of multiple energy-consuming components in the vehicle.

[0048] The energy-consuming components may be components included in the energy-consuming system of the vehicle.

[0049] Specifically, the device can divide the vehicle into various energy consumption systems and determine the energy consumption components corresponding to each energy consumption system.

[0050] For example, energy-consuming components may be electric drive equipment included in the power system, cabin equipment included in the cabin system, components included in the air-conditioning system, components included in the battery aging system, and components included in the intelligent driving system, and are not limited here.

[0051] The energy consumption prediction strategy can be an energy consumption prediction method corresponding to an energy-consuming component. It is understood that each energy-consuming component is different, and therefore the conversion relationship between the operating status and energy consumption of each energy-consuming component also varies. Therefore, by performing targeted analysis and prediction on each energy-consuming component, the predicted energy consumption of each energy-consuming component can be made more accurate and reliable.

[0052] For example, for the energy consumption of the air-conditioning system, the device can estimate the temperature change curve based on the weather data of the day contained in the parameter data, the ambient temperature measured by the vehicle's actual ambient temperature sensor, and the user's air-conditioning usage habits. Then, based on the conversion relationship between energy consumption and temperature, the predicted energy consumption of the components included in the air-conditioning system under the current driving conditions can be obtained.

[0053] The air-conditioning usage habit may be a temperature threshold for users to turn on the air-conditioning system, and the temperature change curve may be a curve showing the temperature difference between the current ambient temperature and the air-conditioning set temperature changing over time.

[0054] The conversion relationship between energy consumption and temperature can refer to a predetermined heat dissipation equation, which may have been calibrated during the vehicle prototype phase. Specifically, based on this conversion relationship, the device can determine energy consumption values ​​at different temperature differences based on the difference between the ambient temperature and the set temperature. Furthermore, by estimating the temperature differences during the predicted driving period, the predicted energy consumption corresponding to the air conditioning system components can be determined.

[0055] It should be noted that the above examples are merely illustrative and do not constitute a limitation to the present disclosure.

[0056] Optionally, the device can predict the driving time based on the user habit parameters included in the parameter data and the ambient temperature and driving time period included in the driving condition parameters.

[0057] S103: Generate the predicted energy consumption of the entire vehicle based on the predicted energy consumption of each energy-consuming component.

[0058] The predicted energy consumption of the entire vehicle may be the total predicted energy consumption of the vehicle.

[0059] Optionally, the device may take the sum of the predicted energy consumption of each energy-consuming component as the predicted energy consumption of the entire vehicle.

[0060] S104: Based on a preset mapping relationship, the corresponding cruising range is determined according to the predicted energy consumption of the entire vehicle.

[0061] The cruising range may be the predicted maximum distance that the vehicle can travel.

[0062] Optionally, if the vehicle is a pure electric vehicle, the predicted energy consumption of the entire vehicle is only the electricity consumption. The device can determine the predicted electricity consumption corresponding to unit driving time based on the predicted driving time and the predicted energy consumption of the entire vehicle, that is, the average electricity consumption. The device can then determine the predicted driving time of the vehicle based on the current battery power of the vehicle and the average electricity consumption. The device can then determine the driving distance corresponding to the predicted driving time as the cruising range based on the predicted average driving speed of the current vehicle.

[0063] For example, if the current predicted energy consumption of a pure electric vehicle is Q and the predicted driving time is t, the average power consumption per unit time x = Q / t can be calculated. Then, based on the current battery power X and the average power consumption x, the predicted driving time T = X / x of the pure electric vehicle can be determined. Then, based on the current predicted average driving speed of the vehicle, the cruising range S corresponding to the current predicted driving time T can be determined.

[0064] It should be noted that the above example is only an illustrative description and is not limited to this disclosure.

[0065] Optionally, if the vehicle is a hybrid vehicle, the predicted energy consumption of the entire vehicle can be the fuel consumption plus the electricity consumption. The device can calculate the cruising range based on the fuel consumption and the electricity consumption respectively, and then add the cruising ranges calculated based on the fuel consumption and the electricity consumption respectively to obtain the final cruising range, which is not limited here.

[0066] In the disclosed embodiment, the parameter data of the vehicle during its current driving is first obtained. Then, based on the preset energy consumption prediction strategies and parameter data of multiple energy-consuming components in the vehicle, the predicted energy consumption of each energy-consuming component is determined. Then, based on the predicted energy consumption of each energy-consuming component, the predicted energy consumption of the entire vehicle is generated. Then, based on the preset mapping relationship, the corresponding cruising range is determined based on the predicted energy consumption of the entire vehicle. In this way, the vehicle can be divided into different energy-consuming components, and the corresponding energy consumption prediction strategies can be applied according to the operating conditions of each energy-consuming component. This makes the predicted energy consumption value of each energy-consuming component more consistent with the actual energy consumption value, thereby making the predicted energy consumption of the entire vehicle more accurate and reliable, and thus making the predicted cruising range more reliable.

[0067] Figure 2 It is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure.

[0068] like Figure 2 As shown, the method for predicting the vehicle's cruising range includes:

[0069] S201: Acquire parameter data of the vehicle currently traveling.

[0070] It should be noted that the specific implementation of step S201 can refer to the above embodiment and will not be described in detail here.

[0071] S202: Acquire each single driving parameter data of each first reference vehicle, wherein the single driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the first reference vehicle in any complete driving process.

[0072] The first reference vehicle may be a vehicle of the same model as the current vehicle.

[0073] The cloud database may contain single-trip parameter data of multiple vehicles. When the current vehicle is connected to the Internet, the device can obtain data from the cloud database.

[0074] Optionally, the device may first determine a plurality of matching first reference vehicles according to the model of the current vehicle from a database in the cloud, and then obtain single driving parameter data corresponding to the plurality of first reference vehicles.

[0075] It should be noted that if the vehicle models in the current cloud database are all different from the current vehicle model, or if the number of vehicles with the same model as the current vehicle is relatively small (less than a preset threshold), then the parameters of the energy-consuming components of the vehicles in the database can be matched with the parameters of the energy-consuming components of the current vehicle to determine multiple vehicles with a matching degree greater than a preset threshold, and these multiple vehicles are determined as the first reference vehicles, which is not limited here.

[0076] The single-trip driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the first reference vehicle during any complete driving process. It is understood that the acquired single-trip driving parameter data for each first reference vehicle can provide data support for subsequent prediction of energy consumption of energy-consuming components of the current vehicle.

[0077] S203: Determine the similarity between the parameter data and each single driving parameter data.

[0078] Specifically, the parameter data of the current vehicle may be matched with the single driving parameter data of each first reference vehicle to determine the matching degree.

[0079] Optionally, the ambient temperature, traffic conditions, vehicle usage times, and driving time periods contained in the parameter data of the current vehicle can be matched with the ambient temperature, traffic conditions, vehicle usage times, and driving time periods contained in each single driving parameter data of the first reference vehicle to determine the degree of matching between the parameter data of the current vehicle and each single driving parameter data, and the matching degree is determined as the similarity between the parameter data of the current vehicle and the single driving parameter data, which is not limited here.

[0080] It can be understood that if the similarity between the current vehicle parameter data and each single driving parameter data is relatively high, it means that the current single driving parameter data has a higher reference value and a greater value.

[0081] S204: Determine each target single driving parameter data having a similarity greater than a preset threshold, and each corresponding second reference vehicle.

[0082] The preset threshold may be a pre-set similarity threshold, and the specific value may be determined based on actual experience.

[0083] The second reference vehicle may be the first reference vehicle whose single driving parameter data and parameter data of the current vehicle have a similarity greater than a preset threshold.

[0084] The target single driving parameter data may be single driving parameter data whose similarity with the parameter data of the current vehicle is greater than a preset threshold.

[0085] It should be noted that if the similarity between the single driving parameter data and the parameter data of the current vehicle is greater than the preset threshold, it means that the vehicle driving condition corresponding to the single driving parameter data is relatively close to the current vehicle. Therefore, the single driving parameter data can be determined as the target single driving parameter data, and the first reference vehicle can be determined as the second reference vehicle.

[0086] S205: Determine the energy consumption of the electric drive device of each second reference vehicle based on each target single driving parameter data.

[0087] Among them, the electric drive equipment mainly consists of motors and power electronic controllers.

[0088] Specifically, the device can determine the energy consumption of the electric drive device of the second reference vehicle based on the energy consumption of each component of the electric drive device during the driving process contained in the target single driving parameter data, such as power consumption and fuel consumption.

[0089] S206: Determine the average of the energy consumption of each electric drive device as the predicted energy consumption corresponding to the electric drive device included in the power system.

[0090] For example, if there are currently three second reference vehicles, and the electric drive equipment energy consumption corresponding to the target single driving parameter data of these three second reference vehicles are S1, S2, and S3 respectively, then (S1+S2+S3) / 3 can be determined as the predicted energy consumption corresponding to the electric drive equipment included in the current vehicle power system.

[0091] It should be noted that the above examples are merely illustrative and do not constitute a limitation to the present disclosure.

[0092] S207: Generate the predicted energy consumption of the entire vehicle based on the predicted energy consumption of each energy-consuming component.

[0093] S208: Based on a preset mapping relationship, determine the corresponding cruising range according to the predicted energy consumption of the entire vehicle.

[0094] It should be noted that the specific implementation of steps S207 and S208 can refer to the above embodiment and will not be described in detail here.

[0095] In the disclosed embodiment, the parameter data of the vehicle during the current driving process is first obtained, and then the single driving parameter data of each first reference vehicle is obtained, wherein the single driving parameter data includes the vehicle parameters, driving condition parameters, and user habit parameters of the first reference vehicle during any complete driving process. The similarity between the parameter data and each single driving parameter data is then determined. Then, each target single driving parameter data having a similarity greater than a preset threshold is determined, as well as each corresponding second reference vehicle. Then, based on each target single driving parameter data, the energy consumption of the electric drive device of each second reference vehicle is determined. Then, the average of the energy consumption of each electric drive device is determined as the predicted energy consumption corresponding to the electric drive device included in the power system. Then, the predicted energy consumption of the entire vehicle is generated based on the predicted energy consumption of each energy-consuming component. Then, based on a preset mapping relationship, the corresponding cruising range is determined based on the predicted energy consumption of the entire vehicle. In this way, the energy consumption of the components of the power system can be predicted according to the energy consumption prediction strategy corresponding to the power system, making the predicted energy consumption corresponding to the power system more accurate and reliable. Since the target single-trip parameter data of the second reference vehicle is selected with a relatively high similarity to the parameter data of the current vehicle, the energy consumption of the electric drive equipment through the target single-trip parameter data of the second reference vehicle can well represent the predicted energy consumption of the electric drive equipment in the power system of the current vehicle during the driving process at this time.

[0096] Figure 3 It is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure.

[0097] like Figure 3 As shown, the method for predicting the vehicle's cruising range includes:

[0098] S301: Obtaining the test energy consumption of the air-conditioning system of the vehicle at a set air-conditioning temperature and multiple constant ambient temperatures.

[0099] The set air-conditioning temperature may be a preset air-conditioning temperature value, which may usually be set to room temperature, such as 20° C., and is not limited here.

[0100] The constant ambient temperature may be a preset operating temperature, and the multiple constant ambient temperatures may be -10°C, 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C, 40°C, etc., without limitation herein. It should be noted that each constant ambient temperature may be used to simulate external ambient temperatures in different seasons and weather conditions.

[0101] The test energy consumption may be the energy consumption determined under the test conditions during the test phase.

[0102] It's important to note that under the same operating conditions and at the same time, vehicles consume more energy in winter. This means the predicted energy consumption of the air conditioning system is related to the temperature difference between the ambient temperature and the vehicle's interior. Therefore, while the vehicle is in the prototype stage, it's appropriate to test the air conditioning system's energy consumption per unit time at the set air conditioning temperature and at various constant ambient temperatures.

[0103] S302: Determine the temperature difference between the set air-conditioning temperature and each constant ambient temperature, and the corresponding relationship between each temperature difference and the test energy consumption.

[0104] It should be noted that the energy consumption of air conditioning system components is closely related to the ambient temperature and the air conditioning temperature. Therefore, during calibration, it is necessary to determine the temperature difference between the constant ambient temperature and the set air conditioning temperature. Each temperature difference corresponds to the measured energy consumption of the air conditioner.

[0105] S303: Determine a conversion coefficient between the temperature difference and the test energy consumption according to the corresponding relationship between each temperature difference and the test energy consumption.

[0106] Among them, the conversion coefficient is used to characterize the conversion relationship between the temperature difference and energy consumption of the air conditioner.

[0107] Specifically, the conversion coefficient between the temperature difference and the test energy consumption may be determined according to the corresponding relationship between each temperature difference and the test energy consumption.

[0108] For example, if the test energy consumption corresponding to the temperature difference a is A, the test energy consumption corresponding to the temperature difference b is B, and if the test energy consumption corresponding to the temperature difference c is C, the conversion coefficient can be determined as (A+B+C) / (a+b+c).

[0109] S304: Acquire parameter data of the vehicle currently traveling.

[0110] It should be noted that the specific implementation of step S304 can refer to the above embodiment and will not be described in detail here.

[0111] S305: Input the air conditioning temperature setting range in the user habit parameters, the current vehicle ambient temperature and the weather data of the day in the driving condition parameters, and the actual air conditioning temperature in the vehicle parameters into the pre-trained temperature prediction model to determine the temperature change curve corresponding to the predicted driving time period.

[0112] The air conditioner temperature setting range may be a temperature range determined based on the user's usual air conditioner temperature setting, such as 20-23°C. The daily weather data may be temperature data for the current date, such as hourly weather temperatures. The actual air conditioner temperature may be the current air conditioner setting temperature.

[0113] The temperature change curve may be a temperature change curve in which the horizontal axis represents time and the vertical axis represents the predicted ambient temperature and the air-conditioning temperature.

[0114] Among them, the temperature prediction model can be a pre-trained neural network model. By inputting the air conditioning temperature setting range, ambient temperature, daily meteorological data and actual air conditioning temperature into the temperature prediction model, the temperature change curve during the predicted driving time period can be determined.

[0115] Optionally, the device can input parameter data of the vehicle's current driving time into a pre-trained time prediction model to obtain the vehicle's current predicted driving time.

[0116] The time prediction model may be a pre-trained neural network model.

[0117] Optionally, traffic condition parameters, weather parameters of the day, ambient temperature parameters, driving time period, and user habit parameters contained in the parameter data of the vehicle's current driving time may be input into the time prediction model to obtain the vehicle's current predicted driving time.

[0118] S306: Based on a preset conversion coefficient and according to the temperature corresponding to the temperature change curve, the predicted energy consumption corresponding to the components included in the air-conditioning system is determined.

[0119] Optionally, the device can determine the temperature difference corresponding to each time point in the predicted driving time period based on the ambient temperature and air conditioner temperature corresponding to the temperature variation curve, thereby determining a temperature difference variation curve. The device can then determine the energy consumption of the air conditioning system affected by the temperature difference based on a preset conversion coefficient and the temperature difference in the temperature difference variation curve. The device can then calculate the power consumption of the air conditioning system, i.e., the predicted energy consumption corresponding to the components of the air conditioning system, based on the air conditioner temperature in the temperature difference variation curve and the corresponding temperature difference.

[0120] S307: Generate the predicted energy consumption of the entire vehicle based on the predicted energy consumption of each energy-consuming component.

[0121] S308: Based on a preset mapping relationship, determine the corresponding cruising range according to the predicted energy consumption of the vehicle.

[0122] It should be noted that the specific implementation of steps S307 and S308 can refer to the above embodiment and will not be described in detail here.

[0123] In the disclosed embodiment, the test energy consumption of the air conditioning system of a vehicle at a set air conditioning temperature and multiple constant ambient temperatures is first obtained. The temperature difference between the set air conditioning temperature and each constant ambient temperature is then determined, as well as the correspondence between each temperature difference and the test energy consumption. Based on the correspondence between each temperature difference and the test energy consumption, a conversion coefficient is then determined between the temperature difference and the test energy consumption. The vehicle's current driving parameter data is then obtained. The air conditioning temperature setting range in the user's habitual parameters, the current vehicle ambient temperature and daily weather data in the driving condition parameters, and the actual air conditioning temperature in the vehicle parameters are then input into a pre-trained temperature prediction model to determine a temperature change curve corresponding to the predicted driving time period. Based on the temperature corresponding to the temperature change curve, the predicted energy consumption of the components in the air conditioning system is then determined based on the preset conversion coefficient. Thus, the energy consumption of the components in the air conditioning system can be predicted based on the energy consumption prediction strategy corresponding to the air conditioning system, making the predicted energy consumption of the air conditioning system more accurate and reliable. Because the temperature change curve is predicted based on the temperature prediction model, it is more reliable and scientific. The temperature is then converted using the pre-calibrated conversion coefficient, making the final predicted energy consumption of the components in the air conditioning system accurate and reliable.

[0124] Figure 4 It is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure.

[0125] like Figure 4 As shown, the method for predicting the vehicle's cruising range includes:

[0126] S401: Acquire parameter data of the vehicle currently traveling.

[0127] It should be noted that the specific implementation of step S401 can refer to the above embodiment and will not be described in detail here.

[0128] S402: Inputting the road condition data in the driving condition parameters, the driving speed range in the user habit parameters, and the actual driving speed in the vehicle parameters into a pre-trained vehicle speed prediction model to determine a vehicle speed change curve corresponding to the predicted driving time period.

[0129] The road condition data may be the road data on which the vehicle is currently traveling, such as expressways, national highways, ring roads, and traffic condition data on the roads, such as school sections and downtown sections, which are not limited here.

[0130] The driving speed range may be a speed at which the user's vehicle is usually driven, such as 40-65 km / h, which is not limited here. The actual driving speed may be the current driving speed of the vehicle.

[0131] The vehicle speed prediction model can be a pre-trained neural network model. By inputting the road condition data in the driving condition parameters, the driving speed range in the user habit parameters, and the actual driving speed in the vehicle parameters into the pre-trained vehicle speed prediction model, a vehicle speed change curve during the current driving process can be obtained.

[0132] The vehicle speed change curve may include vehicle driving data corresponding to the predicted driving time period of the current driving. Specifically, the vehicle speed change curve may be a curve with the horizontal axis representing time and the vertical axis representing the vehicle driving speed.

[0133] S403: Determine predicted energy consumption corresponding to components included in the intelligent driving system according to the vehicle driving speed corresponding to the predicted vehicle speed change curve.

[0134] Specifically, the device can determine the predicted energy consumption corresponding to the components included in the intelligent driving system within the predicted time period based on the vehicle driving speed corresponding to the vehicle speed change curve.

[0135] For example, the instantaneous energy consumption corresponding to each vehicle's driving speed can be determined based on P = FV, where V is the vehicle's driving speed, P is the predicted energy consumption, and F is the proportionality factor, which is a parameter pre-calibrated in the laboratory to correspond to the current vehicle's stress conditions.

[0136] S404: Generate the predicted energy consumption of the entire vehicle based on the predicted energy consumption of each energy-consuming component.

[0137] S405: Based on a preset mapping relationship, determine the corresponding cruising range according to the predicted energy consumption of the entire vehicle.

[0138] It should be noted that the specific implementation of steps S404 and S405 can refer to the above embodiment and will not be described in detail here.

[0139] In the disclosed embodiment, the parameter data of the vehicle during the current driving is first obtained, and then the road condition data in the driving condition parameters, the driving speed range in the user habit parameters, and the actual driving speed in the vehicle parameters are input into the pre-trained speed prediction model to determine the speed change curve corresponding to the predicted driving time period, and then the predicted energy consumption corresponding to the components included in the intelligent driving system is determined based on the vehicle driving speed corresponding to the predicted speed change curve, and then the predicted energy consumption of the entire vehicle is generated based on the predicted energy consumption of each energy-consuming component, and finally, based on the preset mapping relationship, the corresponding cruising range is determined based on the predicted energy consumption of the entire vehicle. In this way, the energy consumption corresponding to the components included in the current intelligent driving system can be predicted based on the vehicle driving speed in the current driving process predicted by the speed prediction model, so that the predicted energy consumption corresponding to the intelligent driving system is more accurate and reliable, and thus the cruising range determined in the end is more accurate and reliable.

[0140] Figure 5 It is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure.

[0141] like Figure 5 As shown, the method for predicting the vehicle's cruising range includes:

[0142] S501: Acquire parameter data of the vehicle currently traveling.

[0143] It should be noted that the specific implementation of step S501 can refer to the above embodiment and will not be described in detail here.

[0144] S502: Obtain the current seating distribution of passengers in the vehicle.

[0145] Among them, whether each seat is occupied can be determined based on the weight sensors on each seat in the vehicle, or the seat distribution of the personnel in the current vehicle can be determined based on the cameras installed in the vehicle.

[0146] For example, if there are 4 seats in the current vehicle, marked as A, B, C, and D, and only seat A is occupied, the seat distribution can be recorded as {A: 1, B: 0, C: 0, D: 0}.

[0147] S503: Determine the first cockpit equipment usage habit parameter corresponding to the occupant seat distribution according to the preset mapping relationship and the user habit parameter.

[0148] The first cockpit device usage habit parameter may be a user's cockpit device usage habit parameter determined based on the current seat distribution. In other words, the user habit parameters may be pre-classified, and cockpit device usage habit parameters corresponding to the same seat distribution may be classified as the same type of cockpit device usage habit parameter, i.e., the cockpit device usage habit parameters corresponding to that seat distribution.

[0149] After determining the current seating distribution of occupants in the vehicle, the device may determine corresponding first cockpit equipment usage habit parameters based on the current seating distribution of occupants in the vehicle.

[0150] S504: Based on pre-set classification conditions, classify the first cockpit equipment usage habit parameters according to the driving time period corresponding to the first cockpit equipment usage habit parameters, wherein the classification conditions include: whether the driving time period belongs to a working day or a non-working day, working hours or off-get off work hours, daytime or nighttime.

[0151] It should be noted that, since users have different usage habits of cockpit equipment in different driving time periods, the usage records of users of cockpit equipment in different time periods can be recorded in advance and classified.

[0152] For example, based on the user's driving time period, the first cockpit device usage habit parameter can be divided into weekday driving time period and non-workday driving time period, working time driving time period and off-get off work time period, daytime driving time period and nighttime driving time period. If the current driving time period is from 4:00 PM to 6:00 PM on Monday, the current driving time period can be determined to be weekday, working time, and daytime, without any limitation here.

[0153] S505: Determine corresponding second cockpit device usage habit parameters from the first cockpit device usage habit parameters according to the current driving time period of the vehicle.

[0154] The second cockpit equipment usage habit parameters include the first cockpit equipment usage habit parameters for the same driving time period.

[0155] After determining the first cockpit device usage habit parameter, the apparatus may filter based on the current driving time period of the vehicle to obtain the first cockpit device usage habit parameter for the same driving time period, that is, the second cockpit device usage habit parameter.

[0156] For example, the second cockpit equipment usage habit parameter may be the first cockpit equipment usage habit parameter corresponding to the current seat distribution of personnel, which are all night, non-working days, and off-duty time, and is not limited here.

[0157] S506: Determine predicted energy consumption corresponding to the cockpit devices included in the cockpit system during the predicted driving time period based on the second cockpit device usage habit parameter and the current cockpit device usage data.

[0158] It should be noted that the device can predict the energy consumption of the cabin equipment during this driving process based on the second cabin equipment usage habit parameters and the current cabin equipment usage data.

[0159] As a possible implementation method, the second cockpit equipment usage habit parameters and the current cockpit equipment usage data can be input into a pre-trained cockpit equipment energy consumption prediction model to obtain the predicted energy consumption corresponding to the cockpit equipment included in the cockpit system during the predicted driving time period.

[0160] Among them, the cockpit equipment energy consumption prediction model can be a neural network model.

[0161] S507: Generate the predicted energy consumption of the entire vehicle based on the predicted energy consumption of each energy-consuming component.

[0162] S508: Based on a preset mapping relationship, determine the corresponding cruising range according to the predicted energy consumption of the entire vehicle.

[0163] It should be noted that the specific implementation of steps S507 and S508 can refer to the above embodiment and will not be described in detail here.

[0164] In an embodiment of the present disclosure, parameter data of the vehicle during current driving is first obtained, followed by the current seating distribution of occupants in the vehicle. A first cockpit device usage habit parameter corresponding to the seating distribution is determined based on a preset mapping relationship and the user habit parameter. The first cockpit device usage habit parameter is then classified based on pre-set classification conditions and the driving time period corresponding to the first cockpit device usage habit parameter. A second cockpit device usage habit parameter corresponding to the first cockpit device usage habit parameter is then determined from the first cockpit device usage habit parameter based on the vehicle's current driving time period. The predicted energy consumption of the cockpit devices included in the cockpit system during the predicted driving time period is then determined based on the second cockpit device usage habit parameter and the current cockpit device usage data. The predicted energy consumption of the entire vehicle is then generated based on the predicted energy consumption of each energy-consuming component. The corresponding range is then determined based on the predicted energy consumption of the entire vehicle based on the preset mapping relationship. Thus, the energy consumption of the cockpit devices included in the cockpit system can be accurately and reliably predicted based on the user's cockpit device usage habits and the current driving time period.

[0165] Figure 6 It is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure.

[0166] like Figure 6 As shown, the method for predicting the vehicle's cruising range includes:

[0167] S601: Acquire parameter data of the vehicle currently traveling.

[0168] It should be noted that the specific implementation of step S601 can refer to the above embodiment and will not be described in detail here.

[0169] S602: Determine current status parameters of a battery in the vehicle, wherein the status parameters include an ambient temperature of the battery, an internal resistance of a battery cell, a battery state of charge, and a discharge power.

[0170] It's important to note that as the vehicle's power source, battery performance is affected by ambient temperature, necessitating a multifaceted approach to energy consumption estimation. Battery parameters such as total current, cell internal resistance, and cycle life all affect the battery's state of charge (SOC), indirectly impacting range estimation. Furthermore, battery degradation and braking energy recovery also impact energy consumption estimation.

[0171] The SOC represents the ratio of the battery's current charge to its rated charge.

[0172] It's understandable that ambient temperature affects the rate of energy consumption within the battery. Vehicles traveling the same mileage at different ambient temperatures will consume different amounts of energy. At lower temperatures, the battery's charge and discharge capacity decreases compared to room temperature, and internal resistance increases nonlinearly, leading to greater battery energy consumption and limiting the vehicle's range.

[0173] S603: Based on a preset mapping relationship and according to the current battery status parameters, the predicted energy consumption corresponding to the components included in the battery aging system within the predicted driving time period is determined.

[0174] It should be noted that when the vehicle is in the prototype stage, energy consumption testing and data recording can be performed on the battery in advance. For example, the discharge curve of the battery at different temperature intervals (5-10°C) and different discharge powers (0.1C-1C) can be confirmed. In addition, the battery internal resistance test feedback is performed at different temperatures and different SOCs (intervals of 5%-10%, with test points set according to the degree of SOC change), so as to obtain basic energy consumption values ​​under different conditions.

[0175] Furthermore, by obtaining the basic energy consumption values ​​under different conditions, the instantaneous energy consumption of the battery corresponding to each condition can be determined. After determining the current battery state parameters, the battery operating condition during the driving period can be predicted based on the battery operating condition corresponding to the current battery state parameters, and the predicted energy consumption corresponding to the components included in the battery aging system can be determined.

[0176] S604: Generate the predicted energy consumption of the entire vehicle based on the predicted energy consumption of each energy-consuming component.

[0177] S605: Based on a preset mapping relationship, determine the corresponding cruising range according to the predicted energy consumption of the entire vehicle.

[0178] It should be noted that the specific implementation of steps S604 and S605 can refer to the above embodiment and will not be described in detail here.

[0179] In the embodiment of the present disclosure, the parameter data of the vehicle during the current driving is first obtained, and then the state parameters of the current battery in the vehicle are determined, wherein the state parameters include the ambient temperature of the battery, the internal resistance of the battery cell, the battery state of charge, and the discharge power. Then, based on a preset mapping relationship, the predicted energy consumption corresponding to the components included in the battery aging system during the predicted driving time period is determined according to the state parameters of the current battery. The predicted energy consumption of the entire vehicle is generated according to the predicted energy consumption of each energy-consuming component. Finally, based on the preset mapping relationship, the corresponding cruising range is determined according to the predicted energy consumption of the entire vehicle. Thus, the predicted energy consumption corresponding to the components included in the battery aging system can be determined according to the state parameters of the battery and the predetermined mapping relationship between the state parameters of the battery and the energy consumption. Since the battery state is analyzed differentially, the energy consumption estimation accuracy of the components of the battery aging system can be improved.

[0180] Figure 7 It is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure.

[0181] like Figure 7 As shown, the method for predicting the vehicle's cruising range includes:

[0182] S701: Obtain parameter data of the vehicle currently traveling.

[0183] It should be noted that the specific implementation of step S701 can refer to the above embodiment and will not be described in detail here.

[0184] S702: Determine the predicted energy consumption of each energy-consuming component in the vehicle according to preset energy consumption prediction strategies for the multiple energy-consuming components and the parameter data.

[0185] It should be noted that the specific implementation of step S702 can refer to the above embodiment and will not be described in detail here.

[0186] S703: Determine the prediction accuracy of each energy-consuming component according to the actual energy consumption and the predicted energy consumption of each energy-consuming component after the vehicle completes the driving.

[0187] The prediction accuracy is used to characterize the accuracy of energy consumption prediction of each energy-consuming component.

[0188] For example, if the actual energy consumption corresponding to any energy-consuming component of the vehicle after driving is 8, and the predicted energy consumption is 7, then the current prediction accuracy of the energy-consuming component can be calculated to be 87.5%.

[0189] It should be noted that the above examples are merely illustrative and do not constitute a limitation to the present disclosure.

[0190] S704: Determine the current driving scenario of the vehicle based on the parameter data.

[0191] Among them, the current driving scene can be an urban driving scene, a rural driving scene, a seaside driving scene, an urban driving scene, a suburban driving scene, a school road driving scene, or a highway driving scene, and is not limited here.

[0192] Optionally, the device can determine the current driving scenario of the vehicle based on the driving condition parameters included in the parameter data.

[0193] S705: Send the current driving scenario and the prediction accuracy of each energy-consuming component in association to a calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component.

[0194] It should be noted that due to different road conditions, traffic rules and users' driving habits in different driving scenarios, the energy consumption of each energy-consuming component also varies.

[0195] In the present disclosure, the prediction accuracy of each energy-consuming component can be associated with the current driving scenario and sent to the calibration system, so that the calibration system can make corresponding adjustments to the energy consumption prediction strategy according to the current driving scenario.

[0196] Among them, the calibration system can be used to adjust the energy consumption prediction strategy. For example, each model can be trained and corrected to improve the accuracy of model recognition.

[0197] Specifically, by associating the current driving scenario and the prediction accuracy of each energy-consuming component and sending them to the calibration system, the calibration system can train and improve the model in the energy consumption prediction strategy of each energy-consuming component of the current vehicle based on the driving scenario.

[0198] S706: Generate the predicted energy consumption of the entire vehicle according to the predicted energy consumption of each of the energy-consuming components.

[0199] S707: Based on a preset mapping relationship, determine the corresponding cruising range according to the predicted energy consumption of the entire vehicle.

[0200] It should be noted that the specific implementation of steps S706 and S707 can refer to the above embodiment and will not be described in detail here.

[0201] In the embodiment of the present disclosure, the parameter data of the vehicle during current driving is first obtained, and then the predicted energy consumption of each energy-consuming component is determined based on the preset energy consumption prediction strategy of multiple energy-consuming components in the vehicle and the parameter data. Then, the prediction accuracy of each energy-consuming component is determined based on the actual energy consumption and predicted energy consumption of each energy-consuming component after the vehicle has finished driving. Then, the current driving scene of the vehicle is determined based on the parameter data. Then, the current driving scene and the prediction accuracy of each energy-consuming component are associated and sent to the calibration system so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component. Then, the predicted energy consumption of the entire vehicle is generated based on the predicted energy consumption of each energy-consuming component. Then, based on the preset mapping relationship, the corresponding cruising range is determined based on the predicted energy consumption of the entire vehicle. In this way, the energy consumption prediction strategy can be calibrated in combination with the current driving scene, so that the energy consumption prediction strategy can more reliably and accurately predict the energy consumption of the energy-consuming components of each driving scene, thereby making the final determined cruising range more reliable and accurate.

[0202] Figure 8 It is a flowchart of a method for predicting vehicle cruising range proposed in another embodiment of the present disclosure.

[0203] like Figure 8 As shown, the method for predicting the vehicle's cruising range includes:

[0204] S801: Obtain parameter data of the vehicle currently traveling.

[0205] S802: Determine the predicted energy consumption of each energy-consuming component in the vehicle according to preset energy consumption prediction strategies for the multiple energy-consuming components and the parameter data.

[0206] It should be noted that the specific implementation of steps S801 and S802 can refer to the above embodiment and will not be described in detail here.

[0207] S803: Obtain single driving parameter data of each third reference vehicle, wherein the single driving parameter data includes vehicle parameters, driving condition parameters and user habit parameters of the third reference vehicle in any complete driving process, wherein the prediction accuracy of the third reference vehicle is higher than the prediction accuracy of the vehicle.

[0208] The third reference vehicle may be a vehicle having a higher prediction accuracy than that of the current vehicle and having the same model as the current vehicle.

[0209] The cloud database may contain single-trip parameter data of multiple vehicles. When the current vehicle is connected to the Internet, the device can obtain data from the cloud database.

[0210] Optionally, the device may obtain single driving parameter data corresponding to multiple third reference vehicles from a database in the cloud.

[0211] The single-trip driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the third reference vehicle during any complete driving process. It is understood that the acquired single-trip driving parameter data for each third reference vehicle can provide data support for subsequent prediction of energy consumption of energy-consuming components of the current vehicle.

[0212] S804: Determine the similarity between the parameter data and each of the single driving parameter data.

[0213] Specifically, the parameter data of the current vehicle may be matched with the single driving parameter data of each third reference vehicle to determine the matching degree.

[0214] Optionally, the ambient temperature, traffic conditions, vehicle usage times, and driving time periods contained in the parameter data of the current vehicle can be matched with the ambient temperature, traffic conditions, vehicle usage times, and driving time periods contained in each single driving parameter data of a third reference vehicle to determine the degree of matching between the parameter data of the current vehicle and each single driving parameter data, and the matching degree is determined as the similarity between the parameter data of the current vehicle and the single driving parameter data, which is not limited here.

[0215] It can be understood that if the similarity between the current vehicle parameter data and each single driving parameter data is relatively high, it means that the current single driving parameter data has a higher reference value and a greater value.

[0216] S805: Determine a fourth reference vehicle corresponding to each target single driving parameter data having a similarity greater than a preset threshold.

[0217] The preset threshold may be a pre-set similarity threshold, and the specific value may be determined based on actual experience.

[0218] The fourth reference vehicle may be a third reference vehicle whose single driving parameter data and parameter data of the current vehicle have a similarity greater than a preset threshold.

[0219] The target single driving parameter data may be single driving parameter data whose similarity with the parameter data of the current vehicle is greater than a preset threshold.

[0220] It should be noted that if the similarity between the single driving parameter data and the parameter data of the current vehicle is greater than the preset threshold, it means that the vehicle driving condition corresponding to the single driving parameter data is relatively close to the current vehicle. Therefore, the single driving parameter data can be determined as the target single driving parameter data, and the third reference vehicle can be determined as the fourth reference vehicle.

[0221] S806: Send the energy consumption prediction strategy of each fourth reference vehicle, the target single driving parameter data corresponding to the fourth reference vehicle, and the prediction accuracy of each energy-consuming component of the current vehicle to the calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component of the current vehicle.

[0222] It should be noted that since the fourth reference vehicle has a high degree of similarity with the current vehicle and its data is better than that of the current vehicle, the energy consumption prediction strategy of each energy-consuming component of the current vehicle can be calibrated through the data of the fourth reference vehicle, so that the predicted energy consumption of the current vehicle is more consistent with the actual energy consumption, thereby improving the reliability of the vehicle's predicted energy consumption and predicted mileage.

[0223] Among them, the calibration system can be used to adjust the energy consumption prediction strategy. For example, each model can be trained and corrected to improve the accuracy of model recognition.

[0224] Specifically, by sending the energy consumption prediction strategy of each fourth reference vehicle, the target single driving parameter data corresponding to the fourth reference vehicle, and the prediction accuracy of each energy-consuming component of the current vehicle to the calibration system, the calibration system can train and improve the model in the energy consumption prediction strategy of each energy-consuming component of the current vehicle based on the target single driving parameter data of the fourth reference vehicle.

[0225] S807: Generate the predicted energy consumption of the entire vehicle according to the predicted energy consumption of each of the energy-consuming components.

[0226] S808: Based on a preset mapping relationship, determine the corresponding cruising range according to the predicted energy consumption of the entire vehicle.

[0227] It should be noted that the specific implementation of steps S807 and S808 can refer to the above embodiment and will not be described in detail here.

[0228] In an embodiment of the present disclosure, parameter data of the vehicle during current driving is first obtained, and then the predicted energy consumption of each energy-consuming component in the vehicle is determined based on the preset energy consumption prediction strategy of multiple energy-consuming components in the vehicle and the parameter data. Then, single driving parameter data of each third reference vehicle is obtained, and then the similarity between the parameter data and each single driving parameter data is determined. Then, a fourth reference vehicle corresponding to each target single driving parameter data having the similarity greater than a preset threshold is determined. Then, the energy consumption prediction strategy of each fourth reference vehicle, the target single driving parameter data corresponding to the fourth reference vehicle, and the prediction accuracy of each energy-consuming component of the current vehicle are sent to a calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component of the current vehicle. Then, the predicted energy consumption of the entire vehicle is generated based on the predicted energy consumption of each energy-consuming component. Finally, based on a preset mapping relationship, the corresponding cruising range is determined based on the predicted energy consumption of the entire vehicle. Therefore, the energy consumption prediction strategy can be calibrated in combination with the single driving parameter data of the fourth reference vehicle, so that the energy consumption prediction strategy can more reliably and accurately predict the energy consumption of each energy-consuming component, thereby making the final determined cruising range more reliable and accurate.

[0229] Figure 9 It is a structural diagram of a vehicle range prediction device proposed in one embodiment of the present disclosure.

[0230] like Figure 9 As shown, the vehicle cruising range prediction device 900 includes an acquisition module 910, a first determination module 920, a generation module 930 and a second determination module 940, wherein:

[0231] The acquisition module is used to obtain the parameter data of the vehicle during its current driving;

[0232] a first determining module, configured to determine the predicted energy consumption of each energy-consuming component in the vehicle based on a preset energy consumption prediction strategy for the plurality of energy-consuming components and the parameter data;

[0233] A generating module, configured to generate a predicted energy consumption of the entire vehicle according to the predicted energy consumption of each of the energy-consuming components;

[0234] The second determination module is used to determine the corresponding cruising range according to the predicted energy consumption of the vehicle based on a preset mapping relationship.

[0235] Optionally, the parameter data includes driving condition parameters, user habit parameters and vehicle parameters.

[0236] Optionally, the energy-consuming component is an electric drive device included in a power system, and the first determining module is specifically configured to:

[0237] Acquiring each single driving parameter data of each first reference vehicle, wherein the single driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the first reference vehicle during any complete driving process;

[0238] determining a similarity between the parameter data and each of the single driving parameter data;

[0239] Determining each target single driving parameter data having a similarity greater than a preset threshold, and each corresponding second reference vehicle;

[0240] determining the energy consumption of the electric drive device of each second reference vehicle based on each target single driving parameter data;

[0241] The average value of the energy consumption of each of the electric drive devices is determined as the predicted energy consumption corresponding to the electric drive devices included in the power system.

[0242] Optionally, the energy-consuming component is a component included in an air-conditioning system, and the first determining module is specifically configured to:

[0243] Inputting the air conditioning temperature setting range in the user habit parameters, the current vehicle ambient temperature and the weather data of the day in the driving condition parameters, and the actual air conditioning temperature in the vehicle parameters into a pre-trained temperature prediction model to determine a temperature change curve corresponding to a predicted driving time period;

[0244] Based on a preset conversion coefficient and according to the temperature corresponding to the temperature change curve, the predicted energy consumption corresponding to the components included in the air-conditioning system is determined.

[0245] Optionally, the energy-consuming component is a component included in an intelligent driving system, and the first determining module is specifically configured to:

[0246] Inputting the road condition data in the driving condition parameters, the driving speed range in the user habit parameters, and the actual driving speed in the vehicle parameters into a pre-trained vehicle speed prediction model to determine a vehicle speed change curve corresponding to a predicted driving time period;

[0247] The predicted energy consumption corresponding to the components included in the intelligent driving system is determined according to the vehicle driving speed corresponding to the predicted vehicle speed change curve.

[0248] Optionally, the energy-consuming component is a cockpit device included in a cockpit system, and the first determining module includes:

[0249] An acquisition unit, used to obtain the current seating distribution of people in the vehicle;

[0250] A first determining unit is configured to determine a first cockpit device usage habit parameter corresponding to the seat distribution of the personnel according to a preset mapping relationship and the user habit parameter;

[0251] a second determining unit, configured to determine, based on a current driving time period of the vehicle, a corresponding second cockpit device usage habit parameter from the first cockpit device usage habit parameter;

[0252] The third determining unit is configured to determine the predicted energy consumption corresponding to the cockpit equipment included in the cockpit system during the predicted driving time period based on the second cockpit equipment usage habit parameter and current cockpit equipment usage data.

[0253] Optionally, the second determining unit is further configured to:

[0254] Based on a preset classification condition, the first cockpit device usage habit parameter is classified according to the driving time period corresponding to the first cockpit device usage habit parameter,

[0255] The classification conditions include: whether the driving time period belongs to a working day or a non-working day, working hours or off-get off work hours, daytime or nighttime.

[0256] Optionally, the energy-consuming component is a component included in a battery aging system, and the first determining module is specifically configured to:

[0257] Determining current state parameters of the battery in the vehicle, wherein the state parameters include the ambient temperature of the battery, the internal resistance of the battery cell, the battery state of charge, and the discharge power;

[0258] Based on a preset mapping relationship and according to the current battery state parameters, predicted energy consumption corresponding to the components included in the battery aging system within a predicted driving time period is determined.

[0259] Optionally, the acquisition module is further configured to:

[0260] Obtain the test energy consumption of the vehicle's air conditioning system at a set air conditioning temperature and multiple constant ambient temperatures;

[0261] Determining the temperature difference between the set air-conditioning temperature and each of the constant ambient temperatures, and the corresponding relationship between each of the temperature differences and the test energy consumption;

[0262] According to the corresponding relationship between each of the temperature differences and the test energy consumption, a conversion coefficient between the temperature difference and the test energy consumption is determined.

[0263] Optionally, the acquisition module is further configured to:

[0264] The parameter data of the vehicle during its current driving is input into a pre-trained time prediction model to obtain the current predicted driving time of the vehicle.

[0265] Optionally, the first determining module is further configured to:

[0266] determining a prediction accuracy rate of each energy-consuming component according to the actual energy consumption of each energy-consuming component after the vehicle has finished traveling and the predicted energy consumption;

[0267] determining a current driving scenario of the vehicle based on the parameter data;

[0268] The current driving scenario and the prediction accuracy of each energy-consuming component are associated and sent to a calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component.

[0269] Optionally, the first determining module is further configured to:

[0270] Obtaining single-trip driving parameter data for each third reference vehicle, wherein the single-trip driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the third reference vehicle during any complete driving process, wherein the prediction accuracy of the third reference vehicle is higher than the prediction accuracy of the vehicle;

[0271] determining a similarity between the parameter data and each of the single driving parameter data;

[0272] Determining a fourth reference vehicle corresponding to each target single driving parameter data having a similarity greater than a preset threshold;

[0273] The energy consumption prediction strategy of each of the fourth reference vehicles, the target single driving parameter data corresponding to the fourth reference vehicle, and the prediction accuracy of each energy-consuming component of the current vehicle are sent to the calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component of the current vehicle.

[0274] In the disclosed embodiment, the parameter data of the vehicle during its current driving is first obtained. Then, based on the preset energy consumption prediction strategies and parameter data of multiple energy-consuming components in the vehicle, the predicted energy consumption of each energy-consuming component is determined. Then, based on the predicted energy consumption of each energy-consuming component, the predicted energy consumption of the entire vehicle is generated. Then, based on the preset mapping relationship, the corresponding cruising range is determined based on the predicted energy consumption of the entire vehicle. In this way, the vehicle can be divided into different energy-consuming components, and the corresponding energy consumption prediction strategies can be applied according to the operating conditions of each energy-consuming component. This makes the predicted energy consumption value of each energy-consuming component more consistent with the actual energy consumption value, thereby making the predicted energy consumption of the entire vehicle more accurate and reliable, and thus making the predicted cruising range more reliable.

[0275] In order to implement the above embodiments, the present disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the vehicle range prediction method proposed in the above embodiments of the present disclosure.

[0276] In order to implement the above embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the vehicle range prediction method proposed in the above embodiments of the present disclosure.

[0277] In order to implement the above embodiments, the present disclosure further proposes a computer program product. When the instruction processor in the computer program product is executed, the vehicle range prediction method proposed in the above embodiments of the present disclosure is executed.

[0278] Figure 10 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 10 The electronic device 12 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present disclosure.

[0279] like Figure 10 As shown, electronic device 12 is implemented as a general-purpose computing device. Components of electronic device 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and a bus 18 that connects various system components (including system memory 28 and processing unit 16).

[0280] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of such architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnection (PCI) bus.

[0281] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0282] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 10 Not shown, often called a "hard drive").

[0283] although Figure 10 Although not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a Compact Disc Read Only Memory (hereinafter referred to as: CD-ROM), a Digital Video Disc Read Only Memory (hereinafter referred to as: DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

[0284] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally implement the functions and / or methods of the embodiments described herein.

[0285] The electronic device 12 can also communicate with one or more external devices 14 (e.g., a keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the electronic device 12, and / or any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). This communication can occur via an input / output (I / O) interface 22. Furthermore, the electronic device 12 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with the electronic device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0286] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the vehicle range prediction method mentioned in the above embodiment.

[0287] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0288] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0289] It should be noted that, in the description of this disclosure, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of this disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0290] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0291] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0292] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0293] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0294] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0295] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0296] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are illustrative and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for predicting vehicle cruising range, characterized in that: include: Acquire parameter data of the vehicle during current driving, wherein the parameter data includes driving condition parameters, user habit parameters, and vehicle parameters; determining predicted energy consumption of each energy-consuming component in the vehicle based on preset energy consumption prediction strategies for multiple energy-consuming components in the vehicle and the parameter data, the multiple energy-consuming components including electric drive equipment included in the power system, cabin equipment included in the cabin system, components included in the air-conditioning system, components included in the battery aging system, and components included in the intelligent driving system; generating a predicted energy consumption of the vehicle according to the predicted energy consumption of each energy-consuming component; Based on a preset mapping relationship, determining the corresponding cruising range according to the predicted energy consumption of the vehicle; The energy-consuming components are components included in the intelligent driving system. The predicted energy consumption of each energy-consuming component is determined based on the preset energy consumption prediction strategy of each energy-consuming component of the vehicle and the parameter data, including: Inputting the road condition data in the driving condition parameters, the driving speed range in the user habit parameters, and the actual driving speed in the vehicle parameters into a pre-trained vehicle speed prediction model to determine a vehicle speed change curve corresponding to a predicted driving time period; determining predicted energy consumption corresponding to components included in the intelligent driving system according to the vehicle driving speed corresponding to the vehicle speed change curve; The energy-consuming component is a cockpit device included in the cockpit system. The predicted energy consumption of each energy-consuming component is determined based on the preset energy consumption prediction strategy of each energy-consuming component of the vehicle and the parameter data, including: Get the current seating distribution of people in the vehicle; Determining a first cockpit device usage habit parameter corresponding to the personnel seat distribution according to a preset mapping relationship and the user habit parameter; determining, based on a current driving time period of the vehicle, a corresponding second cockpit device usage habit parameter from the first cockpit device usage habit parameter; Determining predicted energy consumption corresponding to the cockpit devices included in the cockpit system during a predicted driving time period based on the second cockpit device usage habit parameter and current cockpit device usage data; The energy-consuming components are components included in the battery aging system. The predicted energy consumption of each energy-consuming component is determined based on the preset energy consumption prediction strategy of each energy-consuming component of the vehicle and the parameter data, including: Determining current state parameters of the battery in the vehicle, wherein the state parameters include the ambient temperature of the battery, the internal resistance of the battery cell, the battery state of charge, and the discharge power; Based on a preset mapping relationship and according to the current battery state parameters, predicted energy consumption corresponding to the components included in the battery aging system within a predicted driving time period is determined.

2. The method according to claim 1, characterized in that The energy-consuming component is an electric drive device included in the power system. The predicted energy consumption of each energy-consuming component is determined based on the preset energy consumption prediction strategy of each energy-consuming component of the vehicle and the parameter data, including: Acquiring single driving parameter data of each first reference vehicle, wherein the single driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the first reference vehicle during any complete driving process; determining a similarity between the parameter data and each of the single driving parameter data; Determining each target single driving parameter data having a similarity greater than a preset threshold, and each corresponding second reference vehicle; determining the energy consumption of the electric drive device of each second reference vehicle based on each target single driving parameter data; The average value of the energy consumption of each of the electric drive devices is determined as the predicted energy consumption corresponding to the electric drive devices included in the power system.

3. The method according to claim 1, characterized in that The energy-consuming components are components included in the air-conditioning system. The predicted energy consumption of each energy-consuming component is determined based on the preset energy consumption prediction strategy of each energy-consuming component of the vehicle and the parameter data, including: Inputting the air conditioning temperature setting range in the user habit parameters, the current vehicle ambient temperature and the weather data of the day in the driving condition parameters, and the actual air conditioning temperature in the vehicle parameters into a pre-trained temperature prediction model to determine a temperature change curve corresponding to a predicted driving time period; Based on a preset conversion coefficient and according to the temperature corresponding to the temperature change curve, the predicted energy consumption corresponding to the components included in the air-conditioning system is determined.

4. The method according to claim 1, wherein Before determining the corresponding second cockpit device usage habit parameter from the first cockpit device usage habit parameter according to the current driving time period of the vehicle, the method further includes: Based on preset classification conditions, the first cockpit device usage habit parameters are classified according to the driving time periods corresponding to the first cockpit device usage habit parameters.

5. The method according to claim 3, characterized in that Before obtaining the parameter data of the vehicle currently traveling, the method further includes: Obtain the test energy consumption of the vehicle's air conditioning system at a set air conditioning temperature and multiple constant ambient temperatures; Determining the temperature difference between the set air-conditioning temperature and each of the constant ambient temperatures, and the corresponding relationship between each of the temperature differences and the test energy consumption; According to the corresponding relationship between each of the temperature differences and the test energy consumption, a conversion coefficient between the temperature difference and the test energy consumption is determined.

6. The method according to claim 1, characterized in that After obtaining the parameter data of the vehicle currently traveling, the method further includes: The parameter data of the vehicle during its current driving is input into a pre-trained time prediction model to obtain the current predicted driving time of the vehicle.

7. The method according to claim 1, characterized in that After determining the predicted energy consumption of each energy-consuming component, the method further includes: determining a prediction accuracy rate of each energy-consuming component according to the actual energy consumption of each energy-consuming component after the vehicle has finished traveling and the predicted energy consumption; determining a current driving scenario of the vehicle based on the parameter data; The current driving scenario and the prediction accuracy of each energy-consuming component are associated and sent to a calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component.

8. The method according to claim 1, characterized in that After determining the predicted energy consumption of each energy-consuming component, the method further includes: Obtaining single-trip driving parameter data for each third reference vehicle, wherein the single-trip driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the third reference vehicle during any complete driving process, wherein the prediction accuracy of the third reference vehicle is higher than the prediction accuracy of the vehicle; determining a similarity between the parameter data and each of the single driving parameter data; Determining a fourth reference vehicle corresponding to each target single driving parameter data having a similarity greater than a preset threshold; The energy consumption prediction strategy of each of the fourth reference vehicles, the target single driving parameter data corresponding to the fourth reference vehicle, and the prediction accuracy of each energy-consuming component of the current vehicle are sent to the calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component of the current vehicle.

9. A vehicle range prediction device, characterized in that: include: An acquisition module is used to acquire parameter data of the vehicle during current driving, wherein the parameter data includes driving condition parameters, user habit parameters, and vehicle parameters; a first determination module, configured to determine predicted energy consumption of each energy-consuming component in the vehicle based on preset energy consumption prediction strategies for multiple energy-consuming components in the vehicle and the parameter data, the multiple energy-consuming components including electric drive equipment included in the power system, cabin equipment included in the cabin system, components included in the air-conditioning system, components included in the battery aging system, and components included in the intelligent driving system; A generating module, configured to generate a predicted energy consumption of the entire vehicle according to the predicted energy consumption of each energy-consuming component; A second determination module is configured to determine a corresponding cruising range according to the predicted energy consumption of the vehicle based on a preset mapping relationship; The energy-consuming component is a component included in the intelligent driving system, and the first determining module is specifically configured to: Inputting the road condition data in the driving condition parameters, the driving speed range in the user habit parameters, and the actual driving speed in the vehicle parameters into a pre-trained vehicle speed prediction model to determine a vehicle speed change curve corresponding to a predicted driving time period; determining predicted energy consumption corresponding to components included in the intelligent driving system according to the vehicle driving speed corresponding to the vehicle speed change curve; The energy-consuming component is a cockpit device included in the cockpit system, and the first determining module is specifically configured to: Get the current seating distribution of people in the vehicle; Determining a first cockpit device usage habit parameter corresponding to the personnel seat distribution according to a preset mapping relationship and the user habit parameter; determining, based on a current driving time period of the vehicle, a corresponding second cockpit device usage habit parameter from the first cockpit device usage habit parameter; Determining predicted energy consumption corresponding to the cockpit devices included in the cockpit system during a predicted driving time period based on the second cockpit device usage habit parameter and current cockpit device usage data; The energy-consuming component is a component included in the battery aging system, and the first determining module is specifically configured to: Determining current state parameters of the battery in the vehicle, wherein the state parameters include the ambient temperature of the battery, the internal resistance of the battery cell, the battery state of charge, and the discharge power; Based on a preset mapping relationship and according to the current battery state parameters, predicted energy consumption corresponding to the components included in the battery aging system within a predicted driving time period is determined.

10. The device according to claim 9, characterized in that The energy-consuming component is an electric drive device included in the power system, and the first determining module is specifically configured to: Acquiring single driving parameter data of each first reference vehicle, wherein the single driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the first reference vehicle during any complete driving process; determining a similarity between the parameter data and each of the single driving parameter data; Determining each target single driving parameter data having a similarity greater than a preset threshold, and each corresponding second reference vehicle; determining the energy consumption of the electric drive device of each second reference vehicle based on each target single driving parameter data; The average value of the energy consumption of each of the electric drive devices is determined as the predicted energy consumption corresponding to the electric drive devices included in the power system.

11. The device according to claim 9, characterized in that The energy-consuming component is a component included in the air-conditioning system, and the first determining module is specifically configured to: Inputting the air conditioning temperature setting range in the user habit parameters, the current vehicle ambient temperature and the weather data of the day in the driving condition parameters, and the actual air conditioning temperature in the vehicle parameters into a pre-trained temperature prediction model to determine a temperature change curve corresponding to a predicted driving time period; Based on a preset conversion coefficient and according to the temperature corresponding to the temperature change curve, the predicted energy consumption corresponding to the components included in the air-conditioning system is determined.

12. The device according to claim 9, characterized in that Before determining the corresponding second cockpit device usage habit parameter from the first cockpit device usage habit parameter according to the current driving time period of the vehicle, the first determining module is further configured to: Based on preset classification conditions, the first cockpit device usage habit parameters are classified according to the driving time periods corresponding to the first cockpit device usage habit parameters.

13. The device according to claim 11, characterized in that The acquisition module is further used to: Obtain the test energy consumption of the vehicle's air conditioning system at a set air conditioning temperature and multiple constant ambient temperatures; Determining the temperature difference between the set air-conditioning temperature and each of the constant ambient temperatures, and the corresponding relationship between each of the temperature differences and the test energy consumption; According to the corresponding relationship between each of the temperature differences and the test energy consumption, a conversion coefficient between the temperature difference and the test energy consumption is determined.

14. The device according to claim 9, characterized in that The acquisition module is further used to: The parameter data of the vehicle during its current driving is input into a pre-trained time prediction model to obtain the current predicted driving time of the vehicle.

15. The device according to claim 9, characterized in that The first determining module is further configured to: determining a prediction accuracy rate of each energy-consuming component according to the actual energy consumption of each energy-consuming component after the vehicle has finished traveling and the predicted energy consumption; determining a current driving scenario of the vehicle based on the parameter data; The current driving scenario and the prediction accuracy of each energy-consuming component are associated and sent to a calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component.

16. The device according to claim 9, characterized in that The first determining module is further configured to: Obtaining single-trip driving parameter data for each third reference vehicle, wherein the single-trip driving parameter data includes vehicle parameters, driving condition parameters, and user habit parameters of the third reference vehicle during any complete driving process, wherein the prediction accuracy of the third reference vehicle is higher than the prediction accuracy of the vehicle; determining a similarity between the parameter data and each of the single driving parameter data; Determining a fourth reference vehicle corresponding to each target single driving parameter data having a similarity greater than a preset threshold; The energy consumption prediction strategy of each of the fourth reference vehicles, the target single driving parameter data corresponding to the fourth reference vehicle, and the prediction accuracy of each energy-consuming component of the current vehicle are sent to the calibration system, so that the calibration system adjusts the energy consumption prediction strategy of each energy-consuming component of the current vehicle.

17. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the vehicle range prediction method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: in, The computer instructions are used to enable a computer to execute the vehicle cruising range prediction method according to any one of claims 1 to 8.

19. A vehicle, characterized in that: The vehicle comprises the electronic device of claim 17 .

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