CN116788052A - Method and device for determining remaining mileage of vehicle, storage medium and electronic equipment - Google Patents
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Abstract
The embodiment of the disclosure provides a method and a device for determining the remaining mileage of a vehicle, a storage medium and electronic equipment, wherein the method for determining the remaining mileage of the vehicle comprises the following steps: determining a remaining capacity of a battery in the vehicle; determining an average energy consumption value of the vehicle in a predetermined operating mode based on the historical driving data; and determining the remaining mileage of the vehicle based on the remaining battery level and the average energy consumption value. According to the method and the device for predicting the energy consumption, the energy consumption of the vehicle can be predicted more rapidly and accurately by combining various factors such as historical driving data, vehicle speed, temperature and battery SOC, the response speed is higher, and the calculation accuracy of the remaining mileage of the vehicle is remarkably improved.
Description
Technical Field
The disclosure relates to the technical field of vehicle driving control, in particular to a method and a device for determining the remaining mileage of a vehicle, a storage medium and electronic equipment.
Background
With the continuous increase of the global demand for clean energy, pure electric vehicles are increasingly widely used as a novel clean energy vehicle. However, due to its special energy storage, an electric vehicle makes accurate prediction of the remaining mileage of a pure electric vehicle during driving a challenging task. At present, some methods and systems for predicting the residual mileage of the pure electric vehicle exist in the market, but the methods often need a large amount of real-time data, a complex mathematical model and artificial intelligence assistance, so that the cost is high, and the popularization and the application are not facilitated.
Disclosure of Invention
The embodiment of the disclosure aims to provide a method and a device for determining the remaining mileage of a vehicle, a storage medium and electronic equipment, so as to solve the problems in the prior art.
In order to solve the above technical problems, the embodiments of the present disclosure adopt the following technical solutions:
an aspect of an embodiment of the present disclosure provides a method for determining a remaining mileage of a vehicle, including:
determining a remaining capacity of a battery in the vehicle;
determining an average energy consumption value of the vehicle in a predetermined operating mode based on the historical driving data;
and determining the remaining mileage of the vehicle based on the remaining battery level and the average energy consumption value.
In some embodiments, the determining the remaining power of the battery in the vehicle includes:
determining a rated capacity of a battery in the vehicle based on the historical travel data;
determining a rated total energy of the battery based on the rated capacity;
the remaining power is determined based on the rated total energy and state of charge.
In some embodiments, the determining the rated capacity of the battery in the vehicle based on the historical driving data includes:
grouping the historical driving data based on the ambient temperature to obtain rated capacities in different temperature intervals;
the rated capacity of the battery is determined based on the ambient temperature at which the vehicle is currently located.
In some embodiments, the determining an average energy consumption value of the vehicle in a predetermined operating mode based on the historical driving data includes:
determining a current operating mode of the vehicle based on historical travel data;
determining an average energy consumption estimated value of the vehicle in the working mode based on historical driving data;
and correcting the average energy consumption estimated value to obtain the average energy consumption value.
In some embodiments, the determining the current operating mode of the vehicle based on the historical driving data includes:
grouping the historical travel data based on ambient temperature, average vehicle speed, and state of charge to determine a plurality of operating modes;
and determining the current working mode based on the current environment temperature, average vehicle speed and state of charge of the vehicle.
In some embodiments, the correcting the average energy consumption estimate to obtain the average energy consumption value includes:
predicting a vehicle speed value of the vehicle in a preset future time;
and correcting the average energy consumption estimated value based on a prediction result.
In some embodiments, predicting a vehicle speed value of the vehicle at a predetermined time in the future is implemented based on a BP neural network trained based on historical driving data.
Another aspect of the disclosed embodiments provides a device for determining a remaining mileage of a vehicle, including:
a remaining power determining module for determining a remaining power of a battery in the vehicle;
the average energy consumption value determining module is used for determining an average energy consumption value of the vehicle in a preset working mode based on historical driving data;
and the remaining mileage determining module is used for determining the remaining mileage of the vehicle based on the remaining electric quantity and the average energy consumption value.
The present disclosure also provides a storage medium storing a computer program which, when executed by a processor, performs the steps of any of the methods described above.
The present disclosure also provides an electronic device comprising at least a memory, a processor, the memory having stored thereon a computer program, the processor, when executing the computer program on the memory, implementing the steps of any of the methods described above.
According to the embodiment of the disclosure, the residual mileage estimation offline model is built through historical driving data, short-term vehicle speed prediction is simultaneously carried out by inquiring the average energy consumption estimated value, so that the average energy consumption value is corrected online, and finally the residual mileage estimated value is obtained by combining the residual electric quantity of the battery. According to the method and the device for predicting the energy consumption, the energy consumption of the vehicle can be predicted more rapidly and accurately by combining various factors such as historical driving data, vehicle speed, temperature and battery SOC, the response speed is higher, and the calculation accuracy of the remaining mileage of the vehicle is remarkably improved.
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In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a schematic step diagram of a method for determining a remaining mileage of a vehicle according to an embodiment of the present disclosure;
FIG. 2 is a schematic step diagram of a method for determining a remaining mileage of a vehicle according to an embodiment of the present disclosure;
fig. 3 is a schematic step diagram of a method for determining a remaining mileage of a vehicle according to an embodiment of the present disclosure.
Detailed Description
Various aspects and features of the disclosure are described herein with reference to the drawings.
It should be understood that various modifications may be made to the embodiments of the application herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Other modifications within the scope and spirit of this disclosure will occur to persons of ordinary skill in the art.
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment, given as a non-limiting example, with reference to the accompanying drawings.
It should also be understood that, although the present disclosure has been described with reference to some specific examples, a person skilled in the art will certainly be able to achieve many other equivalent forms of the present disclosure, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in light of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure will be described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure, which may be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the disclosure in unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but merely serve as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the word "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
A first embodiment of the present disclosure provides a method for determining a remaining mileage of a vehicle, which may be an electric vehicle in particular, for estimating the remaining mileage of the vehicle based on big data. The big data are historical data related to the vehicle running, the historical data are generally stored on a cloud server, the cloud server is used for storing the data, real-time access and processing of the data are achieved, and the efficiency and the real-time performance of data analysis are improved.
Specifically, the embodiment uses the signal fed back by the current vehicle to enter a database located at a cloud server based on the signal and transmission rule and the remote data acquisition and transmission device, and stores the vehicle driving data in the database after preprocessing such as data decoding and cleaning, and extracts relevant data in the vehicle driving process to estimate and calculate the remaining mileage.
The process of data acquisition for each vehicle to form big data mainly comprises the following steps:
step 1: and a data acquisition step of acquiring data by installing sensors including, but not limited to, a temperature sensor, a GPS sensor, a current sensor, a voltage sensor, etc. inside and outside the vehicle, and acquiring data of a driving state, environmental information, driving behavior, a power battery state, etc. by the above sensors.
Step 2: and a data cleaning step, namely performing data cleaning work on the collected data to avoid the influence of data quality, wherein the data cleaning work comprises operations such as data denoising, missing data processing, data formatting and the like.
Step 3: and a data storage step, namely transmitting the cleaned data to a cloud server and storing the data in a database.
As described above, the plurality of sensors are used for comprehensively acquiring the running state, the driving behavior, the power battery state and the like of the vehicle, so that the data quality is higher, and the analysis result is more accurate. The driving data generated during each driving of the vehicle is stored in the cloud server in the above manner, and the determination of the remaining mileage is realized during the driving of the current vehicle by using the stored data, as shown in fig. 1, including:
s101, determining a remaining capacity of a battery in the vehicle.
In this step, the remaining capacity of the battery in the vehicle is determined. Specifically, the battery SOC value acquired in the vehicle is utilized to determine the remaining capacity RE of the battery, where the remaining capacity may be expressed as:
RE＝SOC*W bat
W bat ＝C bat *U
in the above formula, SOC is the state of charge of the battery, and is generally used to reflect the remaining capacity of the battery, and is defined as the ratio of the remaining capacity to the battery capacity; w (W) bat Is the nominal total energy of the battery; c (C) bat Is the rated capacity of the battery; u is the terminal voltage of the battery.
It can be seen from the above equation that the remaining power RE of the battery, the SOC value of the battery, and the rated total energy W of the battery bat Related to, however, the rated capacity C of the battery bat Not a fixed value, but will vary with ambient temperature.
For this purpose, the rated capacity C of the battery is determined bat In the process of (1), the current environment temperature of the vehicle is obtained, so that the rated capacity C of the battery at different environment temperatures can be obtained based on the big data in the cloud server bat MAP table of (2) as shown in table 1 below;
table 1 MAP table of rated capacities of the batteries at different ambient temperatures
| Ambient temperature | Rated capacity of battery |
| <-30℃ | C bat1 |
| (-30,-20℃] | C bat2 |
| (-20,-10℃] | …… |
| (-10,0℃] | …… |
| (0,10℃] | …… |
| (10,20℃] | …… |
| (20,30℃] | C bat7 |
| >30℃ | C bat8 |
Thus, the rated capacity C of the battery is obtained by reading the SOC signal and the ambient temperature signal of the battery from the CAN bus of the vehicle and looking up a table bat And finally, determining the residual electric quantity RE of the battery through the formula.
S102, determining an average energy consumption value of the vehicle in a preset working mode based on historical driving data.
After the remaining amount of battery in the vehicle is determined through the above-described step S101, in this step, an average energy consumption value of the vehicle in a predetermined operation mode is determined based on the historical running data. The average energy consumption value E here c Refers to the power loss condition of the vehicle in different predetermined operating modes. Here, the average energy consumption value E is determined c In the process of (1) adopting a layering estimation mode. Specifically, as shown in fig. 2, includes:
s201, determining a current operation mode of the vehicle based on the historical driving data.
In this step, a current operation mode of the vehicle is determined based on the historical travel data. In particular, in the determination of the average energy consumption value, an average energy consumption estimated value, that is, an average energy consumption base value of the vehicle in the current operation mode, is first calculated and determined in an off-line manner based on the upper layer through the history data on the running of the vehicle. The average energy consumption value can be greatly different due to different energy consumption conditions of the vehicle in different working modes. Therefore, it is necessary to first explicitly define and divide the operation mode of the vehicle. In this embodiment, the principal component analysis method is used to extract features capable of distinguishing different operation modes from the historical driving data stored in the database, where the features include at least average vehicle speed, ambient temperature, and battery SOC.
The principal component analysis method is a commonly used feature extraction method, specifically taking into account the average energy consumption value E c Is the average energy consumption of the vehicle in different working modes, the value is comprehensively influenced by a plurality of factors, in order to realize the average energy consumption value E c According to the main component analysis result, the embodiment considers the comprehensive influence of the ambient temperature, the average vehicle speed and the battery SOC to realize the average energy consumption value E c The above-mentioned influencing factors may enable the following subdivision:
the ambient temperature information grouping section is set to: -30 ℃ (-30, -20 ℃ ], 20, -10 ℃ (-10, 0 ℃ ], (0, 10 ℃ ], (10, 20 ℃ ], (20, 30 ℃ ], >30 ℃ for 8 intervals;
the average vehicle speed information grouping section is set to be, taking into account the running state of the vehicle: (0, 20km/h ], (20, 40km/h ], (40, 60km/h ], (60, 80km/h ], >80km/h;
the battery SOC value grouping section is set to: (0, 20], (20, 40], (40, 60], (60, 80], (80, 100);
considering the above three factors, the running of the vehicle is divided into 8×5×5=200 subdivided working modes, and the following table 2 is specific;
operating mode partitioning of the vehicle as described in Table 2
| Mode of operation | Ambient temperature | Average vehicle speed | SOC |
| Mode 1 | <-30℃ | (0,20km/h] | (0,20] |
| Mode 2 | <-30℃ | (20,40km/h] | (0,20] |
| Mode 3 | <-30℃ | (40,60km/h] | (0,20] |
| Mode 4 | <-30℃ | (60,80km/h] | (0,20] |
| …… | …… | …… | …… |
| Mode 198 | >30℃ | >80km/h | (80,100] |
| Mode 199 | >30℃ | >80km/h | (80,100] |
| Mode 200 | >30℃ | >80km/h | (80,100] |
S202, determining an average energy consumption estimated value of the vehicle in the working mode based on historical driving data.
After the operation mode of the vehicle is determined through the above-described step S201, in this step, an average energy consumption estimation value of the vehicle in the operation mode is determined based on the historical traveling data.
In particular, based on memoryHistorical driving data stored in a database of a cloud server, reading average speed information, environment temperature information and battery SOC information of each driving section of the vehicle, grouping the historical driving data based on the determined working modes, and calculating the average value E of energy consumption of all the historical data sections of the vehicle in different working modes in an off-line mode ck (k=1, 2,3., 200) as an average energy consumption estimate, i.e. an average energy consumption base value, in a determined operation mode, and generating a MAP table of average energy consumption estimates in different operation modes.
In particular, here the average energy consumption estimate E in the different modes of operation ck (k=1, 2,3., 200.) the calculation procedure is: the battery current and voltage data of each segment of the historical driving data under different working modes are read, and the average energy consumption estimation sub-value corresponding to the segment can be obtained by dividing the integral of the battery power by the driving mileage represented by the segment, which can be specifically expressed as:
further based on the average energy consumption estimation sub-value, obtaining an average energy consumption estimation value E of all pieces of historical driving data of the vehicle in different working modes ck (k=1, 2,3., 200) can be expressed as:
thus, based on the above formula, the MAP table of the average energy consumption estimated value of the vehicle in different operation modes is obtained, so that when the vehicle is running, the average vehicle speed information, the environment temperature information and the battery SOC information in the first preset time before the current moment are read, wherein the first preset time can be set according to the situation, for example, 180s, and then the operation mode of the vehicle is determined by inquiring the MAP table of the average energy consumption estimated value and the average energy consumption estimated value E in the current operation mode is read ck (k＝1,2,3...,200)。
And S203, correcting the average energy consumption estimated value to obtain the average energy consumption value.
After determining the average energy consumption estimated value of the vehicle in the operation mode based on the history of running data through the above-described step S202, in this step, the average energy consumption estimated value is corrected to obtain the average energy consumption value. Specifically, the average energy consumption estimated value obtained as described above is determined based on the historical running data of the vehicle, and the prediction of the future running condition of the vehicle is not considered. For this purpose, the average energy consumption estimate needs to be predicted at this step.
Specifically, the correcting the average energy consumption estimated value to obtain the average energy consumption value, as shown in fig. 3, further includes:
s301, predicting a vehicle speed value of the vehicle in a preset time in the future.
And S302, correcting the average energy consumption estimated value based on a prediction result.
Specifically, considering that the average energy consumption estimated value is calculated and determined in an off-line manner based on the historical data of the vehicle running on the upper layer, the short-term vehicle speed prediction is mainly realized on the vehicle on the basis of the BP neural network on the lower layer, so that the average energy consumption estimated value obtained by the upper layer algorithm is corrected.
The flow for realizing online energy consumption correction based on vehicle speed prediction at the lower layer comprises the following steps: first, based on the case where the ambient temperature and the battery SOC are rarely changed within an extremely short time interval, it is assumed that the ambient temperature and the battery SOC are less likely to be changed within a second predetermined time in the future. Therefore, only the vehicle speed value within a predetermined time in the future needs to be predicted. The second predetermined time here may be set to 20s, for example.
Specifically, a BP neural network method is adopted to predict the speed value of the running condition of the vehicle in the future 20s, and the average speed v in the future 20s is calculated avg_pre 。
Method for predicting vehicle speed by BP neural network adopted hereThe basic process is as follows: firstly, building and training a BP neural network, and selecting an average vehicle speed v mean Standard deviation sigma of acceleration a Standard deviation sigma of deceleration d Average value of acceleration a mean Idle time ratio r idle And taking the vehicle speed value in the adjacent 5s as an input neuron, extracting vehicle speed information in historical driving data in a database of the cloud server as a neural network training sample, selecting the vehicle speed of 20s in the future as an output neuron, and establishing a BP neural network.
After the BP neural network is trained, the average vehicle speed v in the second preset time in the future, namely 20s in the future is output avg_pre . Thereby obtaining the average energy consumption predicted value E of the battery according to the predicted average vehicle speed, the ambient temperature and the battery SOC by a table look-up mode c_pre 。
Further, the average energy consumption estimated value is corrected through the average energy consumption predicted value, and finally the average energy consumption value is obtained. The average energy consumption value for the remaining mileage estimation here can be expressed as:
E c ＝δE ck +(1-δ)E c_pre
wherein δ is a constant, 0.7;
and S103, determining the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.
After determining the remaining power of the battery in the vehicle through the above-described step S101 and determining the average power consumption value of the vehicle in a predetermined operation mode based on the historical driving data through the above-described step 102, the remaining mileage of the vehicle is determined based on the remaining power and the average power consumption value in this step. The residual electric quantity and the average energy consumption value are input into a residual mileage estimation offline model based on an operating mode, wherein the residual mileage estimation offline model can be built and trained based on historical driving data in a database of a cloud server, and the residual mileage of the vehicle in the residual mileage estimation offline model can be calculated by the following formula:
where RDR is the remaining mileage of the vehicle, RE is the remaining power of the battery in the vehicle, E c The average energy consumption value corresponding to the vehicle in the current working mode, that is, the residual mileage RDR is a function of the residual electric quantity of the vehicle and the average energy consumption value corresponding to the current working mode, where the accuracy of the residual electric quantity and the average energy consumption value of the vehicle needs to be ensured to meet the requirements for accurately estimating the residual mileage of the vehicle.
According to the embodiment of the disclosure, the residual mileage estimation offline model is built through historical driving data, short-term vehicle speed prediction is simultaneously carried out by inquiring the average energy consumption estimated value, so that the average energy consumption value is corrected online, and finally the residual mileage estimated value is obtained by combining the residual electric quantity of the battery. According to the method and the device for predicting the energy consumption, the energy consumption of the vehicle can be predicted more rapidly and accurately by combining various factors such as historical driving data, vehicle speed, temperature and battery SOC, the response speed is higher, and the calculation accuracy of the remaining mileage of the vehicle is remarkably improved.
Based on the same inventive concept as the first embodiment described above, a second embodiment of the present disclosure provides a remaining mileage determining apparatus of a vehicle, including a remaining power amount determining module, an average energy consumption value determining module, and a remaining mileage determining module coupled to each other, wherein:
the residual electric quantity determining module is used for determining the residual electric quantity of a battery in the vehicle;
the average energy consumption value determining module is used for determining an average energy consumption value of the vehicle in a preset working mode based on historical driving data;
the remaining mileage determining module is used for determining the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.
Further, the remaining power determining module includes:
a rated capacity determining unit for determining a rated capacity of a battery in the vehicle based on the history running data;
a rated total energy determining unit for determining a rated total energy of the battery based on the rated capacity;
and a remaining power determining unit configured to determine the remaining power based on the rated total energy and the state of charge.
Further, the rated capacity determining unit includes:
the acquisition subunit is used for grouping the historical driving data based on the ambient temperature and acquiring rated capacities in different temperature intervals;
and the determination subunit is used for determining the rated capacity of the battery based on the current environment temperature of the vehicle.
Further, the average energy consumption value determining module includes:
a current operation mode determining unit configured to determine a current operation mode of the vehicle based on the history running data;
an estimation unit for determining an average energy consumption estimation value of the vehicle in the working mode based on the historical driving data;
and the correction unit is used for correcting the average energy consumption estimated value to acquire the average energy consumption value.
Further, the current operation mode determining unit includes:
a grouping subunit configured to group the historical driving data based on an ambient temperature, an average vehicle speed, and a state of charge to determine a plurality of operation modes;
and the working mode determining subunit is used for determining the current working mode based on the current environment temperature, the average vehicle speed and the state of charge of the vehicle.
Further, the correction unit includes:
a prediction subunit, configured to predict a vehicle speed value of the vehicle in a predetermined time in the future;
and the correction subunit is used for correcting the average energy consumption estimated value based on the prediction result.
Further, predicting the vehicle speed value of the vehicle in a future preset time is realized based on a BP neural network, and the BP neural network is trained based on historical driving data.
According to the embodiment of the disclosure, the residual mileage estimation offline model is built through historical driving data, short-term vehicle speed prediction is simultaneously carried out by inquiring the average energy consumption estimated value, so that the average energy consumption value is corrected online, and finally the residual mileage estimated value is obtained by combining the residual electric quantity of the battery. According to the method and the device for predicting the energy consumption, the energy consumption of the vehicle can be predicted more rapidly and accurately by combining various factors such as historical driving data, vehicle speed, temperature and battery SOC, the response speed is higher, and the calculation accuracy of the remaining mileage of the vehicle is remarkably improved.
A third embodiment of the present disclosure provides a storage medium, which is a computer-readable medium storing a computer program that, when executed by a processor, implements the method provided by the first embodiment of the present disclosure, including steps S11 to S13 as follows:
s11, determining the residual quantity of a battery in the vehicle;
s12, determining an average energy consumption value of the vehicle in a preset working mode based on historical driving data;
and S13, determining the remaining mileage of the vehicle based on the remaining electric quantity and the average energy consumption value.
Further, the computer program, when executed by a processor, implements other methods provided by the first embodiment of the present disclosure
According to the embodiment of the disclosure, the residual mileage estimation offline model is built through historical driving data, short-term vehicle speed prediction is simultaneously carried out by inquiring the average energy consumption estimated value, so that the average energy consumption value is corrected online, and finally the residual mileage estimated value is obtained by combining the residual electric quantity of the battery. According to the method and the device for predicting the energy consumption, the energy consumption of the vehicle can be predicted more rapidly and accurately by combining various factors such as historical driving data, vehicle speed, temperature and battery SOC, the response speed is higher, and the calculation accuracy of the remaining mileage of the vehicle is remarkably improved.
A fourth embodiment of the present disclosure provides an electronic device comprising at least a memory having a computer program stored thereon and a processor that, when executing the computer program on the memory, implements the method provided by any of the embodiments of the present disclosure. Exemplary, the electronic device computer program steps are as follows S21 to S23:
s21, determining the residual quantity of a battery in the vehicle;
s22, determining an average energy consumption value of the vehicle in a preset working mode based on historical driving data;
s23, determining the remaining mileage of the vehicle based on the remaining power and the average energy consumption value.
Further, the processor also executes the computer program in the third embodiment described above
According to the embodiment of the disclosure, the residual mileage estimation offline model is built through historical driving data, short-term vehicle speed prediction is simultaneously carried out by inquiring the average energy consumption estimated value, so that the average energy consumption value is corrected online, and finally the residual mileage estimated value is obtained by combining the residual electric quantity of the battery. According to the method and the device for predicting the energy consumption, the energy consumption of the vehicle can be predicted more rapidly and accurately by combining various factors such as historical driving data, vehicle speed, temperature and battery SOC, the response speed is higher, and the calculation accuracy of the remaining mileage of the vehicle is remarkably improved.
The storage medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.
Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the passenger computer, partly on the passenger computer, as a stand-alone software package, partly on the passenger computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the passenger computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (e.g., connected through the internet using an internet service provider).
It should be noted that the storage medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
While various embodiments of the present disclosure have been described in detail, the present disclosure is not limited to these specific embodiments, and various modifications and embodiments can be made by those skilled in the art on the basis of the concepts of the present disclosure, which modifications and modifications should fall within the scope of the claims of the present disclosure.
Claims (10)
1. A method of determining a remaining mileage of a vehicle, comprising:
determining a remaining capacity of a battery in the vehicle;
determining an average energy consumption value of the vehicle in a predetermined operating mode based on the historical driving data;
and determining the remaining mileage of the vehicle based on the remaining battery level and the average energy consumption value.
2. The determination method according to claim 1, characterized in that the determination of the remaining capacity of the battery in the vehicle includes:
determining a rated capacity of a battery in the vehicle based on the historical travel data;
determining a rated total energy of the battery based on the rated capacity;
the remaining power is determined based on the rated total energy and state of charge.
3. The determination method according to claim 2, wherein the determining the rated capacity of the battery in the vehicle based on the historical running data includes:
grouping the historical driving data based on the ambient temperature to obtain rated capacities in different temperature intervals;
the rated capacity of the battery is determined based on the ambient temperature at which the vehicle is currently located.
4. The method of determining according to claim 1, wherein the determining an average energy consumption value of the vehicle in a predetermined operation mode based on the historical driving data includes:
determining a current operating mode of the vehicle based on historical travel data;
determining an average energy consumption estimated value of the vehicle in the working mode based on historical driving data;
and correcting the average energy consumption estimated value to obtain the average energy consumption value.
5. The method according to claim 4, wherein the determining the current operation mode of the vehicle based on the history of running data includes:
grouping the historical travel data based on ambient temperature, average vehicle speed, and state of charge to determine a plurality of operating modes;
and determining the current working mode based on the current environment temperature, average vehicle speed and state of charge of the vehicle.
6. The method of determining according to claim 4, wherein the correcting the average energy consumption estimation value to obtain the average energy consumption value includes:
predicting a vehicle speed value of the vehicle in a preset future time;
and correcting the average energy consumption estimated value based on a prediction result.
7. The determination method according to claim 6, wherein predicting a vehicle speed value of the vehicle in a predetermined time in the future is implemented based on a BP neural network trained based on historical running data.
8. A device for determining a remaining mileage of a vehicle, comprising:
a remaining power determining module for determining a remaining power of a battery in the vehicle;
the average energy consumption value determining module is used for determining an average energy consumption value of the vehicle in a preset working mode based on historical driving data;
and the remaining mileage determining module is used for determining the remaining mileage of the vehicle based on the remaining electric quantity and the average energy consumption value.
9. A storage medium storing a computer program, which when executed by a processor performs the steps of the method according to any one of claims 1 to 7.
10. An electronic device comprising at least a memory, a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the computer program on the memory, implements the steps of the method according to any of claims 1 to 7.
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| CN202310737193.8A CN116788052A (en) | 2023-06-20 | 2023-06-20 | Method and device for determining remaining mileage of vehicle, storage medium and electronic equipment |
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| CN202310737193.8A CN116788052A (en) | 2023-06-20 | 2023-06-20 | Method and device for determining remaining mileage of vehicle, storage medium and electronic equipment |
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