Vehicle energy consumption monitoring methods, systems, devices, computer equipment, and storage media
By generating personalized vehicle energy consumption analysis models in the cloud and combining vehicle information and driving habit data, the problem of not considering driving habits and vehicle aging in the energy consumption monitoring of new energy vehicles has been solved, and more accurate energy consumption prediction has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG ZEEKR INTELLIGENT TECH CO LTD
- Filing Date
- 2023-09-06
- Publication Date
- 2026-06-30
AI Technical Summary
Existing onboard energy consumption monitoring solutions for new energy vehicles fail to consider changes in energy consumption caused by driving habits and vehicle aging, resulting in errors in energy consumption calculations. Furthermore, the impact of changes in external temperature is not taken into account.
By generating a personalized vehicle energy consumption analysis model in the cloud, and combining the vehicle's own information and driving habit data, a personalized energy consumption analysis model is generated for energy consumption prediction in route planning.
It enables personalized energy consumption prediction based on differences in driving habits, reduces the impact of vehicle aging and external environment on energy consumption calculation, and improves the accuracy of energy consumption prediction.
Smart Images
Figure CN117416211B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of new energy vehicle technology, and in particular to an on-board energy consumption monitoring method, system, device, computer equipment, and storage medium. Background Technology
[0002] Users of existing new energy vehicles often experience battery anxiety over long distances. In-vehicle maps provide the remaining battery power based on route planning to reach the destination. If the current energy consumption is insufficient to reach the destination, it will be displayed intuitively. Common factors involved in energy consumption calculations include: vehicle interior and exterior temperature, vehicle age, total mileage, average acceleration, number of rapid acceleration / decelerations at 100 km / h, and average speed.
[0003] However, existing in-vehicle solutions cannot display changes in energy consumption based on the user's driving habits, which differs from the theory. Vehicle energy consumption is often fixed, but as the vehicle age and mileage change, battery degradation will cause energy consumption to increase. Changes in external temperature also affect energy consumption, and existing solutions cannot display changes in energy consumption. Summary of the Invention
[0004] Therefore, it is necessary to provide a vehicle energy consumption monitoring method, system, device, computer equipment, and storage medium to address the aforementioned technical problems. This solution aims to solve the problem that existing vehicle energy consumption monitoring schemes do not consider energy consumption changes caused by driving habits and the vehicle itself. This solution combines the vehicle itself and driving habits to generate a personalized vehicle energy consumption analysis model, then performs energy consumption analysis on the planned route, and sends the obtained energy consumption analysis results to the vehicle terminal for display, thereby achieving the purpose of vehicle energy consumption monitoring.
[0005] In a first aspect, this application provides a vehicle energy consumption monitoring method applied in the cloud, wherein the cloud interacts with the vehicle terminal, and the method includes:
[0006] Receive energy consumption monitoring instructions transmitted from the vehicle terminal, and extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring instructions;
[0007] Based on the driving identity ID, obtain the corresponding behavioral energy consumption data and the corresponding model body, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model.
[0008] The energy consumption analysis model of the vehicle body is used to perform energy consumption analysis on the planned route, and the energy consumption analysis results are sent to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0009] In one embodiment, prior to the step of receiving the energy consumption monitoring command transmitted by the vehicle terminal, the following steps are included:
[0010] Multiple data points are pre-configured on several vehicles to obtain historical data from each data point. The historical data includes: user driving logs, vehicle basic information, vehicle road condition information, and driving energy consumption information.
[0011] Based on the user's driving log and the corresponding vehicle driving road condition information, acquire and store behavioral energy consumption data related to the user's driving habits. The behavioral energy consumption data includes at least: the number of times the vehicle accelerates / decelerates at 100 km / h and the average vehicle speed. Different driving energy consumption data are matched with different driving behaviors.
[0012] Based on the user's driving log and the vehicle's basic information, the vehicle's energy consumption data is determined. The vehicle's energy consumption data includes at least the vehicle's age, mileage, and interior / exterior temperature.
[0013] Based on the vehicle type, energy consumption features for model training are selected from the behavioral energy consumption data and vehicle body energy consumption data. The model is then trained using historical data of these energy consumption features to obtain the main models for different vehicle types.
[0014] In one embodiment, after the step of obtaining historical data for each data tracking point, the following steps are included:
[0015] Select one or more energy consumption characteristics from the vehicle body energy consumption data according to the vehicle type;
[0016] Assign energy consumption coefficients to the selected energy consumption features and calculate the energy consumption coefficient values corresponding to each energy consumption feature; wherein, the energy consumption coefficients correspond one-to-one with the vehicle body energy consumption data;
[0017] The model body is trained using energy consumption coefficient values and behavioral energy consumption data.
[0018] In one embodiment, the step of assigning energy consumption coefficients to selected energy consumption characteristics and calculating the energy consumption coefficient values corresponding to each energy consumption characteristic includes:
[0019] The vehicle energy consumption data in the historical data is divided according to the driving cycle;
[0020] For a single driving cycle, the energy consumption coefficient is used to construct an energy consumption characteristic relationship;
[0021] Substituting vehicle energy consumption data from different driving cycles into the energy consumption characteristic formula, the energy consumption coefficient value of each energy consumption characteristic is calculated based on polynomial combination; wherein, the characteristic energy consumption formula is:
[0022] w i =w i1 *f1+w i2 *f2+...+w i(j-1) *f j-1+w ij *f j
[0023] Among them, w i The total energy consumption coefficients, f1, f2…f, represent the total energy consumption coefficient values. j These represent historical data for each energy consumption characteristic, w i1 w i2 …w ij These represent the energy consumption coefficient values for each energy consumption characteristic.
[0024] In one embodiment, the step of training the model subject using energy consumption coefficient values and behavioral energy consumption data includes:
[0025] Obtain the corresponding driving energy consumption information based on behavioral energy consumption data;
[0026] The energy consumption coefficient value and driving energy consumption information are substituted into the preset body energy consumption analysis formula for model training. The training results in a model subject corresponding to different car types, and a personalized body energy consumption analysis model is generated.
[0027] Models of different car types are stored in a model repository; the energy consumption analysis formula for the car body is as follows:
[0028]
[0029] Where w1, w2, w3, w4, and w5 represent the energy consumption coefficient values of the selected energy consumption characteristics; l represents the mileage traveled at different speeds; E 21 E represents the kinetic energy as the vehicle speed increases; 22 E4 represents the kinetic energy when the vehicle's speed decreases; E5 represents the mechanical energy when the vehicle's potential energy increases; E6 represents the mechanical energy when the vehicle's potential energy decreases.
[0030] Indicates low voltage energy consumption; This indicates the vehicle's overall energy consumption.
[0031] In one embodiment, the driving identity ID includes: user ID and vehicle ID; the step of obtaining the corresponding behavioral energy consumption data and the corresponding model subject based on the driving identity ID includes:
[0032] Based on the user ID, retrieve behavioral energy consumption data related to the user's driving habits;
[0033] The vehicle type is determined based on the vehicle ID. The corresponding model body is then retrieved from the model repository to input the behavioral energy consumption data and the current vehicle energy consumption data into the model body, thereby generating a personalized vehicle energy consumption analysis model.
[0034] Secondly, this application provides an on-board energy consumption monitoring system, the system including: an on-board terminal and a cloud; the on-board terminal includes: an interface unit, a data acquisition unit, a communication unit and a display unit;
[0035] The interface unit is connected to the data interface installed on the vehicle;
[0036] The acquisition unit is connected to the interface unit to obtain the driving identity ID and the current vehicle energy consumption data, and transmits the driving identity ID and the current vehicle energy consumption data to the communication unit. The current vehicle energy consumption data includes at least one of the following: vehicle age, mileage, and current in-vehicle / outside temperature.
[0037] The communication unit is connected to the cloud and sends the driving identity ID and the current vehicle energy consumption data to the cloud;
[0038] The cloud platform obtains behavioral energy consumption data and model body based on the driving identity ID, and inputs the behavioral energy consumption data and current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model, so as to perform energy consumption analysis on route planning, obtain energy consumption analysis results, and send the energy consumption analysis results to the vehicle terminal.
[0039] After receiving the energy consumption analysis results, the vehicle terminal displays the estimated actual energy consumption upon arrival at the destination via the display unit.
[0040] In one embodiment, the vehicle terminal is loaded with a vehicle map, and the data interface of the vehicle map is connected to the communication unit;
[0041] In response to the activation of the in-vehicle map, the vehicle identification ID and current vehicle energy consumption data are transmitted to the communication unit via the data interface.
[0042] In one embodiment, the cloud includes: a Kafka data pipeline, a data warehouse, and a model warehouse;
[0043] The Kafka data pipeline is used to receive the driving identity ID and the current vehicle energy consumption data, generate message instructions, retrieve the corresponding behavioral energy consumption data from the data warehouse according to the message instructions, and retrieve the corresponding model body from the model warehouse.
[0044] Thirdly, this application provides an on-board energy consumption prediction device, the device comprising: a data extraction module, a model acquisition module, and an energy consumption monitoring module, wherein...
[0045] The data extraction module is used to receive energy consumption monitoring instructions transmitted by the vehicle terminal and extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring instructions.
[0046] The model acquisition module is used to acquire the corresponding behavioral energy consumption data and the corresponding model body according to the driving identity ID, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model.
[0047] The energy consumption monitoring module is used to perform energy consumption analysis on the planned route using the vehicle energy consumption analysis model, obtain the energy consumption analysis results and send them to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0048] Fourthly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0049] Receive energy consumption monitoring instructions transmitted from the vehicle terminal, and extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring instructions;
[0050] Based on the driving identity ID, obtain the corresponding behavioral energy consumption data and the corresponding model body, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model.
[0051] The energy consumption analysis model of the vehicle body is used to perform energy consumption analysis on the planned route, and the energy consumption analysis results are sent to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0052] Fifthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:
[0053] Receive energy consumption monitoring instructions transmitted from the vehicle terminal, and extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring instructions;
[0054] Based on the driving identity ID, obtain the corresponding behavioral energy consumption data and the corresponding model body, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model.
[0055] The energy consumption analysis model of the vehicle body is used to perform energy consumption analysis on the planned route, and the energy consumption analysis results are sent to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0056] The above-mentioned vehicle energy consumption monitoring method, system, device, computer equipment, and storage medium can achieve the following technical effects:
[0057] By employing the method of obtaining corresponding behavioral energy consumption data and a corresponding model body based on the driving identity ID, and inputting the behavioral energy consumption data and current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model; using the vehicle energy consumption analysis model to perform energy consumption analysis on the planned route, obtaining energy consumption analysis results and sending them to the vehicle terminal to display the actual energy consumption estimate upon arrival at the destination, it is possible to generate personalized vehicle energy consumption analysis models based on different driving habits, and to make personalized energy consumption estimates based on differences in driving habits. This can reduce energy consumption calculation errors caused by vehicle aging, and can also reduce the impact of the external environment on vehicle energy consumption calculation. Attached Figure Description
[0058] Figure 1 This is a schematic diagram illustrating the application of the vehicle-mounted energy consumption monitoring system in Example 1;
[0059] Figure 2 This is an interactive schematic diagram of the vehicle-mounted energy consumption monitoring system in Example 2;
[0060] Figure 3 This is a flowchart illustrating the vehicle energy consumption prediction method in Example 3;
[0061] Figure 4 This is a schematic diagram of the data acquisition process in Example 3;
[0062] Figure 5 This is a schematic diagram of the model training process in Example 3;
[0063] Figure 6 This is a schematic diagram of the model architecture in Example 3;
[0064] Figure 7 This is a schematic diagram of the model prediction in Example 3;
[0065] Figure 8 This is a structural block diagram of the vehicle-mounted energy consumption monitoring device in Example 4;
[0066] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0067] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0068] With the increasing popularity of new energy vehicles, users experience significant battery anxiety during long-distance travel due to long charging times and the incomplete availability of charging stations. Existing in-vehicle solutions include some map services that provide remaining battery power based on route planning, indicating the remaining battery level upon arrival at the destination. If the current energy consumption is insufficient to reach the destination, a notification will be displayed. Furthermore, during real-world testing, car manufacturers typically select roads with zero gradient and zero curvature for testing, calculating energy consumption per kilometer within each speed range. For example, a new energy vehicle at 10 km / h consumes 0.452 kW / km. The average energy conversion rate for gradients greater than zero, such as the efficiency of converting electrical energy to mechanical energy, is 0.9. Based on this energy conversion efficiency and energy consumption across different speed ranges, users can estimate the overall route energy consumption by considering road conditions during route planning. This estimation, combined with the vehicle's total battery energy and the energy consumed along the corresponding route, allows for the determination of remaining energy upon arrival at the destination. However, this solution does not take into account the changes in energy consumption caused by users' driving habits, resulting in a difference between theoretical and actual calculated energy consumption; the energy consumption of a vehicle is fixed, but as the vehicle age and mileage change, battery degradation will cause energy consumption to increase; the temperature difference between the inside and outside of the vehicle also affects energy consumption, and current vehicle energy consumption monitoring solutions do not take into account the above effects.
[0069] Example 1
[0070] This application provides a method for estimating vehicle energy consumption, which can be applied to, for example... Figure 1 The vehicle energy consumption monitoring system shown includes an on-board terminal 100 and a cloud-based system 200, which communicate via a vehicle network. The vehicle-mounted terminal 100 includes an interface unit, a data acquisition unit, a communication unit, and a display unit. The interface unit is connected to a data interface installed on the vehicle. The data acquisition unit is connected to the interface unit to acquire a driving identity ID and current vehicle energy consumption data, and transmits the driving identity ID and current vehicle energy consumption data to the communication unit. The current vehicle energy consumption data includes at least one of the following: vehicle age, mileage, and current interior / exterior temperature. The communication unit is connected to the cloud and sends the driving identity ID and the current vehicle energy consumption data to the cloud. The cloud acquires behavioral energy consumption data and a model body based on the driving identity ID, and inputs the behavioral energy consumption data and current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model for energy consumption analysis of route planning, obtaining energy consumption analysis results, and sending the vehicle energy consumption analysis results to the vehicle-mounted terminal. After receiving the energy consumption analysis results, the display unit displays the estimated actual energy consumption upon arrival at the destination.
[0071] In one embodiment, the vehicle terminal loads a vehicle map, and the data interface of the vehicle map is connected to the communication unit; in response to activating the vehicle map, the vehicle identification ID and current vehicle energy consumption data are transmitted to the communication unit via the data interface. In another embodiment, the communication unit can use communication technologies such as 4G, 5G, and V2X to connect to the vehicle network, and then connect to the cloud through the vehicle network.
[0072] In one embodiment, the cloud includes: a Kafka data pipeline 210, a data warehouse 220, and a model warehouse 230. The Kafka data pipeline 210 receives the vehicle identification ID and current vehicle energy consumption data, generates message instructions, and retrieves corresponding behavioral energy consumption data from the data warehouse 220 and the corresponding model body from the model warehouse 230 based on the message instructions. The Kafka data pipeline 210 is a high-throughput, persistent, distributed publish-subscribe message queue system used to determine the legitimacy of access and to access the data warehouse and model warehouse based on the vehicle identification ID sent by the vehicle terminal.
[0073] Exemplary illustration, see attached diagram. Figure 2 As shown, the vehicle terminal opens the in-vehicle map, generates an energy consumption monitoring command, and sends it to the cloud to request the generation of a personalized vehicle energy consumption analysis model. The cloud receives the energy consumption monitoring command and extracts the driving identity ID and current vehicle energy consumption data from it. This current energy consumption data can include the vehicle's current age, mileage, and interior / exterior temperature, among other energy consumption characteristics. Further, the cloud retrieves behavioral energy consumption data corresponding to the user's driving habits from the data warehouse based on the driving identity ID. This behavioral energy consumption data can include the number of rapid acceleration / decelerations per 100 km / h and average speed. The cloud also retrieves a model body corresponding to the vehicle type from the model warehouse based on the driving identity ID; this model body is a general model corresponding to a specific vehicle type. The behavioral energy consumption data and current vehicle energy consumption data are then input into the model body to generate a personalized vehicle energy consumption analysis model. The vehicle energy consumption analysis model is used to perform energy consumption analysis on the planned route, and the results are sent to the in-vehicle terminal. After receiving the energy consumption analysis results, the in-vehicle terminal displays the estimated actual energy consumption upon arrival at the destination. When displaying actual energy consumption estimates, the vehicle-mounted terminal converts these estimates into battery information based on route planning, and then displays the remaining battery power for a specific purpose. Since the energy consumption monitoring command is triggered after the vehicle map is opened, preferably, the vehicle-mounted terminal displays the actual energy consumption estimates through the vehicle map.
[0074] Example 2
[0075] In one embodiment, such as Figure 3 As shown, a method for predicting vehicle energy consumption is provided, and this method is applied to... Figure 1The following steps, S101-S103, are used as an example to illustrate the process.
[0076] Step S101: Receive the energy consumption monitoring command transmitted by the vehicle terminal, and extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring command;
[0077] Step S102: Obtain the corresponding behavioral energy consumption data and the corresponding model body according to the driving identity ID, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model.
[0078] Step S103: Use the vehicle energy consumption analysis model to perform energy consumption analysis on the planned route, obtain the energy consumption analysis results and send them to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0079] In one embodiment, reference is made to the appendix. Figure 4 As shown, before step S101, which is the step of receiving the energy consumption monitoring command transmitted by the vehicle terminal, the steps include: steps S201-S203.
[0080] Step S201: Pre-configure various data points on several vehicles and receive historical data from each data point. The historical data includes: user driving logs, vehicle basic information, vehicle driving road condition information, and driving energy consumption information.
[0081] Step S202: Based on the user's driving log and the corresponding vehicle driving road condition information, acquire and store behavioral energy consumption data related to the user's driving habits. The behavioral energy consumption data includes at least: the number of times the vehicle accelerates / decelerates at 100 km / h and the average vehicle speed. That is, find the patterns of the user's driving habits through historical data, such as the number of times the vehicle accelerates at 100 km / h, the number of times the vehicle decelerates at 100 km / h, and the average vehicle speed. Different driving energy consumption data match different driving habits. In this embodiment, the corresponding driving energy consumption information is acquired based on the behavioral energy consumption data. The driving energy consumption information can include the energy consumption in each driving cycle, such as the driving distance at different speed ranges, the kinetic energy required to increase speed, the change in kinetic energy when speed decreases, the mechanical energy increased when going uphill, the mechanical energy decreased when going downhill, and the energy consumption of low-voltage electrical appliances (such as the energy consumption of the DHU and the energy consumption of the air conditioner).
[0082] Step S203: Obtain vehicle energy consumption data based on the user's driving log and basic vehicle information. The vehicle energy consumption data includes at least the vehicle age, vehicle mileage, and interior / exterior temperature. The basic vehicle information may include: license plate number, vehicle model, engine number, chassis number, vehicle type, etc.
[0083] Step S204: Select energy consumption features for model training from behavioral energy consumption data and vehicle body energy consumption data according to vehicle type, and use historical data of energy consumption features to train the model to obtain the main body of the model for different vehicle types.
[0084] To further explain, this embodiment categorizes vehicles based on their specifications, models, manufacturers, and other configurations. This allows different vehicle types to utilize different energy consumption characteristics for model training, resulting in different vehicle type models. These models are general-purpose vehicle body energy consumption analysis models for their respective vehicle types. In this embodiment, the model body corresponding to the vehicle type is first located in the model repository. Then, based on behavioral energy consumption data related to driving habits and vehicle body energy consumption data, the model body is personalized to generate a customized vehicle body energy consumption analysis model.
[0085] In one embodiment, reference is made to the appendix. Figure 5-7 As shown, after step S201, which is to receive historical data from each data point, the process includes steps S301-S303.
[0086] Step S301: Select one or more energy consumption features from the vehicle body energy consumption data according to the vehicle type; different vehicle types may consider the same, overlapping, or completely different energy consumption features. This embodiment considers different vehicle types to train the model subject corresponding to the vehicle type.
[0087] Step S302: Assign energy consumption coefficients to the selected energy consumption features and calculate the energy consumption coefficient values corresponding to each energy consumption feature; wherein, the energy consumption coefficients correspond one-to-one with the vehicle body energy consumption data; further explanation: at this time, the energy consumption coefficient is just an unknown code. This application first proposes the concept of energy consumption coefficient, and then calculates different energy consumption coefficient values for different energy consumption features, and the total energy consumption coefficient value is the sum of the products of each vehicle body energy consumption data and the energy consumption coefficient value.
[0088] Step S303: Train the model subject using energy consumption coefficient values and behavioral energy consumption data.
[0089] In one embodiment, step S302, assigning energy consumption coefficients to the selected energy consumption characteristics and calculating the energy consumption coefficient values corresponding to each energy consumption characteristic, includes:
[0090] Vehicle energy consumption data in historical data is divided according to driving cycle;
[0091] For a single driving cycle, an energy consumption characteristic relationship is constructed using the energy consumption coefficient;
[0092] Substituting vehicle energy consumption data from different driving cycles into the energy consumption characteristic formula, the energy consumption coefficient value of each energy consumption characteristic is calculated based on polynomial combinations; whereby the characteristic energy consumption formula is:
[0093] w i =w i1 *f1+w i2 *f2+...+w i(j-1) *f j-1 +w ij *f j
[0094] Among them, w i The total energy consumption coefficients, f1, f2…f, represent the total energy consumption coefficient values. j These represent historical data for each energy consumption characteristic, w i1 w i2 …w ij These represent the energy consumption coefficient values for each energy consumption characteristic.
[0095] In one embodiment, step S303, the step of training the model subject using energy consumption coefficient values and behavioral energy consumption data, includes:
[0096] Obtain the corresponding driving energy consumption information based on behavioral energy consumption data;
[0097] The energy consumption coefficient value and driving energy consumption information are substituted into the preset body energy consumption analysis formula for model training. The training results in a model subject corresponding to different car types, and a personalized body energy consumption analysis model is generated.
[0098] Models of different car types are stored in a model repository; the energy consumption analysis formula for the car body is as follows:
[0099]
[0100] Where w1, w2, w3, w4, and w5 represent the energy consumption coefficient values of the selected energy consumption characteristics; l represents the mileage traveled at different speeds; E 21 E represents the kinetic energy as the vehicle speed increases; 22 E4 represents the kinetic energy when the vehicle's speed decreases; E5 represents the mechanical energy when the vehicle's potential energy increases; E6 represents the mechanical energy when the vehicle's potential energy decreases. Indicates low voltage energy consumption; This indicates the vehicle's overall energy consumption.
[0101] The driving energy consumption information includes: kinetic energy when driving speed increases, kinetic energy when driving speed decreases, mechanical energy when driving potential energy increases, mechanical energy when potential energy decreases, and low voltage energy consumption.
[0102] In one embodiment, the driving identity ID includes: user ID and vehicle ID; the step of obtaining the corresponding vehicle energy consumption analysis model based on the driving identity ID includes:
[0103] Based on the user ID, retrieve behavioral energy consumption data related to the user's driving habits; based on the vehicle ID, determine the vehicle type, and retrieve the corresponding model body from the model repository so that the behavioral energy consumption data and the current vehicle energy consumption data can be input into the model body to generate a personalized vehicle energy consumption analysis model.
[0104] The user ID represents the user's identity. Different users have different driving habits, and based on these differences, energy consumption varies significantly among users with different driving habits. The vehicle ID represents the vehicle's VIN (Vehicle Identification Number), which contains information such as the vehicle's manufacturer, year, model, body style and code, engine code, and assembly location. Therefore, vehicle age, model, and other vehicle-related characteristic data can be obtained based on the vehicle ID.
[0105] In the aforementioned vehicle energy consumption monitoring method, the corresponding behavioral energy consumption data and model body are obtained based on the driving identity ID, and the behavioral energy consumption data and the current vehicle energy consumption data are input into the model body to generate a personalized vehicle energy consumption analysis model. The vehicle energy consumption analysis model is used to perform energy consumption analysis on the planned route, obtain the energy consumption analysis results, and send them to the vehicle terminal to display the actual energy consumption estimate when reaching the destination. This method can generate personalized vehicle energy consumption analysis models based on different driving habits and make personalized energy consumption estimates based on differences in driving habits. It can reduce the energy consumption calculation error caused by vehicle aging and reduce the impact of the external environment on the calculation of vehicle energy consumption.
[0106] It should be understood that, although Figure 3-7 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 3-7 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0107] Example 4
[0108] In one embodiment, such as Figure 8 As shown, an on-board energy consumption monitoring device is provided, including: a data extraction module 10, a model acquisition module 20, and an energy consumption monitoring module 30, wherein:
[0109] The data extraction module 10 is used to receive energy consumption monitoring instructions transmitted by the vehicle terminal and extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring instructions.
[0110] The model acquisition module 20 is used to obtain the corresponding behavioral energy consumption data and the corresponding model body according to the driving identity ID, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model.
[0111] The energy consumption monitoring module 30 is used to perform energy consumption analysis on the planned route using the vehicle energy consumption analysis model, obtain the energy consumption analysis results and send them to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0112] Specific limitations regarding the vehicle energy consumption monitoring device can be found in the limitations of the vehicle energy consumption monitoring method described above, and will not be repeated here. Each module in the aforementioned vehicle energy consumption monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0113] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores vehicle energy consumption monitoring data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a vehicle energy consumption monitoring method.
[0114] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0115] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0116] The system receives energy consumption monitoring instructions transmitted from the vehicle terminal, extracts the driving identity ID and current vehicle energy consumption data from the instructions, obtains the corresponding behavioral energy consumption data and the corresponding model body based on the driving identity ID, and inputs the behavioral energy consumption data and current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model. The system uses the vehicle energy consumption analysis model to perform energy consumption analysis on the planned route, obtains the energy consumption analysis results, and sends them to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0117] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0118] The system receives energy consumption monitoring instructions transmitted from the vehicle terminal, extracts the driving identity ID and current vehicle energy consumption data from the instructions, obtains the corresponding behavioral energy consumption data and the corresponding model body based on the driving identity ID, and inputs the behavioral energy consumption data and current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model. The system uses the vehicle energy consumption analysis model to perform energy consumption analysis on the planned route, obtains the energy consumption analysis results, and sends them to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
[0119] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0120] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0121] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A vehicle energy consumption monitoring method, applied in the cloud, wherein the cloud interacts with the vehicle terminal, characterized in that, The method includes: Receive energy consumption monitoring instructions transmitted by the vehicle terminal, extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring instructions, wherein the current vehicle energy consumption data includes at least one of the following: vehicle age, mileage, and current interior / exterior temperature; Based on the driving identity ID, obtain the corresponding behavioral energy consumption data and the corresponding model body, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model. The vehicle energy consumption analysis model is used to perform energy consumption analysis on the planned route, and the energy consumption analysis results are sent to the vehicle terminal to display the actual energy consumption estimate when reaching the destination. The driving identity ID includes: user ID and vehicle ID; the step of obtaining the corresponding behavioral energy consumption data and the corresponding model subject based on the driving identity ID includes: Based on the user ID, retrieve the energy consumption data related to the user's driving habits; The vehicle type is determined based on the vehicle ID, and a model body corresponding to the vehicle type is searched from the model repository. The behavioral energy consumption data and the current vehicle body energy consumption data are then input into the model body to generate a personalized vehicle body energy consumption analysis model.
2. The vehicle-mounted energy consumption monitoring method according to claim 1, characterized in that, Before the step of receiving the energy consumption monitoring command transmitted by the vehicle terminal, the following steps are included: Multiple data points are pre-configured on several vehicles to receive historical data from each data point. The historical data includes: user driving logs, basic vehicle information, vehicle road condition information, and driving energy consumption information. Based on the user's driving log and the corresponding vehicle driving road condition information, acquire and store behavioral energy consumption data related to the user's driving habits. The behavioral energy consumption data includes at least: the number of times the vehicle accelerates / decelerates at 100 km / h and the average vehicle speed. Different driving energy consumption data are matched with different driving habits. Based on the user's driving log and the vehicle's basic information, the vehicle's energy consumption data is determined. The vehicle's energy consumption data includes at least the vehicle's age, mileage, and interior / exterior temperature. Based on the vehicle type, energy consumption features for model training are selected from the behavioral energy consumption data and vehicle body energy consumption data. The model is then trained using historical data of these energy consumption features to obtain the main models for different vehicle types.
3. The vehicle-mounted energy consumption monitoring method according to claim 2, characterized in that, After the step of receiving historical data from each data tracking point, the following steps are included: Select one or more energy consumption characteristics from the vehicle body energy consumption data according to the vehicle type; Assign energy consumption coefficients to the selected energy consumption features and calculate the energy consumption coefficient values corresponding to each energy consumption feature; wherein, the energy consumption coefficients correspond one-to-one with the vehicle body energy consumption data; The model body is trained using energy consumption coefficient values and behavioral energy consumption data.
4. The vehicle-mounted energy consumption monitoring method according to claim 3, characterized in that, The steps of assigning energy consumption coefficients to selected energy consumption characteristics and calculating the energy consumption coefficient values corresponding to each energy consumption characteristic include: The vehicle energy consumption data in the historical data is divided according to the driving cycle; For a single driving cycle, the energy consumption coefficient is used to construct an energy consumption characteristic relationship; Substituting vehicle energy consumption data from different driving cycles into the energy consumption characteristic formula, the energy consumption coefficient value of each energy consumption characteristic is calculated based on polynomial combination; wherein, the characteristic energy consumption formula is: in, This represents the total energy consumption coefficient value. , … These represent historical data for each energy consumption characteristic. , … These represent the energy consumption coefficient values for each energy consumption characteristic.
5. The vehicle-mounted energy consumption monitoring method according to claim 4, characterized in that, The step of training the main body of the model using energy consumption coefficient values and behavioral energy consumption data includes: Obtain the corresponding driving energy consumption information based on the aforementioned behavioral energy consumption data; The energy consumption coefficient value and driving energy consumption information are substituted into the preset body energy consumption analysis formula for model training. The training results in a model subject corresponding to different car types, and a personalized body energy consumption analysis model is generated. Models of different car types are stored in a model repository; the energy consumption analysis formula for the car body is as follows: in, , , , , The energy consumption coefficient value represents the selected energy consumption characteristic; Indicates the distance traveled at different speeds; This represents the kinetic energy as the vehicle speed increases. This represents the kinetic energy as the vehicle's speed decreases. The mechanical energy that increases when the driving potential energy increases; Mechanical energy representing the decrease in potential energy; Indicates low voltage energy consumption; This indicates the vehicle's overall energy consumption.
6. A vehicle-mounted energy consumption monitoring system, characterized in that, The system includes: an in-vehicle terminal and a cloud platform; the in-vehicle terminal includes: an interface unit, a data acquisition unit, a communication unit, and a display unit. The interface unit is connected to the data interface installed on the vehicle; The acquisition unit is connected to the interface unit to obtain the driving identity ID and the current vehicle energy consumption data, and transmits the driving identity ID and the current vehicle energy consumption data to the communication unit. The current vehicle energy consumption data includes at least one of the following: vehicle age, mileage, and current in-vehicle / outside temperature. The communication unit is connected to the cloud and sends the driving identity ID and the current vehicle energy consumption data to the cloud; The cloud platform obtains behavioral energy consumption data and a model body based on the driving identity ID, and inputs the behavioral energy consumption data and current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model. This model is used to perform energy consumption analysis on route planning, obtain energy consumption analysis results, and send the results to the in-vehicle terminal. Specifically, the cloud platform searches for behavioral energy consumption data related to the user's driving habits based on the user ID; it determines the vehicle type based on the vehicle ID, searches for a model body corresponding to the vehicle type in the model repository, and inputs the behavioral energy consumption data and current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model. After receiving the energy consumption analysis results, the vehicle terminal displays the estimated actual energy consumption upon arrival at the destination via the display unit.
7. The vehicle-mounted energy consumption monitoring system according to claim 6, characterized in that, The vehicle terminal is loaded with a vehicle map, and the data interface of the vehicle map is connected to the communication unit. In response to the activation of the in-vehicle map, the vehicle identification ID and current vehicle energy consumption data are transmitted to the communication unit via the data interface.
8. The vehicle-mounted energy consumption monitoring system according to claim 6, characterized in that, The cloud includes: Kafka data pipeline, data warehouse, and model warehouse; The Kafka data pipeline is used to receive the driving identity ID and the current vehicle energy consumption data, generate message instructions, retrieve the corresponding behavioral energy consumption data from the data warehouse according to the message instructions, and retrieve the corresponding model body from the model warehouse.
9. A vehicle-mounted energy consumption monitoring device, characterized in that, The device includes: The data extraction module is used to receive energy consumption monitoring instructions transmitted by the vehicle terminal, and extract the driving identity ID and current vehicle energy consumption data from the energy consumption monitoring instructions. The current vehicle energy consumption data includes at least one of the following information: vehicle age, mileage, and current interior / exterior temperature. The model acquisition module is used to acquire the corresponding behavioral energy consumption data and the corresponding model body according to the driving identity ID, and input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate a personalized vehicle energy consumption analysis model; wherein, the model acquisition module searches for the behavioral energy consumption data related to the user's driving habits according to the user ID; determines the vehicle type according to the vehicle ID, searches for the model body corresponding to the vehicle type in the model repository, so as to input the behavioral energy consumption data and the current vehicle energy consumption data into the model body to generate the personalized vehicle energy consumption analysis model; The energy consumption monitoring module is used to perform energy consumption analysis on the planned route using the vehicle energy consumption analysis model, obtain the energy consumption analysis results and send them to the vehicle terminal to display the actual energy consumption estimate when arriving at the destination.
10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.
11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.