Vehicle energy consumption analysis methods, devices, electronic equipment and storage media

Abstract

The vehicle energy consumption analysis method, apparatus, electronic device, and storage medium provided in this application acquire the operating records of a target vehicle, which characterize the operating status of the target vehicle within at least one first time period. Based on the operating records, an optimization coefficient is obtained, which characterizes the change in energy consumption of the target vehicle relative to a first energy consumption value within at least one first time period. The first energy consumption value is obtained based on the average energy consumption of the target vehicle within a second time period, where the first time period is a sub-interval of the second time period. Based on the optimization coefficient and at least one first energy consumption value, a target energy consumption value is obtained, which characterizes the actual energy consumption within the second time period. By determining the optimization coefficient of the target vehicle relative to the first energy consumption value within at least one first time period in the second time period based on the operating records within the first time period, and then obtaining the target energy consumption value based on the optimization coefficient and the first energy consumption value, the problem of low accuracy in energy consumption data analysis results is solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle energy consumption analysis method, apparatus, electronic device, and storage medium. Background Technology

[0002] Currently, more and more people are choosing hybrid vehicles for their travel, using electricity for short trips and gasoline for long trips. As cars become more intelligent, users are increasingly concerned about fuel and electricity consumption for each journey. To meet user needs, the vehicle's infotainment system calculates energy consumption based on real-time data reported by the vehicle's functional units, providing users with energy consumption analysis results for their trips.

[0003] Currently, vehicle energy consumption analysis is performed using calculation formulas built into the vehicle's electronic control unit. The parameters of these formulas are often simulated operating values ​​set for the vehicle at the factory. The energy consumption data analysis results are obtained through these calculations and provided to users for reference.

[0004] However, in actual use, the actual operating conditions of a vehicle are more complex than the simulated operating conditions, resulting in a discrepancy between the actual energy consumption and the energy consumption calculated by the formula, which in turn leads to low accuracy of energy consumption data analysis results. Summary of the Invention

[0005] This application provides a vehicle energy consumption analysis method, apparatus, electronic device, and storage medium to solve the problem of low accuracy in energy consumption data analysis results.

[0006] In a first aspect, this application provides a vehicle energy consumption analysis method, comprising: acquiring the operation record of a target vehicle, the operation record representing the operation status of the target vehicle within at least one first time period; obtaining an optimization coefficient based on the operation record, wherein the optimization coefficient represents the energy consumption change of the target vehicle relative to a first energy consumption value within at least one first time period, the first energy consumption value being obtained based on the average energy consumption of the target vehicle within a second time period, the first time period being a sub-interval of the second time period; and obtaining a target energy consumption value based on the optimization coefficient and at least one first energy consumption value, the target energy consumption value representing the actual energy consumption of the target vehicle within the second time period.

[0007] In one possible implementation, obtaining the optimization coefficient based on the operation record includes: acquiring driving environment information, the driving environment information representing environmental factors affecting the energy consumption of the target vehicle during the first time period; obtaining an energy consumption coefficient based on the operation record and the corresponding driving environment information; and obtaining the optimization coefficient based on the energy consumption coefficient.

[0008] In one possible implementation, obtaining the target energy consumption value based on the optimization coefficient and at least one first energy consumption value includes: obtaining a first energy consumption type corresponding to the first energy consumption value, wherein the first energy consumption type represents the power source type selected by the target vehicle during the second time period; obtaining a first target energy consumption value based on the first energy consumption type, the optimization coefficient, and at least one first energy consumption value, wherein the first target energy consumption value represents the energy consumption under the corresponding first energy consumption type; and obtaining the target energy consumption value based on the first target energy consumption value.

[0009] In one possible implementation, the method further includes: determining preset information based on the first target energy consumption value, the preset information representing a preset solution to reduce the first target energy consumption value.

[0010] In one possible implementation, the method includes: obtaining the operation record based on the planned trip of the target vehicle; obtaining the target energy consumption value based on the optimization coefficient and at least one first energy consumption value includes: obtaining an estimated energy consumption value based on the optimization coefficient and at least one first energy consumption value, the estimated energy consumption value representing the estimated energy consumption of the target vehicle; and obtaining the target energy consumption value based on the estimated energy consumption value.

[0011] In one possible implementation, the method further includes: obtaining a second energy consumption type used by the target vehicle in the planned trip, the second energy consumption type representing the power source type selected by the target vehicle in the planned trip; obtaining the target energy consumption value based on the estimated energy consumption value includes: obtaining the target energy consumption value based on the estimated energy consumption value and the second energy consumption type.

[0012] In one possible implementation, the method further includes: obtaining a minimum energy consumption value based on the estimated energy consumption value; and obtaining the target energy consumption value based on the minimum energy consumption value and the corresponding second energy consumption type.

[0013] Secondly, this application provides a vehicle energy consumption analysis device, comprising:

[0014] An acquisition module is used to acquire the operation records of a target vehicle, wherein the operation records characterize the operation status of the target vehicle within at least one first time period;

[0015] The first processing module is used to obtain an optimization coefficient based on the operation record, wherein the optimization coefficient represents the change in energy consumption of the target vehicle relative to a first energy consumption value in at least one first time period, the first energy consumption value is obtained based on the average energy consumption of the target vehicle in a second time period, and the first time period is a sub-interval of the second time period.

[0016] The second processing module is used to obtain a target energy consumption value based on the optimization coefficient and at least one of the first energy consumption values, wherein the target energy consumption value represents the actual energy consumption of the target vehicle during the second time period.

[0017] In one possible implementation, when the first processing module obtains the optimization coefficient based on the operation record, it is specifically used to: acquire driving environment information, the driving environment information representing environmental factors affecting the energy consumption of the target vehicle during the first time period; obtain an energy consumption coefficient based on the operation record and the corresponding driving environment information; and obtain the optimization coefficient based on the energy consumption coefficient.

[0018] In one possible implementation, when the second processing module obtains the target energy consumption value based on the optimization coefficient and at least one of the first energy consumption values, it is specifically configured to: obtain a first energy consumption type corresponding to the first energy consumption value, wherein the first energy consumption type represents the power source type selected by the target vehicle in the second time period; obtain a first target energy consumption value based on the first energy consumption type, the optimization coefficient and at least one of the first energy consumption values, wherein the first target energy consumption value represents the energy consumption under the corresponding first energy consumption type; and obtain the target energy consumption value based on the first target energy consumption value.

[0019] In one possible implementation, the second processing module is further configured to: determine preset information based on the first target energy consumption value, wherein the preset information represents a preset solution for reducing the first target energy consumption value.

[0020] In one possible implementation, the acquisition module is specifically used to: obtain the operation record based on the planned route of the target vehicle; when the second processing module obtains the target energy consumption value based on the optimization coefficient and at least one of the first energy consumption values, it is specifically used to: obtain an estimated energy consumption value based on the optimization coefficient and at least one of the first energy consumption values, wherein the estimated energy consumption value represents the estimated energy consumption of the target vehicle; and obtain the target energy consumption value based on the estimated energy consumption value.

[0021] In one possible implementation, the acquisition module is further configured to: acquire a second energy consumption type used by the target vehicle in the planned trip, the second energy consumption type representing the power source type selected by the target vehicle in the planned trip; and when the second processing module obtains the target energy consumption value based on the estimated energy consumption value, it is specifically configured to: obtain the target energy consumption value based on the estimated energy consumption value and the second energy consumption type.

[0022] In one possible implementation, the second processing module is further configured to: obtain a minimum energy consumption value based on the estimated energy consumption value; and obtain the target energy consumption value based on the minimum energy consumption value and the corresponding second energy consumption type.

[0023] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0024] The memory stores computer-executed instructions;

[0025] The processor executes computer execution instructions stored in the memory to implement the vehicle energy consumption analysis method as described in any of the first aspects of the embodiments of this application.

[0026] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the vehicle energy consumption analysis method as described in any of the first aspects of the embodiments of this application.

[0027] According to a fifth aspect of the embodiments of this application, this application provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle energy consumption analysis method as described in any of the first aspects above.

[0028] The vehicle energy consumption analysis method, apparatus, electronic device, and storage medium provided in this application acquire the operating records of a target vehicle, which characterize the operating status of the target vehicle within at least one first time period. Based on the operating records, optimization coefficients are obtained, whereby the optimization coefficients characterize the energy consumption change of the target vehicle relative to a first energy consumption value within at least one first time period. The first energy consumption value is obtained based on the average energy consumption of the target vehicle within a second time period, where the first time period is a sub-interval of the second time period. Based on the optimization coefficients and at least one first energy consumption value, a target energy consumption value is obtained, which characterizes the actual energy consumption of the target vehicle within the second time period. By obtaining the corresponding optimization coefficients based on the operating records within the first time period, the energy consumption change relative to the first energy consumption value within at least one first time period in the second time period is determined. Furthermore, the target energy consumption value is obtained based on the optimization coefficients and the first energy consumption value, thus solving the problem of low accuracy in energy consumption data analysis results and improving the precision of energy consumption data analysis results. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0030] Figure 1This is an application scenario diagram of the vehicle energy consumption analysis method provided in the embodiments of this application;

[0031] Figure 2 for Figure 1 A schematic diagram illustrating the energy consumption data analysis results for the provided application scenarios;

[0032] Figure 3 A flowchart illustrating a vehicle energy consumption analysis method provided in one embodiment of this application;

[0033] Figure 4 for Figure 3 A schematic diagram illustrating the specific implementation steps of step S102 in the illustrated embodiment;

[0034] Figure 5 for Figure 3 The illustrated embodiment provides a schematic diagram of a travel route;

[0035] Figure 6 for Figure 3 A schematic diagram illustrating the specific implementation steps of step S103 in the illustrated embodiment;

[0036] Figure 7 A flowchart of a vehicle energy consumption analysis method provided in another embodiment of this application;

[0037] Figure 8 This is a schematic diagram of the structure of a vehicle energy consumption analysis device provided in one embodiment of this application;

[0038] Figure 9 A schematic diagram of an electronic device provided according to one embodiment of this application;

[0039] Figure 10 This is a block diagram illustrating a terminal device in an exemplary embodiment of this application.

[0040] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0041] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0042] The technical solution of this application involves the collection, storage, use, processing, transmission, provision and disclosure of user personal information and data, which comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0043] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0044] First, let me explain the terms used in this application:

[0045] A Telematics Box (Tbox) is a remote communication terminal that integrates vehicle network and wireless communication functions, providing remote information processing services. It is typically installed below the dashboard. A Tbox is a communication-enabled box based on Android or Linux operating systems, containing a SIM card, and is accompanied by hardware such as a GPS antenna and a 4G antenna.

[0046] The application scenarios of the embodiments of this application are explained below:

[0047] Figure 1 This diagram illustrates an application scenario of the vehicle energy consumption analysis method provided in this application. The vehicle energy consumption analysis method provided in this application can be applied to scenarios involving vehicle remaining energy consumption prediction. For example, as shown... Figure 1 As shown, the execution subject of the method provided in this application embodiment can be a cloud server. The cloud server responds to user operations or automatically executes the vehicle energy consumption analysis method provided in this embodiment according to a set time interval. The target energy consumption value is obtained by processing the target vehicle operation records and average energy consumption reported by the remote communication terminal (i.e., Telematics Box, hereinafter referred to as Tbox). Figure 2 for Figure 1 The provided diagram illustrates the energy consumption data analysis results for various application scenarios, such as... Figure 2As shown, the cloud server obtains the target vehicle's current operating records, including a mileage of 100 kilometers, an average speed of 80 kilometers per hour, and a battery temperature of 55°C. The target vehicle's average energy consumption is 20 kWh / 100 kilometers. Based on the complex operating states of sub-time periods within a 1.25-hour period, the energy consumption of each segment is analyzed and corrected, resulting in a corrected average energy consumption of 20.5 kWh / 100 kilometers. Further, the electricity consumed to travel 100 kilometers is calculated to be 20.5 kWh, thus the target energy consumption value is 20.5 kWh. Then, based on the target vehicle's initial remaining battery power of 63 kWh before starting this segment of travel and the consumed electricity of 20.5 kWh, the energy consumption data analysis result is obtained, resulting in a predicted remaining battery power of 42.5 kWh for the target vehicle.

[0048] Currently, in applications of vehicle energy consumption prediction, energy consumption analysis is typically performed using built-in algorithm models within the vehicle's electronic control unit or cloud services. These models often employ fixed simulated values ​​as parameters, and the energy consumption analysis results are obtained by calculating these simulated values ​​and vehicle driving data. However, in reality, the vehicle's operating state is far more complex during the corresponding time period. If energy consumption analysis is performed using simulated operating state values, ignoring the energy consumption variations corresponding to these complex operating states within sub-time periods, the accuracy of the energy consumption analysis results will be low.

[0049] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0050] Figure 3 A flowchart of a vehicle energy consumption analysis method provided in one embodiment of this application is shown below. Figure 3 As shown, the vehicle energy consumption analysis method provided in this embodiment includes the following steps:

[0051] Step S101: Obtain the operation record of the target vehicle. The operation record represents the operation status of the target vehicle in at least one first time period.

[0052] For example, the operation record is the driving status value of the target vehicle within a time period, such as vehicle speed, vehicle temperature, vehicle speed, average energy consumption per 100 kilometers, and battery health. As the target vehicle drives under different road conditions, the recorded values ​​in its corresponding operation record change accordingly. For example, the operation record of the target vehicle can be generated by collecting data from various sensors installed on the target vehicle. The operation record can be stored locally as offline data and uploaded to a cloud server after a trigger condition is met. In another possible implementation, the generated operation record can also be uploaded to the cloud server in real time via a Tbox.

[0053] Furthermore, for example, the first time period refers to a fixed or non-fixed length time interval during the target vehicle's operation. For instance, within year_1 month_1 day_1, the time period between 3 PM and 4 PM is a first time period. Another example is the time period from the current time point to 30 minutes prior to the current time point. The operation log contains the corresponding operating status for each first time period, such as the target vehicle's average speed and battery temperature during the time period from the current time point to 30 minutes prior to the current time point.

[0054] Step S102: Based on the operation record, obtain the optimization coefficient, wherein the optimization coefficient represents the change in energy consumption of the target vehicle relative to the first energy consumption value in at least one first time period. The first energy consumption value is obtained based on the average energy consumption of the target vehicle in a second time period, and the first time period is a sub-interval of the second time period.

[0055] For example, the second time period refers to a fixed or non-fixed length time interval during the target vehicle's travel, and includes at least one first time period. For instance, within year_1, month_1, day_1, the time interval from 3 PM to 5 PM is the second time period time_1, the time interval from 3 PM to 4 PM is a first time period time_2, and the time interval from 4 PM to 5 PM is a first time period time_3. Further, it can be determined that time_1 includes time_2 and time_3, and time_2 and time_3 are both sub-intervals of time_1. As another example, if the time interval from the current time point to 60 minutes prior to the current time point is the second time period time_4, then the time interval from the current time point to 30 minutes prior to the current time point is a sub-interval of the second time period time_4.

[0056] For example, the first energy consumption value is the average energy consumption value calculated based on a theoretical model. For instance, within the second time period, the average energy consumption value within that time period, i.e., the first energy consumption value, can be obtained by analyzing the data reported by the Tbox. The first time period is a sub-interval of the second time period. By dividing the second time period according to a specified time length or characteristic, at least one first time period can be obtained. Based on the first time period, the operating records of the target vehicle within the first time period are determined.

[0057] For example, the optimization coefficient characterizes the change in energy consumption of the target vehicle relative to the first energy consumption value within at least one first time period. For instance, the first energy consumption value characterizes the average energy consumption per 100 kilometers of the target vehicle. If the first energy consumption value of the target vehicle within at least one first time period is 14 kWh / 100 km, after processing based on the aforementioned operating records, the corresponding optimization coefficient is +0.5 kWh / 100 km. That is, based on the first energy consumption value calculated using the theoretical model, the average energy consumption is increased by +0.5 kWh / 100 km. Therefore, the optimization coefficient obtained based on the operating records is equivalent to a correction value for the first energy consumption value, reflecting the increase or decrease in actual energy consumption caused by changes in the vehicle's operating state during actual operation.

[0058] In one possible implementation, a pre-trained machine learning model is used. More specifically, the machine learning model processes the running records using a "linear regression" classifier to obtain the corresponding optimization coefficients. More specifically, for example, based on data features, running record rec_1 corresponds to optimization coefficient fact_1, running record rec_2 corresponds to optimization coefficient fact_2, and then, based on the target vehicle's running record rec_1 within a sub-time interval, the corresponding optimization coefficient fact_1 is obtained. This machine learning model needs to be trained before use. Training samples are used to determine the mapping relationship between the target vehicle's running records and optimization coefficients. The neural network model is trained using these training samples until convergence, thus obtaining the aforementioned machine learning model. The specific training process will not be elaborated here. Through the scheme of this embodiment, optimization coefficients are obtained by analyzing running records within at least one first time period. Furthermore, the first energy consumption value is corrected based on the optimization coefficients to obtain accurate energy consumption results.

[0059] For example, Figure 4 for Figure 3 The schematic diagram of the specific implementation steps of step S102 in the illustrated embodiment is as follows: Figure 4 As shown, the specific implementation steps of step S102 include:

[0060] Step S1021: Obtain driving environment information. The driving environment information represents the environmental factors that affect the energy consumption of the target vehicle in the first time period.

[0061] For example, driving environment information refers to external information that affects the driving data of the target vehicle during its operation, such as road surface smoothness, road slope, ambient temperature, wind force, and wind direction. The driving environment of the target vehicle is one of the factors affecting its energy consumption. More specifically, for example, during driving, under the same driving environment, the energy consumed by the target vehicle to overcome wind resistance differs depending on whether it is driving with the wind or against the wind.

[0062] Step S1022: Obtain the energy consumption coefficient based on the operation records and corresponding driving environment information.

[0063] Step S1023: Obtain the optimization coefficient based on the energy consumption coefficient.

[0064] For example, the energy consumption coefficient is an energy consumption correction value based on the target vehicle's operating records and driving environment information. The energy consumption coefficient can be a preset value stored in a cloud server. More specifically, based on the first time period corresponding to the target vehicle's operating records, the driving environment information of the target vehicle within the first time period is determined, and thus, the corresponding energy consumption coefficient can be determined. In one possible implementation, during the first time period T1, the target vehicle is traveling against the wind with a wind force of level 2. In this case, the corresponding environmental information is info_1. More specifically, the environmental information can be an array containing multiple elements, for example, info_1 = [para_1, para_2], where para_1 represents the wind force of the target vehicle's travel, for example, para_1 = -2, indicating that the target vehicle is traveling against the wind with a wind force of level 2; para_2 represents the slope of the road segment traveled by the target vehicle, for example, para_2 = +5, indicating that the uphill slope of the road segment traveled by the target vehicle is 5. Then, based on the preset mapping relationship between the environmental information and the energy consumption coefficient, the energy consumption coefficient num_1 corresponding to the environmental information info_1 is obtained. Similarly, if the target vehicle is traveling with the wind at level 2, the corresponding environmental information is info_2. Specifically, info_2 = [para_3, para_4], where para_3 represents the wind force on which the target vehicle is traveling. For example, para_3 = +2 indicates that the target vehicle is traveling with the wind at level 2. para_4 represents the slope of the road segment on which the target vehicle is traveling. For example, para_4 = -5 indicates that the downhill slope of the road segment on which the target vehicle is traveling is 5. Thus, the energy consumption coefficient num_2 corresponding to the environmental information info_2 is obtained.

[0065] Further, based on the energy consumption coefficient num_1, the optimization coefficient fact_1 is obtained. One possible implementation is that the energy consumption coefficient can be a feature matrix representing driving environment information. A pre-trained machine learning model processes the energy consumption coefficient to obtain the corresponding optimization coefficient. This machine learning model uses a linear regression classifier to predict the optimization coefficient. This machine learning model needs to be trained before use. For example, training samples can be obtained by mapping the energy consumption coefficient, composed of combinations of driving environment information (e.g., [para_1, para_2, para_3, etc.]), to the optimization coefficients obtained from actual measurements through the vehicle's infotainment system. These training samples are then used to train the neural network model until convergence, thus obtaining the aforementioned machine learning model. The specific training process will not be detailed here.

[0066] This embodiment establishes a mapping relationship between energy consumption coefficients and optimization coefficients based on operation records and driving environment information, further improving the accuracy of correcting the first energy consumption value. More specifically, by establishing a mapping relationship between energy consumption coefficients and optimization coefficients based on driving environment information such as wind resistance, road slope, and ambient temperature, the first energy consumption value of the target vehicle under specific road conditions can be accurately corrected. For example, if the target vehicle is traveling against the wind for 50% of the journey and with the wind for 50% of the journey, the optimization coefficients for the two periods will be different, resulting in different optimization results for the first energy consumption value.

[0067] Step S103: Based on the optimization coefficient and at least one first energy consumption value, the target energy consumption value is obtained, which represents the actual energy consumption of the target vehicle in the second time period.

[0068] For example, based on actual operating records, a corresponding optimization coefficient is obtained. This optimization coefficient is used to correct the first energy consumption value calculated by the theoretical model to obtain the target energy consumption value, which is the actual energy consumption of the target vehicle in the second time period. More specifically, Figure 5 for Figure 3 The illustrated embodiment provides a schematic diagram of a travel route, such as... Figure 5As shown, when the target vehicle's journey is the journey of the second time period T2 (from point A to point C), the first energy consumption value calculated by the theoretical model within the second time period T2, that is, the average energy consumption per 100 kilometers, is aver_diss_1. According to the target vehicle's journey from point A to point B, and then from point B to point C, there are two sub-time periods within the second time period T2, namely time period T1_1 and time period T1_2, which correspond to two optimization coefficients: fact_1 and fact_2, and two mileages: path_1 (corresponding to the journey: from point A to point B) and path_2 (corresponding to the journey: from point B to point C). Then, the actual energy consumption at this time is diss_1, as shown in equation (1).

[0069]

[0070] By correcting the average energy consumption value per 100 kilometers using the solution in this embodiment, the actual energy consumption during the current or historical journey can be obtained. Furthermore, it can provide users with the total actual energy consumption for any time period selected by the user.

[0071] It should be understood that this application does not limit the correction relationship between the optimization coefficient and the first energy consumption value, but rather illustrates the concept of this application by referring to specific embodiments (“additive” correction relationship).

[0072] In another possible implementation, exemplarily, Figure 6 for Figure 3 The schematic diagram of the specific implementation steps of step S103 in the embodiment shown is as follows: Figure 6 As shown, the specific implementation steps of step S103 include:

[0073] Step S1031: Obtain the first energy consumption type corresponding to the first energy consumption value. The first energy consumption type represents the power source type selected by the target vehicle in the second time period.

[0074] For example, the first energy consumption type is the type of power source used by the target vehicle during operation, such as electric power source, oil power source, and gas power source. More specifically, when using the target vehicle, the user will select different power sources to power the target vehicle according to different scenarios to minimize energy consumption. For example, electric power source is selected for short-distance driving, and oil power source is selected for long-distance driving.

[0075] Step S1032: Based on the first energy consumption type, the optimization coefficient and at least one first energy consumption value, a first target energy consumption value is obtained. The first target energy consumption value represents the energy consumption under the corresponding first energy consumption type.

[0076] Step S1033: Obtain the target energy consumption value based on the first target energy consumption value.

[0077] For example, based on the selected power source to power the target vehicle, the corresponding optimization coefficient is obtained based on the actual operation record. This optimization coefficient is used to correct the first energy consumption value calculated by the theoretical model to obtain the first target energy consumption value. Furthermore, if the target vehicle uses electric energy as a power source to drive, the first target energy consumption value is obtained as the energy consumption based on the electric power source, i.e., the target energy consumption value. More specifically, for example, the cloud server obtains two sub-time periods of the target vehicle in the second time period T2 through Tbox, namely time period T1_1 and time period T1_2. The total driving distance of the target vehicle in the second time period T2 is 100 kilometers. The driving distance in the time period T1_1 is path_1 = 50 kilometers, and the driving distance in the time period T1_2 is path_2 = 50 kilometers. The first energy consumption value in the second time period T2 is 20 kWh / 100 km. According to the operation record in the first time period T1_1, the corresponding energy consumption coefficient num_1 is obtained, and then the corresponding optimization coefficient fact_1 = 0.6 kWh / 100 km is obtained. According to the operation record in the first time period T1_2, the corresponding energy consumption coefficient num_2 is obtained, and then the corresponding optimization coefficient fact_2 = 0.4 kWh / 100 km is obtained. According to formula (1), the first target energy consumption value diss_1 = 20.5 kWh is calculated. The first target energy consumption value is the actual power consumption of the target vehicle in the second time period T2, that is, the target energy consumption value. By correcting the average energy consumption value per 100 kilometers using the solution in this embodiment, the energy consumption type corresponding to the actual energy consumption in the current or historical trip can be obtained. This allows the user to be provided with the actual energy consumption corresponding to the selected energy consumption type under a specific trip. Furthermore, it allows the user to be provided with the total actual energy consumption for any selected time period or the total actual energy consumption classified by energy consumption type.

[0078] In another possible implementation, preset information is determined based on the first target energy consumption value. This preset information represents a preset solution to reduce the first target energy consumption value. For example, during the driving process of the target vehicle, the cloud server can analyze the first target energy consumption value to obtain the factors affecting the target vehicle's energy consumption, i.e., the preset information. This preset information can then be used to provide corresponding solutions to the user. More specifically, for example, if the cloud server detects an abnormal increase in the first target energy consumption value, analysis of vehicle information reported by the Tbox reveals that the target vehicle's windows are currently open, whereas they were closed in the previous period. Therefore, when the windows change from closed to open, the target vehicle's energy consumption increases due to increased wind resistance. The cloud server then sends information about the impact of the open window status on vehicle energy consumption to the vehicle's infotainment system or mobile terminal. The user can then choose whether to close the windows based on prompts on the mobile terminal, or the target vehicle can automatically close the windows based on information from the vehicle's infotainment system to reduce the first target energy consumption. Through the solution in this embodiment, users can intuitively obtain information about factors affecting the current energy consumption of a vehicle via a cloud server or terminal device. For example, opening a window increases wind resistance during vehicle operation, thus increasing energy consumption. Alternatively, the vehicle's gear may not be adjusted to the optimal operating gear based on its current speed, leading to increased energy consumption. Furthermore, based on these influencing factors, users can manually or the vehicle can automatically perform corresponding operations to achieve the goal of reducing energy consumption.

[0079] In this embodiment, the operation records of the target vehicle are acquired, representing the vehicle's operating status within at least one first time period. Based on these records, optimization coefficients are obtained, where each coefficient represents the change in energy consumption of the target vehicle relative to a first energy consumption value within at least one first time period. The first energy consumption value is based on the target vehicle's average energy consumption within a second time period, where the first time period is a sub-interval of the second time period. Based on the optimization coefficients and at least one first energy consumption value, a target energy consumption value is obtained, representing the actual energy consumption of the target vehicle within the second time period. By obtaining the corresponding optimization coefficients from the operation records within the first time period, the change in energy consumption relative to the first energy consumption value within at least one first time period in the second time period is determined. Furthermore, the target energy consumption value is obtained based on the optimization coefficients and the first energy consumption value, thus solving the problem of low accuracy in energy consumption data analysis results and improving the precision of the analysis.

[0080] Figure 7 A flowchart of a vehicle energy consumption analysis method provided in another embodiment of this application is shown below. Figure 7 As shown, the vehicle energy consumption analysis method provided in this embodiment is... Figure 3 Based on the vehicle energy consumption analysis method provided in the illustrated embodiment, step S103 is further refined. Therefore, the vehicle energy consumption analysis method provided in this embodiment includes the following steps:

[0081] Step S201: Obtain the operation record of the target vehicle. The operation record represents the operation status of the target vehicle in at least one first time period.

[0082] Step S202: Based on the operation record, obtain the optimization coefficient, wherein the optimization coefficient represents the change in energy consumption of the target vehicle relative to the first energy consumption value in at least one first time period. The first energy consumption value is obtained based on the average energy consumption of the target vehicle in a second time period, and the first time period is a sub-interval of the second time period.

[0083] Step S203: Obtain the operation record based on the planned route of the target vehicle.

[0084] For example, trip planning involves the target vehicle automatically or manually generating a trip task based on the user's desired trip instructions. For instance, based on the target vehicle's planned trip, the cloud server obtains relevant trip information such as weather, road conditions, and mileage, and then obtains an estimated running record based on the obtained trip information.

[0085] Step S204: Based on the optimization coefficient and at least one first energy consumption value, the estimated energy consumption value is obtained, which represents the estimated energy consumption of the target vehicle.

[0086] Step S205: Obtain the target energy consumption value based on the estimated energy consumption value.

[0087] For example, the estimated energy consumption value is based on the estimated energy consumption of the planned journey of the target vehicle. By obtaining the driving environment information, the first energy consumption value is estimated, and the corresponding optimization coefficient is obtained according to the estimated operation record. Then, the estimated energy consumption value is obtained by using the optimization coefficient and at least one first energy consumption value. For example, the average energy consumption per 100 kilometers aver_diss_2 of the target vehicle in the second time period T2 of the entire planned journey is obtained according to the planned journey of the target vehicle, which is the first energy consumption value. The planned journey of the target vehicle is divided according to the driving environment and other information to obtain at least one sub-planned journey path_3 corresponding to the first time period. According to the sub-planned journey corresponding to the first time period, the operation record of the sub-planned journey and the optimization coefficient fact_3 corresponding to the operation record are determined, and the estimated energy consumption value diss_2 can be obtained, as shown in equation (2), and then the target energy consumption value is obtained.

[0088]

[0089] More specifically, for example, the average energy consumption aver_diss_2 of the target vehicle in the second time period T2 of the entire planned journey is estimated to be 20 kWh / 100 km. The second time period T2 only includes one first time period T1. The planned journey corresponding to the second time period T2 is the sub-planned journey path_3 corresponding to the first time period T1, for example, 100 km. The corresponding optimization coefficient fact_3 = 0.5 kWh / 100 km. According to formula (2), the estimated energy consumption value diss_2 = 20.5 kWh is obtained, which is the target energy consumption value. Through the solution of this embodiment, the user can obtain the estimated energy consumption before the planned journey begins. Based on the estimated energy consumption, the user can charge the target vehicle in time, for example, by charging or refueling.

[0090] In one possible implementation, a second energy consumption type is obtained for the target vehicle during the planned trip. This second energy consumption type represents the power source type selected by the target vehicle during the planned trip. Based on the estimated energy consumption value and the second energy consumption type, the target energy consumption value is obtained. For example, the target vehicle may have both electric and gasoline power sources. The cloud server provides estimated energy consumption values ​​for each energy consumption type based on the target vehicle's planned trip: estimated electric consumption value `diss_ele` and estimated gasoline consumption value `diss_oil`, thus obtaining the corresponding target energy consumption value. For example, for the same trip, the estimated electric consumption value `diss_ele` is 20.5 kWh, and the estimated gasoline consumption value `diss_oil` is 7 liters, thus obtaining the target energy consumption value with both energy consumption type and estimated energy consumption. Through this embodiment, the cloud server can estimate the target energy consumption value for each energy consumption type for the planned trip for the user. Based on the cost corresponding to each target energy consumption value, the user can intuitively understand the cost of each energy consumption scheme for the planned trip.

[0091] In another possible implementation, the minimum energy consumption value is obtained based on the estimated energy consumption value; the target energy consumption value is then obtained based on the minimum energy consumption value and the corresponding second energy consumption type. For example, based on the cloud server's estimated electricity consumption value (diss_ele) and estimated fuel consumption value (diss_oil), the costs corresponding to the estimated electricity consumption value (diss_ele) and the estimated fuel consumption value (diss_oil) are obtained. According to the energy-saving and cost-effective setting rules, the cloud server selects the second energy consumption type with the lowest cost to recommend to the user, thus obtaining the target energy consumption value. More specifically, for example, under the same trip conditions, based on the estimated electricity consumption value (diss_ele) = 20.5 kWh, the cost corresponding to the target energy consumption value is 30; based on the estimated fuel consumption value (diss_oil) = 7 liters, the cost corresponding to the target energy consumption value is 50. Therefore, according to the energy-saving and cost-effective setting rules, the cloud server recommends that the user select electric power source as the second energy consumption type to power the target vehicle during the planned trip, thus obtaining the target energy consumption value of 20.5 kWh and the corresponding cost of 30. Through the solution in this embodiment, the cloud server can prompt the user whether the current initial remaining battery power of the target vehicle can complete the planned trip. If the planned trip can be completed, the cloud server can further provide the user with the lowest cost driving plan and estimate the remaining energy after the planned trip is completed, such as the remaining battery power or the remaining fuel. The user can then arrange the charging or refueling trip plan according to the estimated remaining energy.

[0092] In this embodiment, the implementation of steps S201-S202 is the same as that in this application. Figure 3 The implementation methods of steps S101-S102 in the illustrated embodiment are the same, and will not be described in detail here.

[0093] Figure 8 This is a schematic diagram of the structure of a vehicle energy consumption analysis device provided in one embodiment of this application, as shown below. Figure 8 As shown, the vehicle energy consumption analysis device 3 provided in this embodiment includes:

[0094] The acquisition module 31 is used to acquire the operation record of the target vehicle, which represents the operation status of the target vehicle in at least one first time period.

[0095] The first processing module 32 is used to obtain optimization coefficients based on the operation records. The optimization coefficients represent the change in energy consumption of the target vehicle relative to the first energy consumption value in at least one first time period. The first energy consumption value is obtained based on the average energy consumption of the target vehicle in a second time period. The first time period is a sub-interval of the second time period.

[0096] The second processing module 33 is used to obtain a target energy consumption value based on the optimization coefficient and at least one first energy consumption value. The target energy consumption value represents the actual energy consumption of the target vehicle in the second time period.

[0097] In one possible implementation, when the first processing module 32 obtains the optimization coefficient based on the running records, it is specifically used to: acquire driving environment information, which represents the environmental factors affecting the energy consumption of the target vehicle in the first time period; obtain the energy consumption coefficient based on the running records and the corresponding driving environment information; and obtain the optimization coefficient based on the energy consumption coefficient.

[0098] In one possible implementation, when the second processing module 33 obtains the target energy consumption value based on the optimization coefficient and at least one first energy consumption value, it is specifically used to: obtain the first energy consumption type corresponding to the first energy consumption value, wherein the first energy consumption type represents the power source type selected by the target vehicle in the second time period; obtain the first target energy consumption value based on the first energy consumption type, the optimization coefficient and at least one first energy consumption value, wherein the first target energy consumption value represents the energy consumption under the corresponding first energy consumption type; and obtain the target energy consumption value based on the first target energy consumption value.

[0099] In one possible implementation, the second processing module 33 is further configured to: determine preset information based on the first target energy consumption value, wherein the preset information represents a preset solution for reducing the first target energy consumption value.

[0100] In one possible implementation, the acquisition module 31 is specifically used to: obtain the running record according to the planned route of the target vehicle; when the second processing module 33 obtains the target energy consumption value according to the optimization coefficient and at least one first energy consumption value, it is also used to: obtain the estimated energy consumption value according to the optimization coefficient and at least one first energy consumption value, wherein the estimated energy consumption value represents the estimated energy consumption of the target vehicle; and obtain the target energy consumption value according to the estimated energy consumption value.

[0101] In one possible implementation, the acquisition module 31 is further configured to: acquire the second energy consumption type used by the target vehicle in the planned trip, the second energy consumption type representing the power source type selected by the target vehicle in the planned trip; when the second processing module 33 obtains the target energy consumption value based on the estimated energy consumption value, it is specifically configured to: obtain the target energy consumption value based on the estimated energy consumption value and the second energy consumption type.

[0102] In one possible implementation, the second processing module 33 is further configured to: obtain a minimum energy consumption value based on the estimated energy consumption value; and obtain a target energy consumption value based on the minimum energy consumption value and the corresponding second energy consumption type.

[0103] The acquisition module 31, the first processing module 32, and the second processing module 33 are connected sequentially. The vehicle energy consumption analysis device 3 provided in this embodiment can perform the following... Figures 3-7 The technical solutions of any of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0104] Figure 9A schematic diagram of an electronic device provided in one embodiment of this application, as shown below. Figure 9 As shown, the electronic device 4 provided in this embodiment includes: a processor 41, and a memory 42 communicatively connected to the processor 41.

[0105] Among them, memory 42 stores computer-executed instructions;

[0106] The processor 41 executes computer execution instructions stored in the memory 42 to implement this application. Figures 3-7 The vehicle energy consumption analysis method provided in any of the corresponding embodiments.

[0107] The memory 42 and the processor 41 are connected via a bus 43.

[0108] For relevant instructions, please refer to the corresponding text. Figures 3-7 The relevant descriptions and effects of the steps in the corresponding embodiments are understood, and will not be elaborated on here.

[0109] One embodiment of this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement this application. Figures 3-7 The vehicle energy consumption analysis method provided in any of the corresponding embodiments.

[0110] The computer-readable storage medium can be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0111] One embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements this application. Figures 3-7 The vehicle energy consumption analysis method provided in any of the corresponding embodiments.

[0112] Figure 10 This is a block diagram illustrating an exemplary embodiment of the present application of a terminal device 800, which may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0113] The terminal device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0114] Processing component 802 typically controls the overall operation of terminal device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0115] Memory 804 is configured to store various types of data to support operation on terminal device 800. Examples of this data include instructions for any application or method operating on terminal device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0116] Power supply component 806 provides power to various components of terminal device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to terminal device 800.

[0117] Multimedia component 808 includes a screen that provides an output interface between terminal device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When terminal device 800 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0118] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when terminal device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0119] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0120] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of terminal device 800. For example, sensor assembly 814 can detect the on / off state of terminal device 800, the relative positioning of components such as the display and keypad of terminal device 800, changes in the position of terminal device 800 or a component of terminal device 800, the presence or absence of user contact with terminal device 800, the orientation or acceleration / deceleration of terminal device 800, and temperature changes of terminal device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0121] Communication component 816 is configured to facilitate wired or wireless communication between terminal device 800 and other devices. Terminal device 800 can access wireless networks based on communication standards, such as WiFi, 3G, 4G, 5G, or other standard communication networks, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0122] In an exemplary embodiment, the terminal device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the functions described in this application. Figures 3-7 The method provided in any of the corresponding embodiments.

[0123] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of a terminal device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0124] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a terminal device, enables the terminal device 800 to perform the above-described embodiments of this application. Figures 3-7 The method provided in any of the corresponding embodiments.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0126] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0127] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for analyzing vehicle energy consumption, characterized in that, The method includes: Obtain the operation record of the target vehicle, wherein the operation record characterizes the operation status of the target vehicle within at least one first time period; Based on the operation record, an optimization coefficient is obtained, wherein the optimization coefficient represents the change in energy consumption of the target vehicle relative to a first energy consumption value in at least one first time period, the first energy consumption value being obtained based on the average energy consumption of the target vehicle in a second time period, and the first time period being a sub-interval of the second time period. Based on the optimization coefficient and at least one of the first energy consumption values, a target energy consumption value is obtained, wherein the target energy consumption value characterizes the actual energy consumption of the target vehicle during the second time period; The process of obtaining optimization coefficients based on the running records includes: Acquire driving environment information, wherein the driving environment information represents the environmental factors that affect the energy consumption of the target vehicle during the first time period; The energy consumption coefficient is obtained based on the operation records and the corresponding driving environment information; The optimization coefficient is obtained based on the energy consumption coefficient.

2. The method according to claim 1, characterized in that, The step of obtaining the target energy consumption value based on the optimization coefficient and at least one of the first energy consumption values ​​includes: Obtain the first energy consumption type corresponding to the first energy consumption value, wherein the first energy consumption type represents the power source type selected by the target vehicle during the second time period; A first target energy consumption value is obtained based on the first energy consumption type, the optimization coefficient, and at least one first energy consumption value. The first target energy consumption value represents the energy consumption under the corresponding first energy consumption type. The target energy consumption value is obtained based on the first target energy consumption value.

3. The method according to claim 2, characterized in that, The method further includes: Based on the first target energy consumption value, preset information is determined, and the preset information represents a preset solution to reduce the first target energy consumption value.

4. The method according to claim 1, characterized in that, The method includes: The operation record is obtained based on the planned route of the target vehicle; The step of obtaining the target energy consumption value based on the optimization coefficient and at least one of the first energy consumption values ​​includes: Based on the optimization coefficient and at least one of the first energy consumption values, an estimated energy consumption value is obtained, wherein the estimated energy consumption value represents the estimated energy consumption of the target vehicle. The target energy consumption value is obtained based on the estimated energy consumption value.

5. The method according to claim 4, characterized in that, The method further includes: The second energy consumption type used by the target vehicle in the planned trip is obtained, and the second energy consumption type represents the power source type selected by the target vehicle in the planned trip; The step of obtaining the target energy consumption value based on the estimated energy consumption value includes: The target energy consumption value is obtained based on the estimated energy consumption value and the second energy consumption type.

6. The method according to claim 5, characterized in that, The method further includes: Based on the estimated energy consumption value, the minimum energy consumption value is obtained; The target energy consumption value is obtained based on the minimum energy consumption value and the corresponding second energy consumption type.

7. A vehicle energy consumption analysis device, characterized in that, include: An acquisition module is used to acquire the operation records of a target vehicle, wherein the operation records characterize the operation status of the target vehicle within at least one first time period; The first processing module is used to obtain an optimization coefficient based on the operation record, wherein the optimization coefficient represents the change in energy consumption of the target vehicle relative to a first energy consumption value in at least one first time period, the first energy consumption value is obtained based on the average energy consumption of the target vehicle in a second time period, and the first time period is a sub-interval of the second time period. The second processing module is used to obtain a target energy consumption value based on the optimization coefficient and at least one of the first energy consumption values, wherein the target energy consumption value characterizes the actual energy consumption of the target vehicle in the second time period; When the first processing module obtains the optimization coefficient based on the operation record, it is specifically used to acquire driving environment information, which represents the environmental factors affecting the energy consumption of the target vehicle during the first time period; obtain the energy consumption coefficient based on the operation record and the corresponding driving environment information; and obtain the optimization coefficient based on the energy consumption coefficient.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the vehicle energy consumption analysis method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The system includes a computer program that, when executed by a processor, implements the vehicle energy consumption analysis method according to any one of claims 1 to 6.

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